University of Florida Thesis Or Dissertation

DEVELOPING A RESILIENCE MODEL FOR DESTINATIONS: AN APPLICATION TO ECUADOR

ESTEFANIA MERCEDES BASURTO CEDEÑO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

To my daughter Alicia, my husband Fabio, and my mom Mercedes. Thanks for being there for me and believing in me.

ACKNOWLEDGMENTS

I thank my daughter, my husband, and my mother for being so supportive, understanding, patient, loving, and always giving me words of encouragement during this exciting project. In a special way, I thank my advisor Lori-Pennington-Gray for giving me her support and being willing to share her knowledge with me and guide me through all these years. I deeply thank my committee members for always being ready to help me and understand my learning process.

I will always carry in my heart all those people who were willing to listen and advise me during my time at the University of Florida. I thank my friends, colleagues, members of the administrative staff, teachers, who made my experience wonderful and a pleasant memory that I will always treasure in my heart. In the same way I want to thank the Ministry of Tourism of Ecuador and the University Laica Eloy Alfaro of Manabí for their help in the implementation of this research.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 10

LIST OF FIGURES ...... 13

LIST OF ABBREVIATIONS ...... 16

ABSTRACT ...... 18

CHAPTER

1 INTRODUCTION ...... 19

Defining the Problem ...... 19 Theoretical Background ...... 21 The Resilience Concept ...... 21 Tourism and Resilience ...... 22 Resilience and Sustainability ...... 24 Types of Adaptation in Tourism ...... 25 A Proposed Resilience Model for Adaptation ...... 25 Key Research Questions and Objectives...... 28 Objective 1 Research Questions: ...... 29 Objective 2 Research Questions: ...... 29 Context and Delimitation ...... 29 The Geographical Context of the Study ...... 30 Definitions and Delimitations for the Study ...... 32 Environmental assets ...... 32 Socio-cultural assets ...... 33 Key stakeholders ...... 34 Risk and vulnerability ...... 34 Resilience ...... 35 Risk and Vulnerabilities in Ecuador ...... 35 Significance of the Study ...... 37 Dissertation Format ...... 37

2 LITERATURE REVIEW ...... 44

Resilience Theory ...... 45 Describing the System ...... 48 Resilience of What to What? ...... 48 Desirability of the System, Identifying Key Issues ...... 50 Multiple Space and Time Scales ...... 50 Different Approaches to Achieve Resilience ...... 51

Engineering ...... 51 Ecological ...... 51 Social Ecological System (SES) Resilience ...... 52 Evolutionary Resilience ...... 55 Community Resilience ...... 56 Resilience in the Tourism Field ...... 59 Enterprise Resilience ...... 59 Tourism Community Resilience ...... 61 Resilience in Tourism an Emerging Approach ...... 62 Resilience Research Organizations ...... 68 Resilient organizations ...... 69 Resilience alliance ...... 70 Stockholm resilience centre ...... 70 The Regional Tourism Adaptation Framework RTAF ...... 71 Gaps in the Knowledge ...... 73

3 METHODS ...... 76

Study Site ...... 77 Research Design Foundation ...... 80 Phase 1: Adaptation of the RTAF Model...... 81 Methodology ...... 82 Rationale ...... 82 Data Collection ...... 84 Document Analysis ...... 85 Skimming stage ...... 86 Review/ reading stage ...... 87 Interpretation stage ...... 87 Data Analysis ...... 88 Identifying and labeling codes ...... 89 Document and verifying ...... 90 Comparing and contrasting ...... 90 Validity and Reliability ...... 90 Phase 2: Risk and Vulnerability Assessment ...... 91 Step 1: Document Analysis Process Risk Assessment...... 93 Rationale ...... 93 Data Collection ...... 94 Documents ...... 95 Document Analysis ...... 96 Skimming stage ...... 96 Review / reading stage ...... 96 Interpretation stage ...... 97 Data Analysis ...... 98 Validity and Reliability ...... 99 Step 2: Qualitative Assessment of Risk (Focus Groups) ...... 100 Rationale ...... 100 Data Collection ...... 101

Design of the Study ...... 104 Anticipating Problems ...... 105 Participant’s Characteristics ...... 105 Sampling ...... 107 Recruitment ...... 108 Incentives ...... 108 Interview Questions ...... 109 Opening questions ...... 109 Introductory question ...... 109 Transition question ...... 110 Key questions ...... 110 Ending question ...... 111 Data Analysis ...... 111 Risk Matrix ...... 113 Interpretation of the Qualitative Data by Stages ...... 114 Validity and Reliability ...... 115 Step 3: Adaptation Assessment Document Analysis ...... 115 Rationale ...... 115 Documents ...... 116 Data Collection ...... 116 Data Analysis ...... 117 Content Analysis ...... 117 Validity and Reliability ...... 119 Step 4: Adaptation Assessment: Consultative Workshop ...... 119 Rationale ...... 119 Data Collection ...... 120 Participants ...... 122 Sampling ...... 124 Recruitment ...... 125 Incentives ...... 126 Workshop Itinerary ...... 126 Workshop Interviews Questions and Operationalization ...... 129 Method of Inquiry ...... 132 Data Analysis ...... 133 Validity and Reliability ...... 135 Phase 3: Adaptive Strategies Evaluation ...... 136 The Supply Side Evaluation Variables: Survey Adaptation Assessment ...... 136 Rationale ...... 136 Reducing survey error ...... 138 Data collection ...... 139 Participants ...... 140 Operationalization of variables (independent, dependent) ...... 141 Data analysis ...... 143 Validity and reliability ...... 144 Phase 4: Demand Side Evaluation Variables ...... 146 Rationale ...... 146

Reducing survey error ...... 147 Data collection ...... 148 Participants ...... 151 Operationalization of variables ...... 153 Data analysis ...... 155 Validity and reliability ...... 155

4 RESULTS ...... 177

Stage 1: Adaptation of the RTAF Model ...... 177 Overview ...... 177 Results Obtained ...... 178 Geographical, environmental, and socio – cultural characteristics ...... 178 Characteristics of the tourism system ...... 181 Issues that have affected the country ...... 184 Adaptation strategies applied in the past ...... 189 Differences between the original RTAF and the Ecuadorian context ...... 190 Stage 2: Risk Assessment ...... 191 Overview ...... 191 Stage 2. Phase 1: Results of the Document Analysis ...... 192 Stage 2. Phase 2: Focus Group Sessions Results ...... 193 Respondents’ profile ...... 193 Identification of risks ...... 194 Opportunities ...... 204 Stage 2, Phase 3: Creation of the Risks Matrix ...... 205 Stage 3: Stakeholder Adaptation Assessment ...... 206 Stage 3, Phase 1: Results of Workshop ...... 207 Adaptation strategy #1: to deal with changing policies ...... 208 Adaptation strategy #2: to deal with political instability ...... 210 Adaptive strategy #3: to deal with exaggerated news ...... 212 Stage 3, Phase 2: Stakeholder Survey ...... 213 Evaluation of adaptation strategy #1: create communication channels using social media ...... 217 Evaluation of adaptation strategy #2: create tourism certification campaigns designed for members of the DMOs ...... 219 Evaluation of adaptation strategy #3: create contingency funds ...... 220 Evaluation of adaptation strategy #4: create a public-private partnership for tourism destination management ...... 221 Evaluation of adaptation strategy #5: elaborate on the pre-crisis risk communication plan with academia ...... 222 Evaluation of adaptation strategy #6: create a PR plan with emphasis on communication and social media ...... 224 Stage 4: Tourist Adaptation Assessment ...... 225 Overview ...... 225 Results of the Survey ...... 226 Strategy #1: create communication channels using social media to broadcast sudden changes in the law and policy (within the sector). ... 228

Strategy #2 evaluation: create tourism certification campaigns designed for members of the DMOs ...... 229 Strategy #3 evaluation: creation of contingency funds (collected monthly by the private sector) for destination image promotion, and small loans ...... 230 Strategy #4 evaluation: public- private partnership to guarantee the continuity of tourism plans and join synergies...... 231 Strategy #5 evaluation: elaboration of a pre-crisis risk communication plan by academia and the DMOs ...... 232 Strategy #6 evaluation: creation of a PR plan with emphasis on communication and social media by private sector guilds and academia ...... 233

5 DISCUSSION AND CONCLUSIONS ...... 294

An Overview of the Study: Adaptation of the RTAF Model ...... 294 Practical Conclusions...... 298 Theoretical Contribution ...... 302 Theoretical Implication ...... 303 Practical Implications and Recommendations ...... 307 Limitation and Future Research ...... 310

APPENDIX

A SAMPLES ...... 314

B INSTRUMENTS ...... 318

Instrument 2 Survey to tourism stakeholders ...... 319 Instrument 3 Survey to tourists ...... 323

C SCRIPTS ...... 326

Script 1 Reminder call ...... 326 Script 2. Welcome ...... 327 Script 3. Grand Rules...... 328 Script 4. Phone invitation script for Workshop ...... 329 Script 5. Welcome (Workshop) ...... 330

D SLIDES ...... 333

LIST OF REFERENCES ...... 336

BIOGRAPHICAL SKETCH ...... 355

LIST OF TABLES

Table page

1-1 Definitions of Resilience ...... 39

1-2 Regions of Ecuador ...... 40

1-3 Stakeholders...... 41

3-1 Documents analyzed RTAF ...... 157

3-2 Documents analyzed risks assessment ...... 160

3-3 Documents analyzed economic sectors ...... 162

3-4 Focus groups questions and justification ...... 163

3-5 Documents analyzed adaptation assessment ...... 165

4-1 Focus Groups’ Participants ...... 234

4-2 Focus Groups’ Profile of participants ...... 235

4-3 Risk Matrix ...... 236

4-4 Adaptation strategies mean scores ...... 237

4-5 Evaluation of groups 1, 2 and 7 ...... 238

4-6 Group one evaluation and correlations ...... 239

4-7 Group two evaluation and correlations ...... 240

4-8 Group three evaluation and correlations ...... 241

4-9 Group four evaluation and correlations ...... 242

4-10 Group five evaluation and correlations ...... 243

4-11 Group six evaluation and correlations ...... 244

4-12 Group seven evaluation and correlations ...... 245

4-13 Group eight evaluation and correlations ...... 246

4-14 Group nine evaluation and correlations ...... 247

4-15 Evaluation of groups 3, 5 and 6 ...... 248

4-16 Demographic information of the stakeholder in the survey ...... 249

4-17 Summary table for findings for all the regression analysis: stakeholders in the industry ...... 250

4-18 Relationship among support for strategy one and demographics, knowledge, and feasibility of strategy one: create communication channels using social media to broadcast sudden changes in the law and policy ...... 252

4-19 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy one ...... 253

4-20 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy two ...... 254

4-21 Relationship among support for strategy two and demographics, knowledge, and feasibility of strategy two: tourism certification campaigns destined to the members of the DMOs ...... 255

4-22 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy three ...... 256

4-23 Relationship among support and demographics, knowledge, and feasibility of strategy three: creation of contingency funds ...... 257

4-24 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy four...... 258

4-25 Relationship among support and demographics, knowledge, and feasibility of strategy four: public- private partnership ...... 259

4-26 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy five ...... 260

4-27 Relationship among support and demographics, knowledge, and feasibility of strategy five: elaboration of a pre-crisis risk communication plan by academia and the DMOs ...... 261

4-28 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy six ...... 262

4-29 Relationship among support and demographics, knowledge, and feasibility of strategy six: PR plan with emphasis on communication and social media by private sector guilds and academia ...... 263

4-30 Summary table for findings for all of the regression analysis: tourists ...... 264

4-31 Adaptive strategies rank by tourists ...... 265

4-32 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy one (Tourist) ...... 266

4-33 Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy one: create communication channels using social media to broadcast sudden changes in the law and policy ...... 267

4-34 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy two (Tourist) ...... 268

4-35 Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy two: tourism certification campaigns destined to the members of the DMOs ...... 269

4-36 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy three (Tourist) ...... 270

4-37 Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy three: creation of contingency funds ...... 271

4-38 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy four (Tourist) ...... 272

4-39 Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy four: public- private parthership ...... 273

4-40 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy five (Tourist) ...... 274

4-41 Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy five: elaboration of a pre-crisis risk communication plan by academia and the DMOs ...... 275

4-42 Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy six (Tourist) ...... 276

4-43 Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy six: PR plan with emphasis on communication and social media by private sector guilds and academia ...... 277

LIST OF FIGURES

Figure page

1-1 Regional Tourism Adaptation Model ...... 42

1-2 Proposed Model ...... 42

1-3 Ecuador Map ...... 43

2-1 The Ball in the Basin ...... 75

2-2 Regional Tourism Adaptation Framework Model ...... 75

3-1 Research design ...... 166

3-2 Code frame for RTAF ...... 167

3-3 Map of Ecuador ...... 168

3-4 Code frame for risk assessment impact ...... 169

3-5 Focus group study design ...... 170

3-6 Tourism destination components ...... 171

3-7 Code frame general risk assessment ...... 172

3-8 Risk matrix ...... 173

3-9 Tourism destinations of Ecuador ...... 174

3-10 Code Frame Adapatation Assessment ...... 175

3-11 Workshop itinerary ...... 176

4-1 Supply side gender ...... 278

4-2 Supply side age ...... 278

4-3 Supply side country of origin ...... 279

4-4 Supply side province ...... 279

4-5 Supply side education ...... 280

4-6 Supply side sector ...... 280

4-7 Demand side destination ...... 281

4-8 Demand side age ...... 281

4-9 Demand side province ...... 282

4-10 Demand side gender ...... 282

4-11 Demand side education ...... 283

4-12 Standardized residual plot for support for strategy one (predictors: demographic, knowledge, and feasibility) ...... 283

4-13 Standardized residual plot for support for strategy one (predictors: attributes) 284

4-14 Standardized residual plot for support for strategy two (predictors: demographic, knowledge, and feasibility) ...... 284

4-15 Standardized residual plot for support for strategy two (predictors: attributes) . 285

4-16 Standardized residual plot for support for strategy three (predictors: demographic, knowledge, and feasibility) ...... 285

4-17 Standardized residual plot for support for strategy three (predictors: attributes) ...... 286

4-18 Standardized residual plot for support for strategy four (predictors: demographic, knowledge, and feasibility) ...... 286

4-19 Standardized residual plot for support for strategy four (predictors: attributes) 287

4-20 Standardized residual plot for support for strategy five (predictors: demographic, knowledge, and feasibility) ...... 287

4-21 Standardized residual plot for support for strategy five (predictors: attributes) . 288

4-22 Standardized residual plot for support for strategy six (predictors: demographic, knowledge, and feasibility) ...... 288

4-23 Standardized residual plot for support for strategy six (predictors: attributes) .. 289

4-24 Profile of the domestic tourist taken from MINTUR and Metropolitan District of Quito...... 289

4-25 Demand of destination sites (left) and more populated areas within the country (right) taken from the Ministry of Urban Development and Living ...... 290

4-26 Standardized residual plot for support for strategy one (predictors: demographic, knowledge, and feasibility) demand side evaluation ...... 291

4-27 Standardized residual plot for support for strategy two (predictors: demographic, knowledge, and feasibility) demand side evaluation ...... 291

4-28 Standardized residual plot for support for strategy three (predictors: demographic, knowledge, and feasibility) demand side evaluation ...... 292

4-29 Standardized residual plot for support for strategy four (predictors: demographic, knowledge, and feasibility) demand side evaluation ...... 292

4-30 Standardized residual plot for support for strategy five (predictors: demographic, knowledge, and feasibility) demand side evaluation ...... 293

4-31 Standardized residual plot for support for strategy six (predictors: demographic, knowledge, and feasibility) demand side evaluation ...... 293

5-1 Frequency of responses: variable knowledge of the tourism attractions ...... 313

LIST OF ABBREVIATIONS

CAPRADE Andean Committee for Prevention and Attention and Disasters

CINFOTUR Tourism Information Center

DMO Destination Management Organization

ED Tourism Tax for Ecuadorian Destination Promotion

FLACSO Faculty of Latin American Social Sciences Headquarters Ecuador

GAD Decentralized Autonomous Government

GDP Gross domestic product

IGEPN Geophysical Institute of the National Polytechnic School

INEC National Institute of Statistics and Census

INOCAR Oceanographic Institute of the Navy

MAE Ministry of Environment of Ecuador

MINTUR Ministry of Tourism

PATA Pacific Asia Travel Association

PIMTE Comprehensive Tourist Marketing Plan for Ecuador

PLANDETUR National Tourist Planning of Ecuador

PR Public Relations

RA Resilience Alliance

RQ Research question

RTAF Regional tourism adaptation framework

SQCA Summative Qualitative Content Analysis

UF University of Florida

ULEAM University Laica Eloy Alfaro de Manabí

UN United Nation

UNESCO United Nations Educational, Scientific and Cultural Organization

UNIDSR United Nations International Strategy for Disaster Reduction

UNWTO World Tourism Organization

VIF Variance Inflation Factor

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

DEVELOPING A RESILIENCE MODEL FOR DESTINATIONS: AN APPLICATION TO ECUADOR

Estefania Mercedes Basurto Cedeño

August 2018

Chair: Lori Pennington-Gray Major: Health and Human Performance

This study adapts a resilience model to Ecuador with the objective of analyzing and identifying the main risks that affect the Ecuadorian tourism sector, as well as the strategies to face them as a way to increase resilience of the destination and contribute to the overall sustainability of the system.

Tourism is one of the fastest growing industries in the world, the main source of income in several developing countries as well as a key shaper of community development. Despite the major economic impact of tourism, the industry is vulnerable to natural disasters, technological / man-made disasters, health-related disasters, and conflict bases incidents. Thus, to increase the adaptive capacity of the tourism sector in

Ecuador, “a regional tourism adaptation framework” (RTAF) has been adopted and adapted to the country of Ecuador. The voices of several stakeholder groups have been included in the analysis and several large sample questionnaires have been carried out to determine the best way to increase tourism resilience in the country.

CHAPTER 1 INTRODUCTION

Ecuador is a small country of 109,484 square miles located in South America.

The primary source of revenue for the Ecuadorian Government is petroleum exportation. However, such activity is not sustainable in the long term. For this reason, the Ecuadorian Government, since 2009, has been promoting diversification of the local economy. One of the primary foci of development is the tourism industry.

International and domestic sustainable tourism development is being promoted in its four regions and has become an important source of income for many destinations throughout the country. Unfortunately, on April 16th of 2016, an earthquake measuring

7.8 on the Richter scale struck the coastal region of Ecuador devastating many tourism destinations and compromising the perception of safety at the national and international level. Since then, many destinations stakeholders alongside the federal government have acknowledged the importance of reducing vulnerabilities and increasing adaptation in order to enhance resilience of tourism sector.

Defining the Problem

The tourism industry is a major economic contributor to many countries throughout the world. In fact, it represents 10% of the global GDP, and is a generator of one in eleven jobs globally according to the World Tourism Organization (UNWTO,

2016). Additionally, tourism is socially important as it can elevate the destination image, promoting community cohesion, supporting community well-being, uplifting the sense of pride in a population, and providing leisure and recreation opportunities (du Cros &

McKercher, 2015). Thus, tourism is pivotal if developed with a sustainable approach that fosters environmental conservation, economic growth, and social development,

especially in developing countries. It has been suggested by many scholars (Ritchie,

2004; Pennington-Gray & Pizam, 2011; Pennington-Gray et al., 2011; Holladay and

Powell, 2013; Pennington-Gray et al., 2013; Becken, 2013; Basurto-Cedeno &

Pennington-Gray, 2016; Espiner et al., 2017) that the tourism industry is vulnerable to disasters both natural as well as man-made. The tourism industry is vulnerable to a variety of factors such as: (1) strong exchange rate fluctuations, (2) variations in the price of oil and other commodities, and (3) increased global concern with safety and security (UNWTO, 2016). Fast drivers (variables of change), such as natural disasters, can have a serious impact on a tourism destination because they do not provide enough time to react, and thus have received heightened attention in recent years in planning and development of many destinations.

In order to reduce risks in the tourism industry and enhance adaptation, several destinations have started to adopt crisis management plans as well as risk reduction frameworks (Ritchie, 2004; Holladay and Powell, 2013). The aim of the risk reduction frameworks is to identify possible sources of crisis and provide guideline of what to do in case of disaster. Despite the many benefits of a risk management framework, recent research suggests that risk management frameworks by themselves are not a guarantee that destinations will recover quickly or efficiently (Basurto & Pennington-

Gray, 2016). On the world stage, a more holistic framework has been suggested

(UNIDSR, 2015) the regional tourism adaptation framework (RTAF) adopts a resilience approach, which blends concepts of resilience, resistance and readiness. Few empirical studies have been conducted using the resilience framework; in fact, the majority of both academic and practitioner studies have taken a conceptual approach to adopting a

resilience framework. This study will take the next step and test a conceptual resilience framework proposed by Jopp (2010) to the context of Ecuador. This study will (1) challenge concepts which have not been empirically measurable in the past and propose variables that destinations can adopt which are measurable; (2) it will adopt a mixed method approach to testing the resilience model and (3) it will propose a scalable model for Ecuador which can be used at the national, state and local levels to aid destinations in building more resilient tourism products and locations.

Theoretical Background

The Resilience Concept

The concept of resilience has its beginnings in 1973, when Hollings proposed a new way to understand ecological systems and introduced an “adaptive cycle” (Hollings,

1973) in which different components of the system might influence change in other components, or even in the whole system. Hollings also introduced the “ecological resilience concept”, and defined it as the time required for an ecosystem to return to an equilibrium or steady state following a perturbation.

In the late nineties, the addition of a social component became necessary when researchers acknowledged that people shape ecosystems and infuse a nature of dynamics into the process (Folke, 2006). Since then, the notion of resilience has been adopted and adapted by several fields resulting in a wide variety of terminology and concepts. Some of the most common definitions and conceptualizations of resilience used by scholars are: (1) engineering resilience, which is the capacity of a system to bounce back to the stage prior disturbance (Folke, 2006); this approach is most commonly used to refer to the capacity of infrastructure to manage stress generated from fast and slow drivers of change. (2) Ecological resilience, which is the capacity of

the ecosystems to survive after an unexpected event, and keep up in a desirable stage

(Berkes et al. 2008). (3) Social-Ecological resilience includes the human influence in the analysis of the system, their power to shape the ecosystem, and adapt to different sets of stressors (Folke, 2006). (4) Community resilience, which is the capacity of the population to respond to crisis and recover functionality (Cutter et al. 2008). Finally (5)

Evolutionary resilience focuses on the social component and the ability to change and adjust continually through the intervention of the government. (Sgro et al. 2011). Table

1-1 includes several definitions of resilience, which have been used in the academic literature.

Despite the variability in definitions of resilience, most of the above definitions emphasize the capacity for successful adaptation in the face of disturbance, shock, stress, or adversity (Norris et al, 2008).

Tourism and Resilience

The implementation of a resilience framework is appropriate to the study of communities, destinations, and the tourism sector because it is not linear and recognizes the connections among different components of a system, while at the same time allowing for change and improvement in order to face different types of stressors.

In the tourism field, the concept of resilience planning has emerged as an alternative to the sustainable development paradigm providing a more effective approach that allows for adaptation by building capacity into the system to enable a destination to return to a desirable state following both anticipated and unanticipated disruptions (Lew, 2014). For Strickland–Munro et al. (2009), resilience is achieved through creating adaptability within systems through the enhancement of social, financial, human, natural, physical and technological capital. Tourism systems are

complex, and policies should be adaptable to different scales (Luthe and Wyss, 2014) that include the geographical location, the local community, and different tourism activities within a geographical area (Strickland–Munro et al. 2009).

The identification and mitigation of crises and vulnerabilities within a system are key components of the resilience model (Tierney and Bruneau, 2007). For Becken

(2013), the goal of resilience is to increase the robustness of the system through reduction or assessment of the vulnerabilities and the intensification of the adaptive capacity. Within the assessment of vulnerabilities and possible risks, destinations must consider both slow drivers (e.g., a set of drivers that could provoke a flip in the system) and fast drivers (sudden changes in the systems such as natural disasters, political unrest, war, etc.). According to Holladay and Powell, (2013) the tourism industry is vulnerable to destabilizing forces such war, local-to-global economic complexities, and natural disasters. Therefore, destination needs to increase its adaptive capacity to deal with global and internal stressors that may cause the system to move from a desirable state to an undesirable state. Moreover, Jopp et al. (2010) suggest that tourism is a resource dependent industry, which must deal with external pressures (such as natural disasters and weather change), thus the destination needs to be prepared to address risks to deal with external pressure and embrace change through mitigation (of vulnerabilities) and adaptation. Accordingly, this destination focused resilience approach is complex and needs to take into consideration a tourism stakeholder process. Some adaptation strategies for resilience in the tourism sector employed by Krusel et al.

(2013) includes developing and securing of tourism activity, promoting the destination year-round, and promoting awareness among tourists. Other researchers (Saarinen and

Tervo, 2006; Scott et al., 2009; Dronkers et al., 2016; Dawson et al., 2016; Maier et al.,

2016) recommend construction of sea walls and beach filling to deal with sea rise; implementation of new infrastructure, adoption of green technology, acquisition of snow making and heating/cooling systems to deal with climate change, and pricing marketing and communications strategies to restore destination image.

Resilience and Sustainability

Resilience and sustainability are concepts that have been merged and confused often in literature. For Lew (2014), sustainability and resilience are two separate concepts. For a sector faced with a range of major sustainability challenges (Hall et al.,

2016), a resilience approach may be the best way to frame tourism planning and development. It affords deliberate efforts to build capacity to respond to the diverse social and environmental vulnerabilities of destinations that operate at various scales

(Espiner et al. 2017). For Espiner et al. (2017) and McCool (2016), resilience and sustainability have complementary relationships. Resilience will contribute to the achievement of sustainability within a destination when taking into account the four pillars of sustainability 1) economic, (2) environmental, (3) socio-cultural, and (4) governance (Holladay and Powell, 2013). For Espiner et al. (2017), resilience is necessary but not sufficient for sustainability, due to the idea that resilience could have several levels within a destination: (1) emerging resilience, (2) developing resilience, (3) mature resilience. Therefore, the resilience of the tourism sector of a destination will contribute to the achievement of sustainability. To attain that, a cooperative approach is needed that involves several stakeholders.

Types of Adaptation in Tourism

Adaptation is commonly referred to in the resilience literature. For some researchers adaptation is the measurable outcome of resilience (Adger et al. 2006,

Espiner et al. 2017) while others establish that resilience is achieved by adaptive capacity (Lew, 2014). For Cai et al., (2016) and Jopp et al. (2010) resilience is reached when the vulnerabilities are assessed and the adaptive capacity is enhanced prior to the

“shock” to a system.

In tourism, several types of adaptation need to be included in the resilience analysis (Jopp et al. 2016). The primary types of adaptation in the tourism sector are categorized into three areas: (1) technical adaptation, (2) business management adaptation, and (3) behavioral adaptation. Technical adaptation involves new and traditional technologies that could be implemented to cope with risk and vulnerabilities; for example the acquisition of green technology to minimize gas emissions and water waste, or the implementation of cooling equipment to cope with hot weather. Business management adaptation includes techniques used by the different stakeholders of a field to deal with change, like market pricing. Behavioral adaptation is associated with the consumer (tourists), which should assess the adaptation strategies for a deep evaluation of their effectiveness (Jopp et al. 2010). An example of behavioral adaptation of tourists is the change of clothing according to the new range of temperatures at the destination.

A Proposed Resilience Model for Adaptation

In the present study, a model for increased resilience is proposed which includes resilience, resistance, and readiness as concepts that are inherent to a resilience approach. This study adopts and modifies the adaptation model proposed by Jopp et al.

(2010) “The regional tourism adaptation framework” (RTAF) (Figure 1-1) as both a conceptual framework for resilience as well as the basis for a proposed empirical model, which was tested in and identified the level of resilience in Ecuador. Under the RTAF approach, resilience is defined as the ability to absorb change; resistance is conceptualized as the reduction of impacts that are likely to affect tourism, and readiness is the ability of a destination to capitalize on opportunities that arise.

According to Jopp et al. (2010) there are two phases to managing risk: mitigation and adaptation. Mitigation is considered by Jopp as the first phase to increase resilience and includes the risk and vulnerability assessments of the tourism sector. To that end, assessments were performed including different stakeholders to ensure all parts of the system and its different scales were represented.

The second part of the model focused on the identification and assessment of opportunities (adaptation strategies) that could emerge from sudden changes. This kind of approach is critical to the resilience concept because it includes change (adaptation process), which is a primary characteristic of resilient systems.

The effectiveness of the adaptation process needs to be taken into consideration.

The supply and demand sides of the sector should both be included in the process.

Most studies neglect the opinion of the community as well as tourists, domestic and international when planning for adaptive strategies, even though it is the tourists who will ultimately visit (and revisit) the destination (Jopp et al. 2010).

The effectiveness of the adaptation strategies will lead to the reduction of vulnerabilities of the tourism sector, and will eventually increase resilience, readiness and resistance, and ultimately will contribute to system sustainability.

Through the analysis of the literature review, the researcher has identified some gaps in the current RTAF model. These gaps are detailed bellow:

• There is not a thorough risk/vulnerability assessment of the destination that includes the opinion of different tourism stakeholders across sectors and experts that will allow for identification of key risks.

• There is not an external evaluation (by experts) of the adaptation strategies before being presented to the possible consumers

For that matter and with the purpose of providing a model that could be applied in different destinations, and which could be used to identify any kind of risk (not limited only to the ones related to climate change), a proposed model was developed (Figure 1-

2).

The proposed model includes an exhaustive risk assessment across tourism sectors and an opportunity assessment validated by an impartial third party and potential consumer. Besides in this model, the contribution of resilience in the achievement of overall sustainability of a system is acknowledged.

The model (Figure 1-2) in this study includes 4 phases, the first is focused on (1) the detailed definition of the tourist system, including the study of the geographical characteristics, natural and cultural resources, identification of the actors of the system, and a historical analysis of the risks that have affected the system under study. The second stage (2) includes a thorough risk analysis of the system considering not only the ones that have affected the tourism sector, but all the risks that have caused a crisis in the destination within 10 years. It also contains a qualitative analysis with the participation of experienced tourism stakeholders to identify the risks that have greatest vulnerability for the sector. This type of approach allows to identify all the risks that may

arise in the destination but focus on those that are more likely to affect the tourism sector.

The third phase (3) is an assessment of opportunities and adaptation capacity with the aim to identify adaptation strategies to cope with the main risks for the tourism sector (product of the second phase of the study), and includes the participation of tourism stakeholders from different sectors (food and beverages, accommodation , operation and intermediation, transport, parks and recreation, and public sector).

The fourth and last phase (4) includes the adaptation process, which is the evaluation and implementation of the adaptation strategies developed in the previous phase. The evaluation of the adaptation strategies is carried out before implementation and includes the opinion of both the supply and the demand side.

This model gives greater importance to the voice of tourism stakeholders in comparison to the information gathered in the phases of document analysis (analysis of historical risks) due to active stakeholders in tourism are who know fully the challenges, risks and vulnerabilities that face in the day to day (Saulter and Leisen,1999; Yuksel et al., 1999; Simpson, 2001).

Key Research Questions and Objectives

The main aim of this study is to adapt and apply a framework that will lead to an increase in resilience, resistance, and readiness in Ecuador through the identification of adaptation strategies to variables of rapid change that affect the tourism sector. The objectives of the present study are expressed in the following research objectives:

1. To adapt a model to increase resilience in a tourism destination. 2. Apply and test the model in Ecuador.

Under the same line of research, the following key research questions will be addressed in the study:

Objective 1 Research Questions:

Research Objective 1: To adapt a model to increase resilience in the tourism destination of Ecuador

• RQ 1: What are the elements of the tourism system of Ecuador?

• RQ 2: What is missing for the current RTAF model in order to be used to adapt to fast variables?

Objective 2 Research Questions:

Research Objective 2: Apply and test the model in Ecuador

• RQ 3: What are the top risks and opportunities for tourism in Ecuador derived from change?

• RQ 4: What are the most efficient and applicable options of adaptive strategies to increase destination resilience in Ecuador?

• RQ 5: What strategies do domestic tourists prefer?

The above research questions are planted with the purpose of finding an efficient way to increase resilience in a tourism destination through the reduction of risk and vulnerability while promoting the implementation of adaptive strategies.

Context and Delimitation

The present study was implemented in continental Ecuador. The purpose of a one–destination approach is to examine in depth the intricacies of a destination. The in- depth analysis of one destination will provide insights into the under studied area of research and will offer grounds for further research expansion in other destinations. The

Galapagos Islands were excluded from this study because their unique characteristics make them necessary to see them as independent systems.

The Geographical Context of the Study

Ecuador is an Andean country located on the equator, more specifically in South

America between Colombia and Peru. It has a population of 15.2 million people and is a republic that borders itself as pluri-ethnic and diverse. Ecuador’s economy is highly dependent on commodities like petroleum and agricultural products. However, during the past decade there has been an increasing interest to diversify the economy through commodification of services. Hence a growing interest for the tourism sector has become the focus of many strategies and policies at the national level.

Ecuador has promoted itself internationally as a green and cultural destination.

Thanks to the collaboration of the private and public sectors, it is the recipient of worldwide awards as the leading green destination for three years in a row (2013, 2014,

2015) and more recently in 2016 as having South America’s leading business destination, leading airport, leading destination, leading green destination, and leading luxury train (World Travel Awards).

Located along the equator (Figure 1-3) Ecuador is home to many endemic and exotic species that includes 18000 plants with flowers, 350 species of reptiles, 1600 species of birds, and 400 amphibians. Ecuador is also the home of the smallest orchid on the planet, and it is internationally known as the land of chocolate with the oldest seed of cacao ever registered (3000 BC.). The country has been recently marketed as the country where you can find four worlds in one place due to its four diverse regions:

(1) Galapagos “the enchanted islands” where giant turtles and exotic endemic species can be easily observed, (2) the pacific coast characterized by its historic towns, cuisine, and adventures, (3) the Andes, a place of volcanoes, hot springs and culture; and (4)

Amazon, a reserve of biodiversity. Each region is different from the other (Table 1-2).

The pacific coast is characterized by its warm weather all year long, and it is an ideal place to practice adventure sports like kite surfing, surfing, kayaking, canopy touring, biking etc. The native forest of the region includes tropical dry forest, tropical wet forest, tropical moist evergreen forest, premontane cloud forest, and mangrove forest. The coast is also home of ancient cultures and archeological ruins of the Valdivia and

Manteño-Huancavilca civilizations. Main tourism destinations of this region include the cities of Guayaquil, Manta, Salinas, and Zaruma. Also important destinations of this region are Montañita town, Machalilla National Park, San Lorenzo, Pacoche rain forest,

Calceta, Puyangi, among others (Ministerio de Turismo, 2016).

The Andes region is known for its cold weather, volcanoes, colorful small towns, ethnic diversity, and architecture. The Andes is divided into three cordilleras, which were formed in the Cenozoic era. The Andean cordilleras are part of the Nazca plate, originating beautiful landscapes but also making the location vulnerable to seismic activity and volcanic eruptions. Main tourism destinations of the region are Quito,

Cuenca, Riobamba, Mindo, Baños, Ingapirca Ruins, Chimborazo Reserve, Cochasqui,

Otavalo, Pasochoa, Pululahua, Mitad del Mundo, Los Illinizas reserve, Cotacachi –

Cayapas reserve, among others (Ministerio de Turismo, 2016).

The Amazon region is located on the east side of the country, and it is one of the most wonderful reserves of biodiversity in the world. This region is composed of multiple ecosystems, and home to twelve indigenous communities Huaorani, Zapara, Secoya,

A’l Cofan, Shuar, Achuar, Kichwa, Siona, Tagaeri, Taromenane, Shiwiar, and

Oñamenane. The main attractions of the region include Yasuni National Park,

Cuyabeno reserve, Sangay National Park, and Pococarpus National Park (Ministerio de

Turismo, 2016).

Finally, the enchanted islands “Galapagos” constitute the fourth region of the country. The Galapagos are volcanic islands located 1000 km west of the Pacific coast and are home of the giant Galapagos turtles. They are known internationally due to their unique biological diversity and constitute the main tourism attraction of Ecuador

(Ministerio de Turismo, 2016).

To define the tourism system of Ecuador, the present study adopts the perspective proposed by Jopp (2012) in which tourism comprises the different stakeholders of the tourism system and the tourism attractions of the destination. The determination of the key stakeholders of Ecuador is preponderant for future steps of the analysis, as well as the identification of the geographical boundaries, environmental and socio-cultural assets.

Definitions and Delimitations for the Study

Due to the complexity of the resilience study and the wide variety of definitions that have been disseminated in the literature, there was a need to establish the limits and definitions that have delineated this study. The following sections will provide thorough information of the definitions adopted.

Environmental assets

This study adopts the definition proposed by Repetto (1992) where environmental assets are considered as natural resources that are available in the system and that provide ecosystem services (Wallace, 2007). More specifically, the natural resources of the destination under study are those that offer the possibility of

providing ecological services that allow the development of tourist activity (Russo and

Van Der Borg, 2002).

Due to its geographical location Ecuador is considered an ideal green destination, with a high biodiversity of species and wonderful landscapes. Ecuador has two biodiversity hotspots (Myers et al., 2000) and it is home of 951 endemic species of fish, 1608 species of birds, 403 mammals, 557 amphibious, 450 reptiles, and 17934 vascular plants. The main environmental assets of the country are included in the protected areas. Ecuador has 50 protected areas: 17 in the Andes, 10 in the Amazon region, 21 in the pacific coast and, 2 in Galapagos Islands.

Ecuador is among the 17 most biodiverse countries on the planet, and the first in relative biodiversity and in terrestrial vertebrates. Additionally, Ecuador has 20% of the bird species in the world and presents high levels of endemism (Instituto Nacional de

Estadisticas y Censos, 2015).

Socio-cultural assets

For the understanding of the characteristics of the system’s actors, as well as for the identification of cultural resources, a definition of socio-cultural assets has been adopted that covers all those cultural resources of historical significance and includes both living and non-living assets (Chiesura and De Groot, 2003). A socio-cultural asset must basically have a symbolic value or mean something to someone, and its transcendence can be at the universal, national, or local level (Du Cros and McKercher,

2014).

Ecuador is a pluri-ethnic country where most of the population is mixed, but with an important representation of aboriginal communities in all its regions with five indigenous communities in the coast, twelve in the Amazon, and thirteen in the Andes.

Due to cultural diversity, Ecuador is an idyllic destination to engage in cultural tourism.

Intangible and tangible cultural assets are accessible for tourism activity, and include colonial architecture, archeological ruins of ancient civilizations, gastronomy, festivals, etc.

Key stakeholders

The identification of key stakeholders of the system is extremely important for the analysis in future steps of the study. It allows the achievement of the understanding of the system complexities and provides a multi scale perspective in the assessments.

The RTAF model proposes the inclusion of suppliers, tourism staff, tourists, and local community; however, the definition of tourism stakeholder proposed by the Ministry of

Tourism of Ecuador (MINTUR) was adopted in this study because it provided richer information about the context under study. Additionally, the participants of the different stages of the investigation were familiar with the MINTUR classification. Hence, in the resilience study, the actors of the tourism system were classified as: (1) the accommodation sector, (2) the food and beverage sector, (3) the recreation and parks sector, (4) the operation and intermediation sector, (5) tourist transport sector, (6) local and national governments, and (7) the tourists (Table 1-3).

With representation and inclusion of all stakeholders mentioned above this study aims to achieve a different scale analysis.

Risk and vulnerability

The present study adopts the concept of risk proposed by the Pacific Asia Travel

Association (2014) and Coombs (2014) where risk is a prospect or probability of a negative event that could develop into crisis. In the tourism field the standard way of assessing the gravity of a tourism crisis is through the numbers of lost arrivals, visitors

nights of spending (Laws et al., 2006). This study defines gravity of a crisis using only visitors arrivals due to it is the standard measure used by the Ministry of Tourism of

Ecuador and the Municipalities to measure the growth in the tourism sector.

Vulnerability in this resilience study is defined as the degree in which a tourism system is susceptible (sensibility) to harm due to risk exposure (Calgaro and Lloyd,

2008). Consequently, exposure is defined as the likelihood of occurrence that a risk

(fast driver of change) could impact the tourism system (Calgaro and Lloyd, 2008), and sensibility is conceptualized as the gravity for the tourism system if the risk occurs

(Laws et al., 2006; Calgaro and Lloyd, 2008).

Resilience

The RTAF model was adopted as framework for the present study. In the RTAF model resilience is defined as the capacity of the tourism system to absorb change

(Jopp et al., 2010), hence to survive after an event, and keep up in a desirable stage

(Berkes, et al., 2008). Under that paradigm resilience of the tourism system is focused on adaptation.

Risk and Vulnerabilities in Ecuador

Ecuador is located in the “ring of fire” of the Pacific Ocean, and it is a destination vulnerable to earthquakes, and possible tsunamis (Shoji et al., 1993). Moreover, due to its location on the equatorial line, Ecuador also experiences cyclical weather phenomena like “El Niño” and “La Nina” at least every ten years, and they are becoming even more frequent due to global climate change. Both “El Niño” and “La Niña” have compromised the tourism and non-tourism infrastructure of the country in the past; causing serious mudslides, overflow of rivers, drought, heavy rain, etc. Ecuador is also highly vulnerable to volcano eruptions. There are 250 continental volcanoes in Ecuador

and almost 3000 volcanoes in Galapagos Island, and 27 potentially active volcanoes in the whole country (including the Galapagos Islands).Of those, five have been identified as the most actives, and three of them have erupted in the past ten years. Volcanic eruptions not only affect the geographical destinations nearby, but also create a dust cloud that interferes with airport operations, causing huge economic losses for the tourism sector, and leading to health problems in the community as wells.

Because of Ecuador being located in the tropics, some epidemics could easily affect the tourism destination and even put visitors at risk. Some examples include the

Dengue and Zika outbreaks experienced in the past years.

Due to political changes and characteristics of the country there is a great possibility of political riots and other forms of violent and non-violent protests against the government. Political unrest has been historically a common issue in South America. In

Ecuador the last political riot happened on September 30th 2010, when the police force abandoned their duties and started a strike that lasted for almost a day. The results of such actions were high levels of delinquency in the whole country, forcing citizens and tourists to be under curfew for more than 18 hours. Major news channels like CNN and

Fox News covered the mentioned events, and the country’s image was considerably affected (FLACSO University).

Ecuador, like many other destinations in developing countries, faces high rates of crime. Common crimes that affect tourists in Ecuador are pick pocketing, robbery, and

“Secuestro Express” which is a modality of robbery in which taxi drivers retain passengers against their will and allow others to get into the taxi in order to rob the passengers. After the robbery, the passengers are commonly left in a remote location.

Significance of the Study

The tourism industry is highly dependent on the cultural and natural resources of a destination (attractions) as well as the quality of supporting infrastructure. Sudden changes, like natural disaster, political riots, and fires, directly affect the viability of the tourism industry. The tourism industry is a major booster of the national economy; it is an important generator of jobs, promoter of wellbeing and quality of life, and generator of taxes. When the tourism industry is hit with sudden changes (fast drivers), the economy can be devastated. Thus, having a plan for managing the crisis as well as how to overcome these sudden shocks is critical.

The present study is of great importance for destinations like Ecuador, where the geographical location and intrinsic characteristics of the community and government could lead to sudden changes and or disasters for the tourism sector. The aim of this study is to provide a process and outcome to create a more resilient destination through the reduction of vulnerabilities and enhancement of opportunities to respond to shocks to the system.

The present study provides a holistic proposal including in the analysis both demand and supply side of the sector in the elaboration and election of adaptive alternatives and vulnerability assessment that will lead to the increase of the resilience, resistance, and readiness of the destination under study. These type of studies are extremely valuable in tourism destinations, because they provide an applicable product that could be implemented at different levels.

Dissertation Format

The dissertation will follow a traditional dissertation format with five chapters.

Chapter 1 will offer an overview of the study, including the problem statement,

theoretical background, conceptual model, research questions, and significance of the study. Chapter 2 will be the literature review. Chapter 3 will detail the methods of the various steps in the study; Chapter 4 will present the results of all phases of the study.

Chapter 5 will provide a summary of all the results as well as theoretical and practical implications for the implementation and evaluation of the proposed model in the destination.

Table 1-1. Definitions of Resilience Resilience Definition Citation Engineering resilience for example focuses on the ability of something to (Folke, 2006). face stress and bounce back to a pre-designed stage or function prior disturbance, in other words is about maintaining efficiency of function, constancy of the system and a predicable world near a single steady state.

Ecological resilience allows for change of the stage of an ecological system Berkes, Colding, & after the present of a stressor as long as the new stage is desirable. It is Folke. (2008). about survival of the ecosystem after an unexpected event, and it’s focused on reducing the vulnerabilities, and find equilibrium.

Social – Ecological resilience includes humans and their influence in the (Tyrel & Johnson, analysis of the systems; it is seeking for adaptive capability, learning and 2008). innovation capabilities in a dynamic context, mostly focusing in planning and resource management, and concerning the viability of the tourism industry as well as the authenticity of the local culture . Community resilience is an attribute of the population which is characterized (Cutter, S. L., Barnes, by the active participation of the community’s members in anticipation and L., Berry, M., Burton, response to crises (social or environmental) in order to recover full C., Evans, E., Tate, E., functionality. & Webb, J. (2008).

Evolutionary resilience is focused in the social world, and its ability to deal (Sgro, Andrew, & with constant change through capacity adjustment and adaptation, it deals Hoffmann 2011). with several levels (individual/collective, local/regional) within a destination and its governance, adopting an inclusive stakeholder perspective with a networked and/ or multilevel governance

Table 1-2. Regions of Ecuador Region Tourism destinations Tourism Infrastructure Tourism organization in and attractions available charge Pacific Coast Guayaquil, Salinas, Accommodation (Hotels Ministry of Tourism Manta, Montañita, and Hostels). Ministry of Atacames, Parque Tourism transportation Environment, Nacional Machalilla and by air, water and land. Municipalities, other protected naural Information centers, Indigenous councils áreas, Zaruma, museums. Calceta.

Andes Quito, Cuenca, Accommodation (Hotels Riobamba, Mindo, and Hostels). Baños, Ingapirca Tourism transportation Ruins, Chimborazo by air, water and land. reserve, Cochasqui, Information centers, Otavalo, Pasochoa, museums. Pululahua, Mitad del Mundo, Los Illinizas reserve, Cotacachi – Cayapas reserve

Amazon Yasuni National Park, Accommodation Cuyabeno reserve, (Hostels, cabins). Sangay National park, Tourism transportation and Pococarpus by air, water and land. National Park.

Galapagos Islands Isabela, San Cristobal, Accommodation (Hotels Santa Cruz, and Hostels). Fernandina Tourism transportation by air, and water. Information centers, museums.

Table 1-3. Stakeholders Stakeholders as defined by the Ministry of Tourism Food and Beverage Restaurants, Bar, Fountain of Soda, Cafeteria

Hospitality Hotels, Hostel, Inns, hostels, guest house

Intermediation and Operation Travel agencies (international and wholesalers) and tour operators

Parks, attractions and recreation Recreation centers, Discotheques, Spas and watering places, event and conference centers

Tourism Transportation Airlines, Trains, Buses and land transport,

Government Ministry of tourism

Tourists Domestic and international

Figure 1-1. Regional Tourism Adapted Model

Figure 1-2. Proposed Model

Figure 1-3. Ecuador Map

CHAPTER 2 LITERATURE REVIEW

Tourism is one of the most important economic activities in the world, which was responsible for 10% of the global gross domestic product (GDP) in 2015. With a steady rate of growth across the globe, it constitutes an important source of income in many countries and generates one in eleven jobs internationally. The exports generated by the tourism industry in 2015 were 1.5 trillion dollars, and it is considered one of the largest and fastest-growing economic sectors in the world (UNWTO, 2016).

In Ecuador, the impact and relevance of tourism is growing. According to the

UNWTO Tourism Highlights Report (2016), Ecuador had over 1.542 million tourist arrivals and 1.691 million dollars received in 2015. From 2007 to present, the income generated by tourism activity in Ecuador has had an increase of 13%, and 1 in 20 jobs in Ecuador are directly related with the activity. It is undeniable the importance of tourism activity to the economy thus, the impact to the economic contributions by both natural and man-made disasters can be catastrophic (Laws et al., 2006).

Because of this, managing disasters has become more of a focus in recent years. More recently on the global front, United Nation (UN) organizations such as the

UNIDSR and UNWTO have begun to work together using a resilience framework to try and mitigate, manage, and recover from disasters which impact the tourism industry.

The concept of resilience surpassed the concept of sustainability in the international narrative. This is mainly due to the distinct difference, which identifies resilience as responding to shocks to the system in a temporal manner while sustainability is more about sustaining the assets in the destination over time. One of the major challenges, however, is the adoption of the appropriate model (Butler, 2017). In order to address

change over time, a model with empirical indicators of change seems to make the most sense. Unfortunately, a widely accepted model does not exist at the community level or at the destination level. The present study therefore overviews the community-based models, discusses the nuances of a destination model as well as explains the benefits for destinations to adopt a resilience framework.

Destinations are extremely vulnerable to slow variables (e.g., a set of drivers that could provoke a flip in the system) and sudden changes in the systems (natural disasters, political unrest, war, etc.). Visitors are unfamiliar with emergency responses in case of a disaster (Pennington-Gray et. al., 2011), and destination management organizations must assume the responsibility of keeping the visitor safe. According to

Holladay and Powell (2013), the tourism industry is vulnerable to destabilizing forces such as war, local-to-global economic complexities, and natural disasters. Therefore, the destination needs to increase its adaptive capacity as a whole to bolster recovery and deal with global and internal stressors that may cause the system to move from a desirable state to an undesirable state.

Resilience Theory

Holling introduced resilience theory in 1973, in his article entitled “Resilience and

Stability of Ecological Systems”, where he proposed that the behavior of ecological systems could be defined by two distinct properties: resilience and stability. Holling

(1973, p. 17) stated “resilience determines the persistence of relationships within a system and is a measure of the ability of these systems to absorb change of state variables, driving variables, and parameters, and still persist”. This definition attempts to explain the behavior of the ecological systems when change is imminent and presents resilience as a characteristic of the system that allows for dealing with a stressor but at

the same time ensures its own survival. According to Holling (1973), an ecological system needs to build resilience in order to avoid extinction.

The concept of resilience has been widely adopted and adapted by several fields resulting in a wide variety of terminologies and concepts that will be analyzed in the next section. This chapter attempts to provide an overview of the resilience theory and commonalities presented across the different resilience frameworks.

The resilience concept has three important aspects: latitude (L), resistance (R), and precariousness (P). Latitude is the maximum amount of change that a system can hold without losing its ability to recover (Walker et al. 2004), in other words, how much can the system change or be exposed to a stressor before crossing a threshold or point of no return. Resistance is how much the system can stay in a stage without changing in the presence of a stressor. Finally, precariousness is how close the system is to crossing a threshold.

The relationship among the three aspects of resilience is illustrated with the metaphor of the “ball in the basin” (Figure 2-1). In the metaphor, a ball represents the system, and the basin represents resilience. The ball will remain inside the basin for a short or long period of time depending on its position and the characteristics of the basin. If a system is resilient, the basin will be deep (resistance), but wide enough to provide room for change (latitude), therefore the ball will be far away from the point of no return (precariousness). However, if any of the resilience aspects change, the shape of the basin will change as well, and the ball will be more susceptible to move and possibly pass the point of no return. The various basins that a system may occupy and the boundaries that separate them are known as a “stability landscape” (Walker et al.,

2004). It is also important to consider that even if the system has a good latitude and resistance, the prolonged effect of a stressor could eventually make the system pass its threshold.

Passing a threshold, or point of no return, does not always represent a bad scenario. In some cases, the system is very resilient but it is not desirable. In this case, management strategies focus on reducing the resilience of the system and ensuring conditions that will allow for an improvement of the system. Accoding to Walker et al.

(2004, p.3) “actors can move thresholds away from or closer to the current state of the system, move the current state of the system away or closer to a threshold, or make the threshold more difficult or easire to reach”. Therefore the desirability of the system needs to be assesed before enaging in a resilience framework.

The resilience theory also includes concepts of “stressors” , “drivers” or “agent of change”. A driver is an agent of change that could affect the system to the point to make it “move” to another stage. Drivers could be endogenous if they are part of the sytem, or exogenous if they are not part of the system. At first this classification appears to be simple and straightforward, however, it is difficult to identify what is part or not part of the sytem in a connected world. Humans tend to be analyzed as both endogenous or exogenous variables depending on the framework adopted when conducting a resilience analysis. For example, engineering resilience uses humans classified as an external agent of change, while Social Ecological System Resilience views humans as part of a system and constitutes endogenous agents of change. There is more concensus when resilient frameworks adopt both slow as well as fast drivers.

Slow drivers are agents of change that influence the system gradually for longer periods of time. Slow drivers tend to be ignored because they do not present an inminent threat to the stage of the system. Fast drivers, on the other hand, are agents that cause great stress to the system. The time of exposure tends to be shorter but the effect on the stage of the system is more significant. To illustrate these concepts we can imagine a tourism destination in the Carribbean. Many agents of change or drivers of change can affect the stability of the destination. For example, contamination of the soil and water on a Carribean island will affect the quality of the natural resources, but it will take some time to see the impact in those resources. However, if a Category 4 hurricane hit the destination, changes to the system will be immediate and easy to spot.

In our example, the slow driver is the contamination, while the fast driver is the hurricane, both can have disastrous effects to the system but the time of the exposure is different and thus so are the effects.

Describing the System

Describing the system is the recommended first stage of any resilience assessment (Butler, 2017), and one of the most recommended steps proposed by the

Resilience Alliance which recognizes three important steps to understand the system:

(1) defining the resilience of what to what, (2) identifying the desirability of the stage of the system and identifying key issues within the system, (3) analyzing key issues in multiple space and time scales.

Resilience of What to What?

Despite the overwhelming variety of definitions and conceptualizations of resilience across many fields, one common thread is that the first stage of any resilience analysis needs to start with the delimitation of the “what to what” (Walker and Salt,

2012). The “what to what” approach is a simplistic way to delimitate any study. In other words, one must decide what the destination is resilient to. The process of “what to what” is not simple (Biggs et al., 2015).

An example will illustrate the complexity of delimitating the “what to what” when taking into account different stakeholders and including cross level analysis. Imagine you are a member of the Destination Management Organization (DMO) of a tourism destination located near a nature reserve, which has its primary source of income as tourism, fishing, and timber. Now to make things a little more complex, let us say that your destination depends on the natural resource as its primary tourism asset.

An important part of managing the destination is understanding risks associated with environmental assets in the destination. Natural resources would have risks associated with forest fires, insect contamination, and deforestation, to name a few. If any of these incidents affect the natural resource, destination arrivals may be impacted.

At first when the decline occurs, the community may complain about lack of tourists. If the decline continues, incomes generated in the community may also be impacted. The multiplier effect then may result in increased awareness of the value of tourism. Thus, the incident is recognized as a community issue, not just a tourism issue. Engaging in a resilience framework, which addresses drivers of change may enable quicker more effective responses to shocks to the system. However, when trying to adopt the resilience framework community could face the dilemma between engage in the preservation of the environmental assets (adopting a tourism resilience approach), or the exploitation of the natural resources for commercial purposes.

Desirability of the System, Identifying Key Issues

The second component of the resilience analysis is to understand the desireability of the system (Biggs et al., 2015). This analysis requires stakeholder participation. The main idea is to reach a consensus on the desireability of the system, taking into account social, ecological, economic, and government components. In addition, this analysis requires identifying the areas where the systems may need improvement or if the status quo is acceptable (Basurto-Cedeño et al., 2016). If the system is not desirable, an intervention may be needed in order to move forward to a better state before building resilience. Transformation to a resilient state requires investement, a vision, and concensus from the identified stakeholder groups. In the event that the destination is in the best state, the DMO can move forward and identify key issues that may compromise the resilience of the destination.

Multiple Space and Time Scales

The third step in the resilience process is to analyze and understand time scales

(Berkes et al., 2008). Time scales represent the time periods whereby the stressors may be analyzed. In slow drivers, time periods can be several decades. With fast drivers, the time period may constitute a couple of weeks or months (Berkes et al.,

2008). Use of a multiple space analyses identifies issues in order to determine the relative scope of the shock to the system. For example, to what extent is the driver specific to the destination? which is large, in scope and occurring simultaneously outside the system? Drivers that are large in scale are sometimes difficult to manage, but should be analyzed and planned for, so response measures can be implemented in the system. Drivers that are small in scale are actionable, which makes planning and managing for these drivers easier (Biggs et al., 2015; Butler, 2017).

Different Approaches to Achieve Resilience

The concept of resilience has become widely popular and accepted among different fields. The result of the extensive adoption of the framework across disciplines has resulted in a variety of definitions and metrics available in the literature. The selection of the proper framework of resilience can be overwhelming. However, attention to answering the question “resilience of what to what?” is the critical step to choosing the appropriate framework. In this section, several frameworks from outside the tourism field will be discussed: (1) engineering resilience, (2) ecological resilience,

(3) social ecological resilience, and (4) evolutionary resilience.

Engineering

The first framework to discuss is the engineering resilience framework. This framework outlines resilience as the behavior of the system to return to a state of equilibrium after exposure to a stressor or driver of change. In other words, resilience is measured as how fast a system can return or bounce back to the previous stage in the presence of the stressor. Underlying this paradigm, is a temporal function, whereby an estimation of time is required to determine when the system returns to a “normal” state

(Folke, 2006). In this approach, the process is conceptualized as linear. Engineering resilience “focuses on maintaining efficiency of function, constancy of the system, and a predicable world near a single steady state” (Folke, 2006, pg 256).

Ecological

Second, ecological resilience or ecosystem resilience is the capacity of the ecosystem to withstand shock, and maintain its function. This approach recognizes the ability of a system to absorb a disruption and keep functioning, maintaining its original identity but allowing for different states. The concept of ecological resilience

acknowledges the existence of multiple systems and not just an ideal steady state.

Ecological resilience is defined by its adaptive capacity that is the degree to which a system is capable of reorganization and adaptation (Brown and Williams, 2015).

Adaptation within this framework depends on the diversity of the species and their evolution; therefore, the purpose of ecological resilience is maintaining the system.

Research in this area tends to focus on the transitions between system states and across thresholds and on features of the dynamic nature of the system, identification of alternative stable states, identification of key drivers and system response variables, and early warnings of shocks which may alter existing thresholds

(Brown and Williams, 2015). The typical methods used in the ecological resilience framework tend to be empirical, including mathematical models and experimentation.

Social Ecological System (SES) Resilience

The third resilience framework to review is called the Social Ecological System of

Resilience. Under this approach, the stability of linked systems that include both humans and nature are evaluated. Three variables define this framework: resilience, adaptability and transformability (Walker et al., 2004). The Social Ecological Resilience framework acknowledges that systems are complex and interrelated and that they are in constant change within a cycle. Resilience is defined as the capacity of a system to absorb disturbances and reorganize while undergoing change and still retain essentially the same function, structure, identity, and feedback (Walker et al., 2004). The SES resilience approach adopts an adaptive renewal cycle. The adaptive renewal cycle is also referred to as “Panarchy” and includes four phases: exploitation (r), conservation

(K), release (Ω), and reorganization (α) (Gunderson and Holling, 2002).

The exploitation and conservation phase comprise a slow, cumulative forward loop of the cycle that is predictable. The conservation phase is characterized by a reduction in the flexibility of the system and decrease in its capacity to respond to external shocks (Walker et al. 2004). The conservation phase is typically followed by a collapse to the system (release phase) that leads to reorganization of the system.

The nature of this adaptive cycle allows for interactions across multiple scales. In other words, the system is dynamic and can move from one stage to another and exist at different scales. According to Walker et al. (2004), no social ecological system can be understood at one scale. The interaction of people organized at multiple levels is inevitable and thus, determining the desirability of the state of the system and the effect of an intervention at different scales is necessary. This point of view allows for integration of different perspectives, and results in more holistic strategies with better chances to achieve positive outcomes for the whole system, and not just for a selective group of stakeholders.

Within the SES Resilience framework, the concept of adaptability is important.

Adaptability is considered a function of the social component. In other words, it is the capacity of the human actors to manage the system and influence the resilience of the community (Walker, et al., 2004). The adaptive capacity of the system depends on cultural diversity, learning and innovation (Brown and Williams, 2015). The SES resilience framework recognizes the importance and influence of humans when shaping social and ecological components of a system. Under this paradigm, people play an important role in the pursuit of sustainability of the system. The recognition of humanity as a major force that shapes ecosystems and the planet in general is a cornerstone of

the SES resilience approach (Folke, 2006). In the SES resilience framework, humans are actors within the system that are closely related and highly dependent on the environment. Therefore, it is necessary to evaluate the influence of humans on the resilience of the system.

Biggs, et al., (2015) recognize the importance of humans when shaping their environment. They present seven principles to build resilience using a social ecological systems framework. They suggest two major strategies: (1) governance systems strategies and (2) properties systems strategies.

Under the governance system strategy, resilience could be accomplished with investment in four areas: (1) the improvement of the inclusion and participation of different stakeholders, (2) the adoption of complex systems thinking, (3) inclusion of all types of learning available within a system, and (4) polycentric governance. Under the

“properties” strategy, three main principles can be followed to build resilience: (1) promote diversity and reduce redundancy, (2) allow for connectivity of the actors of the social ecological system, and, (3) identify slow variables of change.

An important element of this approach is “self-organization,” which is the organization of the system by external factors (Adger et al., 2005). Self- organization or a grass roots initiative allows for transformability of the system. Transformability can be positive or negative. The way this process is shaped will depend on the knowledge of the capacity of the system, and the ability of its actors to cope with change in both social and ecological structures.

Under the SES resilience paradigm, resilience is not just about resistance to change and conservation of existing structures but is also about the opportunities that

disturbances offer to recombine, evolve, and in general incur a positive change to the system (Folke, 2006). Another way to measure resilience is as an outcome of the relationship between adaptive capacity and the system capacity of self-organization

(Adger et al., 2005). For Adger (2006), social ecological resilience is measured by built capacity and self-organization, where built capacity is a function of learning, regenerative ability, adaptation, governance and management. According to Folke

(2006), losing resilience implies loss of adaptability.

Evolutionary Resilience

Evolutionary resilience is a newer framework proposed by Davoudi et al. (2012).

This framework is concerned with the social element of resilience and its ability to cope with change. According to Davoudi et al. (2012), resilience is not conceived of as a return to normality, but rather as the ability of complex socio-ecological systems to change, adapt, and, crucially, transform in response to stresses and strains. Systems are conceived as “complex, non-linear, and self-organizing, permeated by uncertainty and discontinuities”.

Moreover, Fabry and Zeghni envision evolutionary resilience as an approach focused on social worlds and decision making, where the aim is to assess management and new trajectories in order to determine actions needed to build adaptation to complex and constant systems. In order to achieve evolutionary resilience, the participation of the government is pivotal; but it is also important to include as many different stakeholders as possible in the process. The evolutionary resilience model takes into account the importance of resources (tangibles and non-tangibles), stakeholder participation, networking, and value change when creating a product. In order to promote resilience, multisectoral governmental policies and constant adaptation

to risk and vulnerabilities is critical for effective implementation. Those goals can be fulfilled with the help of an adaptive, reflexive, and deliberative governance process, which permits stakeholders to be included in the decision-making process.

Community Resilience

Community resilience is an attribute of the population. How resilient a community is depends on active participation of members of the community in planning and response to crises (social or environmental). The greater the planned actions are of the community, the quicker the recovery period of the destination will be. Effective community resilience is not only the volume of community member’s involvement in the process but also the bonds between different groups within the communities. According to Norris (2007) there are four factors that shape and define community resilience: (1) economic development, (2) social capital, (3) information & communication, and (4) community competence.

Community resilience is a concept that has been widely used in social sciences to explain the recovery capacity of a community and its ability to change or evolve in order to overcome a shock to the system. Plenty of studies are available which explain this community resilience phenomenon. In this section, we are going to analyze the main characteristics of community resilience and the ways scholars have measured this resilience.

According to Herrera and Rodriguez (2016), communities are resilient if they have faced a major disaster and have returned to a “new normal” state. Herrera and

Rodriguez use the term “new normal” to refer to a positive change to the system which is still in equilibrium after the disaster. They argue that after a shock of significant magnitude, things will not return to a previous state. Change is imminent in some

communities; the members of the community may be reluctant to return to their homes and thus move to “safer” locations. Under this perspective, resilience of a community needs to be assessed with a longitudinal perspective in order to monitor the evolution of the destination over time. After a disaster, three main dimensions are monitored: (1) ecology, (2) social and (3) economic. Indicators of resilience within the social dimension are measured by demographic characteristics and perceptions of wellbeing. Within the ecology dimension, measures take into account rebounding of parks and forests from shocks. In the economic dimension, number of businesses and diversification of these businesses are considered.

For Cai et al. (2016), community resilience is the ability to prepare, plan, absorb, recover from, and adapt to adverse events. Resilience is based on the relationship between vulnerability (how exposure to a stressor could result in damage to the community), and adaptability (how a community can recover after severe damage) as well as five community resilience dimensions. These dimensions are: (1) social, (2) economic, (3) infrastructure, (4) community, and (5) environmental. According to Cai. et al. (2016), communities can be located in four possible stages: (1) susceptible, (2) recovering, (3) resistance, and (4) usurper. Identification of the stage of the community is important for destination planning and better management of resources when preparing for possible disasters.

Norris et al. (2008) provides another approach of community resilience. For

Norris, resilience is a process which outcome is adaptation after a disaster. Under this paradigm, disasters are defined as a potentially traumatic event that is collectively experienced, has an acute onset, and is time delimited. For this researcher,

communities are composed of four dimensions: (1) built infrastructure, (2) natural environment, (3) social and (4) economic environment. How those mentioned dimensions evolve after a disaster will determine how successful their resilience plan is.

When the community resilience process is successful, the recovery could last several weeks, but generally will involve a stable trajectory of healthy functioning. While a non- resilient community will take a longer recovery process involving periods of dysfunction lasting for several months, followed by a gradual return to pre-event functioning.

Important attributes of community resilience are identified in the approach and are considered areas where investment is needed in order to make the community more adaptable to disasters. Those areas are: (1) robustness, (2) redundancy, and (3) rapidity (first identified by Bruneau et al., 2003). Robustness is the ability of the community to withstand stress without suffering degradation. Redundancy is considered the extent to which elements are substitutable in the event of disruption or degradation.

Finally, rapidity is how quickly the community or visitors can use the resources after a disaster.

The process of resilience is about linking a set of adaptive capacities to a positive trajectory of functioning and adaptiveness after a disturbance. Under this umbrella, adaptation is measured by (1) physiological wellbeing of the community (absence of psychopathologies, healthy patterns of behavior, adequate role functioning at home/ school/work, and perception of high quality of life), and (2) population wellness.

Moreover, the set of capacities (networked resources) that should become the focus of community resilience are economic development, social capital, information and communication, and community competence.

Resilience in the Tourism Field

In the first part of this chapter, we provided an overview of the economic impact of tourism globally and we suggested that without having a resilience framework to manage the destination, the impact to the destination in the event of a natural or man- made disaster can be catastrophic. The recognition that the industry is vulnerable to unplanned disasters has given rise to a new trend in research. This new trend of research acknowledges that change is imminent, thus destinations need to be prepared to face the challenges, particularly when communities depend on tourism as their main economic activity. Similar to community resilience, the selection of the resilience framework depends on the quantification of measures, the definition of the “what to what” and the identification of diverse drivers of change.

The selection of the appropriate framework for a destination will depend on the decision of the destination managers as well as stakeholders and their identification of what they consider the best way to move forward. When the appropriate framework is selected, determining measures that fit the identified variables will need to be determined. In the tourism field, resilience studies have mainly focused on building resilience within tourism enterprises or within the tourism community. This section will provide an overview of both approaches and will provide in detail the main findings of the resilience literature in the past three decades.

Enterprise Resilience

Enterprise tourism resilience is oriented to building adaptability within tourism businesses in order to maintain their existence, level of employment, and income.

Recognizing that destinations can be subject to high levels of volatility in tourism demand businesses must invest in plans that respond to these slow and fast drivers

(Biggs, 2011). The concept of enterprise resilience is a social ecological system resilience-based approach, where the social component of the system (humans) is interested in managing the natural and cultural resources of the destination in order to ensure the stability of the tourism enterprise is maintained in the face of a disturbance or unpredictable change.

In order to build enterprise resilience, an approach developed by Biggs (2011) allows for the use of surrogates, which acknowledges important aspects of resilience may not be observable and must be inferred. Under this paradigm, four factors have been identified as important variables when building enterprise resilience: (1) enterprise age, size, and experience, (2) ecological condition of the system, (3) levels of social and human capital, and (4) lifestyle value of enterprise owners.

Dahles and Prabawa (2015) suggest that resilience uses past experiences of tourism businesses to identify tactics, which are more efficient to achieving resilience.

The most effective tactics are then used as determinants to develop the tourism enterprise resilience plan and identified areas are invested in. The problem with this approach is that it ignores the differences among businesses and only relies on qualitative measures of recovery.

According to Sydnor (2009), the resilience of the tourism industry is a function of the resilience of the community. Thus, variables surrounding the natural capital, human capital, social capital, economic capital and physical capital (infrastructure) must be taken into account. According to Sydnor (2009), the outcome of resilience is recovery.

Measures of resilience include: (1) the number of tourism businesses available, (2) the number of jobs that such business provides and (3) the annual payroll after and before

a disaster, which will be a reflection of the number of businesses, number of jobs, and the annual payroll of the community.

Tourism Community Resilience

Tourism is a human activity, which is highly dependent on the availability of resources within a community. The tourists’ experience is not only affected by the condition of the tourist sites, but also by human contact and other aspects of the destination that are not directly related to tourism activity. Similarly, the tourism sector is affected by the same things that affect the community and thus cannot be seen as a separate function. Based on this philosophy, Lew (2014) proposed a resilience model that recognized that tourism destinations and businesses face shocks, which can impact both an individual entrepreneur as well as the entire community. Therefore, tourism destinations need to be prepared for changes from those shocks, deterioration after the shocks, and even complete loss because of the shocks.

Recognition of the vulnerability of the tourism industry requires a tourism resilience approach. Lew (2014) suggests that any approach needs to separate the notion of sustainability from the notion of resilience. Under the community resilience framework, Lew (2014) identifies four main indicators of resilience: (1) building community capacity so change can be promoted when needed, (2) creation of new environmental knowledge integrating different perspectives, (3) improving living conditions for community members and employees of the tourism sector and; (4) supporting social collaboration.

Assessing community resilience is a complex process due to the nature of people, the community and the environment. However, the more resources a community possesses, the more resilience its industries may have after a disaster

(Sydnor, 2009). Therefore, what is good for the community is also good for the tourism sector. More community capacity can make up for limited resources in local economic development (Leigh & Blakely, 2013).

For Becken (2013), resilience in tourism destinations is about increasing robustness, assessing vulnerability, and increasing the adaptive capacity of the destination. For the researcher this could be obtained through a model capable of implementing the stability landscape in a qualitative analysis using surrogates to measure resilience in its attributes (latitude, resistance and precariousness).

Resilience in Tourism an Emerging Approach

Resilience in tourism emerged as a necessity to achieve sustainability of the sector and with the aim to preserve the natural and cultural resources of tourism destinations while acknowledging that there are many factors (internal and external) in different scales that need to be taken into consideration in tourism planning. Whereas the concept of tourism sustainability has been in the literature for a long time, recent acknowledgement of the limitation of the traditional conceptualization of the triple bottom line approach: “socially acceptable, ecologically viable and economically feasible” (McCool, 2016. p. 10), as overly simplistic, outdated and non-inclusive of the real characteristics of the social ecological systems (McCool, 2016). Hence, a necessity to move forward to a more holistic framework to achieve the desired sustainability has arisen.

Resilience is a concept that has its origins in the ecological field. Hollings proposed it in 1973. However, in the tourism field the concept started to be adopted in the eighties. The first inclusions of the concept of resilience in the tourism literature was used to describe the characteristics of the destination where the tourism activity was

developed (Macnaught, 1982; Pearce, 1985; Singh, 1985; Romeril, 1989) During the nineties, the concept of resilience was mostly used to label how flexible or resistant the destinations and the communities were when facing tourism development and the increasing number of tourists (Piccard, 1990; Milne, 1990; Pilgram, 1992.)

For Holder (1980) and Chib (1980), resilience was also used to characterize the capacity of the tourism industry to recover from the different fluctuation of the macro economic factors. In the late nineties and beginnings of the 2000, the resilience theory and framework started to be widely adopted for the industry as a way to achieve sustainability of the sector and the host destination. Moreover, resilience theory started to be embraced as complementary to the risk science, and proliferation of resilience literature in the tourism field grew exponentially. Pizam and Smith (2000) analyzed the resilience of tourism destinations in the face of terrorism and found that three factors

(product development, diversification and pricing) appeared to be affecting the capacity of recovery of the system after an attack. For Farrell and Twining-Ward (2004), to enhance resilience the destination planners need to consider the different scales of the tourism system and acknowledge its complexities. Hence, resilience could not be accomplished assuming a linear approach, due to systems not being linear and need to be studied taking into consideration different levels of analysis such as global, regional, and local. Zeng, Carter, and DeLacy (2005) found similar results in the analysis of resilience of the tourism sector to short-term crisis. Moreover, they identified five types of short-term crises that might affect the tourism sector (human epidemics, animal epidemics, weather/natural disasters, civil strike/violence, and war or terrorism) and

recommended three adaptation strategies to cope with such crises (reconstruction, management of media, grants and subside for small business).

Johnson et al. (2007), in the same line of research, identified that an increase of awareness and improvement in staff training and preparedness will lead to an increase in resilience. However, they also found that in reality, the tourism sector was unprepared to deal with short-term crises (like tsunamis), hence, identification of the key stakeholders of the tourism system was advised to promote strategies and focus on understanding of vulnerability, and risk. Johnson et al. (2007) also advised to work on emergency planning issues, training (individual and organizational), barriers, and false alarms.

For Tyrell and Johnson (2008) resilience in the tourism field needed to focus on the destinations and its capacity to recover from the tourism activity. They acknowledged that tourism could lead to a deterioration of the cultural and natural resources of the destination, compromising the sustainability of the tourism activity in the future. They argued that in order to enhance resilience in the destination, the identification of the tipping points are necessary. With that aim they developed a mathematical model to analyze the tourism destination quality in three dimensions: (1) ecological-environmental, (2) economic-fiscal, and (3) social - cultural. Tyrell and

Johnson (2008) also recognized the importance of the government to implement policies to control activity and allow for recovery of the destination.

For Sydnor-Bousso (2009) on the other hand, the factors that drive resilience in the tourism industry are natural capital (ecological systems), human capital (levels of education in the sector), social capital (social cohesion and personal involvement),

economic capital (resources in money), and the physical capital (built environment).

Sydnor-Bousso (2009) also acknowledged the complexity of the tourism system and the need for an analysis at different scales, and concluded that the more resources the community possesses, the more resilient the industry will be.

Hence, what is good for the community is inevitably good for the tourism industry, and efforts to increase resilience in the sector must start within the community.

Likewise, Ruiz-Ballesteros (2010) acknowledged that the resilience framework helps to understand the complexity of the tourism systems, and that communities need to (1) learn to live with change and uncertainty, (2) nurture diversity for reorganization and renewal, (3) combine different kinds of knowledge, and (4) create opportunities for self- organization to increase resilience of the system and the tourism sector.

Strickland-Munro et al. (2010) mentioned that adaptability is needed to achieve resilience in protected areas designated for tourism activity. They concluded that adaptability is determined through social, financial, human, natural, physical, and technological capital. Furthermore, systems of governance and institutions also influence the adaptability of the social – ecological system. For Strickland-Munro et al.

(2010) profound understanding of the system is required in the resilience assessment, and the assessment must include the geographical delimitation, the local community, and the tourism component (tourism activities within the geographical area). The investigators also established that for the identification of threshold in the tourism resilience analysis a scenario method is advised.

With a similar approach, Larsen et al. (2011) suggested the use of scenario planning while using the resilience framework. For Larsen et al. (2011), scenarios are

needed to have an idea of the desirable and undesirable future of the system under study. Besides, inclusion of stakeholders (formal and informal) is highly advised to gain a deep understanding of how the system will adapt and respond to uncertainty and surprises associated with risks. The implementation of the stakeholder theory in the resilience tourism literature has been widely utilized to achieve the understanding of the system at different scales (Sydnor-Bousso, 2009; Jopp et al., 2010; Bigg, 2011. Biggs et al., 2011; Lew, 2014; Lew, 2016) and it also allows the identification of crucial factors to increase resilience and mitigate risk.

For Biggs (2011), resilience in tourism should be focused on making the business sector stronger. With that aim, some factors need to be included in the resilience analysis such as enterprise characteristics, environmental conditions, social capital, lifestyle identity, and economics. Using the mentioned factors Biggs found that informal enterprises displayed higher levels of resilience than formal enterprises in the face of crises. One probable cause for such findings is that informal tourism enterprises have greater possibilities to implement adaptation strategies without depending on the approval of shareholders, which would lead to an increase in the speed of implementation. On the other hand, Holladay and Powell (2013) argue that long term planning will lead to resilience of the destination, maximizing the efficacy in the implementation of the strategies. Additionally, domains to measure resilience need to be in line with the sustainability goals for the destination. Hence social, governance, economic, and ecological domains need to be part of the resilience assessment.

Understanding of the resilience theory and application in the tourism field have been done from different angles, leading to different results within the sector. For

example, Becken’s (2013) idea of resilience is not measured through domains, but with the use of surrogates derived from qualitative analysis of the system under study. The surrogates, will allow to measure characteristics of the stability landscape (resistance, latitude, and precariousness).

In a different approach, Kruse et al. (2013) focused on adaptation as the key to achieve resilience in tourism destinations. They identified several potential adaptive strategies that could lead to resilience in the Alpines. They argued that, with the (1) developing and securing of tourism activities, (2) promotion of year-round tourism, and

(3) other adaptation strategies (such as informing tourist of climate change impact, improving natural hazard management and risk reduction, promoting research and development projects, among others) could reduce the negative impact of climate change and increase resilience. Kruse et al. (2013) also identified 3 dimension where tourism destinations need to focus in order to achieve adaptive capacity. The mentioned dimensions were (1) Knowledge awareness, (2) Ability (touristic infrastructure, and technology), and (3) Actions (institutions and economic resources). Furthermore, they found that adaptation strategies are primarily realized in an autonomous, private, and local manner in the tourism sector, and are encouraged by economic incentives to reduce loss and meet future demand.

For Lew (2014), resilience and sustainability are different paths of development.

Whereas sustainability is focused on stability, resilience is promoting change and adaptation. Within the context of rural communities, Lew found that the indicators for measuring resilience and sustainability where different. The resilience indicators according to Lew are (1) building community capacity, (2) creating new environmental

knowledge, (3) improving living conditions for tourism employees, and (4) supporting social collaboration. In the same context, Hamzah and Hampton (2014) found that small rural tourism communities that refuse to adapt and change will reach tipping points in a short term. According to Espiner and Becken (2014), resilience in protected areas is achieved through the increase of self-sufficiency levels, and the ability to respond with only minor operational adjustments. They also identified three major drivers of change in protected areas used for tourism: (1) cost of energy, (2) climate changes, and (3) natural risks; and appointed the preponderant role of governance to increase resilience.

For Calgaro et al. (2014), resilience of a tourism destination could be achieved through risk reduction and vulnerability assessments. They argued that with the decrease of the amount of exposure and sensitivity of the system, the destinations will increase their adaptiveness and hence improve their resilience.

More recently, Orchiston et al. (2016) proposed that organizational resilience is an important concept in the disaster management literature in the tourism sector. They defined organizational resilience as “the capacity of organizations to adapt to disturbance and seize opportunities emerging from changed environment” (Orciston et al., 2016, p. 145). Besides, they identified two dimensions (1) planning and culture and

(2) collaboration and innovation to understand resilience across and within sectors.

Resilience Research Organizations

The resilience framework constitutes a shift in the linear-system paradigm and acknowledges the complexity of systems at different scales (Walker and Salt, 2012).

For its nature, the resilience framework is interdisciplinary, a characteristic that has made possible the formation of resilience research groups or organizations. Resilience organizations have helped to advance the understanding and application of the

resilience framework at different levels and considering different perspectives. The main resilience organizations, which have gained a worldwide scientific reputation, are (1)

Resilient Organizations, (2) Resilience Alliance, and (3) Stockholm Resilience Centre.

This section will provide an overview of each one of the mentioned research groups and their contribution to the resilience science.

Resilient organizations

Resilient Organizations is a research group, which in collaboration with the

University of Canterbury and the University of Auckland in New Zealand has developed the resilience benchmark tool that can be applicable to different enterprises and organizations. For the group, resilience is conceptualized as the ability of an organization to survive crisis and thrive in a world of uncertainty. The resilience benchmark tool developed for the group is used to compare resilience strengths and weaknesses against other organizations within the sector of interest. For the resilient organization group, resilience could be assessed using 13 indicators to measure three dimensions: (1) leadership and culture, (2) network and relationships, and (3) change ready. For the "leadership and culture" dimension the mentioned approach considers the levels of leadership, staff engagement, decision-making, situational awareness, and innovation and creativity. For the "network and relationship" dimension, the indicators that need to be considered are: effective partnership, internal resources, leveraging knowledge, and breaking silos. Finally, for the "change ready" dimension it is necessary to consider indicators such as proactive posture, stress testing plans, planning strategies, and unit of purpose. The strength of the indicators, which make some organizations to survive and thrive in the face of adversity. Therefore, resilience is a strategic capability.

The 13 indicators proposed by the group are the result of the research work conducted in Canterbury after the earthquake of 2010.

Resilience alliance

The Resilience Alliance (RA) is an international, multidisciplinary research organization that explores the dynamics of social-ecological systems. As part of their contribution to social-ecological resilience, they have proposed a workbook for practitioners that includes a conceptual model of a system and provides a general guideline of how to assess resilience where recognizing the role of resources, stakeholders, and institution. The resilience assessment proposed by the RA utilizes a strategy based on the use of questions and exercises to construct a conceptual model of the social-ecological system of interest. The RA proposes a five step approach: (1) describe the system, (2) system dynamics, (3) interactions, (4) system governance, and

(5) acting on the assessment, in order to understand and promote resilience in social- ecological systems.

Stockholm resilience centre

The Stockholm Resilience Centre is an organization that has the backing of the

University of Stockholm and it has focus on the understanding of the resilience framework concentrating in three pillars of research (1) landscape, (2) marine and (3) urban sustainability. Its main research streams are centered in the understanding of the complex adaptive systems and resilience thinking, possible partnership with the

Anthropocene, stewardship and system transformation. The working method of the

Stockholm Resilience Centre is based on innovative and collaborative research with experts of different disciplines and divergent competencies to perform effective teams.

The Regional Tourism Adaptation Framework RTAF

The RTAF approach was developed by Jopp, Delacy and Mair in 2010 to assist regional destinations managers and policy makers to deal with the impact of climate change (Jopp et al. 2013). For the researchers, tourism is an industry highly dependent on natural resources that could be negatively or positively impacted by the climate. With the aim to address the possible alterations generated by climate change, it is suggested to manage risks through mitigation and adaptation.

Adaptation under this framework is oriented to create strategies to adjust the tourism activity in order to reduce vulnerability and improve business certainty.

However, the destination adaptation approach could be complex to achieve due to the large number of stakeholder involved. Vulnerability under the RTAF approach is a function of exposure to climate factors that includes the socio – economic factors in the assessment. The vulnerability assessment of a tourism destination needs to be performed before moving forward to the development of adaptation strategies.

Under the RTAF paradigm, the assessment of vulnerability and the identification of viable adaptation strategies will lead to the increase of resilience, resistance and readiness of the system; and the mentioned concepts taken from Jopp et al. (2010) are defined as follows:

• Resilience is defined as the ability to absorb change

• Resistance is conceptualized as the reduction the impacts that are likely to affect tourism

• Readiness is the ability of the destination to capitalize on opportunities that arise.

The RTAF models includes two phases (Figure 2-2); the first phase focus on the assessment of vulnerabilities and resilience of the destination, and involves three steps:

(1) defining the system, (2) establish risk and opportunities, and (3) determine the adaptive capacity. The definition of the system includes an in depth analysis of the destination under study and its geographical boundaries, inventory of the environmental and social - cultural assets within that geographical delimitation, and the identification of the key system stakeholders (Table 1-3).

For the establishment of risk and opportunities the model recommends the participation of key stakeholders of the system in order to identify, assess and categorize the risk and opportunities that could emerge from change. The final step of this first phase of the model is to determine the adaptive capacity of the system under study through the identification of factors that could limit or enhance vulnerability for the destination. Jopp et al (2013) recommend determining the following elements (taken from Jopp et al. 2013) in order to determine the support over an adaptive strategy:

• Economic development • Dependence of tourism for income and employment • Dependency of tourism resources • Degree of seasonality • Level of access to technology and resources

The second phase of the model is focused on increasing the resilience, resistance and readiness of the system, and involves the process of adaptation which has five steps: (1) identification of adaptation options, (2) assessment of the adaptation options, (3) test with the consumers, (4) implementation, and (5) evaluation. Through the mentioned steps the model aims to obtain both demand and supply side perspectives of the efficacy of the adaptation options for the destination, ultimately leading to increased resilience, resistance and readiness of the destination.

Gaps in the Knowledge

The tourism industry is highly dependent on the availability of both cultural and natural resources within a destination. However, in many cases unsuitable tourism development is a problem in a destination. Unsustainable tourism could lead to degradation of available resources within a destination to the point that it could challenge the very survival of the tourism industry in the future (Basurto et al., 2015).

For that reason, destination managers, the tourism business sector and even the community need to engage in long-term planning and invest in adaptation strategies to respond to shocks to the system. Considering this, Tyrel and Johnson (2008) addressed this aforementioned issue. They proposed the idea of “dynamic resilience” using a mathematical equation model to determine the level of the social ecological systems resilience to tourism-induced stress. In other words, how fast the natural and cultural resources of a destination recover after continued exposure to tourists. This idea goes hand in hand with the concept of carrying capacity; however, the concept of dynamic resilience includes thresholds, which may lead to measures that are more accurate.

There is need in the field of tourism to understand the relative influence of social capital and natural capital on resilience. The majority of the tourism resilience literature has focused on ensuring the survival of the destination in economic terms. However, the quality of the tourism destination and its attractiveness depends on more than just the economic contribution of tourism but also the state of the environment, cultural assets, social assets, and how these components are perceived by the visitor (Tyrell &

Johnson, 2008) and the community.

With the aim of providing adaptive strategies to face uncertainty while taking into account the visitor/ community acceptance, an adaptation to RTAF model is proposed in

this study. Moreover, the majority of the adaptation and resilience approaches are theoretical, and there is a need for a more applicable framework that could be used at different scales. The RTAF approach proposed by Jopp et al. (2010) presents a scalable model that could be applied at the national, state, and local levels. The RTAF approach could be further modified to be able to assess different sources of change and therefore used to increase different levels of resilience in a tourism destination, finally contribute to the achievement of sustainability. The present study proposes to use qualitative and quantitative methodology, taking into consideration the inputs of the demand and supply side of the tourism sector, and across sectors of the destination of

Ecuador to apply the RTAF model at the national level. This way the framework will be useful in the identification of all kinds of possible risks that could affect a tourism destination, and not only the ones related to climate change.

Figure 2-1. The Ball in the Basin

Figure 2-2. Regional Tourism Adaptation Framework Model

CHAPTER 3 METHODS

The purpose of the study was to develop and apply a destination resilience model for the country of Ecuador. The study was conducted in cooperation with

Ecuador’s Ministry of Tourism (MINTUR) and the University Laica Eloy Alfaro de

Manabí (ULEAM). A sequential mixed method approach was applied in the study, which was conducted in four phases (1) Adaptation of the RTAF model, (2) Risk and vulnerability assessment, (3) Supply adaptation assessment, and (4) Demand adaptation assessment. Data analyses for each phase varied from each other and will be explained further in the following sections (Figure 3-1).

This research adopted a mixed method exploratory sequential design, which included a qualitative study followed by quantitative component to address the research problem. This methodology allowed for a deeper analysis of the system under study at different scales and considered the opinion of experts and stakeholders within the destination; this approach allowed for the qualitative and quantitative assessment of the risks and adaptive capacity of the destination, while at the same time provided empirical validation from the community (possible domestic tourists). The level of acceptance of the strategies proposed by the stakeholders provided an idea of the effectiveness of such alternatives to decision makers and destination planners. Finally, the mixed method design made possible to achieve greater validity of the results due to triangulation, and completeness of the understanding of the phenomenon (Creswell &

Clark, 2011).

Study Site

All phases of the study were conducted in continental Ecuador. Ecuador is an

Andean country located in South America between Colombia and Peru (Figure 3-2) to the west of Ecuador is the Pacific Ocean, and the country features 200 nautical miles.

The total area of the country is 109,480 sq. miles, of which 5% is water. The population per the 2010 census was 14,483,499 inhabitants with an estimated 16,144,000 in 2015 according to the INEC (Instituto Nacional de Estadisticas y Censos).

Due to its geographical location along the equatorial line, Ecuador is home to many endemic and exotic species and has 50 protected areas, 17 in the Andes, 10 in the Amazon region, 21 in the pacific coast and, 2 in Galapagos Islands.

Guayaquil is the biggest city in Ecuador, and the tourism industry has had an intensive growth in the past decade. In 2015, more than two million tourists visited the destination (Municipality of Guayaquil, Tourism observatory), of which 1739662 were domestic tourists. The average length of stay in the city is five days, and the average expenditure is 249 dollars.

Quito is the capital of Ecuador, and it is located at the slopes of the Pichincha volcano at 2818 m. altitude. It was founded in 1534 in the ruins of an Inca city. Quito possesses one of the most extensive, best preserve and least altered historic center of

Spanish America. In 1978, UNESCO declared Quito a World Cultural Heritage Site. The

Historic center of Quito is a jewel of preservation of the colonial Spanish. Quito was the second most important territory among the Spanish colonies in America, and the home of the art school “Escuela Quiteña”. The Centre Historic of Quito has kept their traditional architecture and has numerous museums and churches that still display colonial art.

Other important tourism destinations in the country are Atacames, Salinas,

Baños, and Manta. Atacames is located in the Province of Esmeraldas, home of the tallest mangroves in the worlds, and the Marimba. It has the biggest natural beach of

Ecuador, which is visited by around 1 million domestic tourists per year. Salinas is located in the province of Santa Elena. Sun and sand tourism is pervasive in the city. Its main tourist attraction is the beach, and it has extensive hotel offerings. Activities that can be done in the destination are sea sports, gastronomic tourism, and fishing. Baños, according to historic records, does not have a foundation date. It was simply populated by small disperse groups. The city is located on the slopes of the Tungurahua volcano, at 40 km from Ambato. The main tourism attractions of Baños are the Tungurahua volcano, the Pailon del Diablo (close to Puyo), and the Manto de la Novia waterfall.

According to the Ministry of Tourism, the 51.12% of domestic tourists use bus as a way of tourism transportation, 39.59% their own vehicle, 1% airplane, and 8.29% other form of transportation like train, bicycle, etc. The most traveled land routes in

Ecuador from 2011 to 2015 were Latacunga – Salcedo, Esmeraldas- San Mateo,

Guayaquil- Gomez Rendon, Aeropuerto-Duran, Quito-Cumbaya, Aloag- Machachi, and

Salinas – La Libertad; the air routes that had a high demand among tourists in continental Ecuador for the period of 2011-2015 were: Quito -Guayaquil-Quito, Quito-

Manta-Quito, and Quito – Cuenca -Quito.

Ecuador is a country furrowed by the Andean mountain range, and geographically located in the “Ring of Fire” of the Pacific, one of the most complex zones of tectonic activity in the world. More specifically, it lies in the region where the plates of Nazca and South America merge. Ecuador has numerous active volcanoes,

and due to its location in the tropic is vulnerable to hydro-meteorological occurrences like floods, droughts, frost, and el Niño and la Niña phenomena.

In the past decades, the country has been the scene of numerous natural disasters that have affected mostly rural residents. In Ecuador, 36.3% of the population is under the poverty line, and in the rural areas the poverty increases up to 70%.

According to the Risk Management Secretary of Ecuador, the country is vulnerable to diverse natural disaster not only because of its geographical characteristics, but moreover due to diverse actions that are carried out by its habitants (deforestation, contamination, urban setting in dangerous zones, etc.).

The institution in charge of preventing, avoiding, reducing, and repairing the effects of man-induced and natural disasters in Ecuador is the Risk Management

Secretary (Secretaria Tecnica de Gestion de Riesgos). This institution has been in charge of managing risk in the country since 2008. Before 2008, the Civil Defense was charged with this task.

Risk management in the country has had an increase of importance since 2008, when the new constitution established as a duty of the government to prevent and manage risks and disasters based on the principles of immediacy, efficiency, precaution, responsibility, and solidarity (Constitution of Ecuador, Art. 397).

Ecuador is a member of the Andean Committee of disaster prevention and attention (CAPRADE Comite Andino de Prevencion y Atencion y Desastres) since 2008 and collaborated in efforts to reduce risks and vulnerabilities in the region.

In Ecuador the characteristics of the risks management approach in the past decades has been reactive, focusing mostly in interventions post-disaster. However,

recently the state has assumed a proactive attitude. Activities of relocation and evacuation of populations settled in risk zones is one of the novel actions of the

Secretary of Risk Management in collaboration with the Municipalities.

Research Design Foundation

This study adopts a mixed methods design, in which the first component is a qualitative study that includes a detailed document analysis and integrates the participation and voice of different tourism stakeholders of the destination, followed by a quantitative component through the implementation of surveys with large samples of members of both supply and demand side.

The adoption of the mixed method approach is widely used in social research since late 1980s (Creswell and Plano Clark, 2017), but it has its origins in the forties when the discussion about the need for a better way to explain complex phenomenon started (Sale et al., 2002).

Research in social science tends to be extremely complex and calls for answers beyond simple numbers or words (Creswell and Plano Clark, 2017), thus the combination of both quantitative and qualitative approaches increases the sophistication and quality of the analysis of problems and provide a strong source of evidence through triangulation of results (Sale et al., 2002; Johnson et al., 2007; Creswell and Clark,

2011) and implementation of different perspectives. Hence, mixed methods design provides a natural complement to traditional qualitative and quantitative research allowing the adoption of a methodological pluralism which often results in a superior research (Johnson and Onwvegbuzie, 2004). Whereas purist quantitative studies and positivism hold the strict trend that social observations need to be objective and researchers should be separated from time and context, and qualitative studies argue

that such thing cannot be possible (Johnson and Onwvegbuzie, 2004), mixed methods research design adopts a worldview called pragmatism (Creswell and Plano Clark,

2017) where research question is more important than the methods, and integration of different paradigms (positivism and constructivism) is a key concept (Tashakkori and

Creswell, 2007; Creswell and Plano Clark, 2017). Accordingly, mixed methods research attempts to consider multiple viewpoints, perspectives, positions, and standpoints in order to obtain a better approach to knowledge that blends theory and practice

(Johnson et al., 2007).

Considering the advantage of combining multiple methods (and paradigms) the study had four major components. Phase 1 focused on what was missing in the original

RTAF model and proposed an adapted model for the destination under study. Phase 2 consisted of a risk and vulnerability analysis of the supply side of the sector using qualitative analysis in order to identify vulnerabilities and risks that could affect the destination, Phase 3 used a qualitative and a quantitative approach to elaborate and evaluate adaptive strategies for the sector considering the demand side, and Phase 4 included the demand side evaluation of the proposed strategies (Figure 3-1).

Phase 1: Adaptation of the RTAF Model

The purpose of the adaptation phase was to analyze in depth the RTAF model proposed by Jopp et al. (2013), to be applied in the Australian context to cope with climatic changes, and determine the gaps and possible changes needed in order to adapt to the context of Ecuador, and be able to increase resilience in the destination and increase adaptation to diverse forms of risks beyond climate change.

Methodology

The primary source of data collection for the adaptation model analysis was document analysis. Documents were defined as "any written or recorded material" of public documents, and annual reports (Guba and Lincoln (1981). This method allowed for understanding of the context and permitted to identify the needs of the destination and the limitation of the current model to address those needs. The adapted model served as a tool that could be applied to other destinations in South America with similar characteristics.

Rationale

The purpose of a one–destination approach was to examine in depth the particularities and complexities of a destination. The thorough analysis of the destination provided understandings of the area of research and offered grounds for further research expansion in other destinations with similar characteristics. The qualitative approach allowed obtaining more in-depth information about a study phenomenon and its context (Matveev, 2002; Denzin and Lincoln, 2011). Moreover, document analysis allowed for the investigation of different types of sources and dimensions to create a detailed representation of the topic under study.

The main goal of the study was to conduct a thorough analysis of the destination characteristics with the aim to identify the best way to adapt the RTAF model to the country. Understanding of the destination context came with a comprehensive study and analysis of different sources of data. Document analysis allowed for the compilation, examination and evaluation of public documents and annual reports, when at the same time provided enough flexibility to include different types of documents

besides written material (Bowen, 2009; Padgett, 2016). This study analyzed 40 published written and audiovisual documents about the destination.

The documents analyzed were selected taking into consideration the following topics: (1) published scientific material regarding the geographical and cultural characteristics of Ecuador, (2) natural and human-induced issues that have affected the country in the past, (3) characteristics of the tourism system in Ecuador. The topics were selected due to the relevance with the study based on the components of the

RTAF model proposed by Jopp et al. (2013).

This study was unique since it constituted a first effort to develop a model of resilience and adaptation to be applied in Ecuador, which can be scalable at the same time. Furthermore, the product of the present study was context-based and fully covers the needs of the destination under study. The in-depth analysis of the public documentation of official sources (government institutions) allowed the researcher to identify the main sources of risk that can cause a tipping point in the country’s equilibrium and that needed to be managed by the adapted model.

The study was conducted in three steps: (1) identification of government documents for the analysis that covered the relevant themes; (2) read documents, take notes, compare necessities of the country with the RTAF model proposed by Jopp et al.

(2013), (3) Cross reference information for gaps identification. The steps of the study were deeply explained in following sections. Several gaps were identified, and the researcher proposed changes in the original model based on the results of the study and literature review.

Data Collection

Preliminary data collection for this research was conducted from January to

March 2017. The information was gathered using documents available on the Internet, and external public records. Internet documents are considered a growing source of existing data ideal for document analysis (Padgett, 2016), and the issue of inaccuracy and validity of the information is addressed using only official sources. Public records are materials created and kept for the purpose of "attesting to an event or providing an accounting" (Lincoln and Guba, 1985). External records are census and vital statistics reports, county office records, newspaper archives, and local business records that can help the researcher to gain understanding of the community of the destination under study (Denzin and Lincoln, 2011).

The researcher identified government institutions in the country that had information regarding the topics of interest considering the list of public institutions of the Republic of Ecuador published by the country’s Secretary of Planning and

Development. Subsequently, the institutions were selected based on their descriptions of responsibilities (detailed in the “about us” section of the web site) and relationship with the topics of interest. Formerly, the researcher created a list of possible sources and compared that list with an Ecuadorian researcher for validity.

The final list of sources included five institutions (1) Ministry of Tourism, (2)

Secretary of Risk Management, (3) Ministry of Environment, (4) The National Institute of

Statistics and Census (INEC), (5) Geophysical institute of Ecuador (IGEPN). The documents analyzed included information published through official websites of the mentioned government institutions. The information collected included topics like natural and human-made disasters that have affected the country in the past ten years, socio-

cultural characteristics of the country, demography, geographic and environmental information, characteristics of the tourism system, among others. The documents included in the analysis were taken from the library section of each of the mentioned institutions based on their relationship with the topics of interest.

Fieldwork was conducted from April – July 2017, the researcher made an on-site visit to Ecuador, and collected printed official information from the selected institutions, she scheduled appointments with the authorities of each institution, explained the characteristics and the rationale of the study and ask them if there was information regarding the topics of interest (geographical and cultural characteristics of the country, risks and vulnerabilities, characteristics of the tourism system) that they could have and that was not accessible through websites. In all cases, the appointments were scheduled during business hours, and conducted in the corresponding institutional buildings. Meetings were conducted on average for about one hour, where the researcher presented the nature of the study, and shared the information collected through the website of the institution and asked the authorities if there was any other relevant information that could be provided for the analysis. The authorities followed the protocol of Ecuadorian public institutions and appointed a member of the staff to help the researcher with the information. This way, the researcher was able to obtain 5 more documents for the analysis (Table 3-1) one for each institution. A total of forty documents were included in the analysis (Table 3-1).

Document Analysis

For Bowen (2009), documents are defined as “material that contain text and images that have been recorded without a researcher’s intervention” (Bowen, 2009). To

achieve optimal results in the document analysis methodology, the researcher was expected to rely on multiple (at least two) sources of evidence (Bowen, 2009).

Forty official documents were analyzed to understand the context and recognize the needs of the study (Table 3-1). A total of 13 documents were provided by the

Ministry of Tourism, 13 by the Ministry of Environment, 5 by the Risk Management

Secretary, 4 by the National Institute of Statistics and Census, 3 by the Geophysical institute of Ecuador, and 2 were recommended by the Risk Management Secretary but published by two Non-Government Organizations: Tierra Segura, and the Seismic

Observatory of the Southwest corporation.

All documents analyzed in the study were written in Spanish, the official language of the country. For the selection of documents, several searches were conducted using the official digital library of each institution where documents that were in line with the topics under study were collected. In addition, the researcher contacted each institution personally and requested extra information that was not accessible via

Internet.

Documents were analyzed in this study by using a three stage process as suggested by Bowen, (2009). The stages were (1) Skimming - identification (2) Review/ reading, and (3) Interpret: review notes and cross-references.

Skimming stage

In the skimming stage, documents were assessed for completeness (Bowen,

2009) in the sense of being comprehensive. Completeness was achieved covering the topic in a selective manner (Bowen, 2009) where the analysis covered only some specific aspects of the topic. The selective analysis included documentation that contained information from 2007- 2017 (ten year period) regarding three main topics: (1)

geographical and cultural characteristics of Ecuador, (2) major risk, vulnerabilities and disasters occurred in the destination in the past ten years, and (3) characteristics of the

Ecuadorian tourism system. The topics selected were in relation to the dimensions of the RTAF model proposed by Jopp et al. (2013).

Review/ reading stage

During the review and reading stage, the researcher carefully read each document and used the coding frame developed for the study (Figure 3-1) to organize the data available in each one of them. The information was classified according to the components of the RTAF model, and the classification of risks according to Pennington

Gray and Pizam (2011). The frame also provided a section to include additional comments. Finally, a section was included to mark if the original RTAF model could fit the destination based on the characteristics detailed in the document. Reading was performed in two periods within two months using the same coding and performed by the same researcher. The rationale under that approach was to achieve reliability through comparisons across points in time (Schreier, 2012).

Interpretation stage

During the interpretation stage the researcher used the code frame and notes taken from each document and compared the findings with resilience literature, and the

RTAF model proposed by Jopp et al. (2013). First, the researcher made a list of characteristics of the system found in the document analysis and compared them with the categories established in the RTAF model. Every characteristic needed to fit under one of the categories suggested by Jopp et al. (2013). If not, a new category was included in the model, at the end the model included two main additions (1) a multi-risk assessment, and (2) a more detailed and context base description of the system.

Secondly, the researcher made a list of risks that have affected the destination in the past and compared it with the RTAF model. Because the original model was developed with the aim to address only climate change, the researcher had to adapt the model and included the risk classification based on the work of Pennington-Gray and

Pizam (2011).

Finally, the researcher compared the adaptation options used in the past in the destination and identified who oversaw the proposal and execution of such alternatives and compared with the information available in resilience literature that has been detailed in the second chapter.

The interpretation stage showed a couple of gaps in the original model regarding

(1) the components of risk and vulnerability, and (2) the adaptation process. The design and adaptation of the original model involved the review of risk, vulnerability, and adaptation assessments available in literature, particularly those designed for the tourism sector, and the identification of areas of improvement.

Data Analysis

The documents were organized into major themes, categories, and case examples (Bowen, 2009) specifically using content analysis (Carey and Asbury, 2016;

Krueger, 2014). Content analysis is a general term for a number of different strategies used to analyze text (Vaismoradi et al. 2013). Content analysis is a flexible method for analyzing text data (Hsieh et al. 2005). For the study, summative qualitative content analysis (SQCA) was employed. SQCA is a type of content analysis that is characterized for the use of key words as codes, which were predetermined before and during data-analysis and selected based on the interest of the researcher and the literature review (Hsien et al., 2005). The use of a good code frame is advised in

content analysis. With that aim the present study used the procedure to conduct the summative qualitative content analysis (SQCA) suggested by Schreier (2012), and

Padgett (2016), which included: (1) Identify and label codes, (2) document and verify, an (3) compare and contrast.

Identifying and labeling codes

The identification of dimensions and subcategories was carried out taking into consideration the resilience, crisis managements, and RTAF model literature. The dimensions were designed using the RTAF model (Jopp et al., 2013), and for the study included three main categories (1) system characteristics, (2) risk and opportunities, (3) adaptive capacity. For each category a set of subcategories were identified (Schreier,

2012). For system characteristics the subcategories were concept driven (deductive) according to the information provided by the RTAF literature: (1) geographic characteristics, (2) environment, (3) socio cultural assets, and (4) main stakeholders.

For the risk and opportunities dimension, the subcategory identification adopted a deductive approach taking into consideration the risk management literature (Boin and

Lagadec, 2000; Beirman, 2003: Ritchie, 2004; Santana, 2004; Glaesser, 2006; De-

Sausmarez, 2007; Ritchie, 2009; Liddell, 2011; Pennington-Gray and Pizam, 2011;

PATA, 2014; Pennington-Gray, 2014) identifying four subcategories for risks: (1) natural, (2) human-induced, (3) health related, and (4) conflict-based.

Finally, for the adaptive capacity dimension, the approach adopted entailed the identification of who oversaw and applied the adaptive approach to overcome risk

(considering past events occurred in the destination). The subcategories identified were

(1) tourism demand side of the system, (2) tourism supply side of the system, (3) other person or institution.

Document and verifying

The second stage of the data examination process was the document verification. The researcher used the frames filled (Figure 3-1) generated from the analysis of each document revised (Table 3-1) and focused on what dimensions or subcategories were not completely addressed in the original RTAF model. From the mentioned examination, a list was generated.

Comparing and contrasting

Finally, with the list generated from the second stage of data analysis, a thorough

“compare and contrast” process was carried out. The goal of this final stage was to identify which subcategories where not included in the RTAF original model, and to include those subcategories in a new model that was more suitable for the destination context. The product of this stage was a proposed model validated by two experts in the field: a professor of the University of Florida, and a tourism professor of the ULEAM.

Validity and Reliability

According to Schreier (2012), validity and reliability are quantitative terms that can be used in qualitative content analysis based on the Qualitative criteria: credibility, trustworthiness, auditability, and authenticity. For the present study, those criteria were addressed conducting a meticulous assessment of the code frame, and the procedure employed.

For Schreier (2012), reliability is achieved when a coding frame has been applied consistently to the documents, and the interpretation is systematic and reasoned. The mentioned can be accomplished through comparison of the data across points in time performed by the same researcher and using the same coding (frame). In the study both the selection of documents and the analysis were carried out following the

mentioned principle. The use of the coding frame to analyze the same units of coding entailed the accomplishment of stability. In qualitative studies the underlying concept of reliability is called stability (Schreier, 2012), for that matter the coding frame was considered reliable to the extent that results of the analysis remained stable over time

(Schreier, 2012). Furthermore, the code was considered consistent when the coefficient of agreement was more that 75%. The coefficient of agreement for this study was calculated using the formula suggested by Schreier (2012):

Coefficient of agreement = number of units of coding on which the codes (3-1) agrees/ total number of unites of coding *100

For the present study the total units of coding were 920 (23 for each document analyzed). The code units were established taking into consideration the applied code frame. During the comparison across points, the number of units of coding on which the codes agreed were 891; giving a coefficient of 96.73%.

For the achievement of validity in the study, the selection of documents and the code frame were analyzed by two researchers from Ecuador, reaching face validity.

Moreover, the final product (adapted model) was revised and approved by two experts in the field.

Content validity was accomplished through the application of concept and frames derived from the RTAF, resilience, and risk management literature.

Phase 2: Risk and Vulnerability Assessment

This part of the study provided an assessment of the risks and vulnerabilities of the destination and included three steps of analysis conceptualized by Jopp et al.

(2010) and adapted by the researcher to a total of 4 steps. The objective of this section

was to identify the key vulnerabilities of the destination and possible adaptation options, considering different perspectives.

In the first step, the researcher conducted a document analysis of the risk management literature and risks assessments in Ecuador. This step allowed understanding of the risks that have affected the country and its different sectors in the past.

The second step was the risks identification. This process included both a literature review at the national level of anticipated fast drivers or sudden change risks, and an across tourism sectors focus group to determine the range of risks. The focus group study was organized and executed by the researcher with the collaboration of the

Universidad Laica Eloy Alfaro de Manabi, the Municipality of the city of Manta and other educational institutions of the country. In the study, stakeholders of different tourism sectors engaged in a three-step process: (1) risk identification; (2) evaluation of likelihood, and (3) evaluation of gravity of impacts

With the information provided from this process, a matrix was created (likelihood by gravity) and each risk was categorized according to: (a) Needs attention- high likelihood, high severity, (b) Back up plan- high likelihood, low severity, (c) Rare occurrence- low likelihood but high consequence, (d) Eliminate risk- low likelihood and low consequence. All risks in the “a” category received full attention and planning efforts. The present study focused only on the risks that were categorized as Rank a

(needs attention – high likelihood, high severity).

The third step included the identification of opportunities (adaptation options) which may be brought about by sudden changes (fast drivers). Those opportunities were biophysical or socio-economic and were identified via a document analysis.

The fourth step consisted of an identification of opportunities via a stakeholder workshop that was held to solicit feedback and input from the industry.

This workshop asked participants to express:

• What adaptation strategies would you recommend for the tourism sector of Ecuador, given the impact of risk X?

• Which stakeholders should be responsible for the implementation of the adaptation strategy suggested?

• Do you think that the local community would be supportive of that adaptation strategy?

• How effective do you believe this adaptation strategy will be in addressing this risk x impact?

• Are specific destinations in Ecuador that should be the focus of adaptation efforts?

The product of the stakeholder workshop was a list of possible adaptive strategies that could be implemented in the destination to increase resilience, resistance, and readiness. Details of each step will be further developed in the following section.

Step 1: Document Analysis Process Risk Assessment

Rationale

Understanding of the destination is crucial for a primary identification of risks and vulnerabilities that could affect the country. PATA (2014) established that the first function of risk management is to identify risks before they become realities. However, in order to identify risks was necessary to gain an in-depth understanding of the context.

The first approach to understand the system according to the resilience approach

advised by the Resilience Alliance is to gather information of the destination. With that purpose, document analysis was adopted in this study as an initial method for risk assessment. The document analysis process allowed the gathering and evaluation of public documents and reports that can help to gain understanding of the phenomenon under study (Guba and Lincoln, 1981). The documents included in the analysis were selected taking into consideration their relevance with the topic of interest and following the classification of risk provided by Pennington-Gray and Pizam (2011). To ensure the authenticity of the information only government official sources and educational institution sources were taken into consideration in the analysis (Bowen, 2009; Padgett,

2016). The present study constituted an input to the current risk management literature of Ecuador, which can be used in further tourism studies.

The study was conducted following the guideline proposed by Bowen (2009) which entails three stages: (1) skimming of relevant material for the study, (2) review and reading, and (3) interpretation of the information. The outcome of the study was a compilation of risks and vulnerabilities of the destination, which allowed for the thorough understanding of the destination context. The study was conducted using documents published by the Secretary of Risk Management available on Internet.

Data Collection

Data collection for the study was conducted from July and August of 2017. The information was collected using the documents published by the Secretary of Risk

Management, available on Internet. Those types of documents are considered external public records suitable for the document analysis process (Padgett, 2016). The issues of authenticity and accuracy, which are the downside of the document analysis methods, were addressed using only information that has a valid source. The Secretary

of Risk Management was selected as primary source because is the official institution engaged in managing risk in Ecuador. The researcher used the Secretary of Risk management digital library for the exploration. All documents published were scanned for the study, (Table 3-2). The researcher selected the documents published by the

Secretary of Risk Management, which compiled assessment of risks that have affected the country in the past ten years. Moreover, descriptive material of main risks was also included in the analysis. Other types of information were excluded in the present study due to lack of relevance.

Documents

According to Bowen (2009), documents are material that are developed without the intervention of the researcher. For Altheide et al. (2008) a document is any symbolic representation that can be recorded and recovered for description and analysis. Hence, the use of digital official information recorded and-or elaborated by an official institution was ideal in the document analysis process. In the present study, nine documents of the digital library of the Secretary of Risk Management of Ecuador were used in the analysis. The documents were selected taken into consideration the relevance with the topic of interest (risks that have occurred in Ecuador from 2007 to 2017). The source was the Secretary of Risk management, the official institution in charge of monitoring risk in the country. No other sources were used in the analysis.

The list of the documents analyzed is detailed in Table 3-2. All documents used in the study were written in Spanish, which is the official language spoken in Ecuador.

Document Analysis

The process implemented in the study to identify risks that have affected

Ecuador in the period 2007-2017, consisted of three steps as suggested by Bowen

(2009) and detailed before in the document analysis section of phase one.

Skimming stage

During the skimming stage the documentation of the digital library of the

Secretary of Risk Management was assessed for completeness. According to Bowen

(2009) completeness can be achieved in two ways (1) broadly covering an issue, or (2) selectively. In the present study, the selective approach was adopted. Hence, the collection of documents to be analyzed needed to be accomplished using the following parameters (1) be included in the digital library of the Secretary of Risk Management of

Ecuador, (2) be related to the topic of interest: “risks that have affected the country”, and (3) be recorded events occurred in the period of 2007 to 2017.

To perform the skimming process the researcher first accessed to the digital library of the Secretary of Risk Management of Ecuador, and downloaded all the available documentation. Following, the researcher scanned the material and selected the documents that met all the criteria for a further detailed analysis. The researcher selected nine documents for the next stage of the analysis. The opinion of a researcher from the University Laica Eloy Alfaro de Manabi was consulted with respect to the documents analyzed in the study in order to validate the researcher selection.

Review / reading stage

During this stage, the researcher carefully read each document using the code frame developed for the study, with the purpose of organizing the data. The code frame used in the study was developed using the risk categories proposed by Pennington-

Gray and Pizam (2011), and the impact guideline used by Liddell (2011). Consequently, the code frame used for the study included four types of risks (Natural, Technological or man-made, health related, and Conflict-based), and two types of impact categories

(Physical and Social). Each impact category included their subcategories based on the

Disaster impact model (Lindell, 2011). Accordingly, four subcategories were included under physical impact (damage in structures, damage in crops, damage in animal farming, and human casualties), and four subcategories were included under social

Impact (psychological impacts, demographics, economic impacts, and political impacts).

Moreover, the frame had sections where the year of the event, the place of occurrence, and additional comments were included. The code frame used for the analysis is detailed in Figure 3-3

Reading was performed in two periods, with the purpose to allow comparisons across time and reach reliability (Schreier, 2012).

Interpretation stage

During the interpretation stage, the researcher classified the information available in each document using the code frame developed for the study (Figure 3-3) notes and important observations were carried out. Secondly, with the information categorized and summarized, a list was generated which included the risks that have affected the country in the past ten years, their impact and recurrence. Finally, a report was made with the information collected.

The mentioned approach allowed for a deep understanding of the context, which was useful for the researcher in order to carry out the next step of the methodology.

Data Analysis

The documents were organized into categories, subcategories, and case examples (Bowen, 2009) using the summative qualitative content analysis technique

(Carey and Asbury, 2016; Krueger, 2014) as is explained in the data analysis section of the document analysis of phase one.

The development of the code frame was carried out taken into consideration the risk classification of Pennington-Gray and Pizam (2011) and the Disaster Impact Model proposed by Liddell (2011). Hence, the developed framework included three main categories (1) risks, (2) physical impact, and (3) social impact. Each category at the same time include four subcategories each. Thus, for the main risks category, the subcategories were concept driven, using the categorization propose by Pennington-

Gray and Pizam (2011), accordingly they were (1) Natural, (2) Man-induced/ technological, (3) Health related, and (4) Conflict based.

For the physical impact dimension, the subcategories were selected using a deductive approach considering the Disaster Impact Model proposed by Liddell (2011).

They were: (1) damage in structures, (2) damage in animal farming, (3) damage in crops, and (4) human casualties. This classification was concept driven.

Finally, for the Social Impact dimension, four subcategories were identify, taking into consideration the Disaster Impact Model proposed by Liddell (2011). Thus, the mentioned subcategories for the study were: (1) psychosocial impact, (2) change in the demography of the destination under study, (3) economic impact, and (4) political impact.

The document verifying was the second stage of the data analysis process. In this stage the researcher used the code-frames filled, which were generated in the

reading stage of the data analysis process (Figure 3-3).The researcher focused on the risk that were more recurrent and had greater impact in the destination. Form the mentioned analysis a list was created.

Finally, with the list generated, a compare and contrast analysis was carried out to identify the risks that have a more frequency and which generate a greater negative impact in the destination. The product of this stage was a report that provided a deep understanding of the destination, primary risk and impacts.

Validity and Reliability

Validity and reliability was achieved based on criteria of credibility, trustworthiness, auditability and authenticity. For the present study those criteria were addressed in the elaboration of the code frame detailed in previous sections. The consistency of the application of the code frame is also a pillar stone of the SQCA

(Schreier, 2012). Consistency was achieved in the study using the principle of comparison across points in time performed by the same researcher. To accomplish the mentioned, the documents were analyzed using the code frame in two separate occasions.

For the study the units of coding were 333 (37 for each document analyzed). The code units were stablished using the categories and subcategories of the code frame applied. During the comparison across points. The number of units of coding on which the codes agreed were 325, giving a coefficient of 97.60%.

Validity was achieved through the concept driven approach adopted in the elaboration of the frame. Documents and the code frame used in the study were analyzed by other researcher, reaching face validity.

Step 2: Qualitative Assessment of Risk (Focus Groups)

Rationale

The in-depth understanding of the characteristics and intricacies of a destination cannot be achieved by merely analyzing the documentation available for the destination in question, but rather through an in-situ study that considers the contributions and perspectives of its inhabitants. It is for that reason that it was necessary to carry out a qualitative study in which the inputs of the different tourism stakeholders of the destination were taken into consideration.

In the present study the opinions of stakeholders of the different sectors of

Ecuador were listed and included in the risk and vulnerability assessment. This approach allowed the inclusion of different perspectives about the risks to which the country is susceptible.

The methodology selected for this study was focus group. Focus group is a technique whose purpose is to collect rich detailed data using semi structure sessions in an informal setting with the help of a facilitator (Carey and Asbury, 2016). Focus group research is not a consensus technique, its primary functions are to gather information, generate insights, determine how group members reach decision, and encourage group integration (Salking and Rainwater, 2003). Hence, the intent in focus groups was to understand. According to Krueger (2014) focus groups are good method for brainstorming, obtaining a range of option, and gain understanding to see an issue through the eyes and heart of a target audience. For all the mentioned, focus groups were ideal for gaining the understanding of the destinations risks and impacts, needed for the qualitative risk assessment.

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The purpose of the focus groups planned for the study, was to nominate a list of candidates of risks, while evaluating the vulnerability of the different productive sectors of Ecuador to a specific risks, and at the same time allow for the generation of insights regarding opportunities derived from change.

Focus groups were selected for the qualitative risk assessment. Focus groups are a wide spread technique, that is special in term of purpose, size, composition, and procedures (Hughes and DuMont, 1993; Morgan, 1996; Morgan and Krueger, 1998;

Kennedy et al., 2001; Krueger, 2014). In focus groups the selection of participants is akin to the characteristics of the study, and the expected outcomes. Hence, participants in focus groups share common characteristics (commonalities) that are specified by the research in the recruitment process (Carey and Asbury, 2016). A focus group study is carefully planned, and conducted by a moderator in order to ensure a relax environment, and enjoyment of the participants (Burgess-Champoux et al., 2006;

Gibson, 2007; Tates et al., 2009; Krueger, 2014).

The focus group technique has its origins in 1930s as an alternative way of conducting interviews (Krueger, 2014). It was widely adopted in the marker research in the 1950s, and gain popularity among academic researchers in the 1980s.

In this study, the focus group technique was implemented with the aim to obtain rich information and gain a deep understanding of the destination risks and impacts, considering the diverse viewpoints of the tourism stakeholders of Ecuador

Data Collection

The collection of data via focus groups followed a detailed plan that was established by the researcher using the guideline proposed by Krueger (2014), and

Carey and Asbury (2016). A moderator directed the series of focus groups conducted

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for the study, with the help of an assistant moderator. They collected the information, took notes, storage and analyzed the data.

According with Carey and Asbury (2016) the moderator is a person that facilitates the preparation of the focus group, who has a vast knowledge of the people, the topic and the local context. For this study, the moderator was the researcher, she was born in Ecuador, and was aware of the context, speak Spanish and English and it was familiar with the topic and the protocol.

The presence of an assistant moderator was advised as a Co-facilitator of roles for larger groups of more than 7 people. His/her functions were to assist with logistics, recording and management of the room, being in charge of seeing if food is available, escorting arriving members to the session, providing direction to the bathroom, taking messages, and taking notes of nonverbal communication. For the study, the Assistant moderator was a Professor of the University Laica Eloy Alfaro de Manabi (ULEAM).

Collection of data was performed during January of 2018 in the city of Manta, in five scheduled sessions. Each session was conducted during different days and after business hours in a private room of the CINFOTUR (Tourism Information Center).

During the session the collection of data was made through audio recorder, field notes taken by the assistant moderator, and session participants filled out an exercise sheet (which will be detailed in the question section of the study). The first page of each field notes included the following information (Krueger, 2014):

• Name of the study • Date of the focus group • Time of the focus group • Location of the focus group • The type of participants • The number of participants

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• Name of the moderator • Name of the assistant moderator • A diagram of the seating arrangement

After the session, the researcher took field notes especially of non-verbal communication and group interaction. Transcription of every word recorded was performed. The transcripts of the recorded information, along with the notes fields and other documents generated in the study were secured in a locker and taken into consideration on the analysis.

The place and time selected for the study followed the recommendation of

Rabiee (2004) and Krueger (2014). A familiar, comfortable, and quiet environment was designated. The time selected for the sessions was 5:50 pm, taking into consideration that was an ideal time for people who work in an office, so they were more likely to assist. The sessions were conducted in the city of Manta. The destination was selected considering that is the fourth biggest city in Ecuador and an important tourism destination visited by domestic and international tourists. Moreover, the principal campus of ULEAM is located in Manta, and this is the only city in Ecuador where all of the important sectors of the economy are present (services and manufacture). This city is the only one in continental Ecuador that receives cruises, and has an international airport. Although there are bigger cities in the country like Guayaquil, Quito, and

Cuenca, none of those have representativeness of every sector like the destination selected. The local selected for the meetings was the CINFOTUR. The venue is situated in a neutral location in the center of the city, near most administrative offices, and has enough parking lots. CINFOTUR is a well-known establishment that provides a comfortable and private atmosphere, with private rooms.

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Design of the Study

For this study, a single category design was selected. In this approach, the focus groups needed to be run until saturation was achieved (Krueger, 2014). The single category design used for the study is detailed in Figure 3-4. Krueger (2014) advises to conduct at least 4 focus groups to reach saturation and recommends they be conducted in phases (one session per day). For the present study, 5 focus groups were scheduled for 5 separate days. This way it allowed the researcher to take notes and perform transcripts after that every session was conducted. For each focus group four hours the session (at least 4 hours) were dedicated to make the summary notes, observation notes, and to evaluate if everything was going well in the participation, location and information gathered aspects (Krueger, 2014).

Focus groups work better with small groups of people that have the same level of power (Krueger. 2014). The ideal number of participants lies between 5 to 12 participants per groups, because more than a dozen could lead to fragmentation

(Krueger, 2014; Carey and Asbury, 2016). Twelve participants were invited for each session, and each session was planned to follow the guideline proposed by Krueger

(2014). Participants belonged to the four tourism stakeholder groups in line with the classification of the Ministry of Tourism of Ecuador: (1) accommodation and restaurants,

(2) attractions and recreation, (3) operators and facilitators, and (4) tourism transportation. Moreover, other members of the destination were included in the study, following the approach proposed by Pennington-Gray (2017) (1) government officials, media, voluntary sector, citizen and advocacy groups, and experts (Figure 3-5).

As participants arrived the moderator welcomed everyone, and presented them with other participants, while the assistant presented the IRB participants agreement. To

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obtain richer data the moderator emphasized the “commonality” among the participants.

The technique was applied because people tent to share information with other people that they perceive like them (Krueger, 2014). During the welcoming (Script 2) the moderator provided information of the project, share the purpose of the study, informed which organizations were supporting the project. The researcher also informed what data was going to be kept, who was going to have access to the data and the transcription, asked permission to record, and offered summary of the results (Krueger,

2014).

After the session opening, the moderator presented the grand rules of the study

(Krueger, 2014; Carey and Asbury, 2016) (Script 3) and the questions (opening, transitional, key and ending questions), and responded to participants’ comments, avoiding leading. For the study, the following grand rules were applied:

• Positive and negative opinions are welcome • There are not wrong answer • Only one person talks at the time • There are not right answer • Consensus is not the purpose

Anticipating Problems

A common issue that can be presented in focus group studies is low attendance of the participants during the session due to problems with the recruitment. To avoid that, the process of recruitment was performed in phases private transportation was offered to the participants (2014).

Participant’s Characteristics

In focus groups, the participants should have a recommended set of characteristics that allow for a rich collection of data (Krueger, 2014). It must exist a

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commonality among participants in the study (demographic or other). The existence of a commonality will lead to information-rich and will provide more insights to the topic

(Krueger, 2014). For the study, the commonality selected was the positions of the participants as stakeholders of the destination and tourism sector of the country.

Stakeholder is for definition any group of individuals who can affect or are affected by the phenomenon under study (Freeman et al., 1994). According to Mitchell et al. (1997) the stakeholders have at least one of the following characteristics: (1) power, (2) legitimacy, and (3) urgency.

The tourism sectors were specified in the classification of the Ministry of Tourism

(detailed before). The stakeholders selected for the study needed to accomplish at least one of the criteria of power, legitimacy and urgency specified for the study. The criteria for power was defined as the possession of valuable knowledge, and access to information, which was the sum of position, personal and political power (Bourne and

Walker, 2010), therefore participant needed to be in a hierarchal position in the industry or institution, if possible with knowledge of risk management.

Legitimacy was defined in the study as the belonging of the participant to a specific productive sector; hence, they were individuals that were currently legally associated with the firm or institution (Freeman, 1994; Mitchell et al., 1997; Sharp et al.,

1999; Roome and Wijen, 2006; Bourne and Walker, 2010).

Finally, urgency was conceptualized as the imperativeness of the interest that the participants had, regarding the claim for immediate attention to the subject under study

(Freeman, 1994; Mitchell et al., 1997; Sharp et al., 1999; Roome and Wijen, 2006;

Bourne and Walker, 2010).

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Additionally, the second commonality was age. The age of the participants were between 18 to 65 years, obeying to the productive age range provided by INEC specifications in Ecuador.

Sampling

The purposive sampling technique applied in the study was “nominations” where the pool of participants was obtained by asking neutral parties for names to create a pool of participants and then randomly select the participants for the study. Nomination is an effective strategy for focus groups (Krueger, 2014) and if the selection of participants is performed randomly from the pool, it helps to reduce bias. The members for the pool were recruited based on the commonality in the “participant’s characteristics” section. Members of the pool were homogenous in terms of prestige or status such as occupation, age, and education.

The parties consulted for nomination were (1) the chief of the Ministry of Tourism

(MINTUR) Zonal 4, (2) the Dean of the Tourism Department of the University Laica Eloy

Alfaro de Manabi, (3) The Chief of the Tourism Department of the Municipality of Manta,

(4) the President of the Tourism Chamber of the City, and (5) the Chief of the Central

Bank of Ecuador Zonal 4. Each institution was consulted regarding possible participants

(tourism stakeholders that comply with the criteria of legitimacy and power previously detailed) and asked to provide a list of possible candidates for the study. The researcher created the nominees pool confirming that participants were not repeated and that they were complying with the study criteria. From the pool of nominees, 60 participants were randomly select (12 participants per 5 groups). To do that, the researcher divided the total number of the participant pool to 60 (the desired sample) and used the resulting number to decide which participants were selected.

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Figure 3-5 represents the possible stakeholder groups in tourism. Of the groups presented, four will be utilized to capture the overview of possible risks that can impact the tourism industry (Figure 3-5)

Recruitment

The selected participants from the pool were contacted by phone and a meeting was scheduled to deliver the personalized invitation to participate in the study (Sample

1). The invitation included the name of the study, the sponsors, importance, brief description of the project; benefits from participate, and contact information of the researcher. The researcher contacted participants in alphabetic order, and presented the dates and times for each focus sessions, so participants selected in which session they wanted to participate (avoiding conflict with their personal plans). Once a session was full (reached 12 participants per session) it was removed from the list of options

(Krueger, 2014). After the participants agreed to participate a confirmation letter

(Sample 1) was sent a week prior to the focus group (Krueger, 2014); and a reminder call was made (Script 1) the evening before the session was scheduled. Private transportation was offered to the participants from their work to the study premise. The mentioned approach lead to lower rates of no-shows (Casey and Krueger, 1994).

Incentives

According to Krueger (2002), incentives for focus groups could be money, food, an upbeat invitation, the opportunity to share opinions, an enjoyable, convenient and easy to find meeting location, involvement in an important research project, and the possibility to build on existing community, social or personal relationships. For the study, money was not offered to participants, but all other incentives were presented.

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Interview Questions

The guideline of the ideal number of questions in focus groups is not more than

12 questions per hour of session (Krueger, 2014). Additionally, most questions are recommended to be open ended to generate discussion, but the use of few close ended question is admissible, especially when there is the need to conduct a ranking of attributes (Carey and Asbury, 2016). The ideal structure from the question route is to star with one or two general opening questions, follow up with an introductory question, then a transition question, and key questions, to finalize with an ending question

(Krueger, 2014).

Opening questions

They needed to be easy to answer (Krueger, 2014; Carey and Asbury, 2016), and they were not considered for the analysis. The main purpose of those questions was to act as icebreakers, allowing participants to talk and feel comfortable to express their opinions. As a rule the opening questions are facts that help to establish the commonality among the participants (Krueger, 2014; Carey and Asbury, 2016). For the study, 5 minutes were dedicated to the following opening questions:

Could you present yourself to the group and share with us: • Where do you work? (Established that all participants have the commonality of working in an productive sector)

• What are your hobbies? (Established that all participants are people with interest)

Introductory question

The time designated for the introductory question was 5 minutes. The purpose of this question was to evocate the experience of the participants about the topic of study.

Besides, it induced people to start thinking about their connection with the subject

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(Krueger, 2014; Carey and Asbury, 2016). For the study the following question was used as introductory question:

Question: in this first part of the session I would like to ask you to take a moment to think about risks that have affected the country and your sector, they could be natural, technological or man-made, health related, or conflict base. Could you tell me what comes to your mind?

Transition question

The transition question served as link for the key questions of the study (Krueger,

2014); the time dedicated for the question was 5 minutes:

Now we are going to talk about risks for the sector, and I would like for you to express your opinions regarding

Question: What risks (happened in the past or not) do you think is important to address? (Jopp et al. 2010)

Key questions

They are the heart of the study and the information obtained from them drove the analysis, but it is advisable to keep the number of key questions at a maximum of eight, to avoid fatigue. (Krueger, 2014; Carey and Asbury, 2016). In this study, the time designated for each question was between 10 and 20 minutes, as recommended by

Krueger (2014). The key questions were developed taking considering the risk literature

(Weinstein and Diefenbach, 1997; Baccarini and Archer, 2001; Pennington-Gray and

Pizam, 2011; Lindell, 2013; Pennington-Gray, 2014; HilleRisLambers et al., 2014;

PATA, 2014) and in line with the purpose of the study.

The followed questions were used in the focus groups:

• In your opinion, how can [x] risk damage the country’s natural attractions?

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• How do you thing that [x] risk will damage the supportive tourism infrastructure (building, roads, etc.)?

• What do you think would be the social response to [x] risk from the tourists’ perspective?

• What do you think would be the social response to [x] risk from the tourism supply sector?

• In case the risk occurs, how do think it would impact tourist arrivals?

• Could you rank on a 5-point scale (5 being the highest score) how likely “the risk” is to occur in Ecuador?

• Could you rank on a 5-point scale (5 being the worse that could happen) how badly the consequences for tourism could be if the risk occurred?

• If the risk occurs, what opportunities for the tourism sector could be derived from the change?

Thorough information of the justification and reference for each question is detailed in Table 3-4.

Ending question

According to Krueger (2014) and Carey and Asbury (2016) there are three types of ending questions for focus groups (1) all thing considered, (2) summary questions, and (3) final questions. In the study, three summary questions were employed after the researcher provided a two-minute oral summary of the topics discussed with the group, and relevant findings.

The summary questions used in the study were:

• Is this an adequate summary? • Do you think we have missed anything? • Is there anything that we should have talked about but did not?

Data Analysis

The data generated from the analysis of the groups included transcription of the sessions, field notes, and participant sheets (used in the ranking of risks). A great

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amount of information is expected in qualitative focus groups studies (Schreier, 2012;

Krueger, 2014; Carey and Asbury, 2016; Padgett, 2016). To adequately process the information, a detailed analysis was performed, with the use of coding and patterns. For the data analysis of the present study, content analysis was selected and implemented together with the framework by stages proposed by Ritchie and Spencer (2002). The steps proposed in the framework were: (1) familiarization, (2) identification of themes and development of the code frame, (3) managing of the data, (4) interpretation.

The researcher needs to be familiar with the data generated for the analysis. For that matter, this stage entails the listening of the tapes (at least 3 times) and carefully reading of the transcripts, reading observation notes, summary notes, and any other document generated during the sessions.

Identification of themes and development of the code frame was carried out by the researcher using as guideline proposed by Schreier (2012). The identification of dimensions and subcategories was performed taking into consideration the risk management literature (Pennington-Gray, 2014; PATA, 2014; Liddell, 2011;

Pennington-Gray and Pizam, 2011; Baccarini and Archer, 2001). Four dimensions were identified in the framework: (1) Risk category, (2) Risk impact, (3) Risk ranking, and (4)

Opportunities.

For the risk category dimension, the classification suggested by Pennington-Gray and Pizam (2011) was employed. The subcategories for risks adopted in the study were natural, man-induced, health related, and conflict-based. For the risk impact dimension, the classification proposed by Lindell (2013) was implemented. The impacts of risks were measured by their effect on human casualties, damage of country infrastructure,

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damage of the environment (natural), and social-psychological respond of the community. The risk ranking used in the study included two values (1) likelihood, and

(2) gravity of the consequences. The subcategories were taken into consideration based on the formula proposed by Baccarini and Archer (2001), where Risk equals

Likelihood consequence. Finally, the opportunity dimension allowed for compilation of ideas generated from the session. The code framework used for the study is detailed in

Figure 3-6.

The managing of data included the use of a technique named “Long tables, scissors and colored marking pens” recommended by Krueger (2002). For the purpose of the technique, two print outs of the transcripts were used. To differentiate each session, the corresponding transcripts were printed on sheets of different colors, and each line was numerated. One of the printouts was used as a guideline and was not cut, or highlighted; and the other was manipulated by the researcher to perform the content analysis. The researcher performed indexing and charting of the quotes according to the code frame developed for the study. After the information was sorted the interpretation was carried out.

The mapping and interpretation process was carried out in two phases: (1) development of the risk matrix, and (2) interpretation of the qualitative data by stages.

The risk matrix analyses used the information generated from the ranking, whereas the second phases used all the information produced during the sessions.

Risk Matrix

The risk matrix analyses followed the guideline proposed by Baccarini and

Archer (2001) where risks needed to be ranked and prioritized in order to focus the risk

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management efforts on the risks with higher ranks. The formula to perform the risk ranking used in the study was: risk=likelihood * consequence.

With the results of the formula, a matrix that measures the likelihood and the severity was created. A list of all the risks mentioned in sessions with their respective average scores was arranged from higher score to lower score. Subsequently, the list was divided into 4 categories.

Each risk was categorized according to the following criteria: (a) Needs attention- high likelihood, high severity, (b) Back up plan- high likelihood, low severity, (c) Race occurrence- low likelihood but high consequence, (d) eliminated risk- low likelihood and low consequence. All risks in the (a) category received full attention and planning efforts. The study focused only on the risks ranked as A (needs attention – high likelihood, high severity) Figure 3-7.

Interpretation of the Qualitative Data by Stages

The second phase of the analysis focused on the transcripts generated in each session. In this phase the study adopted an interpretation by stages approach (Ritchie and Spencer, 2002; Rabiee, 2004; Krueger, 2014, Carey an Asbury, 2016). First the researcher performed a search for key words that were repeated frequently among the different sessions. Second, the researcher analyzed the context in where the words were mentioned and looked for internal consistency among session’s transcripts and the frequency and intensity of comments. Finally, the specificity of responds across the sessions where used to analyze if the groups reached saturation and what was the most important aspects on the sessions that collaborated to give big picture of the topic under analysis.

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The code frame analysis, and the qualitative analysis by stages was carried out in two times by the same researcher to achieve reliability.

Validity and Reliability

Validity and reliability in content analysis are reached based on criteria of credibility, trustworthiness, auditability and authenticity. With that purpose the development of the code frame used in the analysis was based on risk management literature (Schreier, 2012). Consistency was achieved in the study using the principle of comparison across points in time performed by the same researcher. The coefficient of the agreement for this study was 97%. A concept driven approach was adopted in the study in order to achieve validity and the code frame used in the study was analyzed by other researchers to achieve face validity.

Step 3: Adaptation Assessment Document Analysis

Rationale

With the aim of gaining understanding of the different adaptation strategies applied in the tourism sector of Ecuador, the researcher conducted a document analysis. Document analysis allowed for the detailed examination of documents regarding the topic of interest, and gained understanding of the phenomenon of interest

(Matveev, 2000; Denzin and Lincoln, 2011).

Document analysis is a systematic procedure that requires constant exploration to review and evaluate documents (Bowen, 2009); and in order to reduce bias four sources of information were taken into consideration (1) the Ministry of Tourism, (2) The international Bank, (3) Public Media, and (4) CNN in Spanish.

The collection of documents was done through Internet during the month of

October and November 2017. To ensure credibility a rigorous technique to gather data

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was implemented in the study, and careful analysis was carried out using a frame developed.

The information analyzed in this section allowed the researcher to acquire vast understanding of past adaptation strategies implemented in the tourism sector of

Ecuador to cope with crisis.

Documents

Documents were defined as any symbolic representation of meaning recorded and retrieved for description and analysis (Altheide et al., 2008). Such definition allowed for the inclusion of a wide array of data like recording technologies, print material, electronic media, audiotapes, visual information (photos and videos), clothing and fashion, internet material, field notes, organizational reports, forms, survey data, institutional files and forth (Altheide et al., 2008; Bowen, 2009; Padgett, 2016).

For this study written information available on Internet of official’s sources were taken into examination. Twelve documents were included in the analysis (Table 3-5), nine documents were selected from the Ministry of Tourism website library, one was a

CNN news, one was a Public media news, and the last one was a report of the

International Bank. All documents included in this study were originally written in

Spanish and were available on the Internet for the general public.

Data Collection

The collection of data for the document analysis was carried out during the moths of October and November of 2017. The Ministry of Tourism, International NGOs, and

Media published the information included in the analysis, which is written documentation available by Internet. The procedure implemented by the researcher to gather data in the area of interest was to perform Internet search using the key words: tourisms,

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tourists, Ecuador, adaptation, and strategies (the words were in Spanish), which were the topics of relevance in this study.

After obtaining the result generated from the search, the researcher inspected the files and selected those that were relevant to the study. Over one hundred documents were revised by the researcher, and only twelve documents were included

(due to relevance) in the analysis.

Data Analysis

The analysis of the data was performed in three stages: skimming, review of readings and interpretation, in concordance with the methodology proposed by Bowen in 2009 and explained previously in phase one of this Chapter. The skimming step includes the identification of the relevant material for the analysis. During the skimming the researcher analyzed over one hundred documents generated from the internet search using the key words tourism, touristic, Ecuador, adaptation strategies. The twelve documents used in the analysis were the result of the first steps of the data analysis stage. The second stage of the process was the review of readings, the researcher carefully read the documents under study to become familiar with the information. Finally, the interpretation stage included the use of the code frame developed for the study for the content analysis in two separates occasions to gain reliability.

Content Analysis

The twelve documents considered for this study were evaluated using content analysis. Content analysis is a flexible technique that permits organize data using key words as codes (Hsieh et al., 2005; Vaismoradis et al., 2013; Carey and Asbury, 2016).

For the present study the use of a predetermined code frame was implemented

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(Schreier, 2012; Padgett, 2016). The frame was developed based on the literature available in adaptation strategies and resilience in the tourism field (Smitt and Skinner;

2002; Hertin et al., 2003; DeFreitas et al., 2006; Scott, 2006; Few, 2007; Scott and

Simpson, 2008; Hoffmann et al., 2009; Apine, 2011; Jopp et al., 2013; Jopp et al.,

2015). The elaboration and application of the frame followed the recommendation made by Schreier (2012), the process started with the identification and label of the codes based on literature, followed by the revision of the document and verification of information to fill in the frame, and ends with a compare and contrast of the different outcomes generated for the documents analysis using the frame (Figure 3-9)

The frame employed in this study content 8 domains, the firsts six domains focused on descriptive information of the adaptive strategy implemented like (1) name of the adaptive strategy or measure, (2) breve description of the strategy, (3) organization in charge of the application, (4) why the strategy was implemented, (5) year of implementation, and (6) goals reached or issues emerged during the application.

The seventh domain was derived from literature (Jopp et al., 2013; Jopp et al., 2015;

Apine 2011; Few et al, 2007) and included 2 subdomains of the classification propose by Jopp et al. (2013) Technical, and business. Technical adaptation involved the use of technology and innovative measures, whereas business adaptation included techniques used by tourism operator, regional government, and tourism industry association, that might involve marketing techniques, repositioning strategies, and product market diversification. The behavioral subdomain was not included in the frame because it is usually associated with the consumer (Jopp et al., 2015). The last domain was entitled

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“purpose of the strategy” (Few et al., 2007; Apine, 2011), and had three subdomains (1) defense, (2) adjustment, and (3) retreat.

Validity and Reliability

Reliability was achieved in this study through comparison across points. The researcher performed the reading and analysis of the twelve documents on two separate occasions using the developed code frame (Figure 3-9).

For the present study the total of units of coding were 132 (11 for each document analyzed). The code units were established taking into consideration the applied code frame. During the comparison across points, the number of units of coding on which the codes agreed were 120; giving a coefficient of 90.90%.

Content validity was accomplished through the application of concept and frames derived from literature (Smitt and Skinner; 2002; Hertin et al., 2003; Hoffmann et al.,

2009; DeFreitas et al., 2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008; Apine,

2011; Jopp et al., 2013; Jopp et al., 2015).

Step 4: Adaptation Assessment: Consultative Workshop

Rationale

A consultative workshop with tourism stakeholders was organized in Guayaquil the biggest city in Ecuador, and one of the main tourism destinations within the country.

A workshop is a well-known technique to engage community participation. Community participation allows the maximum use of local knowledge in a developing process

(Street, 1999) because involves different stakeholders and provides them with some share of power in the decision – making process for destination planning (Street, 1999).

Moreover, inclusion of the community in the development of adaptation strategies saves

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costs (Apine, 2011) and supports tourist destinations so they can remain sustainable and competitive in the long term (Jopp et al., 2015) increasing their resilience capacity.

Inclusion is particularly important when planning for the community, there is evidence that suggests the centralized policies and programs generated only by experts, have limited success in resolving problems of a community or a specific sector within the community. Moreover, general solutions do not always fit into particular context or groups of people, and it is through participatory approaches like workshops that it is possible to find appropriate solutions for specific problems (Stringer, 2007).

Workshops are meetings that promote inclusion and discussion among different local actors to suggest solutions to future problems, and identify barriers, and create more effective action plans (Street, 1999; Na et al., 2010), they provide a multilateral communication platform through face to face communication (Na et al., 2010) while boosting broad-based inclusion in the developing of adaptive strategies which have ethical and practical value (Few et al., 2007).

The objective of the action participatory workshop was to provide a forum (Na et al., 2010) for tourism stakeholders to share knowledge of risk awareness and elaborate adaptive strategies to cope with the Rank A risk identified in the focus group study. The result of the workshop adequately reflects the views of the tourism stakeholders (Na et al., 2010) and includes their ideas and experience in the elaboration of adaptive strategies to cope with risk.

Data Collection

The primary objective of the workshop was to gather information about possible and feasible adaptation strategies to implement in the tourism sector of Ecuador, which included the experience and perspective of various stakeholders (Stringer, 2007). With

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that aim one-day workshop was schedule for the collection of data. The destination selected to conduct the workshop was the city of Guayaquil on January 30th. Guayaquil is the biggest city of Ecuador, and one of the main tourism destinations in the country with 1’739662 domestic tourist visits in 2015 (tourism observatory of Guayaquil), and

5184 tourism business registered in the Ministry of Tourism (20.39% of the country).

The location selected to hold the study followed the advice of Rabiee (2004), a familiar and comfortable location, preferably a place where other studies and educational training were performed in the past (Andersen and Jaeger, 1999). For that reason, the workshop was held in one of the conference rooms of the Government Building of

Guayaquil, which is located in the business district of the city and has modern facilities to accommodate all participants. The timeframe selected to conduct the workshop was the month of January, which according to the Ministry of Tourism is considered the low season. The aforementioned facilitated the recruitment of participants. Previous to the workshop the selected participants received a list of risks that could affect the tourism sector of Ecuador (product of the focus group study) which formed the background for the one-day discussion (Street, 1999).

According to Chambers (2002) and Stringer (2007) the workshop should be led by an experience facilitator, and at least one assistant which have vast knowledge of the topic under study and the destination context. In this study the facilitator was the researcher, and his/her assistant was one faculty member of the ULEAM, who received training (given by the researcher) regarding the topic and the protocol to be implemented.

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The researcher is an Ecuadorian professor with 8 years of experience lecturing classes at the ULEAM, and conducting tourism training to the Ministry of Tourism, her functions during the workshop were to lead the plenary session (introduction of the topic, and objectives of the study), and help the participants during the working session.

She also supervised all activities and gave feedback to participants.

The functions of the assistant were to coordinate the logistic of the room and food catering, take field notes during the session, and supervision of the protocol staff.

Two protocol students were also required to help with the participants’ registration, and food service.

The session was audio-tape recorded, and field notes were taken by the researcher’s assistant. Immediately after the session was concluded the researcher performed the transcription of the session (eliminating any identifier) and erased the audiotape to protect the participants from disclosure (Stringer, 2007). The transcription of the session was analyzed using a code frame derived from literature and developed for the study, to organize the information generated from the discussion. Besides, the researcher took field notes, and all documentation generated by the participants and presented in the sessions was saved for posteriori analysis (ppts, and posters used during the presentation).

Participants

In workshop the process of participant selection requires a specific procedure called purposeful sampling (springer, 2007), which entails the selection of participants based on a particular set of attributes. For the present study those attributes were called commonalities, which were defined considering the purpose of the session, leaded to information-rich outcomes (Krueger, 2014). The commonality selected was the positions

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of the participants as tourism stakeholders of the tourism sector of the country. The criteria used to define stakeholders was similar to the one detailed in the focus group method of this chapter. Hence, one criteria was power and in this study was defined as the hierarchical position of the participant within the firm. Legitimacy was defined as the relationship between the stakeholder and the firm, and for this study was contractual; in the sense that the participants should legally work for the firm. Finally, urgency existed when the stakeholder considers the phenomenon under study as important or urgent

(Mitchell et al., 1997; Freeman, 1994).

The aim of the workshop was to develop adaptive strategies to cope with risk in the tourism sector of Ecuador. The list of risks for this study were presented to the participants one week before the session (Street, 1999), and was the product of the focus group study held in the city of Manta.

Considering the aim of the study, participants selected were tourism stakeholders of Ecuador, which belong to the sectors of accommodation, food and beverage, facilitators and tour operator, tourism transportation and recreation; following the classification of the Ecuadorian Ministry of Tourism. Furthermore, members of the government (MINTUR, and Tourism Department of the city of Guayaquil), specialized media (Transport and Ladevi Magazine), and experts on the field (academics) were invited to participate. The mentioned approach was in line with previous research using the workshop technique in the tourism field, where participants consisted of local representatives including policy makers, members of the business community, experts and residents (Andersen and Jaeger, 1999; Street, 1999). Figure 3-5 represents the possible stakeholder groups in tourism.

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The ideal number of participants in workshops was between 15 and 50

(Andersen and Jaeger, 1999; Patt and Schroter, 2008; Na et al., 2010; Gunnarsson-

Östling et al., 2012). It was advisable to avoid large numbers of stakeholders in workshops, because it has practical limitations to inclusion and the democratic process

(Few et al., 2007). For that reason, the desirable number of participants for the present study was between 16 and 30. However, in the recruitment process (which will be explained in the next sections) the contacted stakeholders were 50, anticipating a 20 to

30% rate of no shows. However, in this study the number of participants reached 47.

Sampling

Nomination was the purposive sampling technique applied in this workshop. In the nomination technique the pool of participants was obtained by asking neutral parties for nominees. Nominees were possible participants who complied with the criteria previously defined in the study. Once a list of nominees was gathered, the researcher used random selection to elect the actual participants for the study, which helped to reduce bias. For this study, members of the nominee pool were homogenous in terms of prestige or status such as occupation, age, and education.

In workshops and other types of action research, homogeneity is useful because people are more likely to share information with people whom perceived as equals

(Miles et al., 1984). The parties consulted for nomination were (1) the Ministry of

Tourism (MINTUR), (2) The Catholic University of Santiago de Guayaquil (3) the

Municipality of Guayaquil, department of tourism, (4) and the Tourism Chamber of the city. Each institution provided a list of nominees for the study. The researcher elaborated the nominee’s pool with the information gathered from the mentioned institutions, and verified that participants were not repeated and fulfilled the study

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criteria. Subsequently, the researcher used systematic selection to randomly designate the participants that were included in the study. Systematic selection consists to pick participants from the pool each nth number (Krueger, 2014; Carey and Asbury, 2016).

Fifty participants were randomly selected from the pool of nominees. Ideal sample in workshops is up to 30 participants according to previous studies (Andersen and Jaeger, 1999; Patt and Schroter, 2008; Na et al., 2010; Gunnarsson-östling et al.,

2012)., but with a 30% of possible no-shows (Patt and Schroter, 2008; Na et al., 2010;

Qunnarsson-östling et al., 2012) the sample was elevated to 50, to reduce the probability of not meeting the target accrual. Systematic selection was employed to select the 50 participants. To do that, the researcher divided the total number of the participants to 50 (the desired sample) and used the result number to decide which participants will be selected.

Recruitment

The participants were designated from a systematic selection process were contacted by the researcher by phone and a meeting was scheduled to deliver the personalized invitation to participate in the study. In workshop sessions is customary to provide background information of the topics to discuss during the session and the objectives of the study, so participants are prepared when they attend (Andersen and

Jaeger, 1999; Patt and Schroter, 2008; Na et al., 2010; Gunnarsson-Östling et al.,

2012). For that reason, the invitation included the name of the study, the sponsors, importance, brief description of the project, objectives to achieve in the session, benefits from participate, and contact information of the researcher (Sample 3, and Script 4).

Finally, reminder calls were made the evening before the session was scheduled. The mentioned approach lead to lower rates of no-shows (Casey and Krueger, 1994).

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Incentives

As incentive, the participants were offered a certificate of assistance endorsed by the Tourism Crisis Management Initiative of the University of Florida (TCMI), The

Ministry of Tourism, and the ULEAM.

Workshop Itinerary

The duration of the workshop was 8 hours including coffee breaks and lunch breaks, which correspond to a normal day of work in Ecuador, from 9 am to 6 pm.

Workshop session normally take over one or two days analyzing up to two topics per day (Anderson and Jaeger, 1999; Street, 1999; Apine, 2011; Gunnarsson-Östling et al.,

2012). In this study, the focus of the activities was to create adaptive strategies to cope with the Rank A risks, generated from the focus group study. With that aim the following itinerary was developed taken into consideration the guideline proposed by Chambers

(2002) and other workshop literature (Andersen and Jaeger, 1999; Few et al., 2007;

Stringer, 2007; Patt and Schroter, 2008; Na et al., 2010; Apine, 2011; Gunnarsson-

Östling et al., 2012).

Before starting the session, the researcher, and the assistant, in company of the protocol team arranged the classroom, using the Hollow U sitting modality. This type of arrangement expose participant to eye contact, and it is ideal for plenary sessions

(Chambers, 2007).

The itinerary of the session followed recommendation of studies in the field that adopted the same methodology (Street, 199; Patt and Schroter, 2008; Na et al., 2010), and had 8 parts: (1) Greeting and Opening of the workshop, (2) plenary session with the theme: Risk that might affect the tourism sector in Ecuador, (3) Divide participants into themes, (4) Development of adaptation strategies (or action plans), (5) Presentation, (6)

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evaluation of the strategies, (7) election of the best strategies, (8) Certificate and pictures (Figure 3-10).

The workshop started with greetings (Script 5) of the facilitator and five minutes were dedicated to breaking the ice among participants (Chambers, 2007). The ice breaker consisted of asking the participants to write their first name on a piece of paper that could later be attached to the front part of their desk (Street, 1999; Chambers,

2007; Östling et al., 2012). This type of activity kept participants busy during the first minutes of the working day, and promoted social activity, sharing their names with other participants without spending too much time in introductions. This kind of exercise is useful in groups where many members know each other’s.

Immediately after this activity, the plenary session started. The purpose of the mentioned activity was to present the objectives of the study and provide background of the current situation of the tourism sector of Ecuador and the tentative risks that might occur (Street, 1999). The time estimated for this activity was between 20 to 30 minutes including a round of questions (Östling et al., 2012). The session started providing basic information of the TCMI, and the facilitator.

After that the objective of the study was outlined. Participants were once again informed that the goal of the research was to develop feasible adaptive strategies for the tourism industry of Ecuador in the case a set of specific risk would occur (Risks rank

A). Then, some basic information regarding the current tourism situation of Ecuador

(using information of MINTUR) was shared. During the presentation the researcher clarified the definition of adaptation strategies used for this study (Jopp et al.; 2013,

2015) and emphasized the importance of the adaptation assessment in the resilience

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process of a destination, Finally the researcher summarized the results of the Focus group study (Risks rank A) providing a complete background for the next part of the session. The slides used in the plenary session are detailed in the appendix.

Once the plenary session was concluded and the objectives of the study were established and visually displayed (Street 1999) the researcher proceeded to divide participants into themes (Street, 199; Patt and Schroter, 2008; Na et al., 2010). The groups were formed using a random classification (the researcher used the registration list to form the groups) via systematic selection to ensure that groups would have up to six members (Script 6). Random groups are good ways to wake people up, and hence lead to group discussions (Chambers, 2007). The time used for the mentioned activity was 5 minutes, and after that participants were asked to get into groups and rearrange the seats, so work was more comfortable. Each group analyzed a possible risk and elaborated adaptive capacity to cope with them. Risk were randomly designated to groups and participants were specifically asked to consider the questions detailed in the next section of this study, in the presentation of their work. Participants had three hours to discuss, develop and prepare their presentation. They were instructed to work until lunch break, because the presentations were held after lunch. Participants, were asked in the invitation to bring their computer if was possible, so presentation could be done in power point, but they were provided with paper, markers, magazines, scissors, glue, and other office supplies when they preferred to prepare their presentation in that way.

After lunch was provided the participants presented their proposed adaptive strategies. The time suggested for each presentation was up to 20 minutes, and 5 minutes for feedback and questions (Street, 1999; Patt and Schroter, 2008; Na et al.,

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2010). The order of the presentation was designated by the researcher based on the list of risks and their rankings provided in the focus group. (Chambers, 2007). Once the presentations were concluded participants were asked to perform a vote of the most viable adaptation strategies per risk, considering the criteria proposed by Jopp et al.,

2013, 2015) and detailed in the next section of this study.

Finally, as closure of the workshop, participants received their assistance certificate, and pictures were taken. The facilitator thanked the participants and concluded the session.

Workshop Interviews Questions and Operationalization

The art and craft of workshop using action research is to create a favorable learning process that allows for the construction of valuable information (Stringer, 2007).

The tools that facilitate the construction of meaningful information are the activities and questions that the researcher elaborates to engage the stakeholders in the formulation of effective solutions to the research problem (Stringer, 2007). As part of this process the construction of a preliminary context was necessary. With that aim, participants received in advance to the session a list of the main risks that could affect the tourism sector of Ecuador, which was elaborated in the risks assessment part of the study

(Focus groups sessions). Furthermore, participants knew the topic and objectives of the workshop. Besides, to provide a more complete picture and background, the researcher gave a lecture in the plenary session; so, all participants could be on the same page before starting the activities.

Using as a reference the literature in adaptation and resilience field (Smitt and

Skinner; 2002; Hertin et al., 2003; DeFreitas et al., 2006; Scott, 2006; Few, 2007;

Hoffmann et al., 2009; Scott and Simpson, 2008; Apine, 2011; Jopp et al., 2013; Jopp et

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al., 2015;), the researcher adapted the questionnaires elaborated by Jopp et al. (2013,

2015) and Saarinen and Tervo (2006), and fitted it into the context of this study.

The questions presented in the first part of the session (adaptation strategy development) were the following:

• What adaptation strategies would you recommend for the tourism sector of Ecuador, given the impact of Risk X?

• Which stakeholders should be responsible for the implementation of the adaptation strategy suggested?

• Do you think that the local community would be supportive of that particular adaptation strategy?

• How effective do you believe this adaptation strategy will be in addressing this particular Risks X impact?

• Are specific destinations in Ecuador that should be the focus of adaptation efforts?

The questions were presented to the participants, and then the facilitator asked then to consider each one of them in the elaboration of the adaptive strategies and their presentation to the group. The participants were also instructed that they needed to support their answer to the questionnaire based on previous experience, and their knowledge on the topic. Finally, the researcher requested participants to be as detailed as possible when giving their presentation (which was oriented to address the mentioned questions).

The questions were selected because they addressed the goal of the study, which was to create feasible adaptive strategies in the tourism sector of Ecuador. Each one of the mentioned questions, promoted the participation of the participants evoking their experiences and inviting them to the detailed and thorough elaboration.

Additionally, the questions allowed participants to focus on the context and analyze real

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possibilities of application, considering the possible impact of their implementation in the

Ecuadorian community.

After participants concluded each presentation, the group was asked to rate the presented adaptive strategies in a five-point scales (five being the higher score) considering five criteria (1) possible economic development, (2) possible impact in employment, (3) dependency of tourism resources, (4) degree of seasonality, and (5) level of technology implemented, taken from Jopp et al. (2015) and referred as key characteristics of support for adaptive measure (Hertin et al., 2003; DeFreitas et al.,

2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008; Hoffmann et al., 2009;).

Participants were also asked to evaluate (1) the feasibility of the implementation,

(2) and provide a grade for support of the presented strategy in a five-point scale (Smitt and Skinner; 2002; Hertin et al., 2003; DeFreitas et al., 2006; Saarinen and Tervo,

2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008; Hoffmann et al., 2009; Apine,

2011).

Therefore, the dependent variable was support for the presented adaptive strategy, and the independent variables were the characteristics of the adaptive measure, and its feasibility of implementation. The mentioned approach follows the guidelines provided in literature, which establishes that the preference over an adaptation strategy is due to the perception of its characteristics and the ease of implementation in the destination (Smitt and Skinner; 2002; Hertin et al., 2003;

DeFreitas et al., 2006; Saarinen and Tervo, 2006; Scott, 2006; Few, 2007; Scott and

Simpson, 2008; Hoffmann et al., 2009; Apine, 2011).

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Method of Inquiry

In qualitative studies the method of inquiry is considered to be the procedure implemented to create new knowledge (Stringer, 2007) and includes a detailed description of the research question, and the rigor of the research.

The research question that drove this portion of the study was the objective 2, research question number 3:

Research question: What are the most efficient and applicable options of adaptive strategies to increase destination resilience in Ecuador?

With the aim of answering the mentioned question the workshop was implemented using action research, which is ideal to find appropriate solutions for a problem in a local situation accounting for the social, cultural, interactional, and emotional factors that affect all human activity (Stringer, 2007; Herr and Anderson,

2015). Rigor in this type of research was oriented to guarantee that the outcomes originated in the process are trustworthy. According to Guba and Lincoln (1994) trustworthiness can be established through credibility, transferability, dependability, and confirmability.

Credibility was achieved in this study through the conscientious selection of stakeholders of the tourism sector, the obtaining of the endorsement of the Ministry of

Tourism of Ecuador, the use of a frame designed for the study and based on literature review and the triangulation of results using a quantitative examination as part of the data analysis.

Transferability and dependability was accomplished by providing a detailed description of the context, activities carried out in the events, and the procedure used in the data analysis, so replication of the study could be done in the future. Finally,

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confirmability was reached with a carefully planned protocol approved by the IRB of the

University of Florida.

Data Analysis

Two types of analysis were carried out from the workshop; the first was a qualitative assessment using action research, and the second one was a quantitative analysis to explore the relationship between the independent variables and the dependent variable.

For the qualitative analysis the following procedure was carried out. The workshop was conducted with the aim to develop adaptive strategies to cope with risk identified in a previous study (focus groups). For that matter, not only the outcome of the workshop, but all the process needed to be included in the data analysis. Hence, the complete session was audio recorded, and transcription was performed immediately after the session was concluded. Besides, field notes and participants rankings provided a broad quantity of information to analyze. The transcripts and the field notes were the focus of the qualitative analysis.

The information provided in both the transcripts and the field notes were analyzed using qualitative content analysis (Hsien et al., 2005; Bowen, 2009; Shreier,

2012; Kueger, 2014; Carey and Asbury, 2016; Padgett, 2016). Data frame used in the analysis was created for this study using as reference the available literature in adaptive strategies in the tourism field (Smitt and Skinner; 2002; Hertin et al., 2003; DeFreitas et al., 2006; Saarinen and Tervo, 2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008;

Hoffmann et al., 2009; Apine, 2011). Three stages were implemented in the analysis

(Bowen, 2009) and were (1) elaboration of frame, (2) verification of information, and (3) comparison and contrast.

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The elaboration of the code frame was carried out using as reference the adaption literature (Smitt and Skinner; 2002; Hertin et al., 2003; DeFreitas et al., 2006;

Saarinen and Tervo, 2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008;

Hoffmann et al., 2009; Apine, 2011; Jopp et al., 2013; Jopp et al., 2015) and included seven categories: (1) name of the strategy, (2) description, (3) to cope with, (4) responsible of the implementation, (5) support of the community, (6) effectiveness, (7) destination where implementation is needed. No subcategories where used in this frame.

The second stage of the analysis included the verification of information. Each strategy presented was analyzed by the researcher using the mentioned frame (Figure

3-11) on two separate occasions to reach reliability (Shreier, 2012). This step permitted a thorough analysis of each strategy presented by the participants.

Finally, the comparison and contrast stage was carried with the aim to find out difference and similarities across the different presentations.

For the second part of the data analysis the participants ranking sheets were used in the analysis. Descriptive statistics were performed to identify the adaptive strategies with higher means in the overall ranking. The top strategies were then used for the phase 3 of the study. Furthermore, a correlation analysis was performed to understand the relationship between the dependent variable (support) and the independent variables (characteristics and feasibility of implementation) (Hair et al.,

2015). Due to the size of the sample other inferential statistical analyses were not performed.

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Validity and Reliability

Validity of the study was achieved through trustworthiness (Guba and Lincoln,

1994), which was explained in detail before in this study (rigor of the method of inquire).

Moreover, other way to achieve validity for the study was through the application of a concept driven approach adopted in the elaboration of the code frame used in the qualitative content analysis.

For reliability the consistency of the application of the code frame is also a pillar stone in content analysis (Schreier, 2012). Consistency was reached through the principle of comparison across points in time performed by the same researcher. For that matter the documents generated from the workshop were analyzed using the code frame in two separate occasions, with a coefficient of agreement greater of 96.32%, using the formula proposed by Schereir (2012):

Coefficient of agreement = number of units of coding on which the codes (3-2) agrees/ total number of unites of coding *100 For this study 517 units of code (11 for each document analyzed). The code units were established using the categories of the code frame applied (seven categories or dimensions). During the comparison across points the number of units of coding on which the codes agreed were 498, giving a coefficient of 96.32%.

For the quantitative analysis performed, concept validity was achieved with literature to identify the types of relationships among the constructs analyzed. Moreover, the instruments used in the analysis were previously tested in other published studies

(Saarinen and Tervo, 2006; Jopp et al., 2013, 2015) and adjusted to the context of the present study. However, results cannot be generalizable to the population due to the size of the sample.

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Phase 3: Adaptive Strategies Evaluation

For the second part of the study the focus was to identify, which adaptive strategies generated in the workshop with tourism stakeholders were more feasible to implement in the destination to cope with risks rank A. The assessment of the adaptive capacities included the supply side evaluation.

The Supply Side Evaluation Variables: Survey Adaptation Assessment

The list of adaptation options was the product of the workshop conducted on

January 2018 to evaluate the feasibility of the implementation of such strategies in

Ecuador. Different stakeholders of the tourism sector were contacted through internet and telephone surveys to obtain their opinions regarding the adaptation options that could be effectively implemented in the destination.

Rationale

Previous steps of the study have provided a list of risks that might affect the productive sector of the country, as well as with their ranking. Risks targeted as "Rank

A" were presented to tourism stakeholders of Ecuador in a workshop and adaptive strategies that could address the mentioned risks in the tourism industry were elaborated.

To evaluate the adaptive strategies generated in the workshop, and gaining representativeness of the sector, a survey with a large sample was conducted through the Internet and by phone using Qualtrics, Skype and CNT Ecuador. The approach adopted was a tailored design to reduce survey error (Fowler; 2013; Dillman et al.,

2014).

The survey method is the most frequently used in the tourism field (Goeldner and

Ritchie, 2006), and more specifically, opinion surveys can provide invaluable

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information, and produce excellent results when they are properly constructed

(Goeldner and Ritchie, 2006).

The use of internet surveys has become increasingly pervasive in the tourism sector (Goeldner and Ritchie, 2006), mainly because they are inexpensive, cost- effective, easily delivered, easy to respond, and do not present major complication for data cleaning and analysis (Sills and Song, 2002). Besides, researchers have taken seriously the internet potential as a tool for conducting scientific research (Sills and

Song, 2002). However, one downside of internet surveys frequently mentioned in literature is that might exist issues when try to reach all members of the target population (Sills and Song, 2002); therefore, increasing the coverage error (Fowler,

2013; Dillman et al., 2014). Conversely, the use of internet is wide spread among the

Ecuadorian population. According to the Ministry of telecommunication and the

Superintendent of telecommunication of Ecuador, 66 out of 100 Ecuadorians were frequent users of the Internet in 2014. Moreover, in 2017 the 90% of the national territory had internet coverage. In the case of the tourism sector, the Ministry had employed several tactics to increase the number of active internet users in the sector

(Barquet et al. 2011). One of the most popular is the inclusion of businesses that have websites and valid email addresses to the database of www.ecuador.travel, which is the official promotional website of the country. Consequently, the tourism stakeholders are a special population, which is very familiar with the use of the internet, and the Ministry of Tourism frequently contacts them through email. For that reason the population has become accustomed to using internet and emails on a daily basis. However, with the aim to reach a larger number of participants and to ensure that every member of the

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population had an equal chance to be included in the study, a phone survey was carried out whenever the registered stakeholder did not have an active email account.

The Ministry of Tourism of Ecuador was a key contributor in this phase of the study. The authorities of the Zonal 4 of the country provided the updated (2017) database of tourism stakeholder of the country.

Reducing survey error

The use of a tailored design was recommended to reduce survey error (Dillman et al., 2014). The tailored design was employed to address the coverage, sampling, non-response, and measurement errors.

The coverage error was addressed and explained in the data collection section of the study. Specifications of the population, the sample frame, coverage rate are also explained in the data collection section.

Sampling error was addressed through increasing of the sample size and stratification (Fowler, 2013; Dillman et al., 2014) the procedures employed to reduce the sampling error are explained in detail in the “participants” section.

The non-response error was reduced with the mixed mode survey and with the increase of benefits, the decreasing of the cost of participation, and the establishment of trust (Dillman et al., 2014); further information is detailed in the data collection section.

Finally, measurement error was addressed establishing the concepts that needed to be measured in the survey, which were based on the research questions of the study (Dillman et al., 2014). The rationale used for the questions in the survey is explained in the operationalization of variables section.

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Data collection

In this section the coverage, and non-response errors were addressed. The defined target population for the study was registered tourism stakeholders; for that matter, the sample frame used in this section was the list of registered stakeholders provided by the Ministry of Tourism. The list had a total of 25427 stakeholder registered,

100% of them have an active telephone line, and the 39% of them have an active email accounts. All target population was coverage in the study, decreasing significantly the coverage error.

Non-response error was addressed using a combined method of recruitment, also known as mixed mode survey (Dillman et al., 2014). The survey was sent through a link by email to the selected participants, and whenever the randomly selected participant did not have a registered email, the researcher contacted them by telephone.

In order to achieve a high response rate, the approach suggested by Dillman et al.

(2014) was adopted, and contact with the participants was carried out on five occasions in the course of 22 days. The first day an announcing letter of the survey was sent by email, and in the case of the telephone calls, an appointment was scheduled for day 4.

During day 4, invitation with the survey link was sent and the phone calls were done continuously from 8 am to 5:30 pm (business hours). The next contact was done on day

10, a second email request was sent and calls that were not answered where done again. A similar approach was carried out on day 18 and 22.

During each contact the researcher specified the usefulness of the survey to the tourism sector, and mentioned that the study was sponsored by the University of

Florida, the Ministry of Tourism, and the University Laica Eloy Alfaro de Manabí. The researcher also provided contact information in case participants wanted to confirm the

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legality of the study (Dillman et al., 2014). Also, to increase the perceive benefit, the researcher stressed that the opportunities of respond were limited and that only a small number of people had the opportunity to participate, and that the time for participation was limited (Dillman et al., 2014).

To decrease the cost of participation, the length and complexity of the survey were kept to a minimum, as well as the request for personal and sensitive information.

Participants

The participants for the study were selected from the list of registered stakeholders provided by the Ministry of Tourism. Stratified samples were employed; sample units were divided into strata prior selection, decreasing this way the sampling error (Dillman et al., 2014). Proportionate sampling strata was employed, where the samples are proportional to the population under study, and each strata represented a tourism subsector.

The population size for the study was 25427, from which 66% belonged to the food and beverage subsector, 17.99% to accommodation, 7.72% to operation (travel agencies), 5.03% to parks and recreation, and 3.26% to tourism transportation.

The desired level of confidence was 95%, with a confidence interval of 5 (Dillman et al., 2014). In order to decrease sample error the sample size was defined using the finite population sampling formula (Fowler, 2013; Dillman et al., 2014).

(N∗p∗q) n = (3-3) MoE 2 ⦗(N−1)∗( ) +(p∗q)⦘ z

Where:

n= complete sample size needed for desired level of precision

N= is the size of the population

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p= the proportion being tested

q= 1-p

MoE= the desired margin of sampling error

Z= the z score or critical value for the desired level of confidence

With the use of the formula, the sampling error decreases below 5 percent at a complete sample size of 379. However, anticipating the probability of non-response, the size of the sample was duplicated (increase of sample size) to 758. Using the principle of stratification, the strata were established as follows: 500 participants of the food and beverage subsector, 138 from accommodation, 58 from operations, 38 from parks and recreation, and 26 from transportation. Participants for each stratum were selected randomly from the list, using a computer program (research randomizer).

Operationalization of variables (independent, dependent)

The evaluation of the adaptive strategies is a core component in the RTAF resilience framework, and a quantitative approach using a large sample will provide results that could be generalizable to the population of interest. For that matter the survey instrument was developed considering the questionnaire used by Jopp et al

(2013, 2015) with few changes that allow for its implementation in the context of this study and the use of the findings of previous phases of the research project. Moreover, the guidelines provided in adaptation and resilience literature where included in the modification and implementation of the tool (Behringer et al., 2000; Hoffman et al.,

2009; Apine 2011, Preston et al., 2011; Jopp et al, 2015; Shaireef et al., 2015; Shaireef et al., 2015).

The design of the questionnaire was focused in three areas (1) demographics,

(2) knowledge of risk and adaptation, and (3) list of adaptations to evaluate. The Risks

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awareness component was included in this questionnaire because several studies have shown that awareness of the risks and knowledge of adaptation influence the attitude of residents and tourists toward possible adaptation preferences (Behringer et al., 2000;

Hoffman et al., 2009; Na et al., 2010; Apine 2011, Preston et al., 2011; Jopp et al, 2015;

Shaireef et al., 2015), hence it was expected that respondents with greater knowledge of risks and adaption were more likely to have a positive opinion of the adaptation strategies proposed in the survey.

Knowledge was measured using a similar scale ranging from very good to very poor. The results of this study helped to identify the strategies preferred by the tourism stakeholders (ones with higher score) and to understand how demography and knowledge play a role in such preference. Finally, each adaptive strategy was rated in a five-point scale (five being the higher score) considering five key characteristics (1) possible economic development, (2) possible impact in employment, (3) dependency of tourism resources, (4) degree of seasonality, and (5) level of technology implemented, taken from Jopp et al. (2013). Participants were also asked to evaluate (1) the feasibility of the implementation, (2) and provide the support of the presented strategy in a five- point scale ranging from highly positive to highly negative (Smitt and Skinner; 2002;

Hertin et al., 2003; Hoffmann et al., 2009; DeFreitas et al., 2006; Saarinen and Tervo,

2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008; Apine, 2011).

The dependent variable was support for the presented adaptive strategy, and the independent variables were the characteristics of the adaptive measure, knowledge, feasibility of implementation, and demographics. The approach followed the guidelines provided in literature (Behringer et al., 2000; Smitt and Skinner; 2002; Hertin et al.,

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2003; DeFreitas et al., 2006; Saarinen and Tervo, 2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008; Hoffman et al., 2009; Na et al., 2010; Apine 2011, Preston et al.,

2011; Jopp et al, 2015; Shaireef et al., 2015 Apine, 2011). The questions asked in the survey were developed with the aim of answering the following research question as outlined in the previous chapter.

Research question. What adaptive strategies could be successfully implemented in Ecuador?

The first part of the questionnaire is dedicated to gather demographics from the respondents, so latter in the analysis could be determinate if there was any difference in the preference in among adaptive strategies presented based on gender, age, nationality, place of birth, education, and tourism sector. This section was designed to enable differences within various groups based on their demographic characteristics.

The second part of the instrument was developed with the purpose of measuring the familiarization of respondents with risks identified as important to address in the tourism sector, and adaptation knowledge surrounding the mentioned risks.

Finally, the last part of the instrument asked respondents to rate the different adaptive strategies elaborated in the workshop held in Guayaquil, based on characteristics, ease of implementation and the positive effect or negative effect of such strategy if implemented. The survey instrument is detailed in the appendix section.

Data analysis

The method of analysis selected for this study was multiple regression. Multiple regression is the appropriate method when the research problems include a single metric dependent variable related to several (more than two) independent metric variables (Hair et al., 2015). Moreover, the objective of this analysis is to predict the

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changes in the dependent variable (support of the adaptive strategy) in response to changes in the independent variables (characteristics, knowledge of risks and adaptation). Its basic formulation is

Y1 = X1 + X2 + ⋯ Xn (3-4)

In multiple regression, the procedure weights each independent variable to determine the maximum prediction from the set of independent variables. Multiple regression is based on correlation, but it is necessary to have a sound theoretical or conceptual reason to run the analysis (Pallant, 2013). Multiple regression is advised to identify how well a set of variables can predict an outcome, and which variable in a set is the best predictor of the outcome.

The regression technique is a straightforward analysis that allowed prediction and explanation. For this study standard multiple regression was employed. In this type of regression all the independent variables were entered in the equation at the same time, and each variable was evaluated accordingly to their predictive power (Pallant,

2013). With this approach results shown how much variance in the dependent variable can be explained by the set of independent variables, and how much unique variance is explained by each independent variable.

The recommended sample size for this analysis lies between 285 to 360 considering all independent variables used in the survey instrument (Hair et al., 1998;

Field, 2009; Pallant, 2013). Test to detect multicollinearity and outliers was run, and due to sample size assumptions of normality, linearity and homoscedasticity were met.

Validity and reliability

For Field (2009) one of the most important aspect of research and a way to reduce the measurement error is through validity and reliability. This section will present

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details about the validity and reliability measures used in the survey to tourism stakeholder in Ecuador.

Reliability is the property which reflects the capacity of the instrument of being consistent with the measurement across samples (Field, 2009). In human studies due to people’s own heterogeneity is it improbable that the results of this sample will be exactly consistent with another sample of respondent in a different context and at a different time (Jopp et al., 2013). Yet, it is expected that it would provide an accurate representation of the population under study, providing a good understanding of the tourism stakeholders of Ecuador.

Furthermore, statistical reliability of the instrument was tested using the

Cronbach’s alpha coefficient to test for internal consistency. It is important for any study to determine if the scale implemented in reliable, and an ideal Cronbach alpha coefficient should be above .7 (Pallant, 2013). For this study the instrument implemented was adopted from Jopp et al. (2015) and adjusted to the context of the study. The Cronbach alpha coefficient, achieved in this study was .90

Validity is the property that reflect that ability of the instrument to measure what is intended to measure (Field, 2009). Two checks for validity were performed in this study

(1) face validity, (2) content validity, (3) criterion-based validity.

Face Validity is a non-statistical method of validity that provides a representation of how well the instrument will measure the construct under study (Field, 2009).

Normally, this is reached through revision of experts. In this study the instrument face – validation was achieved through the revision of the instrument for two other experts on the field, besides the researcher (one from the University of Florida and one of the

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ULEAM). Moreover, the instrument implemented in the study is based on the one developed by Jopp et al. (2015), which has been tested in several occasions in the Surf

Coast.

Content validity was another non-statistical measure, which was reached through the degree to which individual item represent the construct being measured (Field,

2009). One way to check content validity is through expert validation. A process of peer review addressed the content validity of this study by expert faculty at the University of

Florida, and ULEAM.

Phase 4: Demand Side Evaluation Variables

The final step in the methodology section included a survey to tourists. The survey was conducted in continental Ecuador, in the principal tourism hotspots of the destination. The aim for the survey was to obtain the perspective of the tourists regarding the possible adaptive strategies elaborated by the tourism supply stakeholders.

Rationale

Tourists are an important part of the tourism system, and adaptive planning should be oriented taking into consideration the locals and the tourists opinions (Wall and Allister, 2006; Jopp et al. 2013, Kelly et al., 2007). Cooperation in tourism planning is fundamental and inclusion of different components of the system is advised to achieve a successful tourism development (Wall and Allister, 2006). Adaptive strategies implemented in tourism destinations can impact traveler’s perceptions and their experience (Plummer and Fennell, 2009); besides, tourists are whom make the final decision of engaging in the adaptive strategy, and if they feel that the strategy approach is unappealing they might decide not to visit a destination (Jopp et al., 2013).

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In this study, the tourists’ opinion was considered as a final filter in the evaluation of the adaptive strategies generated in the workshop with the supply side tourism stakeholder. The information generated from this helped destination planners to consider the tourists’ opinions when deciding to engage in an adaptation strategy. With this section the inclusion of all components of the tourism destination was achieved for the resilience study.

With the aim to gather tourists’ opinions, a survey method was adopted. Surveys and the use of structured questionnaires are widely used in empirical research in tourism (Goeldner and Ritchie, 2012; Robinson et al., 2013); Moreover, opinion surveys have been very successfully implemented in the past, providing with invaluable information and excellent results when they are conducted properly (Goeldner and

Ritchie, 2012).

The survey study was conducted on the principal tourism hot spots of Ecuador, and data was collected in person. For this type of study there are not limit of the modes of data collection (Fowler, 2013), but paper questionnaire was selected. The advantage of this kind of approach is that is the most effective way of enlisting cooperation; besides, the use of paper questionnaire allowed for the responders to answer the questions without sharing their response with the researcher (Fowler, 2013).

Reducing survey error

Tailored design was implemented in the study to reduce survey error (Dillman et al., 2014), addressing issues as coverage, sampling, non-response, and measurement errors.

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In the data collection, participants, and the operationalization of variables sections of this study it will be explained how the coverage, sampling, non-response, and measurement errors were reduced.

Data collection

The coverage, and non-response errors were addressed in this section. The target population for the study was tourists in Ecuador. The sample frame was composed of domestic tourists that visit principal tourism destinations of continental

Ecuador (Guayaquil, Quito, Esmeraldas, Salinas, Baños) and their main attractions.

This type of approach is normally applied when there is not an advance list from where to sample (Floyd, 2013). The destinations were selected taking into consideration the data available in the Ministry of Tourism. The preferred forms of transportation in

Ecuador for domestic tourism are bus 51.12%, own vehicle 39.59%, and airplane 1%

(Ministry of Tourism, 2011; Ministry of Tourism, 2016). The city with the highest land traffic (both bus and private vehicle) is Guayaquil. The number of domestic tourists in

Ecuador that used air transportation in 2014 was 3416600, according to the Ministry of

Tourism Statistic Report; from those the 42% corresponded to the route Quito-

Guayaquil-Quito, the 11.5% traveled in the route Quito- Cuenca- Quito, and the route

Quito – Manta – Quito registered 6.6%. Moreover, all air routes in the country have as starting point two of the cities included in the analysis. Additionally, the 41.69% of all passenger of the tourism train routes start in the cities of Guayaquil and Quito, and

95.34% of the routes are within 300 Km, of the mentioned cities. Furthermore, the most visited national parks and protected areas (excluding Galapagos) are located at not more than 280 Km of distance of Guayaquil, Quito, Esmeraldas, Salinas, and Baños.

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The principal tourism destinations of the country (for domestic tourism) and their main tourist sites were selected as the multistage areas of sampling (Fowler, 2013).

This approach was selected because there was no “list of participants” from where a sample could be drawn. Instead, official information was provided by the Ministry of

Tourism of the most visited cities in Ecuador and their principals attractions (Ministry of

Tourism, 2011; Ministry of Tourism, 2015).

The cities of Guayaquil, Quito, General Villamil Playas, Salinas, and Atacames were the most visited cities in the country by domestic tourist in 2011, whereas

Guayaquil, Quito, Cuenca, and Baños were the most visited by international tourist.

Considering the mentioned information, the cities of Guayaquil, Quito, Salinas,

Baños and Atacames were selected as sites for collection of data (Figure 3-8). The city of General Villamil Playas was not included in the analysis due to its proximity to the city of Salinas, and Baños was included due to its proximity to Puyo (an Amazon destination), besides most tour packages offer both destinations together, therefore tourists that visit Baños most likely will visit Puyo. The selection of the mentioned destination ensured the inclusion of tourists that visit the three regions of the continental

Ecuador: Andes, Coast, and Amazon. The tourism sited selected within those cities were selected following the recommendation of each city department of tourism.

The selected sites for the study in Guayaquil were based on the ranking of most visited tourism attractions in the city and nearby, provided by the Municipality. The tourism sites included in the study were: (1) Santay Island, (2) Pier Simon Bolivar- Las

Peñas – Cerro Santa Ana the same year.

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In Quito, the tourism sites included in the study as points for data collection were

(1) Historic Center, (2) Volcano Route- train station, and (3) Mitad del Mundo, all of which belong geographically to the city of Quito, or were located within two hours.

In the case of Atacames and Salinas the pier of each destination (located in front of the beach) was selected as point of collection of data for this study. And in Baños the points of collection of data were (1) pailon del Diablo, (2) el manto de la novia, and (3) casa del arbol.

To address non-response error the surveys were conducted in person, using self-response paper questionnaires. This type of approach is a dominant data-gathering tool in tourism studies due to its ease of implementation and relative low cost (Orams and Page, 2000). Besides, it increases the probabilities of higher levels of recruitment, and low rates of non-respondents (Floyd, 2013).

The researcher asked for permission from the Municipalities of each city to conduct data collection in points of major tourist flow near the main tourist attractions.

Each city tourism department appointed the specific areas for collection of data. To implement the data collection the researcher needed the collaboration of five tourism students from the University Laica Eloy Alfaro de Manabi whom served as surveyors.

Each student received two days of preparation regarding the topic of the study and the approved protocol. In addition, to obtain a high rate of responses the students were instructed to present their credential and identification when conducting the survey

(Dalton et al., 2000).

The on-site survey was conducted during 3 days on February of 2018 (10 to 12), one day was dedicated to each attraction. The surveys were conducted simultaneously

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and two days were dedicated in the cities of Guayaquil, Three days were dedicated in

Quito, one day in Atacames, and one day in Salinas, and three days in Baños from 8:00 am to 6:00 pm, to take advantage of the daylight. The surveyors approached tourists whom they identified as locals, outlined the purpose of the research, and invited them to participate in the survey (Pearce et al., 2013; Lee and Taylor, 2005).

They confirmed the tourist’s nationality (Ecuadorian) before distributing the survey. After receiving consent to participate, the self-administered questionnaire was presented to the respondent. To ensure diversity in the sample, the surveyors delivered one questionnaire to any travel party of less than 5 people, and for tour groups (usually consisting of 20-35 people) a maximum of four surveys were presented to the group

(Schneider and Sonmez, 1999; Vitterso et al, 2000; Hillery et al., 2001; Lee and Taylor,

2005; Pearce et al., 2013). For this study purposive sample was employed (addressing all tourist parties identified by the surveyors), lack of randomization could produce a sample biases; however, the tourists survey represented only one part of the project, thus due to time and resources constraints the number of questionnaire completed and the representativeness of the sample was deemed sufficient (Jopp et al., 2015).

369 of surveys were collected in the study but only 354 were usable, from them

21% corresponded to the city of Quito, 13.6% to the city of Guayaquil, 20.1% to

Atacames, Baños 25.1%, and 20.1% Salinas.

The study obtained a 95.84% response rate, of which 91.95% were complete and admissible in the analysis.

Participants

The target population for this study was domestic tourists of continental Ecuador.

The study focused only in domestic tourists because they would probably support

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destinations within the country that have recently suffered a disaster; whereas, international tourists are more likely to avoid a destination associated with risks and change their travel plans (Sonmez, 1998; Sonmez and Graefe, 1998; Sonmez et al,

1999; Leep and Gibson, 2003; Reisinger and Mavondo, 2005; Fuchs and Reiche,

2006).

Participants in the study were adults (older than 18 years old) considered tourists. The definition of domestic tourist applied followed the guidelines of the Ministry of Tourism of Ecuador. Thus, domestic tourists are Ecuadorians whom travel to a destination different than their usual environment, for a duration of less than one year, for any main purpose (leisure, business, health, education or other personal reason) other than to exercise a paid activity for a resident entity in the country or place visited

(Ministry of Tourism, 2016).

According to the Ministry of Tourism of Ecuador, in 2013 the income generated for domestic tourism was 5000 millions of dollars; Ecuadorian travel 1.7 times per year with an approximate of 6 million people mobilizing to various tourist destinations within the country (Barquet, 2014). When the exact number of the population is unknown, the size of the sample can be established taking into consideration the standard normal deviation set at 95% confidence level (1.96), maximum heterogeneity (50% = 0.5) and the confidence interval (0.05 = ±5) (Dillman et al., 2014) employing the following formula:

(z2∗p∗q) n = (3-5) MoE2

Where:

n= complete sample size needed for desired level of precision

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p= the proportion being tested

q= 1-p

MoE= the desired margin of sampling error

z= the z score or critical value for the desired level of confidence

With the use of the formula, the sampling error decrease below 5 percent at a complete sample size of 385.

In this study 8 hours per day for 10 days were dedicated to collect the sample.

The total number of participants recruited was 354.

Operationalization of variables

Evaluation of adaptive strategy is fundamental in adaptation planning (Preston et al., 2011), and community support is an important factor so it was worthwhile to obtain the perception and attitude towards possible adaptive strategies (Apine, 2011) to be implemented in Ecuador, from the people whom actually are going to live with such measures and are more directly affected (domestic tourists). Tourists play an important role in adaptation of the destination through their choice of holiday destination (Jopp et al., 2015), therefore the adaptation options need to be tested with possible consumers

(domestic tourists) before implementation.

The questions asked in the survey instrument applied in this study were taken from the tool designated by Jopp et al. (2015) and modified to apply in the context of

Ecuador. They helped to answer the following research question:

• What strategies do domestic tourists prefer? The first section of the questionnaire was focus on demographics to understand the distinctions within various groups based on gender, age, place of birth, and level of

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education, each variable was evaluated in order to identify differences in opinions regarding the adaptation strategies.

The second part of the instrument focused on determining the level of familiarity of the visitors with the tourism sites and their knowledge of risk and adaptation. With that aim the questions posted by Jopp et al. (2015) were adopted and adapted. The resulting question were:

• Q1. Is this the first time that have you visited this tourism site in your lifetime?

• Q2. How would you rate your knowledge of the tourist attractions and activities available in this site?

• Q3: How would you rate your knowledge of the issues surrounding risk 1- 3?

• Q4. How would your knowledge of the issues surrounding risk 1-3 adaptation?

Questions were used to determine the number of first time visitors to the site and their knowledge of the tourist attractions and activities available to help determine if knowledge of the tourist areas and attractions influenced respondents’ opinions regarding adaptation in the tourism site. Besides, respondent were asked to use a 5- point scale to identify their perceived knowledge regarding the identified risks and adaptation. Several studies have shown that awareness of the risks and knowledge of adaptation influence the attitude of residents and tourists towards possible adaptation preferences (Behringer et al., 2000; Hoffman et al., 2009; Na et al., 2010; Apine 2011,

Preston et al., 2011; Jopp et al, 2015; Shaireef et al., 2015) it was expected that respondents with greater knowledge of risks and adaption were more likely to have a positive opinion of the adaptation strategies proposed in the survey.

The final section of the survey was dedicated to evaluate the tourists’ preferences over the different adaptive strategies presented. A list of possible strategies

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were presented, and respondents were asked to provide a score for support of the strategy.

For this study a multiple regression analysis was conducted to understand the effect of the independent variables (1) demographics, and (2) knowledge (of risk, of adaptation, of the site) in the overall support for the adaptation strategies (dependent variable). The survey instrument is detailed in the appendix section.

Data analysis

Multiple regression was selected as the appropriate method to predict the changes in the dependent variable (support for the adaptive strategy) in response to changes in the independent variables (demographics and knowledge). The basic formulation used in multiple regression is

Y1 = X1 + X2 + ⋯ Xn (3-6)

For this analysis simultaneous multiple regression was employed, this type of regression provides understanding of how much variance of the dependent variable can be explained by all independent variables as a set, and how much unique variance is explained by each independent variable (Pallant, 2013).

The recommended sample size for this analysis is at least 146 considering all independent variables used in the survey instrument (Hair et al., 1998; Field, 2009;

Pallant, 2013). A test to detect multicollinearity and outliers was run, and due to sample size assumptions of normality, linearity and homoscedasticity were met.

Validity and reliability

An extremely important aspect of research is to reduce the measurement error through validity and reliability (field, 2009). This section will present details about the validity and reliability measures used in the survey instrument used for the tourists.

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Reliability is defined by Field (2009) as the property of the instrument used in the statistical analysis, which reflects its capacity of being consistent with the measurement across samples. The statistical reliability of the survey instrument used in this study was tested using the Cronbach’s alpha coefficient to test for internal consistency. According to Pallant (2013) the ideal Cronbach alpha coefficient should be above 0.7. For this study the instrument implemented was adopted from Jopp et al. (2015) and adjusted to the context of the study. The Cronbach alpha coefficient, achieved in this study was .74

Another fundamental property of the instrument is Validity. Validity is conceptualized as the property that reflects the ability of the instrument to measure what is intended to measure (Field, 2009). In this study both face-validity and content validity were achieved through peer review (experts of the University of Florida, and ULEAM), and accurate implementation of the measurements used in past studies. Face – validity and content validity are non-statistical methods that provide a representation of how good the instrument will measure the construct under study, and the degree to which individual items represent the construct being measured (Field, 2009). The survey instrument implemented in this study is based on the questionnaire developed by Jopp et al. (2015), which has been tested on several occasions in the Surf Coast.

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Table 3-1. Documents analyzed RTAF N Document name Source Information analyzed 1 Viaje de la semana Andes Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 2 Viaje de la semana Costa Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 3 Viaje de la semana Amazonia Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 4 Viaje de la semana Galápagos Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 5 Catastro Nacional de Turismo Ministry of tourism Tourism stakeholders Zonal 4 6 Revista ama la vida vol. 1 Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 7 Revista ama la vida vol. 2 Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 8 Revista ama la vida vol. 3 Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 9 Revista ama la vida vol. 4 Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 10 Revista ama la vida vol.5 Ministry of tourism Tourism attractions web site Environment Socio cultural assets 11 Revista ama la vida vol. 6 Ministry of tourism Tourism attractions web site Environment Geography Socio cultural assets 12 America Turistica Ministry of tourism Environment web side Geography Socio cultural assets 13 Ecuador culinario Ministry of tourism Socio cultural assets web site 14 Ambiente 2035 vol .1 Ministry of the Environment environment web site Adaptation strategies and decisions implemented by the government, and community

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Table 3-1. Continued N Document name Source Information analyzed 15 Ambiente 2035 vol .2 Ministry of the Environment environment web site Adaptation strategies and decisions implemented by the government, and community 16 Ambiente 2035 vol .3 Ministry of the Environment environment web site Adaptation strategies and decisions implemented by the government, and community 17 Ambiente 2035 vol .4 Ministry of the Environment environment web site Adaptation strategies and decisions implemented by the government, and community 18 Ambiente 2035 vol .5 Ministry of the Environment environment web site Adaptation strategies and decisions implemented by the government, and community 19 Ambiente 2035 vol .6 Ministry of the Environment environment web site Adaptation strategies and decisions implemented by the government, and community 20 El Nino / La Nina hoy Ministry of the Risk and vulnerability environment web site (natural) 21 Segunda comunicacion de Cambio Ministry of the Risk and vulnerability Climatico environment web site (natural, and man induced ) 22 Informe factor de emission CO2-2012 Ministry of the Risk and vulnerability environment web site (man induced ) 23 Factor de emission 2013 Ministry of the Risk and vulnerability environment web site (man induced ) 24 Quinto Informe Nacional para el Ministry of the Environment Convenio de la Diversidad Biologica environment web site Adaptation strategies and decisions implemented by the government, and community

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Table 3-1. Continued N Document name Source Information analyzed 25 Libro Biodiversidad Ministry of the Environment assets environment web site 26 Reporte Huella ecologica 2013 Ministry of the Risk and vulnerability environment Zonal 4 (man induced ) 27 Estudio de caso en el Ecuador referente Secretary of Risk Risks (natural) a la vulnerabilidad y riesgos Management web site 28 Mapas de amenaza de inundacion por Secretary of Risk Risks (natural) provincias Management web site 29 Mapas de probabilidad de incendios Secretary of Risk Risks (natural) forestales Management web site 30 Mapas de Tsunamis Secretary of Risk Risks (natural) Management web site 31 Información consolidada de eventos Secretary of Risk Risk natural, man adversos Management (Zonal induced, healt related, 4) conflict base 32 Estadistica demograficas en el Ecuador INEC web site Context: socio-cultural 33 Pais atrevido: la nueva cara INEC web site Context: socio-cultural sociodemográfica del Ecuador 34 Reconstruyendo las cifras luego del INES web site Risk: natural, health. sismo Memorias Adaptation decision: other 35 Una Mirada historica a la estadistica del INEC Context: environment, Ecuador socio cultural 36 Mapas de Peligro: vulcanologia IGEPN web site Risk: natural 37 Mapa de Sismos IGEPN web site Risk: natural 38 Informe Sismico Especia N 7- 2017 IGEPN Risk: natural 39 Malecon de La Libertad cerrado por Tierra Segura Risk: man- induced, mancha aceitosa en el mar health, conflicto based, natural 40 Perdidas por desastres de impacto Corporativo OSSO Risk: man- induced, extremo, grande y menor en Ecuador, health, conflicto based, 1970-2007 natural

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Table 3-2. Documents analyzed risks assessment N Document name Source Information analyzed 1 En tierra segura : desastres Secretary of Risks: naturales tendencia de la tierra Risk • El niño management • Mud slid La Josefina • Earthquake • Volcano eruption • Flood • Drought

Affected population 2 Consolidado de eventos Secretary of Risks adversos Risk • Air crash management • Car accidents • Accidents using nautical transportation • Train accident • Volcano eruption • Avalanche • Social conflict • Contamination • Mud slide • Epidemic • Explosion • Frost • Fire • Forest fire • Drought • Flood • Earthquake • Storm • Tsunami • Wind storm (gale)

Frequency of Risk 3 Perdidas por desastres de Secretary of Risks impactos extremo, grande y Risk • Hydro meteorological menor en Ecuador 1970-2007 management • Geological • Anthropological • Epidemics and plagues

Intensity, affected and fatalities 4 Revista informativa de la Secretary of • Volcano eruption Secretaria de Gestion de Risk consequences Riesgos management 5 Plan de Gestion de Riesgos Secretary of Risks reduction procedures Institucional Risk management

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Table 3-2. Continued N Document name Source Information analyzed 6 Plan Familiar de Emergencias Secretary of Risks reduction procedures 2013 Risk management 7 Plan de continuidad de Secretary of Risks reduction procedures actividades ante emergencias: Risk instituciones y empresas management 8 Manual Del Proceso De Secretary of Risks reduction procedures Gestión De Capacitación En Risk "Gestión De Riesgos" management - Modalidad Presencial 9 Guía Para La Incorporación De Secretary of Risks reduction procedures La Variable Riesgo En La Risk Gestión Integral De Nuevos management Natural Risks Proyectos De Infraestructura

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Table 3-3. Documents analyzed economic sectors ECONOMIC SECTOR SUBSECTORS (as Source categorized by the Central Bank of Ecuador) Oil and mines • Oil extraction Central Bank of Ecuador • Mines • Production of Energy

Agriculture, livestock and • Agriculture (banana) Central Bank of Ecuador fishing • Aquaculture (shrimp) • Floriculture • Fishing

Construction • Real estate Central Bank of Ecuador • Civil Engineering

Manufacture • Food manufacturing Central Bank of Ecuador • Textiles • Metallurgy

Transport • Ground transportation Central Bank of Ecuador • Marine transport • Air transport

Commerce • Wholesales Central Bank of Ecuador • Minor

Tourism • Hospitality Central Bank of Ecuador • Restaurants and Catering • Tourism services

Health, education and social • Health Central Bank of Ecuador services • Education • Social services

Financial services • Banks Central Bank of Ecuador

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Table 3-4. Focus groups questions and justification Type of Question Question Justification and Reference Introductory Please take a moment to think about risks Introductory questions are questions that have affected the country and your questions to evocate sector, They could be natural, technological experience of the participants or man-made, health related, or conflict about the subject (Krueger base. Could you tell me what comes to your 2014) mind? According to Pennington-Gray and Pizam (2011) disasters could be natural, technological or man-made, health related, or conflict base. They are also referred in PATA (2014)

Transitional What risks (happened in the past or not) do Questions than go deeper questions you think are important to address ? than introductory question and serve as links for key questions (Krueger, 2014)

Identification of risk asking stakeholders using focus groups (Jopp et al. 2010) Key question In your opinion, how can (x) risk damage Damage of the environment the country’s natural attractions? (animals, crops) is a measure of physical impact (Lindell, 2013)

Natural environment is an element in the destination impact framework proposed by Pennington Gray, 2014 Key question How do you thing that (x) risk will damage Damage of infrastructure is a the supportive tourism infrastructure measure of physical impact (building, roads, etc.)? (Lindell, 2013; Pennington Gray, 2014) Key question What do you think would be the social Lindell, 2013; Pennington respond to (x) risk from the tourists Gray, 2014. perspective? Social- psychological measures of social impact. What do you think would be the social Other measures like respond to (x) risk from the tourism supply demographics, economical, sector? economical, and political impact can be access through secondary data. Key question In the case the risk occurs, how do you Decline in tourists’ arrivals is think it would be the impact tourist arrivals? a measure of disaster impact (Linder, 2013; Pennington Gray, 2014; Pennington gray and Pizam 2011)

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Table 3-4. Continued Type of Question Question Justification and Reference Intervention of moderator: So far you have, identified these “x” numbers of risks (name the mentioned risks) taking into consideration all that you have said so far. Could you … Key questions Could you rank in a 5-point scale (5 being PATA (2014) For likelihood of the highest score) how likely is “the risk” to Transform risks into decision- happening occur in Ecuador? making information by For overall evaluating the probabilities, ranking of the risk Could you rank in a 5-point scale (5 being time-frames and potential and likelihood of the worse that could happen) how badly impact of each risk and then happening could be the consequences for the tourism classifying and prioritizing sector if “the risk” occured? them

Rank and prioritize risks is needed in a project in order to focus the risk management effort on the higher risks (Baccarini, and Archer, 2001).

Risk= likelihood x consequence (Baccarini,and Archer, 2001).

After evaluating 3 types of scales to measure likelihood of risk (verbally labeled five- seven point scales, percentages scales, and 9 point logarithmic odd scales) the verbally labeled scales were judged best (Weinstein, and Diefenbach, 1997) Key question If the risk occurs, what opportunities for the Change represent opportunities tourism sector could be derive from the opportunities to gain change? fundamental insight into ecological and evolutionary processes HilleRisLambers et al. (2014)

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Table 3-5. Documents analyzed adaptation assessment N Document name Source Information analyzed 1 Ecuador ama la vida Ministry of Tourism Adaptation strategies 2 Noticias MINTUR: En Atacames, Ministerio Ministry of Tourism Adaptation strategies to de Turismo socialize Proyecto de cope with earthquake reconstruccion para Esmeraldas y Manabi of 2016 3 Noticias MINTUR: Turismo solidario Ministry of Tourism Adaptation strategies to cope with earthquake of 2016 4 Noticias MINTUR: Turismo solidario Ministry of Tourism Adaptation strategies to paquete turistico 1 cope with earthquake of 2016 5 Noticias MINTUR: Turismo solidario Ministry of Tourism Adaptation strategies to paquete turistico 2 cope with earthquake of 2016 6 Noticias MINTUR: Turismo solidario Ministry of Tourism Adaptation strategies to paquete turistico 3 cope with earthquake of 2016 7 Noticias MINTUR: Turismo solidario Ministry of Tourism Adaptation strategies to promocion Esmeraldas cope with earthquake of 2016 8 PLANDETUR 2020 Ministry of Tourism Adaptation strategies to cope with Climate change 9 Noticias MINTUR: Plan de apoyo para la Ministry of Tourism Adaptation strategies to reactivacion economica y financier para la cope with earthquake reconstruccion en zonas afectadas of 2016 10 Ecuador: mitigacion y adaptacion al International Bank Adaptation strategies to Cambio Climatico cope with Climate change 11 El pujante sector turistico ecuatoriano Medios Publicos Adaptation strategies to teme duras secuelas tras el terremoto cope with earthquake of 2016 12 El Turismo en Ecuador se recupera CNN español Adaptation strategies to lentamente despues del terremoto cope with earthquake of 2016

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Figure 3-1. Research design

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Figure 3-2. Code frame for RTAF

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Figure 3-3. Map of Ecuador

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Figure 3-4. Code frame for risk assessment impact

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Figure 3-5. Focus group study design

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Figure 3-6. Tourism destination components

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Figure 3-7. Code frame general risk assessment

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Figure 3-8. Risk matrix

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Figure 3-9. Tourism destinations of Ecuador

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Figure 3-10. Code Frame Adapatation Assessment

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Figure 3-11. Workshop itinerary

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CHAPTER 4 RESULTS

This chapter presents the results of a four-stage sequential study that was designed with the aim to achieved two objectives through five research questions.

Accordingly, the chapter is sub-divided into four sections which correspond with the design of the study proposed in Chapter three. Each section will present results in a comprehensible and coherent way for the reader, hence it will include the research question, an overview of the methodology used, and the results of the study.

Stage 1: Adaptation of the RTAF Model

Overview

The first stage of the study included the adaptation of the RTAF model proposed by Jopp et al. (2012), and pursued to give answer to the research question one and two:

• RQ 1 What are the elements of the tourism system of Ecuador?

• RQ2: What is missing for the current RTAF model in order to be used to adapt to fast variables?

The RTAF was originally designed to address climate change in the Victoria’s

Surf Coast, whereas the adapted model aimed to address different types of stressors in

Ecuador. To determine what was missing in the RTAF model proposed by Jopp et al.

(2012) to be used to adapt to fast variables and fit into the Ecuadorian context a document analysis was conducted. Forty official public published written documents were included in the analysis (Table 3-1) collected from January to July 2017 both through internet and on-site visits. The topics analyzed were: (1) natural and man-made disasters that have affected the destination in the past ten years, (2) geographic and environmental characteristics of the country, (3) characteristics of the Ecuadorian tourism system. Finally, a cross-comparison between the original model of the RTAF

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and the characteristics of the destination under study was carried out. Additionally, the destination’s analysis gave answered to the RQ: 1, expressly in the section

"characteristics of the tourist system", While the section “Differences between the original RTAF model and the context” accurately responded the RQ: 2.

Results Obtained

The results of the document analysis provide an insightful portrait of the

Ecuadorian context, which made evident the need for adaptation of the original RTAF model. The condense summary of the documentation analyzed is presented in this section in three parts (1) geographic and environmental characteristics, (2) characteristics of the tourism system, and (3) issues that have affected the country.

In this section, the information analyzed has been summarized to provide a concise overview of the Ecuadorian tourism system. A total of 17 of the 40 documents analyzed contained information regarding the characteristics of the Ecuadorian tourism system. In this section, the results will be shown based on the previously established categories.

Geographical, environmental, and socio – cultural characteristics

According to the seventeen documents analyzed (Table 3-1) the following generalities were established. Ecuador is a small country of 109,484 sq. miles located in South America between Colombia, Peru and the Pacific ocean. It is in the ring of fire of the Pacific, in the northern volcanic zone of The Andes. Ecuador is divided in 23 continental provinces and one insular (Galapagos). It has 11 international airports and 7 regional airports. The official language spoken is Spanish, and the capital is Quito. The largest city in the country is Guayaquil, and they main sources of income are the export of oil, banana, flowers, and wood.

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The country has vast natural resources which are distributed in its four regions

(1) Andes, (2) Pacific coast, (3) Amazon, and (4) Galapagos; each of which offer a different and unique climate, elevation, biodiversity, and topography.

According to the MINTUR publications (America Turistica, 2012; Viaje de la

Semana, 2016; Ama la vida magazine edition 1 to 6, 2013) Ecuador has about 1640 different types of endemic birds, 4500 species of butterflies, 345 of reptiles, 358 of amphibious, and 258 variety of mammals. The unique geographical location of the country and the exceptional habitats and microclimate, have located the country within the 17 most biodiverse in the world, and the one that has greater diversity per square kilometer. Also, the country has one of the highest concentration of volcanos (active and dormant) in South America. Ecuador has 12 national parks, 5 biological reserves, 9 ecological reserves, 1 geobotanic reserve, 4 flora and fauna production reserves, 10 wildlife refuge, 4 marine reserves, and 6 areas of natural recreation. Due to the location of the country in the middle of the planet, Ecuador does not have extreme changes in the weather, and most of the country experiments only two seasons in the year (rain season and dry season).

In the coastal region, the ecological reserve Cayapas - Mataje has the highest mangroves in the world, some can reach up to 64 meters high, and in the coast line sightings of humpback whales can be seen during in the months of July and August.

Fifteen documents contributed to the analysis of the socio-cultural aspects and assets of Ecuador (Table 3-1). The summary of the information collected and recurrent topics across the data is detailed in this section.

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The population of Ecuador is 15.2 million people according to the information provided by the INEC. The country is multi- ethnic and cultural diverse, the majority group are the mestizos, which correspond to 70% of the population, followed by the montubios and blacks with 14%, the indigenous 8%, whites 6%, while the remaining 2% corresponds to the Asians. The official language is the Spanish, but there are 14 ancestral languages legally recognized by the Ecuadorian government, and for the indigenous groups the traditional languages is also accepted as official idiom (INEC).

According to the census of 2001, 50% of the indigenous population consider the ancestral language as their mother tongue. Ecuador is a secular nation, for which there is freedom of worship. However, most of the population are Christians, except for the indigenous group who have their own conviction.

Ecuador was colonized by the Spanish and Catholicism was imposed on the indigenous population, this brought with it a mixture of Hispanic and pre-Hispanic customs, which resulted in a wide range of tangible and intangible cultural manifestations that are unique and of great value (America Turistica, 2012). Proof of this is seen in the festivals, mythology, music, and other intangible assets that have a high component of the Ecuador’s indigenous beliefs (Ama la Vida magazine vol 1- 6,

2013; America Turistica, 2012).

One recurrent topic across the documents was the value of the Ecuadorian people and their high levels of community cohesion "the Ecuadorian are open and warm people, despite their poverty they are willing to give you everything they have, because they are extremely hospitable" (Ama la vida magazine vol 1, 2013).

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Ecuadorians are very proud of their culture (INEC), Indigenous people proudly wear their typical clothing every day as an insignia to be recognized as members of a specific community (Viaje de la semana Andes, 2016).

The Ecuadorian gastronomy is also a source of pride among Ecuadorians regardless of their ethnicity. Ecuadorian food is characterized by its high caloric content.

The food of the Andes is prepared based on animal protein (pigs, sheep, beef, guinea pig) potatoes, cassava, and grains that grow in the area; while the food of the coast is based on seafood, plantain, peanut, avocado, chicken, corn, and leaves of banana and corn (Ecuador culinario, 2013).

The tangible cultural heritage of the country is globally recognized. There are numerous churches and colonial and pre-Hispanic architecture that can be seen throughout the country, and mainly in the Andes. Ecuador has received five global awards due to its cultural and natural heritage, the award-winning destinations have been: Galapagos, the historic center of the city of Quito, Sangay National Park, the historic center of the city of Cuenca and the Zapara culture (America Turistica).

The following quote from the famous Alexander Humboldt, is presented recurrently through the documents analyzed as a fairly accurate description of

Ecuadorians, their culture and behavior:

Ecuadorians are strange and unique beings: they sleep peacefully surrounded by roaring volcanoes, they live poor among incomparable riches and they become happy listening to sad music.

Characteristics of the tourism system

This study used the concept of tourism system proposed by Jopp et al. (2011), in which the stakeholders and the natural and cultural attractions of the destination form the system. To determine the tourism characteristics of the destination, a document

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analysis of 11 official documents provided by the Ministry of Tourism of Ecuador was carried out (Table 3-1). The summary of the information analyzed and the key aspects for this study are detailed in the following section.

Ecuador has been internationally promoted as a destination of nature and culture, its main strength being the natural biodiversity that the country possesses. The main natural attractions of Ecuador are located in the 51 protected areas of the country and distributed throughout its four regions (Ambiente 2035 vol 1- 6, 2016; The biodiversity book of MAE, 2016; America Turistica, 2012; Ama la Vida magazine vol 1-

6, 2013). The place of accommodation of international visitors is usually located in the metropolitan centers, since the surface of the country is small, and visitors tend to stay in urban centers and get to the natural and cultural attractions by land, air or sea

(America turistica, 2012).

The main tourism destinations are the cities of Quito, Guayaquil, Cuenca, Manta,

Salinas, Atacames and Baños (Report of the Ministry of Tourism, 2017). The main entrance of international tourists is through the borders with Colombia and Peru, and with flights to the airports of Quito and Guayaquil. Domestic tourism occurs mainly by land during the national holiday calendars that have been designed to favor national tourism (Report of the Ministry of Tourism, 2017).

In terms of cultural tourism, the country offers a wide variety of tangible and intangible cultural attractions in its four regions, predominantly the cultural legacy of the

Andean region and the great variety of ethnicities and living culture that is distributed along the mountain range of the Andes and the Amazon (Ama la Vida Magazine vol 1 -

6, 2014; Viaje de la semana Amazonia, Viaje de la semana Andes, America Turistica,

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2016). The feasibility of carrying out an “experiential tourism” in which the visitor has the opportunity to live with aboriginal communities in an authentic environment is undoubtedly a strength of the Ecuadorian cultural tourism (America Turistica, 2012;

Revista Ama la vida vol. 1, 2013).

The Tourist Attractions Inventory of the Ministry of Tourism (2004) includes 3,500 tourist attractions nationwide with their respective classification and ranking. An updated inventory is being carried out since 2016, and the Ministry projects an increase in the number of attractions of at least 27% (Ministerio de Turismo del Ecuador, 2004;

Ministerio de Turismo del Ecuador, 2011; Ministerio de Turismo del Ecuador, 2016).

According to the national consolidated cadaster of the Ministry of Tourism, as of

April 2017 there were 25,427 tourist businesses registered, of these, 66% corresponded to the food and beverages sector, 17.99% to lodging, 7.72% to operation and intermediation, 5.03% to parks and recreation, and 3.26% to tourist transport.

According to data from the Ministry of Tourism, there are 5,177 registered accommodation businesses, which corresponds to a total of 96,717 rooms, 160,247 beds, and a maximum load capacity of 224,317 guests per night. Regarding the size of the hotel sector, 4,647 were registered as micro companies, 473 small companies, 47 medium and 10 large companies and / or international chains. Furthermore, the hotel sector employs 34,306 workers.

In the food and beverages sector, the data provided by the Ministry of Tourism recorded 17,695 businesses, of which 16,290 correspond to micro-enterprises, 1,380 to small-sized companies, and 25 medium-sized companies. All the food and beverage businesses offer a maximum capacity for 745,399 consumers per day. And

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intermediation, recreation and tourism transportation sectors employed 15,078 people in

2017.

According to interviews with tourism stakeholders and members of the tourism chambers of Manta and Guayaquil, in Ecuador most of the decisions of tourism development are taken at the governmental level, and the tourism stakeholders of the private sector are rarely consulted. The participation in trade associations for the sector is optional and meetings of stakeholders from different subsectors is very sporadic.

The Ministry of Tourism has a program called "Invest in Ecuador" which openly proclaims the desire to provide tourism advice and thus offer a favorable environment for investment.

Issues that have affected the country

To establish the typology and recurrence of disasters in Ecuador, a document analysis of 16 official documents provided by the Secretary of Risk Management, the

Geophysical Institute of Ecuador (IGEPN), and the Ministry of the Environment was conducted. The analysis of these documents was performed with the objective of determining the characteristics of the Ecuadorian context. Four themes were found that were identifiable in most of the documents (15 documents, see Table 3-1) analyzed (1) natural disasters (2) man-induced disasters, (3) effects on the tourism sector, and (4) adaptation strategies used and who oversaw them.

The time frame of the analysis was 10 years, starting in 2007 and finalizing in

2017. This section will list the main disasters that have affected the Ecuador in the

2007-2017 period according to the document analyzed, and will detail their typology, how they affected the tourist sector of the country, and what adaptation strategies were used to deal with them.

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Flood: In Ecuador, floods are categorized as natural disasters and are a constant threat, which can be caused by rain, overflowing rivers or lakes, dam rupture, among others. During the 2007-2017 study period, two floods of catastrophic dimensions affected the country (Risk Management Secretary Website). The first occurred in 2008 and affected primarily the provinces of the coast and south-central

Andes. This flood brought a total of 300 million dollars in agricultural losses, 62 deaths and 28, 9122 victims. The 2012 flood affected all the provinces of the coast, Azuay, and

Loja. The total losses due to floods in 2012 amounted to 237.9 million dollars, 20 deaths and 300,000 victims.

Forest fires: With regard to forest fires, Ecuador has reported 13,969 hectares of forest affected during the year 2017, with the provinces of Pichincha, Loja, Azuay,

Santa Elena, Guayas, and Manabí being the most affected (Consolidado de eventos adversos, 2017).

Tsunami: As Ecuador is country bordered by the Pacific Ocean, there might be tsunamis. Despite the numerous tsunami warnings, the number of disastrous events caused by tsunamis is minimal. Historically, the Oceanographic Institute of the Navy

(INOCAR) reports only two tsunamis occurred in the last hundred years, both without fatalities. The most recent occurred on March 10, 2011 (due to the earthquake in

Honsho, Japan), and affected mostly the Galapagos archipelago with waves of 2.6 to

6.1 meters.

Volcanic eruption: Ecuador has 84 volcanoes that are constantly in observation by the IGEPN. Ecuador is a country that has registered a high volcanic activity in the last decade, in this section it will be detailed those volcanoes that have brought

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catastrophic consequences and that have been cataloged by the Secretary of Risk

Management as threats.

The volcanoes that are currently in process of eruption in Ecuador are (1)

Tungurahua, (2) Sangay, (3) Cotopaxi, and (4) Reventador. They are closely observed by the IGEPN and hey constantly throw volcanic materials (magma) and ashes. In some cases, they can register more than 2000 activities per day, but most of them do not bring disastrous consequences for the population. However, during the years 2007,

2014, 2015 and 2016 there have been significant expulsions of pyroclastic material and smoke columns that have intervened with the country's tourist activities (including the closure of airports due to the ash).

Earthquakes: Ecuador is in an area of strong seismic activity due to the friction of movements of two plates: Nazca and Sudamericana (continental plate). In the country 10 major earthquakes have been recorded since 106 to 2018. Due to the length of time used in this study (2007-2017) only one telluric movement of catastrophic dimensions was included. The earthquake of April 16, 2016 had a 7.8 magnitude with epicenter in Pedernales, affecting mainly the provinces of Manabí and Esmeraldas in which 671 were reported deaths and 243,000 injured.

Environmental pollution: The main polluter of Ecuador according to the

Ministry of Environment, is the mining expansion and oil exploitation, followed by vehicular pollution in cities with high urban growth or high levels of heavy transport flow.

Ecuador is one of the 20 most biodiverse countries in the world (America Turistica,

2012),

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Oil reserves are found in areas of high biodiversity and natural tourist attractions of the country (Pacayacu, Yasuni, etc.). Because petroleum is the main source of income, there is an atmosphere of tension between conservationists and the government. In a similar way, in the growing cities we can observe a sewer system and water and soil management that is quite deficient, according to members of different tourism chambers and hoteliers of the country (taken from the focus group sessions).

All this has generated a mood of dissatisfaction both between the supply and tourist of the Ecuadorian tourism sector (taken from the focal sessions) and influenced the quality of the tourist experience. In the past members of the tourism chamber, the

Ministry of Environment and NGOs have undertaken numerous awareness campaigns and political movements to create pressure with the authorities. However, this struggle

"has brought more obstacles because the news presents a climate of conflict that is not really a reflection of Ecuadorian tourism" (focus session with an hotelier).

Political instability: Historically, Ecuador has been a country of political uprisings, protests, and presidential takeovers. From 1996 to 2006, the country presented high rates of political instability, with governments unable to complete their terms due to coups. However, from 2007 to 2017, the government of Rafael Correa showed stability most of the time. On September 30, 2010, a coup attempt by the armed forces against the president was carried out. During the span of one day the president lost the support of the armed forces due to changes he wanted to impose on them. A minority group of the military took advantage of the climate of chaos, and launched an attack against the president that was not successful. In addition, during the period 2007-

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2017 numerous non-violent protests have been carried out by a right-wing group, in response to the socialist economic measures of the Correa’s government.

Impact in the tourism sector: Based on the documentation compiled for the documentary analysis and through interviews with the officials of the government institutions consulted for the study, there is not at the moment an analysis of causality between the different disasters detailed and the impact on the national tourism balance and / or the number of tourism arrival. However, a correlation can be inferred between the decrease in the number of visitors and the number of disastrous events that affected the country at any given time.

According to the statistical bulletins of the Ministry of Tourism during the years

2013, 2015, and especially in 2016 there was a decrease in the number of visitors.

More specifically during April and May 2016 there was a considerable decrease in visitors’ arrivals in comparison to previous years. Likewise, in 2013 and 2016 there was a decrease in income from receptive tourism.

Although the documents included in this analysis cannot determine causality between the decrease in the number of visitors or income from tourism and the natural disasters that have impacted the country, the various interviews with representatives of the Ministry of Tourism and stakeholders in the sector allow us to conjecture that the

Ecuadorian tourism sector is affected by various natural and man-induced disasters.

In this regard, several statements by tourism stakeholders assert that:

Every time it floods, there are mudslides, volcanic eruptions, strikes, or any other phenomenon that hinders the roads of transportation we are affected as sector (MINTUR, official).

The way they perceive us through the news affects us negatively or positively, and when there is political instability, when images of the

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natural disasters that hit us continuously come out in the news the number of visits to our establishments is affected (member of the private sector)

Not only these disasters affect the number of visitors, but also the experience they have (member of the private sector).

Adaptation strategies applied in the past

The institution responsible for strengthening tourism in Ecuador is the Ministry of

Tourism (MINTUR). MINTUR has been in charge of creating tourism policies, accessing development plans for destinations and promoting the country. For many years they had their own funds that they collected through tourist taxes (tourism promotion funds), which they managed autonomously. With the use of these funds, the Strategic Plan for the Development of Sustainable Tourism for Ecuador (PLANDETUR 2020), and the

Marketing Plan of the Tourism Ministry (PIMTE), advised by TOURISM & LEISURE, were partially developed, and implemented.

The plans provided a portfolio of tourism products, policies and strategies to strengthen the sector. Specifically, the Marketing Plan of the Tourism Ministry (PIMTE) provided a range of options that included the strengthening of the destination brand, cooperative advertising, use of marketing via traditional routes (such as fam trips, fairs and events) and novelty (such as marketing), strengthening of public relations (PR) a through synergies among all the actors in the sector and with workshops and presentations. However, currently most of the strategies proposed on the Marketing

Plan of the Tourism Ministry (PIMTE) and the Strategic Plan for the Development of

Sustainable Tourism for Ecuador (PLANDETUR 2020) are no longer viable due to lack of funds.

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Differences between the original RTAF and the Ecuadorian context

The specific characteristics of Ecuador were determined by careful analysis of forty official documents. The information collected was organized and analyzed using a code-frame design created specifically for the study. Through the code-frame it was possible to identify the category of information that was originally not included in the

RTAF model. Disasters that historically had not affected the destination were not in the original model, however scholars (reference) suggest this is necessary to create an informative risk assessment.

Through examination of the 40 documents, several themes emerged. First, the analysis showed a larger range of risks as well as the intensity of the risks and disasters that have affected Ecuador. The document analysis indicated scope, intensity and frequency are variables which were not included in the original model.

Finally, the original model did not include adaptation strategies. The revised model suggested that examination of past strategies provided a scope of what, how and for how long certain strategies were employed by the tourism industry after a disaster had occurred which impacted the industry. The document analysis revealed that for 10 of the 40 documents an adaptation strategy was mentioned and chronicled.

The new model is different from the original model in three ways: first, a document analysis is employed to determine (1) geographical boundaries, (2) environmental assets, (3) socio cultural assets, and (4) key stakeholders in the tourism industry who are involved with disaster management. Hence, the method to define the system is more broad and richer in context than the original RTAF model.

Second, the new model includes a more thorough risk assessment of the destination. The risk assessment portion of the document analysis used a broadened

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list of possible risks which included both those the destination had experienced in the past as well as those which they may not have experienced. This broadening of the possible risks in the model allowed for a greater understanding of the risks to the system.

Third, as part of the risk assessment, a qualitative study with Ecuadorian tourism stakeholders was conducted (through focus groups) to identify and prioritize risks that have greater impact on the tourism sector. This kind of approach allows for a deeper understanding of the variety of risks that might affect a destination.

Finally, the new model acknowledges the list of adaptation options as an outcome of the adaptation assessment. The adaptation process includes the presentation, evaluation, and implementation of the adaptation strategies. This was included in order to provide a portfolio of ideas (which can be implemented or not) of ways to adapt to risks in the destination.

Stage 2: Risk Assessment

Overview

A risk analysis was carried out in stage 1 of the study. This risk analysis occurred in three phases: (1) document analysis: an analysis of 9 official documents of the risk management provided by the Secretary of Ecuador; (2) focus group analysis: five focus groups carried out with tourism stakeholders of the city of Manta; and (3) creation of the risks matrix. These were completed in order to determine the three top risks that affect the national tourism sector and thus answer the research question: What are the top risks and opportunities for tourism in Ecuador derived from change?

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Stage 2. Phase 1: Results of the Document Analysis

The document analysis consisted of decoding 9 official documents (Table 3-2) provided by the Secretary of Risk Management. According to the "consolidated adverse events" report of 2017, natural and man-made disasters which resulted in higher percentages of human losses and affected the image of the country included: seismic movements, floods, landslides and volcanic eruptions.

Other disasters of less impact, which affected the country, during the same period were: political and social conflicts, pollution, epidemics, industrial explosions, forest fires, hurricane winds, and avalanches. Thus, based on a synthesis of the risks determined from the report of “Lost by extreme impact disasters of the risk management secretary” includes: (1) hydro-meteorological, (2) geological (3) anthropological, (4) epidemics and plagues.

Since Ecuador is considered a country with multiple risks, the Risk Management

Secretary has carried out numerous plans (e.g., institutional risk management plan,

2013 emergency family plan, contingency plan for emergency activities, management process manual of training in risk management, guidance for the incorporation of the risk variable in the comprehensive management of new projects) and capacity building efforts in order to increase awareness of implementing plans, identifying risks and reducing risks.

Five documents focused mainly on increasing public awareness of different risks and the importance of prevention to increase the capacity for adaptation. The documents also included pamphlets that have been used to socialize the emergency measures and steps to follow in case of disasters. It should be noted that all the

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documentation used by the Secretary is only in the Spanish language and is directed only to Ecuadorian residents but not to tourists.

Stage 2. Phase 2: Focus Group Sessions Results

Five sessions of focus groups were held in the city of Manta, Ecuador. A total of

60 actors from the tourism industry were invited to the sessions, of which 23 attended the different sessions with a total attendance rate of 38.3%.

The sessions were conducted during 5 consecutive working days from January

10 to 16, 2018. Each session lasted about an hour and a half. During the sessions, participants were exposed to risks and rated the risks that based on their experience as well as their perceptions of how the risks have affected the development of the sector at the national level.

The attendance in the number of participants during the days in which the sessions range between six to four per session (Table 4-1). The highest day of attendance were January 10th (six participants) a Wednesday, and January 11th (five participants) a Thursday, while the least attended days were January 12 to 16 (four participants per session) Friday, Monday and Tuesday respectively. The fluctuation of the days could have been since Monday and Friday are usually the days of greatest work commitments.

Respondents’ profile

The participants of the focal sessions in the city of Manta were active members of the national tourism sector and that were nominated by (1) the Chief of the Ministry of

Tourism (MINTUR) Zonal 4, (2) the Dean of the Tourism Department of the University

Laica Eloy Alfaro de Manabi, (3) The Chief of the Tourism Department of the

Municipality of Manta, (4) the President of the Tourism Chamber of the City, and (5) the

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Chief of the Central Bank of Ecuador Zonal 4, for their active participation in the developing of the tourist sector of Ecuador.

Of the 23 participants that attended the sessions, 21 were Ecuadorians and 2 foreigners; 18 lived in the city of Manta, and 5 lived in other destinations in the country.

Gender distribution was evenly distributed with 12 women and 11 men participating.

The age of the participants ranged between 23 and 60 years of age, with a marked majority of participants between 25 and 40 years old (16 participants).

Additionally, during the sessions there was inclusion of all the subsectors of the system: 4 participants belonged to the academy, 6 were tour operators and / or wholesalers and intermediaries, 6 were hotel managers and hostels of luxury and limited service category, 1 participants belonged to the tourism transportation sector, 3 members of the recreation sector were present, and there was also the participation of 1 member of the Ministry of Tourism, 1 member of the Municipal Autonomous

Government of Manta, and 1 representative of the rural area of the canton (Table 4-2).

Identification of risks

Participants were asked to take a moment to describe past experiences in the last 10 years that had negative consequences for the Ecuadorian tourism sector. They were given a definition of risk and asked to limit themselves to the conceptualization provided by the researcher at the beginning of the sessions:

• Risk: The present study adopted the concept of risk proposed by the Pacific Asia Travel Association (2014) and Coombs (2014): Risk is a prospect or probability of a negative event that could develop into crisis.

• Context of the study: continental Ecuador

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The findings from these sessions have been organized by the themes that emerged. In the very beginning of the sessions participants tended to list the risks that have affected the tourist sector of the country in a broad context:

Some risks that have affected the country are the economic crisis of 99, fluctuation in the price of the oil, earthquake, volcano eruptions… Let’s remember that Ecuador is the country of the four worlds, and we have several natural disasters that might affect us. (participant 3)

There are risks of catch tropical diseases, such as those transmitted through mosquitoes or food. (participant 16)

Other risk is the oil extraction that is contaminating the national parks of the Amazon. (participant 8)

Natural disasters are pretty commons in Ecuador, and we are still learning how to live with all the earthquakes, volcano eruptions, etc.(participant 20)

This type of generic response was expected at the beginning of the sessions, therefore, the researcher included probe questions to dig deeper into the meaning of the responses. This caused the answers and the quality of information provided to be significantly enriched. The main risks that were identified for the participants and their impact in the sector were (1) sudden changes in the policy, (2) political instability, and

(3) exaggerated news.

Risk 1 sudden changes in the policy: During the session, several participants

(n= 19) identified that sudden changes in the tourism policy of the country as risks that have negatively affected the development of the tourism activity of the destination. One participant mentioned that in the past there was a tourism tax name “ED” that was used for promotion of the country and was administered by the MINTUR, but then the government suddenly decided that the mentioned tax needed to be administered by the

National Treasury, and now this income is practically gone and the Ministry of Tourism does not have enough budget for international promotion. According to participant #1:

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This has affected the destination marketing, and currently we are in disadvantage in comparison with other neighbor countries like Colombia and Peru.

Moreover, participant # 17 agreed with this claim saying:

There are not enough funds for promotion of the country because the MINTUR does not have budget, cause now every single penny is administered by the National treasury… this policy was implemented without consultation, or socialization.

A concern expressed on numerous occasions was that due to this deficit of budget, the ministry is not providing infrastructure or maintaining infrastructure as in previous years:

Before the MINTUR built many infrastructure… we have a huge list of infrastructures delivered by the ministry, but there is no maintenance (participant 17)

The main issue is that we have inadequate policies and they change all the time in our sector, we as sector generate a lot of income and taxes, and before we (MINTUR) were entailed to administrate that money by ourselves and we did good, not perfect, but very good… we had a lot of international promotion and developed projects all around the country... but, not long ago the government changed the policies and now we don’t administer our own money… we need to get together as a guild and start doing thigs to deal with those changing policies and become stronger. (participant 17)

Under the same topic other participants mentioned that these sudden changes in the policy prevent the continuation of the activity and therefore endanger the tourist and the sector in general:

I think the main risk we have here in Ecuador is that the tourism policy of each DMO in the country changes all the time, suddenly and unexpected, so because there is not a firm policy everybody does whatever they want to do, there are not standards, there is not mandatory training, there is not even a clear or comprehensive development tourism plan. (participant 7)

In some similar way other participants commented that:

The sudden changes in policy within the DMOs, interfere with continuity and implementation of tourism plans (participant 11)

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There is a lack of identification of exclusive zones to carry out tourist activities, and in a general way, industrial development gets in the way of tourism development and preservation of the resources, this is due to deficient laws and policies, or as a result of changes in the government… when a tourism development plan is approved in the ministry something happens, the authorities are removed and everything remains in the air. (participant 9)

The participants agreed that the changes not socialized in the policies entail more problems than natural disasters:

Many natural disasters can affect the tourist attractions, but in my experience the greatest threat for tourism attractions are bad governments and bad policies, what is happening in Yasuni is a clear example. (participant 8)

We need strong policies that are not fluctuating every time someone in the position of power has an agenda. (participant 23)

These problems of incompatible and changing policies are generated in the central government and in the autonomous governments or municipalities, for this reason, there are no updated and consensual regulations consulted with the stakeholders of the sector to regulate the tourist activity of our country. (participant 2)

Based on these comments during the sessions, it was deduced that the sudden changes in sector policies were a risk for which the stakeholders had not prepared themselves.

Risk 2 political instability: The second theme to emerge was the fear of political instability of the central government and the municipal governments and how this negatively influenced the image of the country before the world. About this, participants expressed different points of view. On the one hand, there were those who focused on the inefficiency of some municipality officials and their lack of preparation regarding tourism, as a factor that has influenced it fluctuation and instability:

I think the real problems are the Municipality and the DMOs, they are the ones in charge of tourism planning now and the ones that have the budge now, but we have DMOs where the staff has no idea of tourism, they are

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not familiar with the industry, they do not know the policy, so they “create” new policies that are not useful. (participant 13)

They self-proclaim as tourism destinations, but we have just couple of hotels and the main economic activity of the destination is not tourism, and then they wonder why they are removed all the time. (participant 10)

The Municipalities and the DMOs don’t provide basic services to the sector, but they charge you for a service they did not provide, and off course people complain and as result there is a lot of rotation in the staff. (participant 14)

On the other hand, there were participants who were more focused on the impact of political instability of the destination:

The development plans in our country are planned for a long period, but then the government and the DMOs suddenly changes everything, and we do not know what to do. We are constantly doing paper work because they changed their mind, and we do not focus in become stronger as a sector. (participant 5)

There is a lack of clear policy and laws in tourism, and tourism plans are just a copy of plans of other destinations with different reality, and whenever a good plan is developed sudden changes in the tourism department of the destination gets in the way of implementation, there is no continuity. (participant 13)

Regarding the impact of the mentioned risks in the sector, participants of the different sessions tended to agree that both the political instability and the abrupt changes of the laws and policies for the sector result in negative repercussions in the tourist destinations of the country:

As sector we don’t have policy that contemplates how to deal with anything, and the DMOs do not socialize any plan for tourism development with us, they don’t hear our voices or listen to our ideas. For example, after the earthquake we experienced a low season, and that was understandable, but a month after the quake we were ready to receive tourists and we needed them to come, a couple of stakeholders got together and we thought that maybe we could contact some national and international artist to come to the destination and do some presentations and this way attract visitors, but nobody listened to us, and this was just an idea of many, but they just do not listen. (participant 18)

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There is not a clear policy to stablish category in the accommodation sector. (participant 15)

The municipality, and the DMOs do not have a policy that call for mandatory training in the sector, and this is a problem because every time the University or the MINTUR offer training nobody assist. There is a need for us to work together. (participant 19)

Tourism policy is too permissive, and everybody can figurate as a tourism business, when in reality there are not serious business, and because the policy is changing all the time there is not a plan to accurate supervise the quality of the services provided by different business within the sector. (participant 4)

Because the is not a real supervision of the DMOs people do dangerous things. For example the other day I went with some tourists to visit some beaches, and there were some traditional restaurants that have the stove right next to a container with gasoline, I was so alarmed that I talked with the owner and expressed my concern and warned him that if he did not remove that risk I would call the police. They listened to me and removed the container, but the DMO does not supervise the tourism businesses, or demand for training of the owners of tourism establishments. (participant 21)

There is not control, both MINTUR and DMOs are not controlling the quality standards of new businesses. In here people buy a house with five rooms and they proclaim their selves as hotel owner! And the same happens with restaurants and the controlling organizations just don’t care. (participant 22)

Other risk for hoteliers is Airbnb, but don’t get me wrong, it is not because it will affect the hotel business, because I think competition is good to improve quality service, but because in here there is not regulation of how these business can operate. I talked with the Minister about my concern, and his responds was “we have to allow AIRBNB because is a trend” … common I am not saying that they can ban the entrance to the market to new form of business just regulate them! (participant 2)

According to the participants, these flaws within the policies and the inefficiency of the control organizations are the results of the constant change of the authorities in said organizations. The concerns regarding this issue were evident during the sessions.

Other problem about this topic was that:

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Municipal authorities sometimes have little tourism knowledge and very rarely involve stakeholders or academia in decision-making. They have the will to promote the activity, but lack experience and / or academic training managed. (participant 4)

Risk 3 the risk of media: The third theme that emerged during the sessions was the risk that the media can have a large impact on the perception of the destination.

According to participants, the harm and the impact of the media can be much worse than any natural disaster that affects the country. The participants mentioned repeatedly that Ecuador is a country that due to its geographical location is susceptible to numerous natural disasters, and because of this they have learned to recover and to be united as a community to face such disasters. However, the way in which the media often exaggerates reality and how they project a panorama of devastation that is not a reflection of the reality of the destination has a greater impact on the number of visitors.

In reference to this, the participants stated that:

Volcano eruptions, landslides, and floods are pretty commons in Ecuador, and I think both the citizens and the tourists are familiar with that, however rumors and exaggerated news are serious threats for the tourism industry. (participant 6)

Ecuador is geographically located in the ring of fire of the pacific, but there are other countries that are affected by natural disasters, like Chile and Mexico, and they receive more visitors and their tourism industry is stronger than ours. So, I don’t think that the main problems are the natural disasters per se, but how we handle them as sector. (participant 23)

We have a greater risk in our sector: the news… for example a week after the earthquake, we as country were ready to receive tourists, especially in the areas that were not directly affected, but in every media in the country and outside, there were images of how devastated some destinations were, and those news in the most cases exaggerated the reality, and they caused more damage than the earthquake itself, that’s why I think that exaggerated news are a great danger for our sector. (participant 3)

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Natural disasters are recurrent risks in Ecuador, but the most important risk under my perception is the media, media keeps broadcasting Ecuador like a dangerous unstable country when this is not true. (participant 10)

We had learned to be resilience to those disasters, we stand after them, we keep working despite the devastation, and we are ready to provide a quality service after a crisis. However, we are still struggling with perceptions that are portrait by the media, and that are not a reflex of the reality. (participant 2)

Some participants said that one problem was that the Ecuadorian community has not seen themselves as a tourist destination, and they are not aware of the impact of the activity on the economy, so they continue to allow these discrediting campaigns:

We as country are not focused, we don’t see tourism as a booster of the economy and proof of that is that every media channel promotes “news” that are exaggerated… for example there is a rumor of “possible tsunami in the social media” and immediately you see news about tsunamis and how dangerous they can be… and this is all because of a rumor! They scare the tourists for nothing. (participant 13)

News are always making a huge deal about earthquakes and tsunamis because there is a rumor and that make sales, but they never rectify that everything is fine in the destination, I think we as stakeholders of the sector should demand for clarification of the media or work together to deal with those dangerous rumors and exaggerated news, maybe use social media or something to contrasts the effect of those horrendous news. (participant 17)

The media talks about normal phenomenon’s like there were horrible natural disasters, for example every now and then they mention that there is going to be an “aguaje” in the coast, but they don’t explain what is an aguaje, they don’t explain that is a normal phenomenon, that depends of the moon cycle, and it only increases the sea level, that is all… what they do is to increase fear, they talk about drowned, they scare the tourists without a reason. (participant 19)

Risk 4 contamination: Contamination is not considered a fast variable in the resilience literature, but participants stated that in the case of Ecuador, it was important to include it. This theme referred to a polluting process which can occur quickly (e.g. oil spills, drainage explosion, etc.) and impact the tourism industry. According to the

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participants of this study, the rapid contamination processes can occur when industrial zones are allowed to overlap recreation and tourism zones. Many of the participants thought that these pollution processes occur due to poor management of the municipalities and lack of guidelines regarding the impact of industrial activities on tourist destinations:

Our politicians don’t care about how industry pollute the natural attractions, they just don’t do anything. (participant 6)

Contamination is a huge problem in Ecuador, and people in the different municipalities just don’t care. (participant 16)

In every municipality that I have visited, I listen a recurrent problem: contamination, but the competent authorities don’t do anything. (participant 17)

One can complaint, but the main polluters are industries and they have arrangements with politicians (participant 15).

The stakeholders were very concerned with the issue of rapid contamination because according to their experience this has a negative effect on the satisfaction of the tourist:

Many tourists who come to the hotel complain about the pollution of the beaches, the auditory contamination, and especially when there is a sudden explosion in the sewers. (participant 2)

Pollution affects the satisfaction and experience of the tourists, they had told me. (participant 14)

Risk 5 other issues: During the sessions, the participants revealed many problems that have affected the sector. Many participants emphasized that the national tourism sector is in a process of development, but has not reached maturity as a destination. It is for this reason that the stakeholders feel that there is not enough skilled labor and therefore the quality of the service provided requires many improvements:

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There is not awareness of the economic impact of the tourism industry in Ecuador. We have beautiful landscapes, great natural and cultural resources, but there is not enough skilled labor… there is not law that requires the hotel sector has a minimum of people that speaks English, and know about international gastronomy, international tourists are not familiar with the traditional food, they sometime get sick. (participant 10)

There is not infrastructure or skilled labor to properly receive the international visitors. (participant 21)

Training is needed in the country, but here is not policy that allow for mandatory training, and this need to be done. (participant 23)

For the participants, not having enough skilled labor is a problem in the sense that they are limited in the quality of the service they provide:

We have a problem in the quality of the service, it is very difficult to find skilled labor. Every student at the university wants to graduate as administrator, but what we really need is a qualified housekeeper. (participant 14)

In this discussion, there was generalized agreement that the root of most of the problems is no real empowerment or sense of belonging in many communities. Such comments included:

In my work with the community, I have identified that there is lack of awareness and interest about its own future, and this complicates things, you can deliver infrastructure, but they won’t maintain them. (participant 4)

People relies on the government to solve their problems when the real issue is lack of empowerment, I agree that we need policy, but we also need empowerment and training. (participant 11)

Empowerment needs to start with kids… what we feed our kids… what we watch in the TV… everything, so they can feel proud of being Ecuadorians. (participant 3)

We need to be more in control of our destiny as tourism destination, and form groups, work together because in every crisis there is also an opportunity… In the earthquake for example there was a lot of help that could be receive properly, and we waste our chances. (participant 4)

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Opportunities

In general, participants agreed that there are always opportunities after adversities. However, those opportunities are not the same for everybody within the sector. Thus, there is a need to be prepare and organized in guilds to take advantages of such opportunities when they appear. Several participants shared their experiences dealing with disaster, lost and opportunities:

After the earthquake of 2016 it was a very difficult time for me, I lost a huge part of my hotel and my father died when the building collapsed, so many people contacted me to offer me help for me and the community... and there was the case of this guy who lives in Unites States and he is the manager of this big company… he was born in my town, so he offered us lots of money, but he required that we (the community I mean) create some kind of organization (legally I mean) to officially give us the money, but we did not, and that money was lost, at the end the same guy had to give the money to other organization in other country. (participant 17)

There are lots of opportunities after crisis, let’s think in Tarqui for instance … that was a disaster before the earthquake, the market that existed there was simply a total disorder, a great environmental pollutant, and very ugly, you know that the earthquake practically erased that area, and now there is the opportunity to reconstruct it and create a park in memory of those who died that terrible day, but the authorities have no personal interests in that area and until this day is still abandoned.. but if we get together and demand results, this could be a great opportunity for the city, to pay homage to the fallen and built a recreation area. (participant 16)

There are lots of opportunities after a crisis, the only thing you need to assume is that opportunities are not for everybody… for instance the tourism sector benefited a lot after the economic crisis of 99, because many people traveled to Europe, you can go and see in the SRI website and see how much money we collected in 2000 when people started to migrate. (participant 12)

Many comments regarding the opportunities after a crisis were oriented to the possibility of forming associations and taking advantage of the help that comes from outside or having a space to share as a sector:

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After the earthquake many lost everything, but also many won, and not only money but knowledge… many people came from different parts of the world and taught us how to deal with devastation. (participant 3)

We need to unite more as a destiny... get together... there is the desire, what is missing is the initiative. (participant 2)

Stage 2, Phase 3: Creation of the Risks Matrix

The next phase of the risk assessment was having the participants’ grade every risk analyzed during the sessions using a 5-point scale. The criteria used in the analysis is the one proposed by Baccarini, and Archer, 2001, were to assess the vulnerability of the destination is necessary to perform a risk ranking and rate the likelihood, and possible consequence (gravity) to the destination in case the risk occurs (Table 3-4)

Twelve risks were identified during the sessions (1) political instability, (2) changing policies, (3) exaggerated news, (4) rapid contamination, (5) earthquakes, (6) volcanic eruptions, (7) floods, (8) tsunamis, (9) epidemic by mosquito bites, (10) mud slides, (11) risks caused by industrial failure, (12) terrorism. Participants were asked to anonymously rank each risk in both criteria in a ranking sheet. After all sessions were concluded the researcher processed the information and create a risk matrix.

Four categories of risks were identified in the matrix based on the index of importance which is the result of the multiplication of the mean score of the likelihood of occurrence, by the mean of the severity of the impact (Table 4-3). The four categories identified were (1) Risk Rank A: need immediate attention, (2) Risk Rank B: need for a backup plan, (3) Risk Rank C: rare occurrence, and (4) Risk Rank D: eliminate - none a priority.

The focus of this study were Rank A Risks, because they were identified as the ones that needed to be addressed immediately in order to increase the resilience of the

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tourism sector of Ecuador, this prioritization approach followed the guidelines proposed by Baccarini and Archer (2001). Thus, the risks with higher index score according to the risk matrix (Table 3.2) were (1) Changing policies with an index score of 23.22

(likelihood = 4.73, impact = 4.91), (2) Exaggerated news with an index score of 21.80

(likelihood = 4.67, impact = 4.67) and (3) Political instability with an index score of 21.72

(likelihood = 4.66, impact= 4.66).

All the results provided during the focus group sessions helped to give answer to the RQ3, and to move forward into the implementation of the adaptation section of the adapted RTAF model. Once the top risks for the tourism sector of Ecuador were identified, the researcher conducted the adaptation study, which results are detailed in the next section.

Stage 3: Stakeholder Adaptation Assessment

The objective of the stakeholder adaptation study was to identify viable strategies that could be implemented by the actors of the tourist sector of Ecuador. For the elaboration of the adaptation strategies the researcher accounted with the help and inputs of the Ministry of Tourism Staff (zone 4) and 47 stakeholders who attended to a workshop. There were two phases to this stage: (1) the workshop, (2) survey of stakeholders.

The product elaborated in the workshop was a portfolio of adaptation strategies to deal with rank-A risks. During the workshop the evaluation of the feasibility of the strategies was carried out, and then this information was validated through a questionnaire on a large scale with members of the sector randomly chosen using the cadaster of the MINTUR. Through this process of preparation and evaluation of adaptation strategies, the following research question was answered: What are the

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most efficient and applicable options of adaptive strategies to increase destination resilience in Ecuador?

Stage 3, Phase 1: Results of Workshop

During the workshop, the researcher included a small lecture in which she shared the basic concepts that delimited the study. Thus, included definitions of resilience, risks, and adaptation. As mentioned in Chapter 3, all participants received along with their invitation the risk sheet that was the product of the analysis of the results of the focal sessions, so they were prepared to engage into a productive discussion.

During the lecture, the A-Rank risks for the sector were discussed and the guidelines and criteria exposed to all participants. A total of 9 groups were created, thus each of the A-rank risks were analyzed by 3 groups. Presentations were carried out by each group and evaluated by the rest of the participants.

As discussed in Chapter 3, the criteria used for the evaluation of each adaptation strategy presented included (1) support for the strategy, (2) feasibility of the implementation, (3) possible economic impact, (4) possible impact in employment, (5) dependency of tourism resources, (6) impact in seasonality, and (7) technology necessary for the implementation.

The evaluation was performed by 47 participants, but only 33 valid score sheets were included in the analysis, four were left in black and ten had missing information.

All the adaptation measures proposed in the groups received good overall grades, with mean scores of 3.58 and above (Table 4-4). Detailed information of each adaptation strategy presented is provided bellow.

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Adaptation strategy #1: to deal with changing policies

The risks were randomly assigned to all groups, and the ones in charge to elaborate adaptation strategies to cope with changing policies were groups 1, 2 and 7.

Group 1 proposed the use of technology and more specifically the use of social media

(Facebook and twitter) to transmit sudden changes in the policies.

With a similar approach, Group 2 came up with a diffusion campaign to broadcast the sudden changes in policy, thus inform not only the members of the sector but the population in general. They manifested that:

Chaos emerges every time the media mention something about a policy to limit the numbers of visitors to Galapagos Island or any other natural tourism attraction in the country, the community does not understand, and everybody gets mad… so we have decided to create a campaign to deal with these changes in policy through social media diffusion. (participant of group 2)

Group 7 presented the idea of a training program to certify the DMO staff so they can have a clear knowledge about the characteristics of the sector; hence they can create better policies and / or maintain the current ones:

We have identified that the source of the problem lays on the DMOs… The staff in such institutions are designated by politicians and almost never have any tourism knowledge or background… we cannot complain anymore about that because that it is not going to change, we need to work together with the DMO and MINTUR and conduct trainings and meeting so they can be more aware of the reality of the private sector… work with the academia and offer certifications. (participant of group 7)

The detailed information about the evaluation of each strategy presented by group 1,2, and 7 is presented in Table 4-5. Group 2 received the overall highest score

(support = 4.15), followed by Group 7 (support = 3.88), and finally Group 1 (support =

3.58). The general evaluation of other attributes under analysis was favorable in all cases with mean scores above 3.30.

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The relationship between support for the strategy presented and the attributes

(feasibility, economic impact, and impact in employment, dependency of tourism resources, seasonality, and technology) was investigated using Pearson product- moment correlation coefficient. Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, and homoscedasticity.

In the specific case of the evaluation of Group 1 (Table 4-6) there was a strong positive correlation (r= .63 to .79, n = 33, p<.0001) between support for the strategy and the rest of the attributes, with higher levels of support associated with higher levels on the rest of the attributes, especially between support and economic impact (r=.79, n=33, p<.0001).

Group 2 indicated a strong positive correlation among support for the strategy and feasibility (r=.72, n=33, p<.0001), economic impact (r=.87, n=33, p<.0001) employment (r=.75, n=33, p<.0001), and dependency of tourism resources (r=.83, n=33, p<.0001) [see Table 4-6 for further detail].

Finally, in the analysis of the strategy evaluation of Group 7 (Table 4-12) there was a strong positive correlation among support for the strategy and all other attributes

(r=.68 to .92, n=33, p<.0001) with higher correlation founded between support for the startegy and possible impact in employment (r=.92, n=33, p<.0001).

Despite the autonomy of each group, there was a strong similarity among all strategies presented, especially between Group 1 and Group 2. Due to the extreme similarities found, the researcher combined the strategies, so only two strategies to cope with changing policies were used for the next part of the study:

• Create communication channels using social media to broadcast sudden changes in the law and policy (within the sector).

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• Tourism certification campaigns destined to the members of the DMOs.

In both cases, the people or organizations in charge of the implementation of the strategies were the private sector through associations and with help from Academia.

Adaptation strategy #2: to deal with political instability

The three groups who revealed adaptive strategies to cope with political instability were Groups 3, 5, and 6. All groups recognized that there was little they could actually do to reduce political instability within the sector, but they recommended to learn to be more independent.

In this regard, Group 3 suggested creating contingency funds administered by the private sector:

The contingency funds will be supported by a monthly contribution of the members of the collegiate bodies and guilds… but transparency is necessary in the management of the funds. (participant of group 3)

According to Group 3, the funds could be used to provide loans during times of political instability, and to promote the destination to counteract the negative perceptions that political instability could generate nationally and internationally.

With a similar approach, Group 5 suggested creating and / or activating the current associations and join forces with Academia in order to create promotional campaigns to improve the destination image:

We need to strengthen the country brand to balance the perception of a political instable destination. (participant of group 5)

Those campaigns need to be funded, but we cannot count on MINTUR, so we need to create our own funds and administrate them, as Galapagos has been doing since the eighties. (participant of group 5)

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Finally, Group 6 suggested that Ecuador is not the only country in the region that suffers of political instability, but tourism arrivals in Ecuador are less than in other countries with some similar problems:

We are not the only destination that has political instability, there are neighboring countries that face tougher political realities than us and yet their tourism sector is stronger than ours, and the number of tourists visiting the country is also much higher. (participant of group 6)

The group proposed that the best way to deal with the political instability is become stronger as a sector and recommended to:

Establish alliances between the private sector, be autonomous and detached. (participant of group 6)

Create public-private partnership to work together and make a better use of the resources for promotion. (participant of group 6)

After groups conducted their presentation, the evaluation of the strategies proposed were carried out. Group 3 received the highest score (support = 4.03), followed by Group 6 (support= 3.88), and lastly Group 5 (support = 3.82). In all cases, attributes received favorable evaluations with mean scores above 3.18 (Table 4-15).

Group 3 also presented a strong positive correlation among support for the strategy and the rest of the attributes (r= .71 to .87, n = 33, p<.0001), with a stronger relationships between support of the strategy and dependency of tourism resources

(r=.87, n = 30, p<.0001). Group 5 presented a strong positive correlation among support for the strategy and feasibility (r=.83, n = 33, p<.0001), economic impact (r=.78, n = 33, p<.0001), employment (r=.76, n = 33, p<.0001), dependency of tourism resources

(r=.75, n = 30, p<.0001) and seasonality (r=.68, n = 33, p<.0001). Lastly, Group 6 showed a strong positive correlation among support for the strategy and all other attributes (r=.69 to .87, n= 33, p<.0001).

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Due to strong similarities among strategies presented (especially between

Groups 3 and 5), the researcher opted to merge the proposals presented into two strategies, which were used in the next stage of the study:

• Creation of contingency funds (collected monthly by the private sector) for destination image promotion, and small loans.

• Public- private partnership to guarantee the continuity of tourism plans and join synergies.

Adaptive strategy #3: to deal with exaggerated news

Groups 4, 8 and 9 elaborated adaptation strategies to cope with exaggerated news. Group 4 suggested to create a crisis communication plan using social media with responses elaborated beforehand, hence the DMOs and Municipalities could rectify the information (emphasizing in communication) to limit the harm:

DMOs and the Municipality should not ignore the exaggerated news… They need to act immediately, thus they need to have a risk communication plan to deal with those risks and crisis. (participant of group 4)

In a similar way, Group 9 recommended to use a pre-crisis planning approach with the help of the Academia. They also focused on the use of social media to broadcast the real state of the destination (post crisis) and eliminate misperceptions.

Finally, Group 8 proposed a public relation (PR) plan to counteract the effect of negative news. The group highlighted the importance of conducting the PR approach through guilds to take advantage of the experience of several stakeholders of the private sector and the academia. The use of social media was highly recommended by the group, but they also suggested including some “old-fashion techniques” such familiarization trips:

We need to take advantage of the technology and use the social media channels, but we also recommend the use of some old-fashion strategies… we can organize familiarization trips for the media in

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exchange of media coverage of the real state of the destination. (participant of group 9)

The evaluation of the strategies presented, displayed the following results. Group

9 had the highest mean score (support = 4.18) followed by Group 4 (support = 4.09) and Group 8 (support = 3.97). A strong positive correlation was found among support for the strategy and the rest of the attributes in all the groups evaluated (Tables 4-9, 4-

13, and 4-14).

In Group 4, there was a strong positive correlation between support for the strategy and all other attributes (with the exception of technology), r = 5.14 to 6.57, n=33, p<.001, with high levels of support associated with higher levels of the attributes

(1) economic impact, (2) impact in employment, (3) dependency of tourism resources,

(4) seasonality, (5) technology needed.

Group 8 showed strong positive correlations among support for the strategy and all other attributes (r=.50 to .80, n=33, p<.001), and the same was observed in Group 9

(r=.53 to .83, n=33, p<.001).

Following the same approach employed in the previously mentioned group presentations, the researcher summarized the strategies exposed by the groups into only two strategies due to overlapping and similarities.

The two strategies were considered for the next section of the study:

• Elaboration of a pre-crisis risk communication plan by academia and the DMOs.

• PR plan with emphasis on communication and social media by private sector guilds and academia.

Stage 3, Phase 2: Stakeholder Survey

A quantitative assessment of stakeholders was performed which randomly selected 758 participants from the stakeholder list of MINTUR. The survey was sent to

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518 emails and 240 attempted calls were made. The rate of response of usable surveys for this study was 35% for email surveys (183 completed and usable surveys) and 44% for survey phone calls (105 completed and usable surveys). Together the surveys collected totaled 288, thus completing an adequate sample size for multiple regression analysis, following the guideline of Field (2009) where ideal sample should include 20 observations for each independent variable in the analysis.

Participants in this study showed an equitable distribution of gender with 42% of women and 58% of men within the sample. The majority of the respondents had a university diploma (undergraduate 37.8%, graduate 22.6%) and 23.6% reported having a high school diploma (Figures 4-1 to 4-6).

Additionally, there was participation of all members of the tourism sector with a majority representing the accommodation sector (48.3%), food and beverages sector

(28.8%) and the operation and intermediation sector (13.2%). Parks, recreation, and tourism transportation accounted for the 9.7% of the participants. Although this does not reflect the exact distribution of the total population where food and beverage sector is the 66% of the population, accommodation is the 17.99%, operation and intermediation is the 7.72%, parks and recreation is the 5.03%, and transportation is 3.2%; it does include the opinion of all stakeholders in the Ecuadorian tourism sector.

Hence, the sample for this study has an overrepresentation of the members of the accommodation sector for more than double comparing to the population. There was also an under representation of the food and beverage sector by half compared to the population and an overrepresentation of intermediation sector by almost a third.

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Although these results vary from the real population, they include all the sectors under study, therefore providing an inclusive evaluation of the adaptation strategies.

Regarding other demographic data, there was an uneven representation of all the provinces of the country with a majority percentage of participants from Azuay

(12.8%), Guayas (10.4%), Manabí 12.5%, and Pichincha (23.6%) which are the most established tourism destinations in the country (Table 4-16).

All adaptation strategies were highly evaluated during the quantitative assessment of the stakeholder with mean scores for support above 3.40. Strategy 6:

Creation of A PR Plan with Emphasis on Communication and Social Media by Private

Sector Guilds and Academia, had the highest score (support= 3.99), followed by

Strategy 1: Creation of Communication Channels Using Social Media to Broadcast

Sudden Changes in the Law and Policy (support =3.97).

Strategy 5: Elaboration of a Pre-Crisis Risk Communication Plan by Academia and the DMOs, also received high levels of support (support =3.92), as well a Strategy

4: Creation of a Public- Private Partnership to Guarantee the Continuity of Tourism

Plans and Join Synergies (support =3.84).

The strategies with lower ratings were Strategy 2: Tourism Certification

Campaigns Destined to the Members of the DMOs which had a support mean score of

3.82, and Strategy 3 Creation of contingency funds (collected monthly by the private sector) which had a mean score of 3.40. These results supported those previously presented in the qualitative evaluation section and offered the possibility to generalize.

The surveys also allowed the opportunity to validate the risk ranking of the qualitative section. The results reaffirmed that the identified risks reflected the national

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reality and not only the destination where the study was conducted, presenting higher mean scores for political instability (mean = 3.34), changing policies (mean = 3.34) and exaggerated news (mean = 3.24).

To gain a deeper understanding of the relationships among variables, two standard multiple regression analyses were conducted for each adaptation strategy included in the study (12 in total). Standard multiple regression was selected as the adequate analysis for this study due to it has been identified as an effective regression technique to assess the relationship among variables when there is a strong theoretical background (Field, 2009; Pallant, 2013). Due to small samples sizes with the stakeholder group (N=288), the independent analysis was run as two separate multiple regression analyses.

The first analysis focused on exploring the relationship between the predictors

(demographics, knowledge, and feasibility of implementation) and the DV (dependent variable): Support for the Strategy. (Behringer et al., 2000; Hoffman et al., 2009; Na et al., 2010; Apine 2011, Preston et al., 2011; Jopp et al, 2015; Shaireef et al., 2015). The analysis aimed to understand how the independent variables influenced the scores for the support of every strategy. Thus, regarding demographic variables the study sought to expose how age, gender, place of birth, sector of belonging, and level of education influence the response of the participants when rating support. This analysis allowed to reveal how social and cultural characteristics of participants in a developing country differed from previous studies performed in more mature tourism destinations.

The independent variables knowledge of risks, and knowledge of adaptation measures were included in this study because previous research have shown that

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awareness of the risks and knowledge of adaptation influence the attitude of residents toward possible adaptation preferences and support (Behringer et al., 2000; Hoffman et al., 2009; Na et al., 2010; Apine 2011, Preston et al., 2011; Jopp et al, 2015; Shaireef et al., 2015), hence it was possible that respondents with better knowledge of risks and adaption were more probable to have a positive opinion of the adaptation strategies proposed in the study. Participants were also asked to evaluate the feasibility of the implementation of the proposed strategies, and this measure was then used as predictor of support, following the guideline of previous adaptation assessment studies

(Smitt and Skinner; 2002; Hertin et al., 2003; Hoffmann et al., 2009; DeFreitas et al.,

2006; Saarinen and Tervo, 2006; Scott, 2006; Few, 2007; Scott and Simpson, 2008;

Apine, 2011).

In the second analysis, the IV were supported by Jopp’s study and included variables he had tested in his study. Hence, the second analysis explored the relationship among the predictors (possible economic impact, possible impact in employment, dependency of tourism resources, seasonality, and technology) and the

DV: Support for the Strategy. Results of the analyses conducted are detailed in the next sections (Table 4-17).

Evaluation of adaptation strategy #1: create communication channels using social media

The first strategy under evaluation was “create communication channels using social media to broadcast sudden changes in the law and policy”, this served as the dependent variable. Standard multiple regression was used to assess the ability of the predictors demographics (gender, age, nationality, province, level of education, and sector), knowledge (knowledge of risks, knowledge of system, knowledge of adaptation)

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and feasibility of implementation to influence the dependent variable: Support for the

Adaptive Strategy #1 (Table 4-18).

In both studies, preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity, and homoscedasticity. No multicollinearity was detected based on the results of the variance inflation factor (VIF <

4.57) with values bellow 10 (Field, 2009). Furthermore, during the tolerance diagnostic there were not values bellow 0.2 (tolerance > .21) implying non-violations of the multicollinearity assumption (Field, 2009) (Table 4-19). To assess linearity and homoscedasticity a scatterplot of the values of the residuals against the values of the outcome predicted by the model was created. Results showed that for both analysis the data was normal (Figures 4-12, and 4-13)

The total variance explained by the model was 16.6%, F (14, 273) = 3.88, p<.001. In the model, only three predictors (1) knowledge of law and policies within the sector (beta= -.16, p=0.05), (2) knowledge of adaptation strategies for risk 3: political instability (beta=.43, p<.001), and (3) feasibility of implementation (beta=.21, p=0.001) showed statistically significant impact on the dependent variable.

The second standard multiple regression analysis was performed to understand the relationship between the predictors attributes proposed by Jopp et al., 2013

(possible economic impact, possible impact in employment, dependency of tourism resources, seasonality, technology), and the dependent variable Support for the

Adaptive Strategy #1 (Table 4-20), results showed that the total variance of the model was 8%, F(5, 282) = 4.88 p <.001. Degree of seasonality was the only predictor that presented a significant beta value (beta =.18, p =0.05).

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Evaluation of adaptation strategy #2: create tourism certification campaigns designed for members of the DMOs

The second strategy under study was “tourism certification campaigns designed for the members of the DMOs”. The same approach used for the evaluation of strategy

1 was carried out, and preliminary analysis to ensure no violation of the assumptions of normality, linearity, multicollinearity, and homoscedasticity were performed. The data showed that the variance inflation factor was below 10 in both regressions with maximum scores of 4.57, and the tolerance diagnostic was above .2 in all cases (Table

4-20) this results indicated that were not violation of the assumption of multicollinearity

(Field, 2009) . Moreover, data was found normal with no violation of homoscedasticity or linearity based on the scatterplots of the standardized residuals (Figures 4-14, and 4-

15).

The results of the first standard multiple regression displayed a 33% of the variance explained by the model F (14, 273) =9.61, p<.001. Five total predictors were found statically significant. The demographic variables age (beta = -.130, p = 0.01), and level of education (beta = .109, p = -0.04) reported high values of beta and statistical significance (Table 4.21), the variables knowledge of adaptation strategies to cope with changing policies (beta = -.229, p = 0.01) and knowledge of adaptation strategies to cope with exaggerated news (beta = .189, p = 0.02) also reported high values of beta.

However, feasibility of implementation showed the highest degree of predictability with

Support for adaptive strategy #2 (beta = .52, p<.001).

A second standard multiple regression was performed to understand the relationship between the predictor attributes (1) economic impact, (2) impact in

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employment, (3) dependency of tourism resources, (4) seasonality, (5) technology needed, and the dependent variable support for the strategy #2.

Results showed that 33% of the variance was explained by the model F (5, 282)

= 27.504 , p< .001, and the attribute with higher beta score was impact in employment

(beta = .25, p < .000), Economic impact (beta = .210, p = 0.02) and seasonality (beta =

.202, p = .03) were found statistically significant predictors of the dependent variable, support for strategy 2. However, the rest of the attributes (dependency of resources, and technology needed) were not significantly related to the DV.

Evaluation of adaptation strategy #3: create contingency funds

Strategy #3 focused on the creation of contingency funds (collected monthly by the private sector) for destination image promotion, and small loans. A standard multiple regression was performed to understand the relationship between the predictors gender, knowledge, feasibility and the dependent variable support for the Strategy #3.

Several analysis were carried out to ensure that there were no violation of the assumptions of multicollinearity, linearity, homoscedasticity, and normality. Results showed that the VIF in both studies was lower than 10 (VIF < 7.70) and the tolerance was always above .20 (tolerance > .21) assuring non-violation of the multicollinearity assumption (Table 4-22). Furthermore, normality of the data and non-violations of the assumptions of linearity and homoscedasticity were established through a scatterplot of the standardized residuals. The graphics showed there was not abnormal distribution of the standardized residuals (Figures 4-16, and 4-17).

Results revealed that the model explained 44.2 % of the variance F (14,273) =

15.47, p<.001 The predictor with the highest value was feasibility of implementation

(beta= .612, p<.001). Other statically significant predictors were gender (beta = -.094, p

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= .043), and age (beta = .038, p = 0.038). However, none of the variables related to knowledge were statistically significant, (Table 4-23).

With the aim to understand the relationship between the attributes proposed by

Jopp et al. (2010) (1) possible economic impact, (2) possible impact in employment, (3) dependency of tourism resources, (4) seasonality, and (5) technology as predictors of the dependent variable, Support for Strategy #3, a standard multiple regression was carried out. Results revealed that 45.6% of the variance in the dependent variable was explained by the model F(5,282) = 47.31, p <.001. However, only dependency of tourism resources was statically significant, while other predictors included in the analysis were not statistically significant.

Evaluation of adaptation strategy #4: create a public-private partnership for tourism destination management

The 4th strategy suggested was to create a public- private partnership to guarantee the continuity of tourism plans and join synergies. Two multiple regressions were performed to understand the relationship among variables. To ensure that there was not violation of the assumption for multiple regression several analyses were performed. For this data the values of VIF were lower than 10 (VIF < 7.14 ) and the tolerance was above .20 for all variables in the analyses (tolerance >.210 ), this results ensure there were not violation of multicollinearity (Table 4-24). Likewise, normality was assessed through the creation of a scatterplot of the standardized residuals. Results ensured not abnormal distribution of the data, and therefore non-violation of the assumption of homoscedasticity, and linearity (Figures 4-18, and 4-19).

The first standard multiple regression was run to test the relationship between the independent variables demographics, knowledge, and feasibility of implementation

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as factors that influence the dependent variable support for the strategy #4. The analysis demonstrated that the model explained the 37.5 % of the variance F(14,273) =

11.684, p < .001 of the dependent variable. However, the only statistically significant predictor was feasibility of implementation (beta=.593, p < .001). Other independent variables in the model (demographics and knowledge) were not found statistically significant in the analysis suggesting that changes in the predictors demographics and knowledge are not associated with changes in the dependent variable for this data

(Table 4-25).

A second standard multiple regression was carried out to understand the relationship between the predictors attributes: (1) economic impact, (2) impact in employment, (3) dependency of tourism resources, (4) seasonality, (5) technology needed and the dependent variable support for the strategy #4.

Results showed that 41.8% of the variance of the dependent variable was explained by the model, F (5,282) = 40.46, p <. 001. Economic impact had the highest beta value and the only one in the analysis that was statistically significant (beta=.375, p

< .001), other predictors were not statistically significant. This would mean that only economic impact is associated with changes in the dependent variable Support for the

Strategy #4.

Evaluation of adaptation strategy #5: elaborate on the pre-crisis risk communication plan with academia

The 5th adaptation strategy focused on “elaborate on a pre-crisis risk communication plan by the academia”. The evaluation of this strategy showed high levels of acceptation with a mean score of 3.92. Further analysis of the relationship among variables was explored using two standard multiple regression.

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Several analysis were carried out to ensure the non-violation of the assumptions for multiple regression. There was non-violation of the assumption of multicollinearity based on the results of the VIF (VIF < 4.78), and tolerance (tolerance >.21 ) (Table 4-

26). Likewise, the normality of the data was established through a scatterplot of the standardized residuals (Figures 4-20, and 4-21) the distribution of the data was normal assuring non-violation of the linearity and homoscedasticity assumptions.

The first analysis was carried out to analyze the capacity of the variables demographic, knowledge, and feasibility to influence the dependent variable, Support for Strategy #5. The regression demonstrated that the model was able to explain 34.8% of the variance F(14,273) = 10.427 p <.001 on the dependent variable. For this study the predictor with higher beta values and statistically significance was feasibility of implementation (beta=.568, p < .001). Other significant predictor was gender (beta = -

.103, p = 0.04). Both feasibility of implementation and gender were the ones that most significantly influenced support for “elaboration of pre-crisis risk management plan”

(support for strategy 5). The other variables in the analysis were not statistically significant, hence there are not associated with the changes in the dependent variable

(Table 4-27).

The second standard multiple regression analysis explored the relationship between the dependent variable, support for strategy #5, and the independent variables attributes (economic impact, impact in employment, dependency of tourism resources, seasonality, technology needed). The results showed that the model explained 39.8% of the model F (5,282) = 37.25, p <.001, where the variable economic impact (beta = .250, p = 0.01) and the variable degree of technology needed for implementation (beta =

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.271, p = 0.001) were the best predictors of support for Elaboration of pre-crisis risk management plan. The other predictors in this model were not found statistically significant, therefore there are not likely to add much explanatory power to the model

(Field, 2009; Hair et al., 2010).

Evaluation of adaptation strategy #6: create a PR plan with emphasis on communication and social media

Adaptation Strategy #6 included A PR Plan with Emphasis on Communication

And Social Media By Private Sector Associations And Academia. This strategy received the highest mean score (support = 3.99), thus, had the greatest chance of acceptancy in the sector. To understand the relationship among the predictor variables demographic, knowledge, and feasibility and the dependent variable support for Strategy #6, a standard multiple regression analysis was carried out following the same approach conducted in previous strategies. The analysis was used to assess the ability of demography, knowledge, and feasibility of implementation to predict levels of Support for Strategy #6.

Results of the of the variance inflation factor (VIF < 5.18) and tolerance

(tolerance >.20) of both standard multiple regression analysis ensure that the multicollinearity assumption was not violated (Table 4-28). Normality, linearity, and homoscedasticity was determined through a scatterplot of the standardized residuals

(Figures 4-22, and 4-23).

Results exhibited a 28.3% variance explained [F (14, 273)= 7.68, p <.001]. Only the predictor variables: gender (beta= -.127, p = .02), and feasibility (beta= .502, p <

.001) were statistically significant (Table 4-29), other predictors in this analysis

(knowledge and other demographic variables like age, nationality, place of living, level

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of education and sector of belonging) were not statistically significant, and therefore not associated with changes in the dependent variable.

Finally, a second standard multiple regression analysis was conducted to explore the relationship between the predictors economic impact, impact in employment, dependency of tourism resources, seasonality, technology needed and the dependent variable Support for the Creation of A PR Plan with Emphasis on Communication and

Social Media (Strategy #6). The analysis demonstrated that the model explained 29.5% of the variance F(5, 282)= 23.621, p< .001. However, the only predictor that was statistical significant was possible economic impact (beta=.288, p = .001), other predictors included in the model were not statistically significant therefore they didn’t have an influence on the dependent variable.

Stage 4: Tourist Adaptation Assessment

Overview

The final part of the study included the evaluation of the strategies by the possible consumers. A large survey was carried in five tourism destinations of the continental Ecuador to determine the preference of the domestic tourists about the strategies presented and understand the relationship between preference (DV: overall grade of the strategy) and the predictors demographic, and knowledge of risk and adaptation. Descriptive statistical analysis was performed to reveal characteristics of the sample and identify the higher ranked strategy. The demographic variables gender, age, place of living, level of education were included in the analysis to seek the influence of demographics over support for the strategy, considering the cultural difference between the destination under study in comparison with studies carried out in more developed destinations.

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The independent variables previous knowledge of the destination and the tourism attractions, knowledge of risks, and knowledge of adaptation measures were included in the study to help determine if knowledge of the tourist areas influenced respondents’ opinions regarding adaptation in the tourism site. Besides, previous studies have shown that awareness of the risks and knowledge of adaptation influence the support of tourists toward possible adaptation preferences (Behringer et al., 2000; Hoffman et al.,

2009; Na et al., 2010; Apine 2011, Preston et al., 2011; Jopp et al, 2015; Shaireef et al.,

2015), therefore it was probable that respondents with superior knowledge of risks and adaption were more likely to support the adaptation strategies proposed in the survey.

This approach helped to give respond to the research question 5: What strategies do domestic tourists prefer?

The reliability of the instrument used in this survey was assessed, and the analysis demonstrate that it had a good internal consistency, with a Cronbach alpha coefficient of .74.

A multiple regression was performed for this study, the IV were supported by

Jopp’s study and included variables he had tested in his studyThe findings for all multiple regressions carried out are detailed in the next sections (Table 4-30).

Results of the Survey

The sample sized (n=385) was deemed sufficient for the number of independent variables included in all statistical analyses. From the sample, 354 questionnaires were usable (91.95% response rate), the other were excluded because they were incomplete.

The representation of the destinations in the sample was equally distributed with a 21.2% of the surveys collected in Quito, 20.1% in Salinas, 20.1% in Atacames, 25.1% in Baños, and 13.6% in Guayaquil.

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In the sample, the majority were men (55.9%), followed by 41.2% women, 2.5% of people who expressed being of another gender, and a 0.3% who preferred not to say.

The participants were mostly adults, between 18 and 34 years old (Figures 4-7 to 4-11) with a significant participation of people from 35 to 44 years old, which is in line with the domestic tourist profile provided in the MINTUR statistical bulletin (Ministerio de

Turismo del Ecuador, 2017), the MINTUR report of the tourism experience (Ministerio de Turismo del Ecuador, 2011), and the report of the domestic tourism of Quito

(Empresa Publica DMQ, 2015), where the domestic tourists in Ecuador is mostly men

(51%) of an average age of 37 years, and who mostly have some type of higher education (61%) Figure 4-24.

Regarding the level of education, most of the participants declared having a high school diploma (40.7%) and 21.8% having completed undergraduate university studies.

Additionally, there were tourists from all provinces of the country, with a majority number of citizens of Esmeraldas (11.6%), Guayas (10.2%), Manabí (24%), and Pichincha

(13.3%). This distribution is quite similar to the population distribution of the INEC demographic reports (Ministerio de Turismo del Ecuador, 2011; Empresa Publica DMQ,

2015; Ministerio de Desarrollo Urbano y Vivienda, 2016; Ministerio de Turismo del

Ecuador, 2017) (Figure 4-25).

Most surveyed tourists planned to spend the night in the destination (69.5%) in a hotel establishment (47.7%) spending an average of $367 dollars during their travel.

They also planned to stay in the destination for about 2.7 nights (mean score) and had visited destination more than once (61%). Most of the tourists surveyed declared traveling with someone and only 11.9% said that were traveling alone.

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The data showed that all strategies presented were highly ranked by the tourists with mean scores of 3.35 and above. The scores obtained were very similar to the ones of the stakeholder with the only a marked difference in Strategies 3 and 1. The best ranked was Strategy 6: PR plan with emphasis on communication and social media by private sector guilds and academia (support= 3.61), followed by Strategy 3: Creation of contingency funds (collected monthly by the private sector) for destination image promotion, and small loans (support = 3.54), Strategy 4: Public- private partnership to guarantee the continuity of tourism plans and join synergies (support = 3.52), Strategy

5: Elaboration of a pre-crisis risk communication plan by academia and the DMOs

(support = 3.51), Strategy 2: Tourism certification campaigns destined to the members of the DMOs (support = 3.49), and finally Strategy 1: Create communication channels using social media to broadcast sudden changes in the law and policy (support = 3.35).

In order to understand the relationship between demographic variables, prior knowledge of the destination, knowledge of the risks and adaptation, and the overall support for the strategy (dependent variable), a standard multiple regression analysis was performed for each of the strategies under study.

Strategy #1: create communication channels using social media to broadcast sudden changes in the law and policy (within the sector).

A standard multiple regression assessed the ability of the variables demographics, prior knowledge of the destination, and knowledge of risk and adaptation to predict levels of support for the creation of communication channels using social media to broadcast sudden changes in the law and policy (Strategy #1).

The model explained 8.7 % of the variance F (12, 341) = 2.71, p = .002. Only the demographic independent variables level of education (beta = -.167, p = 002) and

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province / place of living (beta = -.118, p = .02) were found statistically significant recording a high beta value. Other variables in the study (prior knowledge of the destination, and knowledge of risk and adaptation) were not significant for this analysis, hence they did not have influence in the dependent variable (Table 4-33).

Prior to the analysis, the data was analyzed with the intention of identifying violations the assumptions of multicollinearity, normality, linearity, homoscedasticity, and independence. Tests assured that there were no violations to the assumptions in any of the regressions performed. The results of the variance inflation factor (VIF <

2.89) and tolerance (tolerance >.35) determined that there was not violation of the multicollinearity assumption (Table 4-32). A scatterplot of the standardized residuals of the analysis were computed to examine the data distribution and ensure non- violation of the assumptions of normality, linearity, and homoscedasticity (Figure 4-26), results showed the data was normal distributed.

Strategy #2 evaluation: create tourism certification campaigns designed for members of the DMOs

A standard multiple regression was carried out to assess the relationship between the independent variables demographic, prior knowledge of the destination and knowledge of risk / adaptation, and the dependent variable support for the Creation

Of Tourism Certification Campaigns Destined To The Members Of The DMOs. The results of the variance inflation factor (VIF < 2.89) and tolerance (tolerance >.35) determined that there was not violation of the multicollinearity assumption (Table 4-34).

Normality, linearity, and homoscedasticity were ensured through a scatterplot of the standardized residuals. Results exhibited that the data was normal distributed and there were not violation of the assumptions (Figure 4.27).

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The model explained the 9.9 % variance F = (12, 341) = 3.115, p < .001. The variables level of education (beta = -.175, p = .001) and knowledge of adaptation measures to cope with changing in policies (beta = -.194, p = .02) were found statistically significant in this analysis. Other demographic variables (gender, age, province of living), and knowledge variables (prior knowledge of the destination, knowledge of the tourism attractions, knowledge of changing policies, knowledge of exaggerating news, knowledge of political instability, knowledge of adaptation measures to cope with exaggerated news, and knowledge of adaptation measures to cope with political instability) were not found significant during this analysis (Table 4-35).

Strategy #3 evaluation: creation of contingency funds (collected monthly by the private sector) for destination image promotion, and small loans

Multiple standard regression was performed between the predictors demographics, prior knowledge of the destination, knowledge of the tourism attraction, knowledge of risks (political instability, exaggerated new, and change in policy), knowledge of the adaptation measures (to cope with political instability, exaggerated new, and change in policy) and the dependent variable support for the creation of contingency funds (collected monthly by the private sector) for destination image promotion, and small loans (strategy 3) showed that the model explained 10.6 % of the variance F(12, 341) = 3.358 , p < .001.

The variables level of education (beta= -.174, p= .001), knowledge of the risk of political instability (beta= .188, p= .01), knowledge of adaptation measures to cope with changing policies (beta= -.298 , p= .003) and exaggerating news (beta= -.165 , p = .04) deemed to be statistically significant and with higher beta values than the rest of the variables in the analysis (Table 4-37). The other variables in the analysis were reported

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non-statistically significant, hence there were not influencing the dependent variable for this analysis.

Several test were computed to ensure assumptions of homoscedasticity, linearity, normality, and multicollinearity were met (Table 4-36). Non-violation of multicollinearity was reported through the results of the variance inflation factor (VIF <

2.89) and tolerance (tolerance > 2.89). Likewise, normality of the distribution of the data was assessed with a scatterplot of the standardized residuals, ensuring the non- violation of the assumption of normality, linearity, and homoscedasticity (Figure 4-28).

Strategy #4 evaluation: public- private partnership to guarantee the continuity of tourism plans and join synergies

A standard multiple regression analysis was performed to assess the relationship between the independent variables demographics, prior knowledge of the destination, knowledge of the tourism attraction, knowledge of risks (political instability, exaggerated new, and change in policy), knowledge of the adaptation measures (to cope with political instability, exaggerated new, and change in policy) and the dependent variable support for the creation of public – private partnership to guarantee the continuity of tourism plans and join synergies (Strategy #4).

The analysis demonstrated that the model explained 7.6% of the variance F (12,

341) = 2.35, p < .007 in the dependent variable. The predictors level of education (beta

= -.111, p = .04), knowledge of adaptation measures to cope with changing in policies

(beta = -.187, p = .04), and knowledge of adaptation measures to cope with exaggerated news (beta = .161, p = .05) reported higher values of beta, and statistically significance (Table 4-39), other variables in the model (gender, age, province, prior knowledge of the destination, knowledge of the tourism attractions, knowledge of

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changing policies, knowledge of exaggerating news, and knowledge of adaptation measures to cope with political instability) were found not significant for the analysis, hence they did not have influence in the dependent variable. Additionally, the assumptions of homoscedasticity, linearity, normality, and multicollinearity were met for this analysis (Table 4-38). Non-violation of multicollinearity was reported through the results of the variance inflation factor (VIF < 2.62) and tolerance (tolerance >.346).

Besides, a scatterplot of the standardized residuals was computed to ensure the non- violation of the assumption of normality, linearity, and homoscedasticity (Figure 4-29), and the results showed the data had a normal distribution.

Strategy #5 evaluation: elaboration of a pre-crisis risk communication plan by academia and the DMOs

A standard multiple regression was run between the independent variables demographics (gender, age, province of living, level of education) prior knowledge of the destination, knowledge of the tourism attractions, knowledge of risks (changing policies, exaggerated news, political instability), knowledge of adaptation measures (to cope with changing policies, exaggerated news, political instability), and the dependent variable Support for the elaboration of a pre-crisis risk communication plan by academia and the DMOs (Strategy #5).

Results showed that the model for this analysis explained only the 11.1 % of the variance F (12, 341) = 3.54, p <.001 in the dependent variable. Moreover, only the measures level of education (beta = -.195, p < .001) and knowledge of the political instability risks (beta = .196, p =.01) were reported statically significant with higher beta values with respect to other variables in the model (Table 4-41).

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Non violation of the assumption of multicollinearity was assumed based on the results of the VIF (VIF <2.89) and the tolerance (tolerance >.346). Likewise, normality, linearity, and homoscedasticity were determined through computation of the scatterplot of the standardized residuals (Figure 4-30).

Strategy #6 evaluation: creation of a PR plan with emphasis on communication and social media by private sector guilds and academia

Finally, a standard multiple regression analysis was computed to assess the relationship between the independent variables demographics, prior knowledge of the destination, knowledge of the tourism attraction, knowledge of risks (political instability, exaggerated new, and change in policy), knowledge of the adaptation measures (to cope with political instability, exaggerated new, and change in policy) and the dependent variable Support for the creation of a PR plan with emphasis on communication and social media by private sector guilds and academia (Strategy #6).

The analysis demonstrated that the model explained 10.8 % of the variance in the dependent variable (support for strategy six) F (12, 341) = 3.45, p <.001 with only the variable level of education (beta = .142 , p =.007 ) reporting a high value of beta and statistically significance (Table 4-43), the rest of the variables in this analysis were not found statistically significant, therefore they did not influence the dependent variable.

The results of the variance inflation factor (VIF < 2.89) and tolerance (tolerance

>.35) determined that there was not violation of the multicollinearity assumption (Table

4-42). A scatterplot of the standardized residuals of the analysis was computed to examine the data distribution and ensure non- violation of the assumptions of normality, linearity, and homoscedasticity (Figure 4-31).

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Table 4-1. Focus Groups’ Participants

Date Number of % of attendance Women Men Time of the participants session

January 6 50% 4 2 1:36:55

10th

January 5 41.67% 2 3 1:45:32 11th

January 4 33.33% 2 2 1:09:53

12th January 4 33.33% 3 1 1:06:55

15th

January 4 33.33% 1 3 00:59:53 16th

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Table 4-2. Focus Groups’ Profile of participants Participant Gender Age Nationality Place of living Sector

Participant 1 Male 36-45- Ecuadorian Portoviejo Hotels Participant 2 Male 25-35 Ecuadorian Manta Hotels Participant 3 Male 25-35 Ecuadorian Manta Hotels Participant 4 Male 25-35 Ecuadorian Manta Academia Participant 5 Male 36-45 Ecuadorian Manta Recreation Participant 6 Male 55 or more Ecuadorian Jipijapa Academia Participant 7 Male 55 or more Ecuadorian Manta Academia Participant 8 Male 25-35 Ecuadorian Manta Tour Operator Participant 9 Male 25-35 Ecuadorian Manta Recreation Participant 10 Male 55 or more Ecuadorian Quito Tour Operator Participant 11 Male 25-35 Ecuadorian Portoviejo Rural sector Participant 12 Male 36-46 Ecuadorian Guayaquil Transportation Participant 13 Female 46-55 Cuban Manta Hotels Participant 14 Female 36-45 Dutch Manta Hotels Participant 15 Female 46-55 Ecuadorian Manta Hotels Participant 16 Female 25-35 Ecuadorian Manta Academia Participant 17 Female 36-45 Ecuadorian Bahia de MINTUR Caraquez Participant 18 Female 25-35 Ecuadorian Manta Municipality of Manta Participant 19 Female 25-35 Ecuadorian Manta Recreation Participant 20 Female 36-45 Ecuadorian Manta Tour Operator Participant 21 Female 25-35 Ecuadorian Manta Tour Operator Participant 22 Female 36-45 Ecuadorian Manta Tour Operator Participant 23 Female 25-35 Ecuadorian Manta Tour Operator

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Table 4-3. Risk Matrix Risk Likelihood (mean) Impact (mean) Index Score Rank

Changing policies 4.73 4.91 23.22 A

Exaggerated news 4.67 4.67 21.80 A

Political instability 4.66 4.66 21.72 A

Earthquakes 4.33 4.73 20.48 B

Rapid Contamination 4.00 4.53 18.12 B

Volcano Eruptions 4.26 3.00 12.78 B

Flood 3.98 3.88 15.44 C

Tsunamis 2.5 4.30 10.75 C

Epidemics 2.66 2.75 7.32 C

Mud slides 2.21 1.98 4.38 D

Terrorism 1.00 1.98 1.98 D

Industrial Failure 1.15 1.22 1.40 D

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Table 4-4. Adaptation strategies mean scores N Mean Std. Deviation

Adaptation strategy proposed by 33 3.58 1.091 group 1

Adaptation strategy proposed by 33 4.15 .906 group 2

Adaptation strategy proposed by 33 4.03 1.185 group 3

Adaptation strategy proposed by 33 4.09 .765 group 4

Adaptation strategy proposed by 33 3.82 1.074 group 5

Adaptation strategy proposed by 33 3.88 1.269 group 6

Adaptation strategy proposed by 33 3.88 1.193 group 7

Adaptation strategy proposed by 33 3.97 .951 group 8

Adaptation strategy proposed by 33 4.18 1.044 group 9

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Table 4-5. Evaluation of groups 1, 2 and 7 Mean Std. Deviation Group 1: support 3.58 1.091 Group 1: feasibility 3.67 .990 Group 1: economic impact 3.39 .966 Group 1: impact in employment 3.52 .972 Group 1: dependency of tourism resources 3.42 1.324 Group 1: seasonality 3.30 1.132 Group 1: technology 3.94 1.223 Group 2: support 4.15 .906 Group 2: feasibility 4.21 .960 Group 2: economic impact 3.73 1.126 Group 2: impact in employment 3.70 1.132 Group 2: dependency of tourism resources 3.85 1.149 Group 2: seasonality 3.73 1.126 Group 2: technology 3.88 1.219 Group 7: support 3.88 1.193 Group 7: feasibility 3.82 1.158 Group 7: economic impact 3.73 1.257 Group 7: impact in employment 3.76 1.251 Group 7: dependency of tourism resources 3.76 1.200 Group 7: seasonality 3.52 1.278 Group 7: technology 3.61 1.223

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Table 4-6. Group one evaluation and correlations

G1_ Support Sig. (1-tailed) Pearson Group 1: support 1.000 . Correlation Group 1: feasibility .647 .000 Group 1: economic impact .786 .000

Group 1: impact in employment .625 .000

Group 1: dependency of tourism .627 .000 resources Group 1: seasonality .715 .000

Group 1: technology .636 .000

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Table 4-7. Group two evaluation and correlations

G2_ Support Sig. (1-tailed) Pearson Group 2: Support 1.000 . Correlation Group 2: feasibility .716 .000 Group 2: economic impact .870 .000

Group 2: impact in employment .748 .000

Group 2: dependency of tourism .834 .000 resources Group 2: seasonality .379 .015

Group 2: technology .385 .013

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Table 4-8. Group three evaluation and correlations

G3_support Sig. (1-tailed) Pearson Group 3: support 1.000 . Correlation Group 3: feasibility .744 .000 Group 3: economic impact .838 .000

Group 3: impact in employment .776 .000

Group 3: dependency of tourism .869 .000 resources Group 3: seasonality .726 .000

Group 3: technology .705 .000

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Table 4-9. Group four evaluation and correlations

G4_ support Sig. (1-tailed) Pearson Group 4: support 1.000 . Correlation Group 4: feasibility .623 .000 Group 4: economic impact .602 .000

Group 4: impact in employment .540 .001

Group 4: dependency of tourism .657 .000 resources Group 4: seasonality .514 .001

Group 4: technology .218 .111

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Table 4-10. Group five evaluation and correlations

G5_ support Sig. (1-tailed) Pearson Group 5: support 1.000 . Correlation Group 5: feasibility .830 .000 Group 5: economic impact .783 .000

Group 5: impact in employment .760 .000

Group 5: dependency of tourism .746 .000 resources Group 5: seasonality .682 .000

Group 5: technology .472 .003

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Table 4-11. Group six evaluation and correlations

G6_ support Sig. (1-tailed) Pearson Group 6: support 1.000 . Correlation Group 6: feasibility .852 .000 Group 6: economic impact .869 .000

Group 6: impact in employment .862 .000

Group 6: dependency of tourism .786 .000 resources Group 6: seasonality .740 .000

Group 6: technology .698 .000

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Table 4-12. Group seven evaluation and correlations

G7_ support Sig. (1-tailed) Pearson Group 7: support 1.000 . Correlation Group 7: feasibility .730 .000 Group 7: economic impact .853 .000

Group 7: impact in employment .922 .000

Group 7: dependency of tourism .678 .000 resources Group 7: seasonality .678 .000

Group 7: technology .759 .000

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Table 4-13. Group eight evaluation and correlations

G8_ support Sig. (1-tailed) Pearson Group 8: support 1.000 . Correlation Group 8: feasibility .805 .000 Group 8: economic impact .804 .000

Group 8: impact in employment .795 .000

Group 8: dependency of tourism .503 .001 resources Group 8: seasonality .642 .000

Group 3: technology .654 .000

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Table 4-14. Group nine evaluation and correlations

G9 support Sig. (1-tailed) Pearson Group 9: support 1.000 . Correlation Group 9: feasibility .829 .000 Group 9: economic impact .674 .000

Group 9: impact in employment .589 .000

Group 9: dependency of tourism .547 .000 resources Group 9: seasonality .531 .001

Group 9: technology .588 .000

247

Table 4-15. Evaluation of groups 3, 5 and 6 Mean Std. Deviation Group 3: support 4.03 1.185 Group 3: feasibility 3.82 1.211 Group 3: economic impact 3.94 1.197 Group 3: impact in employment 3.76 1.300 Group 3: dependency of tourism resources 3.67 1.315 Group 3: seasonality 3.61 1.248 Group 3: technology 3.79 1.317 Group 5: support 3.82 1.074 Group 5: feasibility 3.61 1.144 Group 5: economic impact 3.70 1.159 Group 5: impact in employment 3.45 1.252 Group 5: dependency of tourism resources 3.39 1.223 Group 5: seasonality 3.24 1.300 Group 5: technology 3.18 1.424 Group 6: support 3.88 1.269 Group 6: feasibility 3.79 1.219 Group 6: economic impact 3.67 1.267 Group 6: impact in employment 3.61 1.298 Group 6: dependency of tourism resources 3.61 1.298 Group 6: seasonality 3.61 1.345 Group 6: technology 3.61 1.391

248

Table 4-16. Demographic information of the stakeholder in the survey Demographic Variables Frequency Percent Gender female 121 42.0 male 167 58.0 Age 18-24 18 6.30 25-34 74 25.7 35-44 84 29.2 45-54 52 18.1 55 or more 50 17.4

Nationality Ecuadorian 272 94.4 Other 16 5.60 Level of Education Graduate 65 22.6 Undergraduate 109 37.8 Technician 19 6.60

High school diploma 68 23.6

Primary school 9 3.10

Rather not to say 18 6.30

Tourism sector Food and Beverage 83 28.8

Accommodation 139 48.3

Operation/ Intermediation 38 13.2

Parks and Recreation 6 2.10

Transportation 3 1.00

Other 19 6.60

249

Table 4-17. Summary table for findings for all the regression analysis: stakeholders in the industry Strategy Strategy Strategy Strategy Strategy Strategy 1 2 3 4 5 6 Sig. Sig. Sig. Sig. Sig. Sig. Gender .075 .163 .043* .460 .040* .016*

Age .081 .014 .038* .245 .691 .642 Nationality .027* .173 .848 .286 .776 .578

Province .511 .654 .538 .371 .978 .343 Level of education .285 .039* .058 .272 .754 .773

Sector .996 .113 .210 .488 .409 .268 Knowledge of risks 1 .718 .303 .973 .715 .960 .323 (changing in policies)

Knowledge of risk 2 .574 .114 .535 .079 .517 .298 (political instability)

Knowledge of risk 3 .184 .348 .274 .899 .147 .227 (changing laws)

Knowledge of laws and policies .055* .765 .501 .577 .406 .662 to deal with risk in the sector

250

Table 4-17. Continued Strategy Strategy 2 Strategy 3 Strategy 4 Strategy 5 Strategy 6 1 Sig. Sig. Sig. Sig. Sig. Sig. Knowledge of .540 .905 .557 .474 .495 .614 adaptation strategies to cope with Risk 1 Knowledge of .239 .013* .913 .242 .890 .278 adaptation strategies to cope with Risk 2 Knowledge of .000* .024* .334 .342 .479 .552 adaptation strategies to cope with Risk 3 Feasibility of .001* .000* .000* .000* .000* .000* implementation Economic Impact .247 .023* .057 .001* .014* .001* Impact in .841 .005* .161 .765 .208 .998 employment Dependency of .843 .492 .022* .197 .824 .366 tourism resources Seasonality .055* .034* .078 .104 .854 .288 Technology needed .691 .771 .359 .451 .001* .238

251

Table 4-18. Relationship among support for strategy one and demographics, knowledge, and feasibility of strategy one: create communication channels using social media to broadcast sudden changes in the law and policy Unstandardized Standardized Model Coefficients Coefficients t Sig. B Std. Error Beta 1 (Constant) 4.290 .521 8.241 .000 Gender -.220 .123 -.101 -1.786 .075 Age -.086 .049 -.102 -1.751 .081 Nationality -.588 .264 -.125 -2.224 .027 Province .006 .009 .038 .658 .511 Level of education .047 .044 .063 1.071 .285 Sector .000 .048 .000 -.005 .996 Knowledge of risks 1 (changing in policies) .023 .065 .026 .361 .718 Knowledge of risk 2 (exaggerated news) .047 .084 .053 .563 .574 Knowledge of risk 3 (political instability) -.117 .088 -.132 -1.331 .184 Knowledge of laws and policies to deal -.138 .072 -.162 -1.926 .055 with risk in the sector Knowledge of adaptation strategies to -.055 .090 -.061 -.614 .540 cope with Risk 1 Knowledge of adaptation strategies to -.112 .095 -.121 -1.180 .239 cope with Risk 2 Knowledge of adaptation strategies to .379 .081 .432 4.659 .000 cope with Risk 3 Feasibility of implementation .189 .055 .207 3.422 .001

252

Table 4-19. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy one Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .954 1.048 Age .894 1.118 Nationality .965 1.036 Province .937 1.068 Level of education .876 1.141 Sector .958 1.044 Knowledge of risks 1 (changing in policies) .586 1.706 Knowledge of risk 2 (political instability) .345 2.902 Knowledge of risk 3 (changing laws) .311 3.215 Knowledge of laws and policies to deal with risk in .430 2.325 the sector Knowledge of adaptation strategies to cope with .314 3.185 Risk 1 Knowledge of adaptation strategies to cope with .290 3.446 Risk 2 Knowledge of adaptation strategies to cope with .355 2.816 Risk 3 Feasibility of implementation .833 1.200 Economic Impact .379 2.637 Impact in employment .416 2.403 Dependency of tourism resources .326 3.072 Seasonality .392 2.549 Technology needed .437 2.290

253

Table 4-20. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy two Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .949 1.054 Age .898 1.114 Nationality .968 1.033 Province .943 1.061 Level of education .885 1.129 Sector .955 1.047 Knowledge of risks 1 (changing in .578 1.729 policies) Knowledge of risk 2 (political instability) .345 2.896 Knowledge of risk 3 (changing laws) .311 3.217 Knowledge of laws and policies to deal .441 2.265 with risk in the sector Knowledge of adaptation strategies to .316 3.168 cope with Risk 1 Knowledge of adaptation strategies to .291 3.436 cope with Risk 2 Knowledge of adaptation strategies to .353 2.831 cope with Risk 3 Feasibility of implementation .905 1.105 Economic Impact .280 3.567 Impact in employment .296 3.376 Dependency of tourism resources .219 4.573 Seasonality .263 3.801 Technology needed .270 3.698

254

Table 4-21. Relationship among support for strategy two and demographics, knowledge, and feasibility of strategy two: tourism certification campaigns destined to the members of the DMOs Unstandardized Standardized Model Coefficients Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.743 .516 5.319 .000 Gender -.167 .120 -.071 -1.399 .163 Age -.118 .048 -.130 -2.478 .014 Nationality -.349 .255 -.069 -1.366 .173 Province .004 .009 .023 .448 .654 Level of education .088 .042 .109 2.075 .039 Sector .074 .046 .081 1.591 .113 Knowledge of risks 1 (changing in -.065 .063 -.067 -1.033 .303 policies) Knowledge of risk 2 (exaggerated news) .129 .081 .134 1.585 .114 Knowledge of risk 3 (political instability) -.080 .085 -.084 -.940 .348 Knowledge of laws and policies to deal -.021 .069 -.022 -.299 .765 with risk in the sector Knowledge of adaptation strategies to -.010 .087 -.011 -.119 .905 cope with Risk 1 Knowledge of adaptation strategies to -.228 .091 -.229 -2.495 .013 cope with Risk 2 Knowledge of adaptation strategies to .178 .079 .189 2.262 .024 cope with Risk 3 Feasibility of implementation .509 .051 .519 9.968 .000

255

Table 4-22. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy three Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .958 1.044 Age .900 1.111 Nationality .955 1.047 Province .936 1.068 Level of education .886 1.128 Sector .953 1.050 Knowledge of risks 1 (changing in .581 1.722 policies) Knowledge of risk 2 (political instability) .344 2.904 Knowledge of risk 3 (changing laws) .311 3.213 Knowledge of laws and policies to deal .429 2.331 with risk in the sector Knowledge of adaptation strategies to .315 3.172 cope with Risk 1 Knowledge of adaptation strategies to .289 3.461 cope with Risk 2 Knowledge of adaptation strategies to .345 2.896 cope with Risk 3 Feasibility of implementation .901 1.110

Economic Impact .255 6.460 Impact in employment .230 7.703 Dependency of tourism resources .208 4.805

Seasonality .263 6.118 Technology needed .212 4.722

256

Table 4-23. Relationship among support and demographics, knowledge, and feasibility of strategy three: creation of contingency funds Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 2.095 .545 3.844 .000 Gender -.255 .126 -.094 -2.028 .043 Age -.105 .050 -.099 -2.088 .038 Nationality .052 .272 .009 .192 .848 Province -.006 .009 -.029 -.617 .538 Level of education .085 .045 .091 1.901 .058 Sector .061 .049 .058 1.255 .210 Knowledge of risks 1 (changing in -.002 .067 -.002 -.033 .973 policies) Knowledge of risk 2 (exaggerated news) .053 .086 .048 .621 .535 Knowledge of risk 3 (political instability) -.098 .089 -.089 -1.096 .274 Knowledge of laws and policies to deal -.050 .073 -.047 -.674 .501 with risk in the sector Knowledge of adaptation strategies to -.054 .092 -.047 -.589 .557 cope with Risk 1 Knowledge of adaptation strategies to -.011 .097 -.009 -.109 .913 cope with Risk 2 Knowledge of adaptation strategies to .082 .084 .074 .968 .334 cope with Risk 3 Feasibility of implementation .597 .047 .612 12.840 .000

257

Table 4-24. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy four Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .955 1.047 Age .898 1.114 Nationality .944 1.060 Province .943 1.061 Level of education .889 1.125 Sector .956 1.046 Knowledge of risks 1 (changing in .590 1.696 policies) Knowledge of risk 2 (political instability) .345 2.896 Knowledge of risk 3 (changing laws) .307 3.256 Knowledge of laws and policies to deal .441 2.267 with risk in the sector Knowledge of adaptation strategies to .316 3.168 cope with Risk 1 Knowledge of adaptation strategies to .290 3.442 cope with Risk 2 Knowledge of adaptation strategies to .356 2.809 cope with Risk 3 Feasibility of implementation .936 1.069

Economic Impact .264 6.106 Impact in employment .240 7.142 Dependency of tourism resources .279 5.593

Seasonality .210 4.753 Technology needed .298 3.357

258

Table 4-25. Relationship among support and demographics, knowledge, and feasibility of strategy four: public- private partnership Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 1.398 .526 2.658 .008 Gender -.087 .118 -.036 -.740 .460 Age -.055 .047 -.059 -1.164 .245 Nationality .273 .255 .053 1.070 .286 Province .008 .009 .044 .896 .371 Level of education .046 .042 .056 1.102 .272 Sector -.032 .046 -.034 -.694 .488 Knowledge of risks 1 (changing in .023 .062 .023 .365 .715 policies) Knowledge of risk 2 (exaggerated news) .141 .080 .144 1.762 .079 Knowledge of risk 3 (political instability) .011 .084 .011 .127 .899 Knowledge of laws and policies to deal -.038 .068 -.040 -.558 .577 with risk in the sector Knowledge of adaptation strategies to -.061 .086 -.061 -.717 .474 cope with Risk 1 Knowledge of adaptation strategies to -.106 .090 -.104 -1.173 .242 cope with Risk 2 Knowledge of adaptation strategies to .074 .078 .076 .951 .342 cope with Risk 3 Feasibility of implementation .580 .048 .593 11.992 .000

259

Table 4-26. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy five Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .953 1.050 Age .889 1.125 Nationality .955 1.047 Province .942 1.062 Level of education .887 1.127 Sector .959 1.043 Knowledge of risks 1 (changing in .587 1.702 policies) Knowledge of risk 2 (political instability) .339 2.952 Knowledge of risk 3 (changing laws) .306 3.271 Knowledge of laws and policies to deal .441 2.267 with risk in the sector Knowledge of adaptation strategies to .315 3.170 cope with Risk 1 Knowledge of adaptation strategies to .288 3.469 cope with Risk 2 Knowledge of adaptation strategies to .347 2.881 cope with Risk 3 Feasibility of implementation .894 1.119

Economic Impact .210 4.752 Impact in employment .216 4.627 Dependency of tourism resources .209 4.780

Seasonality .226 4.418 Technology needed .345 2.901

260

Table 4-27. Relationship among support and demographics, knowledge, and feasibility of strategy five: elaboration of a pre-crisis risk communication plan by academia and the DMOs Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 2.441 .497 4.913 .000 Gender -.234 .114 -.103 -2.062 .040 Age .018 .045 .021 .398 .691 Nationality -.070 .244 -.014 -.285 .776 Province .000 .008 -.001 -.027 .978 Level of education .013 .040 .016 .313 .754 Sector -.036 .044 -.041 -.827 .409 Knowledge of risks 1 (changing in -.003 .060 -.003 -.050 .960 policies) Knowledge of risk 2 (exaggerated news) -.051 .078 -.055 -.650 .517 Knowledge of risk 3 (political instability) .118 .081 .129 1.455 .147 Knowledge of laws and policies to deal -.054 .065 -.061 -.833 .406 with risk in the sector Knowledge of adaptation strategies to -.056 .082 -.059 -.683 .495 cope with Risk 1 Knowledge of adaptation strategies to -.012 .087 -.013 -.138 .890 cope with Risk 2 Knowledge of adaptation strategies to .054 .076 .059 .709 .479 cope with Risk 3 Feasibility of implementation .523 .048 .568 11.000 .000

261

Table 4-28. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy six Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .955 1.047 Age .892 1.121 Nationality .965 1.036 Province .932 1.073 Level of education .889 1.125 Sector .960 1.042 Knowledge of risks 1 (changing in .591 1.692 policies) Knowledge of risk 2 (political instability) .332 3.008 Knowledge of risk 3 (changing laws) .303 3.301 Knowledge of laws and policies to deal .441 2.267 with risk in the sector Knowledge of adaptation strategies to .315 3.170 cope with Risk 1 Knowledge of adaptation strategies to .290 3.445 cope with Risk 2 Knowledge of adaptation strategies to .348 2.873 cope with Risk 3 Feasibility of implementation .902 1.109

Economic Impact .313 3.192 Impact in employment .235 4.257 Dependency of tourism resources .200 5.178

Seasonality .275 3.632 Technology needed .297 3.368

262

Table 4-29. Relationship among support and demographics, knowledge, and feasibility of strategy six: PR plan with emphasis on communication and social media by private sector guilds and academia Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 2.809 .509 5.525 .000 Gender -.284 .117 -.127 -2.426 .016 Age -.022 .047 -.025 -.466 .642 Nationality .140 .251 .029 .557 .578 Province -.008 .009 -.050 -.949 .343 Level of education .012 .041 .016 .289 .773 Sector -.050 .045 -.058 -1.109 .268 Knowledge of risks 1 (changing in -.061 .061 -.066 -.989 .323 policies) Knowledge of risk 2 (exaggerated -.085 .081 -.093 -1.043 .298 news) Knowledge of risk 3 (political .102 .084 .113 1.212 .227 instability) Knowledge of laws and policies to deal .029 .067 .034 .438 .662 with risk in the sector Knowledge of adaptation strategies to .043 .085 .046 .505 .614 cope with Risk 1 Knowledge of adaptation strategies to -.098 .090 -.103 -1.088 .278 cope with Risk 2 Knowledge of adaptation strategies to .047 .078 .052 .596 .552 cope with Risk 3 Feasibility of implementation .475 .051 .502 9.300 .000

263

Table 4-30. Summary table for findings for all of the regression analysis: tourists Strategy 1 Strategy 2 Strategy 3 Strategy 4 Strategy 5 Strategy 6 Sig. Sig. Sig. Sig. Sig. Sig. Gender .708 .991 .367 .678 .238 .195 Age .729 .282 .513 .352 .436 .701 Province .024* .171 .081 .883 .818 .263 Level of education .002* .001* .001* .037* .000* .007* Fist time visitor .931 .737 .742 .756 .887 .878 Knowledge of laws .386 .221 .187 .898 .489 .430 and policies to deal

with risk in the sector Knowledge of risks .866 .385 .928 .531 .354 .806 1 (changing in policies) Knowledge of risk 2 .838 .412 .646 .995 .558 .559 (political instability) Knowledge of risk 3 .165 .506 .015* .266 .011* .086 (changing laws) Knowledge of .136 .027* .003* .036* .394 .218 adaptation strategies to cope with Risk 1 Knowledge of .121 .150 .043* .053* .287 .074 adaptation strategies to cope with Risk 2 Knowledge of .311 .176 .605 .107 .664 .084 adaptation strategies to cope with Risk 3

264

Table 4-31. Adaptive strategies rank by tourists N Mean Std. Deviation

Adaptive strategy 6 354 3.61 1.043

Adaptive strategy 3 354 3.54 1.040

Adaptive strategy 4 353 3.52 1.045

Adaptive strategy 5 354 3.51 1.002

Adaptive strategy 2 354 3.49 .994

Adaptive strategy 1 354 3.35 .974

Valid N (listwise) 353

265

Table 4-32. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy one (Tourist) Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .978 1.023 Age .956 1.046 Province .979 1.022 Level of education .957 1.045 Fist time visitor .919 1.088 Knowledge of laws and policies to deal .676 1.480 with risk in the sector Knowledge of risks 1 (changing in .408 2.449 policies) Knowledge of risk 2 (political instability) .395 2.534 Knowledge of risk 3 (changing laws) .441 2.266 Knowledge of adaptation strategies to .346 2.890 cope with Risk 1 Knowledge of adaptation strategies to .396 2.523 cope with Risk 2 Knowledge of adaptation strategies to .381 2.623 cope with Risk 3

266

Table 4-33. Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy one: create communication channels using social media to broadcast sudden changes in the law and policy

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 3.172 .348 9.108 .000 Gender .035 .092 .020 .375 .708 Age .015 .043 .018 .347 .729 Province -.022 .010 -.118 -2.264 .024

Level of education -.098 .031 -.167 -3.166 .002

Prior knowledge of the -.009 .108 -.005 -.086 .931 destination Knowledge of the tourism .051 .059 .055 .869 .386 attractions in the destination Knowledge of risks 1 .012 .071 .014 .169 .866 (changing in policies) Knowledge of risk 2 .015 .072 .017 .204 .838 (exaggerated news) Knowledge of risk 3 .093 .067 .108 1.393 .165 (political instability) Knowledge of adaptation 1 -.118 .079 -.132 -1.496 .136 Knowledge of adaptation 2 .113 .073 .128 1.556 .121 Knowledge of adaptation 3 .073 .072 .085 1.014 .311

267

Table 4-34. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy two (Tourist) Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .978 1.023 Age .956 1.046 Province .979 1.022 Level of education .957 1.045 Fist time visitor .919 1.088 Knowledge of laws and policies to deal .676 1.480 with risk in the sector Knowledge of risks 1 (changing in .408 2.449 policies) Knowledge of risk 2 (political instability) .395 2.534 Knowledge of risk 3 (changing laws) .441 2.266 Knowledge of adaptation strategies to .346 2.890 cope with Risk 1 Knowledge of adaptation strategies to .396 2.523 cope with Risk 2 Knowledge of adaptation strategies to .381 2.623 cope with Risk 3

268

Table 4-35. Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy two: tourism certification campaigns destined to the members of the DMOs

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 3.046 .353 8.625 .000 Gender -.001 .094 -.001 -.012 .991 Age .047 .043 .057 1.077 .282 Province -.013 .010 -.071 -1.371 .171

Level of education -.105 .031 -.175 -3.332 .001

Prior knowledge of the .037 .109 .018 .336 .737 destination Knowledge of the tourism .074 .060 .077 1.226 .221 attractions in the destination Knowledge of risks 1 .063 .072 .070 .870 .385 (changing in policies) Knowledge of risk 2 .060 .073 .067 .822 .412 (exaggerated news) Knowledge of risk 3 .045 .068 .052 .666 .506 (political instability) Knowledge of adaptation 1 -.178 .080 -.194 -2.219 .027 Knowledge of adaptation 2 .106 .074 .118 1.443 .150 Knowledge of adaptation 3 .099 .073 .113 1.357 .176

269

Table 4-36. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy three (Tourist) Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .978 1.023 Age .956 1.046 Province .979 1.022 Level of education .957 1.045 Fist time visitor .919 1.088 Knowledge of laws and policies to deal .676 1.480 with risk in the sector Knowledge of risks 1 (changing in .408 2.449 policies) Knowledge of risk 2 (political instability) .395 2.534 Knowledge of risk 3 (changing laws) .441 2.266 Knowledge of adaptation strategies to .346 2.890 cope with Risk 1 Knowledge of adaptation strategies to .396 2.523 cope with Risk 2 Knowledge of adaptation strategies to .381 2.623 cope with Risk 3

270

Table 4-37. Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy three: creation of contingency funds

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 3.577 .368 9.710 .000 Gender -.088 .098 -.047 -.903 .367 Age .030 .045 .034 .655 .513 Province -.018 .010 -.091 -1.751 .081

Level of education -.109 .033 -.174 -3.332 .001

Prior knowledge of the -.038 .114 -.018 -.330 .742 destination Knowledge of the tourism .083 .063 .082 1.322 .187 attractions in the destination Knowledge of risks 1 -.007 .075 -.007 -.091 .928 (changing in policies) Knowledge of risk 2 .035 .076 .038 .460 .646 (exaggerated news) Knowledge of risk 3 .173 .071 .188 2.437 .015 (political instability) Knowledge of adaptation 1 -.249 .084 -.259 -2.975 .003 Knowledge of adaptation 2 .156 .077 .165 2.033 .043 Knowledge of adaptation 3 .040 .076 .043 .518 .605

271

Table 4-38. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy four (Tourist) Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .978 1.023 Age .956 1.046 Province .979 1.022 Level of education .957 1.045 Fist time visitor .919 1.088 Knowledge of laws and policies to deal .676 1.480 with risk in the sector Knowledge of risks 1 (changing in .408 2.449 policies) Knowledge of risk 2 (political instability) .395 2.534 Knowledge of risk 3 (changing laws) .441 2.266 Knowledge of adaptation strategies to .346 2.890 cope with Risk 1 Knowledge of adaptation strategies to .396 2.523 cope with Risk 2 Knowledge of adaptation strategies to .381 2.623 cope with Risk 3

272

Table 4-39. Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy four: public- private parthership

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 2.939 .376 7.808 .000 Gender .041 .100 .022 .415 .678 Age .043 .046 .050 .933 .352 Province -.002 .010 -.008 -.148 .883

Level of education -.070 .033 -.111 -2.091 .037

Prior knowledge of the -.036 .116 -.017 -.311 .756 destination Knowledge of the tourism .008 .064 .008 .129 .898 attractions in the destination Knowledge of risks 1 .048 .077 .051 .627 .531 (changing in policies) Knowledge of risk 2 .000 .077 .001 .006 .995 (exaggerated news) Knowledge of risk 3 .081 .072 .087 1.114 .266 (political instability) Knowledge of adaptation 1 -.180 .086 -.187 -2.108 .036

Knowledge of adaptation 2 .152 .078 .161 1.939 .053 Knowledge of adaptation 3 .126 .078 .137 1.618 .107

273

Table 4-40. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy five (Tourist) Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .978 1.023 Age .956 1.046 Province .979 1.022 Level of education .957 1.045 Fist time visitor .919 1.088 Knowledge of laws and policies to deal .676 1.480 with risk in the sector Knowledge of risks 1 (changing in .408 2.449 policies) Knowledge of risk 2 (political instability) .395 2.534 Knowledge of risk 3 (changing laws) .441 2.266 Knowledge of adaptation strategies to .346 2.890 cope with Risk 1 Knowledge of adaptation strategies to .396 2.523 cope with Risk 2 Knowledge of adaptation strategies to .381 2.623 cope with Risk 3

274

Table 4-41. Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy five: elaboration of a pre-crisis risk communication plan by academia and the DMOs

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 3.219 .354 9.099 .000 Gender -.111 .094 -.061 -1.182 .238 Age .034 .043 .041 .780 .436 Province -.002 .010 -.012 -.231 .818

Level of education -.118 .031 -.195 -3.738 .000

Prior knowledge of the -.016 .109 -.008 -.143 .887 destination Knowledge of the .042 .060 .043 .693 .489 tourism attractions in the destination Knowledge of risks 1 .067 .072 .074 .928 .354 (changing in policies) Knowledge of risk 2 -.043 .073 -.048 -.587 .558 (exaggerated news) Knowledge of risk 3 .173 .068 .196 2.545 .011 (political instability) Knowledge of -.069 .080 -.074 -.853 .394 adaptation 1 Knowledge of .079 .074 .086 1.066 .287 adaptation 2 Knowledge of .032 .073 .036 .434 .664 adaptation 3

275

Table 4-42. Variance Inflation Factor and Tolerance diagnostics for regression analysis of strategy six (Tourist) Collinearity Statistics Model Tolerance VIF 1 (Constant) Gender .978 1.023 Age .956 1.046 Province .979 1.022 Level of education .957 1.045 Fist time visitor .919 1.088 Knowledge of laws and policies to deal .676 1.480 with risk in the sector Knowledge of risks 1 (changing in .408 2.449 policies) Knowledge of risk 2 (political instability) .395 2.534 Knowledge of risk 3 (changing laws) .441 2.266 Knowledge of adaptation strategies to .346 2.890 cope with Risk 1 Knowledge of adaptation strategies to .396 2.523 cope with Risk 2 Knowledge of adaptation strategies to .381 2.623 cope with Risk 3

276

Table 4-43. Relationship among support and demographics, prior knowledge of the destination, and knowledge of risk and adaptation of strategy six: PR plan with emphasis on communication and social media by private sector guilds and academia

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 3.374 .369 9.149 .000 Gender -.127 .098 -.067 -1.299 .195 Age -.017 .045 -.020 -.384 .701 Province -.011 .010 -.058 -1.121 .263

Level of education -.089 .033 -.142 -2.707 .007

Prior knowledge of the -.017 .114 -.008 -.153 .878 destination Knowledge of the .049 .063 .049 .790 .430 tourism attractions in the destination Knowledge of risks 1 .019 .076 .020 .246 .806 (changing in policies) Knowledge of risk 2 -.044 .076 -.048 -.585 .559 (exaggerated news) Knowledge of risk 3 .122 .071 .133 1.722 .086 (political instability) Knowledge of -.103 .084 -.107 -1.234 .218 adaptation 1 Knowledge of .138 .077 .146 1.795 .074 adaptation 2 Knowledge of .132 .076 .143 1.732 .084 adaptation 3

277

Figure 4-1. Supply side gender

Figure 4-2. Supply side age

278

Figure 4-3. Supply side country of origin

Figure 4-4. Supply side province

279

Figure 4-5. Supply side education

Figure 4-6. Supply side sector

280

Figure 4-7. Demand side destination

Figure 4-8. Demand side age

281

Figure 4-9. Demand side province

Figure 4-10. Demand side gender

282

Figure 4-11. Demand side education

Figure 4-12. Standardized residual plot for support for strategy one (predictors: demographic, knowledge, and feasibility)

283

Figure 4-13. Standardized residual plot for support for strategy one (predictors: attributes)

Figure 4-14. Standardized residual plot for support for strategy two (predictors: demographic, knowledge, and feasibility)

284

Figure 4-15. Standardized residual plot for support for strategy two (predictors: attributes)

Figure 4-16. Standardized residual plot for support for strategy three (predictors: demographic, knowledge, and feasibility)

285

Figure 4-17. Standardized residual plot for support for strategy three (predictors: attributes)

Figure 4-18. Standardized residual plot for support for strategy four (predictors: demographic, knowledge, and feasibility)

286

Figure 4-19. Standardized residual plot for support for strategy four (predictors: attributes)

Figure 4-20. Standardized residual plot for support for strategy five (predictors: demographic, knowledge, and feasibility)

287

Figure 4-21. Standardized residual plot for support for strategy five (predictors: attributes)

Figure 4-22. Standardized residual plot for support for strategy six (predictors: demographic, knowledge, and feasibility)

288

Figure 4-23. Standardized residual plot for support for strategy six (predictors: attributes)

Figure 4-24. Profile of the domestic tourist taken from MINTUR and Metropolitan District of Quito.

289

Figure 4-25. Demand of destination sites (left) and more populated areas within the country (right) taken from the Ministry of Urban Development and Living

290

Figure 4-26. Standardized residual plot for support for strategy one (predictors: demographic, knowledge, and feasibility) demand side evaluation

Figure 4-27. Standardized residual plot for support for strategy two (predictors: demographic, knowledge, and feasibility) demand side evaluation

291

Figure 4-28. Standardized residual plot for support for strategy three (predictors: demographic, knowledge, and feasibility) demand side evaluation

Figure 4-29. Standardized residual plot for support for strategy four (predictors: demographic, knowledge, and feasibility) demand side evaluation

292

Figure 4-30. Standardized residual plot for support for strategy five (predictors: demographic, knowledge, and feasibility) demand side evaluation

Figure 4-31. Standardized residual plot for support for strategy six (predictors: demographic, knowledge, and feasibility) demand side evaluation

293

CHAPTER 5 DISCUSSION AND CONCLUSIONS

This section will provide (1) an overview of the study, (2) practical conclusions, (3) theoretical contributions, (4) theoretical implications, (5) practical

Implications and recommendations, and (6) limitations of the research.

An Overview of the Study: Adaptation of the RTAF Model

The process of adapting the RTAF model was justified due to the need to extend the model to include unique characteristics and geographic locations which may warrant different perspectives on risk, risk awareness, and adaptation.

The first step in extending the model was to examine the risks presented in the original model and examine the potential risks in the destination of study.

Three processes were undertaken to extend the original model: document analysis, focus groups and a stakeholder survey to confirm potential risks.

The new model proposes a more detailed and destination-oriented system definition approach, where the elements of the system follows the guideline and classification of the Ministry of Tourism of Ecuador, whereas the original model adopted a system definition proposed by Leiper (2004) that cannot be applied in all destinations, especially in those where little research has been done. It is for this reason that the new model is more focused on a holistic, systematic approach which reflects the intricacies of a place of study.

The original RTAF model was designed to face climate change, to understand which of the different impacts could greatly affect the tourism sector of the destination under study. The original model and process did not include a document analysis; however, it was felt that this step would help to narrow down

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the existing information on risks which existed in relevant documents within the country. This served as a starting point to understand the scope of risks for

Ecuador.

Document analysis of the destination’s characteristics, and history of the risks that have affected the destination in the past allowed to gain a deeper understanding of the system. Information gathered in the analysis was processed using content qualitative analysis with a predetermined frame code that was designed for the study with the aim to expose all the type of risks that have affected the destination in the past and hence could affect it again in the future.

Gravity of the risks, and likelihood of occurrence were not analyzed in this phase of the study, and the product of the analysis was only informational. The results of this phase showed that the risks literature for the country (included in this study) were primary focused on natural disasters.

To determine which risks identified in the documentary analysis were more likely to affect the tourism sector, a qualitative study was carried out including the voices of different tourism stakeholders (focus group sessions). The risks evaluation by tourism stakeholders was the determining point for the elaboration of the risk matrix, and henceforth the identification of the Rank “A” risks (need for attention: high likelihood and severity). The opinion and assessment of tourism stakeholders therefore had an important weight in this study, given that they are whom know the strengths and weaknesses of the sector and whom are aware of the needs and priorities for the destination

(Saulter and Leisen, 1999; Yuksel et al., 1999; Simpson, 2001; d’ Angella and

Go, 2009; Calgaro et al., 2014; Dales and Susilowati, 2015).

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The original process used a panel of experts through a Delphi study to narrow down the list of risks and determine which risks were pertinent. While

Delphi studies are a good method to receive expert opinions, the goal of a risk audit is to cast a wide net and generate a list which is exhaustive for the destination. The author felt that using a variety of stakeholders from the tourism industry was a better method to understand risks to the tourism industry. Thus, focus groups, which consisted of representatives from academia, the tourism industry, local government, and national government were included.

Finally, the new model recognizes that the list of adaptation options is a product of the adaptation assessment (a portfolio of ideas), and not part of the adaptation process per se. The list of adaptation options is generated from the inputs of the workshop with the stakeholders of the destination, and the adaptation process in the new model is more complex and inclusive. It includes the presentation, evaluation, and implementation of adaptation strategies.

Due to time constraints, this study only covers up to the evaluation stage of the strategies. It is important to emphasize that the new model carries out a triple evaluation of the strategies that begins in the stakeholder workshop (first evaluation) and culminates with a large-scale survey of both the stakeholders in the industry (second evaluation) and the tourist (third evaluation). This procedure is different from the original model in which the strategies were created and evaluated by a group of experts through a Delphi study, and then evaluated by tourists.

The approach implemented in the new model allows the strategies to be socialized and evaluated by a greater number of members of the system and to

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generalize the achievements at the country level. Moreover, the tourist evaluation phase focused on domestic tourists as possible consumers of tourist services after a disaster, given that past studies have concluded that local and tourist opinions need to be taken into consideration when planning adaptation measures

(Wall and Allister, 2006; Kelly et al., 2007; Jopp et al. 2013). Additionally, domestic tourists are more likely to support the destination after a disaster, whereas, international tourists are more likely to avoid destinations associated with risk (Sonmez, 1998; Sonmez and Graete, 1998; Sonmez et al, 1999; Leep and Gibson, 2003; Reisinger and Mavondo, 2005; Fuchs and Reiche, 2006).

To summarize, the new model generated from the documentary analysis had three new components:

• It included a more detailed definition of the system incorporating the definitions and polices employed within the country.

• It added a detailed risk/vulnerability assessment of the destination that included the opinion of different tourism stakeholders across sectors and experts using qualitative techniques such focus group sessions. The results from the sessions (especially the categorization of risks and creation of the risk matrix) were subsequently validated through quantitative methods, which allowed us to assert that the results were generalizable to the country.

• It included external evaluation (by experts) of the adaptation strategies before being presented to the possible consumers (tourist). This allowed to identify those strategies that had a greater preference among stakeholders at the national level and compare if they coincided with the preferences of domestic tourists. The analysis focused on domestic tourists as possible consumers of tourist services after a disaster.

Once the model was developed and validated, the implementation of creating the resilience plan was recommended.

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Practical Conclusions

The present study carries out the modification, improvement, and implementation of the RTAF model with the aim of increasing resilience of the

Ecuadorian tourism sector. The parts of this sequential study revealed valuable information both for the theory of resilience as well as for practice. The findings of the study can be summarized as follows:

Finding 1. Ecuador is a destination that due to its geographical location and the characteristics of its cultural context is subject to various types of risks, both natural and man-made, which can negatively affect the tourism sector.

[document analysis]

Finding 2. Twelve possible risks for the tourist sector of Ecuador were identified in the focus group sessions: (1) political instability, (2) changing policies, (3) exaggerated news, (4) rapid contamination, (5) earthquakes, (6) volcanic eruptions, (7) floods, (8) tsunamis, (9) epidemic by mosquito bites, (10) mud slides, (11) risks caused by industrial failure, (12) terrorism. [document analysis & focus groups]

Finding 3. The risks that were a priority for the travel industry in Ecuador were: (1) political instability, (2) changing policies, and (3) exaggerated news, and adaptation measures focused on targeting those risks. [focus groups]

Finding 4. Several adaptation strategies emerged from the workshop conducted with the tourism stakeholders [focus group & workshop]. These adaptations extended the original categories of strategies to include:

• Social media • Certification/training of the key informants in the travel sector • Funding support • Organizational management structures within the industry 298

• Investment in crisis management planning

Finding 5. Participants of the focus group study as well as the workshop emphasized the need to work in synergies (academia, private sector, public sector) to face the different risks that affect the tourism? sector. [focus group & workshop]

Finding 6. Participants in the private sector agreed that they cannot depend on the public sector to increase their capacity for resilience, so they proposed to take the reins and empower themselves. [focus group]

Finding 7. All strategies presented in the study received a high overall score for both the stakeholder and the tourist evaluation with mean scores above

3. Overall, the adaptation strategy that received the highest score was:

Sstrategy #6: Elaboration of the PR plan with emphasis on communication and social media by private sector guilds and academia. [workshop]

Finding 8. Using the data generated from the workshop it was established that there is a strong positive correlation between overall score of the adaptation strategies and viability of the implementation, possible economic impact, possible impact in employment, dependency of tourism resources, impact in seasonality, and technology necessary for the implementation for all strategies evaluated.

[stakeholder evaluation survey]

Finding 9. According to the analysis from the stakeholder survey data, the regression models explained only up to 37.5% of the variance in the dependent variable. Thus there must be a combination of variables not included in the model that have a greater influence when it comes to support for the proposed strategies. [Tourist evaluation survey]

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Finding 10. Feasibility of Implementation was the independent variable that showed statistical significance across all six adaptive strategies. Findings showed that when perception of feasibility increased, so too did the values of support for the strategies. This suggests that if stakeholders are not confident that these adaptive strategies are possible, if the government and/or private sector is not willing or able to invest in them. [tourist evaluation survey]

Finding 11. Findings in the stakeholder evaluation indicated that demographic variables were sometimes significantly related to adaptive strategies. Across the six adaptive strategies, gender was significant in 3 analyses; age was significant in 2 analyzes; and nationality was significant in 2 analyzes. The findings suggest that Ecuadorian men, who are 35 years and older tend to rate the strategies more negatively.

Finding 12. Knowledge of the ability to cope with political instability was significant for 2 analyses and tended to positively influence the support for strategy #1 (creation of communication channels using social media to broadcast sudden changes in the law and policy, and strategy #2 (creation of tourism certification campaigns destined to the members of the DMOs).

Finding 13. The predictor attributes proposed by Jopp et al. (2010) (i.e., economic impact, impact in employment, dependency of tourism resources, seasonality, and technology needed for implementation) suggested that when stakeholders perceived there was going to be a favorable economic impact, they were more likely to support a strategy (economic impact was significant in 4 of 6 analyses). Other attributes were not as consistently significant across the strategies. Degree of seasonality was only positively significant in two analyses.

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Dependency of resources was negatively significant in two analyzes. And, possible impact in employment was only positively significant in one analysis.

Finding 14. When assessing the tourist evaluation of adaptive strategies, only “level of education” was significant across all strategies. Findings exhibited that tourists were more likely to support and adaptive strategy if they have a higher level of education. The variables prior knowledge of the destination, knowledge of the tourism attraction, knowledge of risks (political instability, exaggerated new, and change in policy), knowledge of the adaptation measures

(to cope with political instability, exaggerated new, and change in policy) were not found statistically significant consistently across the strategy evaluation.

Knowledge of adaptation measures to cope with changing policies and exaggerated news were only found significant in the evaluation of three strategies (creation of certification campaigns, creation of contingency funds, public-private partnership) and they tended to imply that when tourists have greater knowledge of adaptation they were not likely to support the adaptive strategies proposed.

Finding 15. Knowledge of the attractions in the destination, was not statistically significant. This suggests one of two things, either Ecuadorians do not see a relationship between the tourism product and the strategies to protect it in times of crisis or there is no variable in knowledge. The mean score of knowledge of attraction was 3.25, and the majority of responses (135) were centered around the average rating (3), (Figure 5-1)

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Theoretical Contribution

Finding 1. Resilience models need to include a risk management component that include both slow and fast drivers of resilience. A process needs to allow for a comprehensive and exhaustive list of identification of risks. There are many types of risks that can affect a destination. The vulnerability of the tourism sector to those risks can vary from one context to another (Calgaro et al.,

2014). Hence, an integrated approach needs to include broader frameworks of risk-related research and consider multiple threats (Wilbanks and Kates, 2010).

Finding 2. Resilience models need to extend beyond infrastructural planning to include marketing and management planning. When tourist destinations are affected by natural and man-induced disasters, not only do they deal with infrastructure losses, but also with negative publicity and the perception of risk of the destination (Scott, 2008). In many cases, tourists choose to cancel their reservations leaving the destinations dealing with the recovery and reconstruction without the income generated by the activity (Carlsen and

Hughes, 2008; Scott, 2008). Proactive risk management planning and post - disaster marketing are key elements in the resilience of tourist destinations that have been ignored in most resilience studies but are recognized as a fundamental piece of recovery in the risk management literature (Gurther, 2007;

Ritchie, 2008; Scott, 2008; Orchiston and Highman, 2016).

Finding 3. Resilience planning needs to be more than a government process, it needs to include the voices of different stakeholders within the tourism sector. Strong partnership, collaboration, and community participation are core factors to enhance resilience in destinations (Sheppard, 2015) since they lead to

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the identification of the tourism sector’s needs (Becken and Hughey, 2013). In summary, an inclusive approach allows for better use and allocation of resources

(Calgaro et al., 2014).

Finding 4. Resilience planning needs to be mindful of the stage of development that the destination is in and its placement on the destination lifecycle curve. Planning, elaboration, and implementation of adaptation strategies must be conscience of the stage of development of the sector and the tourist destination. Therefore, resilience research should also understand the feasibility and barriers that adaptation efforts could face based on the destinations’ stage in the lifecycle.

Theoretical Implication

Resilience theory has had a growing level of acceptance in the tourism literature in the past decades. However, one limitation is that most literature does not indicate how to move from theory to practice (Espiner et al., 2017). The present study contributes to the current literature on tourism and resilience providing a comprehensive and condensed explanation of the foundation of the resilience theory and its application in the tourism field. It also takes steps to adapt the RTAF model and implement it in a smaller, more volatile, less developed tourism destination.

The results of this study indicate the need to integrate a multiple risk analysis in the RTAF model. This is particularly true given that tourism destinations are affected by different risks and the magnitude of the sector's vulnerability vary (Menoni et al., 2012; Becken et al., 2013; Becken and Khazai,

2017). Although several destinations in a region may be affected by the same

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risks, the way in which they are prepared and respond to them determines their vulnerability (Ritchie, 2008; Wilbanks and Kates, 2010).

The RTAF model seeks to reduce vulnerability and increase the capacity and adaptability of a destination, thus increasing the resilience of the system.

However, to enhance resilience it is necessary to prioritize the adaptation of those risks. Therefore, a multi-risk assessment component that includes an analysis of the history of disasters that have affected the destination

(documentary analysis), and the participation of the members of the systems

(tourism stakeholders) as evaluators of the risks is highly advised (Faulke, 2001).

The inclusion of the stakeholders' voices, and the integration of the risk component of the RTAF model allowed the association between the risk management literature and the stakeholder theory to be integrated into the resilience model. Although this relationship has been implicit in much of the current resilience research (Buckle et al., 2000; Tompkins and Adger, 2004; Jopp et al., 2010; Menoni et al., 2012; Becken and Hughey, 2013; Calgaro, 2014;

Sheppard, 2015; Becken and Khazai, 2017), this study contributes to the validation of the importance of merging these two lines of research with the resilience theory.

It is important to mention that the results of this study show the need to advance the understanding of the steps that lead to resilience in tourist destinations. The results indicate that the risk management and tourist planning by the DMOs can facilitate or hinder the process of resilience of the destinations depending on the level of preparation in which they have invested (Buckle et al.,

2000; Faulke, 2001; Ritchie, 2008; Becken et al., 2013; Becken and Khazai,

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2017). Merely an infrastructural response (post-crisis) guarantees the reconstruction of the infrastructure in a disaster, however it does not ensure that the tourism activity will be maintained or rebuilt after a catastrophic event

(Tompkins and Adger, 2008; Scott, 2008; d’Angella and Go, 2009).

The results of both the focus group and the workshop show that marketing to improve the image of the destination after an adverse event allows better management to counter negative images. Therefore, this component of resilience management is a crucial strategy for the recovery of destinations. These findings are consistent with studies in tourism risk management (Gurther, 2007; Carlsen and Hughes, 2008; Scott, 2008; Orchiston and Highman, 2016).

Many studies indicate that recovering destinations face numerous difficulties when maintaining tourist reserves and stimulating new post-crisis visits

(Gurther, 2007; Carlsen and Hughes, 2008; Ritchie, 2008; Scott, 2008; Orchiston and Highman, 2016). Therefore, resilience planning should include marketing and destination planning considering the stage of the destination life cycle.

(Buckle et al., 2000; d’Angella and Go, 2009).

Previous studies have emphasized the importance of the identification of the destination’s stage in its life cycle for planning and the implementation of effective tourism policies (Martin and Uysal, 1990; Cooper, 1992; Gets, 1992;

Tooman, 1997; Farell and Twining-Ward, 2004). However, there are few that have focused on the importance of the destination life cycle theory as a complement of the resilience framework (Farell and Twining-Ward, 2004). While it is implicit that within the study of the characteristics of the system its included the recognition of the state of development of the tourism sector (Jopp et al.,

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2013), a thorough analysis using the destination life cycle framework would allow a greater knowledge of the tourism system and understanding of the results of the resilience study. For instance, in this research it was observed that Ecuador as a tourist destination is at an early stage in the life cycle (involvement stage), so it was to be expected that there were shortcomings in the destination planning and elaboration of policies for the sector. These findings are crucial to understand why the different tourism stakeholders identified as main risks those that were directly related to destination planning and marketing, even in the face of possible disastrous events such as volcanic eruptions and earthquakes. The inclusion of the destination life cycle theory as a companion to the resilience framework for the tourism sector could identify which regulatory and planning strategies could be advised for the destination under study (Martin and Uysal,

1990, Getz, 1992).

Another significant contribution to the resilience tourism theory is the validation of the importance of including the voices of the different members of the system. In this study the opinions, suggestions, experiences, and knowledge of different stakeholders of the Ecuadorian tourism sector were considered. This allowed a high quality of information collection and provided greater knowledge of the destination, its risks, vulnerabilities, and opportunities. In addition, the implementation of the stakeholder approach in this resilience study provided a space for open communication between different members of the system, as well a medium to promote synergies and effective use of resources (Buckle et al.,

2001; d’Angella and Go, 2009; Becken and Hughey, 2013; Calgaro et al., 2014;

Dahles and Susilowati, 2015).

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Practical Implications and Recommendations

The aim of this study was to provide a process and outcome to create a more resilient destination through the reduction of vulnerabilities and enhancement of opportunities to respond to shocks to the system.

The present study provided a holistic approach that should lead to the increase of resilience, resistance, and readiness of the destination under study.

Main findings suggest that in order to achieve tourism resilience for the sector improvements in the planning and policy creation for the sector need to be adopted in the destination across levels (considering the macro, meso, and micro levels). This kind of approach is fundamental in destinations that are in an early stage in the destination life cycle (Martin and Uysal, 1990; Getz, 1992).

Based on the results of this study and considering the specific context of

Ecuador, first steps to enhance resilience for the tourism sector should be focused on the following aspects.

Macro: the measures suggested in this level include the ones that need to be taken at the country level, hence need to be adopted by the government through the Ministry of Tourism (MINTUR), or by associations that have at least a national impact. Some macro measure identified in this resilience study included the consultation and participation of different tourism stakeholders for the development and implementation of tourism destination plans (focus groups and workshop). Additionally, participants (focus groups and workshop) felt that the marketing and advertising strategies for the destination needed to be socialized with the members of the productive sector, as well as the training programs (to improve quality of service).

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Another suggested measure was a tourism tax reform with the aim to increase of the resilience of the destination. Participants suggested that transparency in the management of the funds and friendly measures for new and small business will help in the development and strengthened of the sector.

Meso: The meso level for the destination of Ecuador adopted the government political division by zones. Ecuador is divided in 9 zones according to the number of habitants and the geographical location (Senplades, 2012). The meso measures proposed in the study included public-private partnership between the representatives of MINTUR for each zone and the main tourism stakeholders. Through the partnership, the implementation of the adaptation strategies proposed in this study could be carried out more easily.

Micro: The municipalities and key stakeholders of the destination could be advised by the MINTUR zonal representatives to carry out specific resilience and adaptation studies for their context. Furthermore, strategies proposed in this study could be implemented by the DMOs of the main tourism destinations for the country, promoting this way the empowerment of the tourism stakeholders and reduce the dependency on the government.

Although not all of the measures proposed in this study could be implemented successfully in the destination (due to numerous cultural and political constraints) the researcher considered that it was opportune to inform them as possible options for the future, or for other destinations interested in increasing its capacity for adaptation and resilience.

Findings from this study provided a portfolio of adaptive strategies, which has been elaborated on and tested, including the voices of the tourism

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stakeholders as well as tourists, in order to improve the resilience of the tourism sector. Although the strategy with the greatest feasibility of implementation was the elaboration of a public relations plan to deal with the rumors and exaggerated news, it is suggested to start with the reactivation and creation of associations and collegiate bodies. In this way the communication channels between the private sector, the public sector, and the academy would allow the exchange of ideas and better use of resources for a common good. This type of initiative allows the inclusion of different members of the system, thus favoring the collective and detailed knowledge of the destination under several points of view.

The use of social networks as official communication channels to inform tourists and members of the supply sector about steps to follow in case of emergency, as well as to provide truthful information about the real risk in a destination is a strategy that has proven effective in previous studies (Ritchie,

2004; Ritchie, 2009; Pennington-Gray et al., 2010; Hyas et al., 2013; Jin et al.,

2014; Schroeder and Pennington- Gray, 2015). Social Media is also an excellent tool for the promotion of destinations and does not generate excessive expenses.

However, it is necessary for the organization that disseminates the information to have credibility and followers, in other words to be considered as an influencer of the target market.

It is important to emphasize that for the elaboration of this work we counted on the help and collaboration of the Ministry of Tourism and the

Universidad Laica Eloy Alfaro de Manabí. Both organizations have shown a marked interest in helping in the campaign of dissemination of the results of this study to increase the probabilities of adoption of the tourist resilience framework

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by the municipalities of the different cantons of Ecuador. In the same way, private organizations have contacted the researcher on numerous occasions to know the details of the researcher's publications). There is therefore a favorable climate for the dissemination of this study and adoption of adaptation strategies development.

Given that this study presented a proposal at the national level and with the support of the leading organization of tourist activity in Ecuador (Ministry of

Tourism), it is more likely that the municipalities will adopt it.

Limitation and Future Research

One difficulty that can be faced when carrying out a sequential design study, is that the results of an early phase can affect the importance of the inclusion of a final phase (Robbins, 1985). In a resilience study, the importance of the evaluation of the adaptive strategies by the demand side (tourists) become evident in the case that the suggested measures were of an infrastructural nature

(such as seawalls, implementation of snow machines, etc.). The risks identified for this study were focused on the management and planning of destinations, thus adaptation strategies developed were focused on dealing with such risks.

Therefore, the results of the evaluation provided by tourists, although they contribute to the understanding of the resilience framework of the destinations, must be taken very carefully when trying to generalize, due to tourists included in the study could not be aware of the implication and intrinsic characteristic of tourism planning. However, the researcher argues that the importance of the demand evaluation component is of great importance for the resilience model especially in cases where the proposed adaptation measures for the destinations

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are infrastructural and could affect the experience and satisfaction of tourists

(Jopp et al., 2010), and therefore it is suggested to maintain the demand evaluation component and verify its usefulness in future resilience studies.

It is also important to point out that despite the exhaustive document analysis carried out in this study, it must be considered that there is also a limitation, since all the documents included in the study were official and prepared by the Ecuadorian State this could lead to bias. To counteract this limitation, the voice of different stakeholders from several tourism destinations in the country were included, and surveys were employed that used a large sample size. Although generalizations of the findings of this study could be warranted in destinations with similar characteristics, it is advisable to consider several limitations.

The data for the focus study, workshops and surveys was collected between the months of January and February of 2018, almost two years after the earthquake, and less than a year after the change of the presidential government of Rafael Correa. Thus, it was expected that recent changes in the policy of the country which affected the sector influenced the response of the participants of this study. Additionally, although there are many similarities in developing tourism destinations, not all have the history of problems regarding political instability that

Ecuador has faced.

Additionally, the survey conducted with the members of the national list of stakeholders from the Ministry of Tourism excluded the voice of those small businesses and new businesses that have not yet registered. Likewise, the opinion of people who changed their telephone number and / or who do not use

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the Internet in the usual way (such as elderly people) were excluded. There could be a significant difference between the people who completed the questionnaire and those who did not. To deal with the limitations of the study, a large sample was included, which in theory will be enough to represent the characteristics of the population of interest.

It is important to point out that Ecuador is a small country where the risks that affect a destination are likely to affect the country in a general way. Though, this does not hold true for large countries whose geographic extensiveness and cultural characteristics may be different across geographic locations.

With respect to the measurements, while careful reflection has been adopted in the reliability and validity of all tools used in the study, further refinement in future studies is recommended.

Finally, it is recommended that in order to support the statement that the model is scalable, other studies need to be carried out in Ecuador at the level of municipalities and provinces to demonstrate viability of the adapted model.

Furthermore, it is also advisable to move forward with the elaboration and evaluation of adaptive strategies to cope with the risks Rank B identified in the study. The results showed that although the most important risks for the sector were all linked to planning and marketing, the following risks in the matrix were all linked to possible natural disasters. It would therefore be interesting to continue with the development of adaptation strategies for these risks, and in the future to be able to carry out a comparative study of the evaluation of the proposed strategies on both the supply and demand side.

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Figure 5-1. Frequency of responses: variable knowledge of the tourism attractions

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APPENDIX A SAMPLES

Sample of invitation (examples taken from Krueger 2014, Carey and Asbury 2016)

Universidad Laica Eloy Alfaro de Manabi and University of Florida Date Name of the person Institution

The University of Florida and the ULEAM are conducting a study of risk, vulnerabilities and opportunities assessment for Ecuador. We are extending an invitation to participate in 2 hours discussion, to be held in CINFOTUR at 5:50 pm on ______2017, because you were identified as an important stakeholder of the tourism sector of Ecuador. Refreshments will be provided. Please contact me if you have any question or want more information.

Sincerely

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Sample of confirmation letter (examples taken from Krueger 2014, Carey and Asbury 2016)

Universidad Laica Eloy Alfaro de Manabi and University of Florida Date Name of the person Institution Thanks for agreeing to join us for a conversation about risk, vulnerabilities and opportunities assessment for Ecuador. We are interested in the ideas of important stakeholders of the economic sectors of the country as yourself. The group will be held: Date From 5:50 to 7:50 pm CINFOTUR We will be offering refreshments. If for some reason you won’t be able to join us, please call as soon as possible. If you have any questions, please give me a call at xxxxxxxxxx. We are looking forward to meeting you.

Sincerely

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Sample of invitation for Workshop

University of Florida, Universidad Laica Eloy Alfaro de Manabi and the Ministry of Tourism Date Name of the person Institution

The University of Florida, the MINTUR, and the ULEAM are organizing a workshop to elaborate adaptive strategies for the tourism sector. You have been nominated as an important stakeholder of the tourism sector of Ecuador and we would like to extend you an invitation to participate in the workshop that will be conducted on January xx of 2017 at 9:00 am in the UCSG room # of the xxxx department. After completion of the workshop you will received a certificate of assistance. Background of the topics to discuss is attached to this invitation.

Refreshments will be provided. Please contact me if you have any question or want more information.

Sincerely

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Sample of background infromation for Workshop

A previous focus group study conducted on December xx of 2017 with tourism stakehokders of the country identified the following risk as important to address for the tourism sector of Ecuador, due to their likelihood of happening and possible intensity of the impact. - Risk one - Risk two - Risk three - Risk four Please consider them in the developing of the adaptive strategies for the sector that will be conducted in the Workshop.

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APPENDIX B INSTRUMENTS

Instrument 1 Ranking sheets

Name of the strategy: ______Risk that the strategy will address ______Group number ______

Please rate the presented adaptive strategies in a five-point scales (five being the higher score) Criteria 0 1 2 3 4 5 Support of the presented strategy Feasibility of the implementation Possible economic development Possible impact in employment Dependency of tourism resources Degree of seasonality Level of technology implemented

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Instrument 2 Survey to tourism stakeholders

Demographics What is your gender? Female Male Transgender female Transgender male Gender variant/ non-conforming Other What is your current age? 18-24 25-34 35-44 45-54 55 and over I prefer not to say What is your country of birth as shown in your identification or passport? Ecuador Other What is your province of birth as shown in your identification or passport? Azuay Bolivar Cañar Carchi Chimborazo Cotopaxi El Oro Esmeraldas Galapagos Guayas Imbabura Loja Los Rios Manabi Morona Santiago Napo Orellana Pastaza Pichincha Santa Elena Santo Domingo de los Tsachilas Sucumbios Tungurahua Zamora Chinchipe What is the highest level of education you have completed? Post graduate qualification Tertiary University degree 319

Vocational / technical training Secondary school Primary school Prefer not to answer To what tourism sector do you belong? Food and beverage Accommodation Operation and travel agencies Parks, recreation and entertainment Tourism transportation

Familiarity with risks and adaptation

From 1 to 5 (five being very good and 1 very poor) How would you rate? Your knowledge of current policies, laws, or regulations to 1 2 3 4 5 accommodate risk in the sector?

Your knowledge of the issues surrounding risk 1? Your knowledge of the issues surrounding risk 2? Your knowledge of the issues surrounding risk 3? Your knowledge of the issues surrounding risk 1 adaptation? Your knowledge of the issues surrounding risk 2 adaptation? Your knowledge of the issues surrounding risk 3 adaptation?

Evaluation of the strategies

For the “X” adaptation strategy please rate in a five-point scales (five being the higher score) the following criteria Criteria 1 2 3 4 5 Feasibility of the implementation Possible economic development Possible impact in employment Dependency of tourism resources Degree of seasonality

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Level of technology implemented

For the “Y” adaptation strategy please rate in a five-point scales (five being the higher score) the following criteria Criteria 1 2 3 4 5 Feasibility of the implementation Possible economic development Possible impact in employment Dependency of tourism resources Degree of seasonality Level of technology implemented

For the “Z ” adaptation strategy please rate in a five-point scales (five being the higher score) the following criteria Criteria 1 2 3 4 5 Feasibility of the implementation Possible economic development Possible impact in employment Dependency of tourism resources Degree of seasonality Level of technology implemented

Please rate in a five-point scales (five being very positive score, and 1 very negative) the following adaptation strategy 321

Criteria 1 2 3 4 5 Adaptation strategy x Adaptation strategy y Adaptation strategy z

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Instrument 3 Survey to tourists

Tourism site ______Demographics What is your gender? Female Male Transgender female Transgender male Gender variant/ non-conforming Other What is your current age? 18-24 25-34 35-44 45-54 55 and over I prefer not to say What is your province of birth as shown in your identification or passport? Azuay Bolivar Cañar Carchi Chimborazo Cotopaxi El Oro Esmeraldas Galapagos Guayas Imbabura Loja Los Rios Manabi Morona Santiago Napo Orellana Pastaza Pichincha Santa Elena Santo Domingo de los Tsachilas Sucumbios Tungurahua Zamora Chinchipe What is the highest level of education you have completed? Post graduate qualification Tertiary University degree Vocational / technical training Secondary school 323

Primary school Prefer not to answer

Tourism component

What was your primary destination on your trip (city)? ______

Is your visit today……?

A day trip? If so, how many hours in total are you planning to stay in the area? (hours) An overnight visit? If so, how many nights in total are you planning to stay in the area? (nights)

What types of accommodations (Check all that apply)

❑ Motel/hotel ❑ Bed and breakfast ❑ Public campground ❑ Commercial ❑ Friends/Relatives ❑ Resort campground home ❑ Something else (please describe) ______

Is this the first time you've visited this area? Yes (If • How did you hear about this Yes) area?

No • About how many times have you visited this area in the (If last 12 months? No) • What year did you visit this area for the first time?

How many people are in your group? ______Number of people

Who is/are in your group? ❑ Alone ❑ Family ❑ Friends ❑ Friends & ❑ Tour group Family ❑ Other (please describe) ______

Please estimate how much you spent overall on this trip (total for entire party and entire trip) $______

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On a scale of 1 to 10, with 10 being a perfect trip, how would you rate the overall quality of your experience during your trip? _____

Familiarity with risks and adaptation Is this the first time that have you visited this tourism site in your lifetime? Yes No From 1 to 5 (five being very good and 1 very poor) How would you rate? Your knowledge of the tourist attractions and activities 1 2 3 4 5 available in this site? Your knowledge of the issues surrounding risk 1? Your knowledge of the issues surrounding risk 2? Your knowledge of the issues surrounding risk 3? Your knowledge of the issues surrounding risk 1 adaptation? Your knowledge of the issues surrounding risk 2 adaptation? Your knowledge of the issues surrounding risk 3 adaptation?

Evaluation of the strategies

Please rate in a five-point scales (five being very positive score, and 1 very negative) the following adaptation strategy

Criteria 1 2 3 4 5 Adaptation strategy x Adaptation strategy y Adaptation strategy z

Assuming you were considering a return trip to this tourism site, how would the implementation of the following adaptation options positively or negatively affect your decision?

Options Highly Negative No affect Positive Highly negative positive 1 2 3 4 5 Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

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APPENDIX C SCRIPTS

Script 1 Reminder call

Script: This is xxxxxxxxx from UF and ULEAM, I am calling to talk with xxxxxx. Can you communicate me with him/her. Hellos Mr/Mrs. Xxxxxx I am calling you to remind you about the focus group session regarding risk, vulnerabilities and opportunities assessment for Ecuador, that will be held tomorrow at 5:50 pm in CINFOTUR. We are looking forward to seeing you tomorrow at 5:50 pm. Are you still able to make it?

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Script 2. Welcome

Script: Hello, good afternoon, I am very thankful that you could join us to our study regarding risk, vulnerabilities and opportunities assessment for Ecuador. My name is xxxxx I am a professor here at ULEAM, and a Ph.D candidate at UF, and this is xxxxx, who will be helping me, she/he will be takin notes, because sometimes it is hard for me to really listen to you and write at the same time. This study constitutes a first stage of a resilience study. The purpose for the present study is to collect information across sectors of Ecuador to identify risks and vulnerabilities that could affect the country. The University of Florida and the ULEAM are supporting this research. The data collected in this session will be tape-recorded, However names will be deleted, and non-identifying information will be disclosed. Only researchers affiliated with this project of the University of Florida and ULEAM will have access to the information, and the same researchers will perform transcription. With your permission, I would like to start the recording of the session. It is important to mention that if you would like to receive the results of the study, you can give us your email so we can send you the information. The contact sheet is right here in the table. It is very important to take into consideration than the context is all continental Ecuador, and that the risks we want to identify are the ones that we call “fast variables” because they happen “fast” . Examples of those types of risks are volcano eruptions, floods, etc.) Before starts, I would like to share with you the definitions of the terminology we are going to use, so everybody will be in the same page: • Risk: The present study adopts the concept of risk proposed by the Pacific Asia Travel Association (2014) and Coombs (2014): Risk is a prospect or probability of a negative event that could develop into crisis. • Fast drivers or fast variable: variables that strongly shape the system and that happens in a short frame of time (Walker et al. 2012) • Context of the study: continental Ecuador Please let us know if you have any doubt regarding the mentioned definitions

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Script 3. Grand Rules

Script: For the present study, I would like to indicate that there are not right or wrong answer, and every opinion is equally important, for that matter I would appreciate it if we can take turns to talk, so everybody’s opinion could be listened. Negative comments are also welcome. What we really want is to know your opinions regarding the topic, so please feel comfortable to share it with us, and remember that the purpose of the study is to gather information not to achieve consensus. Besides, because we don’t want to miss any comment we are recording this session, but be assure we won’t share your personal information with anybody. For that matter, we want to communicate to day using first names only. As you can see in front of you there are market and cards where you can write your first name, or whatever name you want us to call you today.

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Script 4. Phone invitation script for Workshop

Script: This is xxxxx from UF and ULEAM, I am calling to talk with xxxxxx. Can you communicate me with him/her. Hellos Mr/Mrs. Xxxxxx I am calling you because you have been identified as an important stakeholder of the tourism sector of Ecuador, and been nominated to participate in an adaptation strategy workshop that will be help in the UCSG, on January XX of 2018 from 9 am to 6 pm. In this workshop you will receive a certificate of assistance endorsed by UF, ULEAM, and the MINTUR. We would like to have your participation. You will be receiving a formal invitation in the next days.

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Script 5. Welcome (Workshop)

Script: Hello, good morning, I am very thankful that you could join us to our workshop to develop adaptive strategies for the tourism sector of Ecuador. My name is xxxxxxx I am a professor at ULEAM, and a Ph.D candidate at UF, and this is xxxxx, who will be helping me, she/he will be takin notes, because sometimes it is hard for me to really listen to you and write at the same time. This study constitutes a second stage of a resilience study. The purpose for the present study is to develop adaptive strategies to cope with the risks rank A for the tourism sector, which were identified in a previous study and presented to you as a background when you received your invitation. To gain a deeper understanding of the mentioned risks and the purpose of this session. I will present some slides. I want to let you know that the data collected in this session will be tape-recorded, However names will be deleted, and non-identifying information will be disclosed. Only researchers affiliated with this project of the University of Florida and ULEAM will have access to the information, and the same researchers will perform transcription. With your permission, I would like to start the recording of the session. It is important to mention that if you would like to receive the results of the study, you can give us your email so we can send you the information. The contact sheet is right here in the table. First I would like to do an activity with you. As you can see in your desks are papers, markets and tape, please write the name you want to be called in big letters and tape it in a way that other participants can see it, and address you in the future for that name. Now we will start or workshop.It is very important to take into consideration that for this study the context is all continental Ecuador, and that the adaptive strategies we want to develop need to be focus on addressing the mentioned risks. I want to share with you also the itinerary of the workshop, which was also provided to you in the registration. As you can see we have a complete day of work, we will start with the presentation of the slides in the plenary session, and then around 9:50 we will work in groups, and at 13:30 we will have a break for lunch, which will be served by the protocol team that help you with the registration. We will return at 2:30 to start group presentations, evaluation and the ceremony of closure, where you will receive your certificates. Before starts, I would like to share with you the definitions of the terminology we are going to use, so everybody will be in the same page: • Risk: The present study adopts the concept of risk proposed by the Pacific Asia Travel Association (2014) and Coombs (2014): Risk is a prospect or probability of a negative event that could develop into crisis. • Adaptive strategy: Any measure proposed or implemented for the tourism industry to cope with risk. The adaptive strategies that can be technical, business management or behavioral (Jopp et al., 2013, 2015) • Context of the study: continental Ecuador Further information about this concepts will be presented in the slide presentation, and please consider that after the presentation we will dedicate some time for answering questions.

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Script 6 Workshop script (activities) Now that I have provided a background for the study and we had revised the risks A for the tourism sector, it is time to form groups. The participants for each group have been selected randomly, and each group will analyze a risk and will develop the strategies for that specific risk. Then after the lunch each group will present the adaptive strategies to the group and they will be evaluated. It is very important to address that these ideas need to be feasible. For that matter, each group can develop up to three strategies, but you need to provide full detail of each one. So, in order to develop them you need to address the following questions:What adaptation strategies would you recommend for the tourism sector of Ecuador, given the impact of Risk X? • Which stakeholders should be responsible for the implementation of the adaptation strategy suggested? • Do you think that the local community would be supportive of that particular adaptation strategy? • How effective do you believe this adaptation strategy will be in addressing this particular Risks X impact? • Are specific destinations in Ecuador that should be the focus of adaptation efforts? Please remember you need to support each point in your presentation. So now I will read the members of each group: Group one is formed by : a, b, c,d, e, please all members raise your hands, you are going to analyze Risk X, So please come together and form the group so you can start working (and so on with the rest of the participants).

Please attention everybody, now that you are formed in groups, remember the goal of this workshop is to develop adaptation strategies for your risks, and for the presentation you need to provide a detailed description of the strategy and ansewed the questions posted. Both the objective and the questions will be display in the screen so you can have them present. Now please free to work, I am going to be available to support each team, and we will present the strategies after lunch. …….. Hello, I just wanted to inform you that lunch we will be served in 15 minutes in the same place where you registered. So please enjoy the food, and We will be returning to the classroom at 2:30 pm to start presentations and evaluations …… Thanks a lot again for coming, I hope you enjoyed your lunch. As you know this part of the workshop is dedicated to the presentation of the adaptive strategies proposed by you as key stakeholders of the tourism sector of Ecuador. I will be calling you by groups to present your work, I just ask the rest of the groups that pay close attention to the presentation, so we can perform the evaluation. Without any comments I will ask group #1 to come and present the work …….. Thanks a lot for your presentation, you had very interesting ideas, I want to take a moment to see if there is any question regarding this presentation ………

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Ok, I believe we run of time, so now we are going to start the evaluation process of the adaptive strategy presented. My assistant had delivered a sheet where you can write down the name of the strategy, and provide your ranking. Remember it is a 5-point scale ranking where 5 is the maximum grade, and just remember that the evaluation is anonymous. …….

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APPENDIX D SLIDES

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BIOGRAPHICAL SKETCH

Estefania Mercedes Basurto Cedeño is an associated professor in the tourism and hospitality department at the Universidad Laica Eloy Alfaro de Manabí in Ecuador

(ULEAM). She has taught 7 years to undergraduate courses in the areas of tourism development, air traffic, tourism inventory, tourism projects, introduction to hospitality, hospitality management, human resources in tourism, and languages. She was awarded with The SENESCYT scholarship, the highest governmental recognition for outstanding students and professors in Ecuador in 2014. Estefania was the academic supervisor for the department of tourism in ULEAM during 2013-2014 academic year.

Her areas of research are tourism resilience, heritage tourism, and sustainable tourism development. She has worked with the Ministry of Tourism in Ecuador the destination management organizations (DMOs) of the country in research and tourism training. She has authored and coauthored 5 manuscripts in internationally renowned academic journals, and has presented her research internationally.

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