Fair Trade Website Content: Effects of Information Type and Emotional Appeal Type

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Songyee Hur

Graduate Program in Fashion and Retail Studies

The Ohio State University

2014

Thesis Committee:

Leslie Stoel, Adviser

Robert Scharff

Soobin Seo

Copyrighted by

Songyee Hur

2014

Abstract

The primary objective of current research was to explore the impacts of information type and emotional appeal type for fair trade store information presented in the online shopping environment. The Stimulus-Organism-Response (S-O-R) model forms the foundation for investigating the relationships among environmental stimuli

(information type and emotional appeal type), consumer cognitive response (perceptions of information quality), affective response (feelings of pleasure, arousal), and behavioral response (purchase intention).

The design of the proposed model was a 2 information type (concrete vs. abstract)

X 2 emotional appeal type (happiness vs. sadness) between-subjects experiment.

Dependent variables were information quality, pleasure, arousal, and purchase intention.

The findings of this study revealed that information type and emotional appeal type influence consumer’s cognitive and emotional responses that in turn lead to purchase intention. The findings of this study will allow fair trade retailers to develop and manage their online website communication strategies in ways to enhance consumer purchase intention.

Acknowledgments

First and foremost, I would like to take this opportunity to express my profound gratitude to my advisor, Dr. Stoel. I cannot thank her enough for her inspiring guidance, patience, continued support, and encouragement throughout my graduate program and the course of this thesis. Without her, this thesis would not have been possible. She has always offered constructive suggestions and a tremendous amount of feedback while allowing me to work in my own way. Her illuminating views and fresh perspectives, in addition to her pain-staking effort to proofread my drafts, have been greatly appreciated.

Her exemplary guidance will take me far on my imminent journey toward completing my

PhD. One simply could not wish for a better advisor.

I offer my sincere appreciation for the learning opportunities provided by the members of my master’s committee: Dr. Robert Scharff and Dr. Soobin Seo. I appreciate their contributions of time and their helpful career advice, which made my research experience both productive and stimulating. While it is not feasible to mention everyone here, I would like to thank a number of people at Ohio State, especially my fellows,

Boram Park, Pielah Kim, Hyejin Park, and Euisun Lee for their friendship and support along the way. Their care and encouragement helped me overcome setbacks and stay focused, even during the toughest times of my master’s pursuit. iii

Last, but not least, none of this would have been possible without the support of my family: my father (Changsik Heo), my mother (Kemja Park), my sister (Boram Heo), my brother (Sejin Heo), and my brother-in-law (Hyeongro Lee). I would especially like to express my heartfelt gratitude to my parents, to whom I have dedicated this thesis, for their unconditional support both financially and emotionally throughout this endeavor.

Vita

December 1987 ...... Born in Daegu, Korea

February 2006 ...... B.S., Kyungpook National University Major: French Language and Literature Daegu, Korea

August 2012 to present ...... M.S., The Ohio State University Major: Fashion and Retail Studies

Fields of Study

Major Field: Fashion and Retail Studies

Table of Contents

Abstract ...... ii

Acknowledgments ...... iii

Vita ...... v

List of Tables ...... x

List of Figures ...... xii

Chapter 1: Introduction ...... 1

1.1. Introduction ...... 1 1.2. Problem Statement ...... 5 1.3. Purpose of the Study ...... 8 1.4. Significance of the Study ...... 8 1.5. Definition of Terms ...... 9

Chapter 2: Literature Review ...... 11

2.1. Fair trade ...... 12 2.1.1. Overview of Fairtrade ...... 12 2.1.2. Fairtrade Certification ...... 13 2.1.3. Fairtrade Marketing Principles ...... 16 2.1.4. Problems Related to Using Persuasive Fairtrade Principles to Influence Purchasing Behavior ...... 18 2.1.5. The Impact of Fairtrade Websites Attributes on Consumers ...... 19 2.2. Concrete and Abstract Information ...... 22 2.2.1. Overview of Concrete and Abstract Information ...... 22 vi

2.2.2. Effect of Concrete and Abstract Information in Consumer Behavior ..... 26 2.3. Emotional Appeals ...... 30 2.3.1. Overview of Emotional Appeals ...... 30 2.3.2. Emotional Appeals (Happiness vs. Sadness) ...... 31 2.4. Theoretical Framework ...... 33 2.4.1. The S-O-R Model ...... 33 2.4.2. Application to Fairtrade Context ...... 37 2.4.3. Proposed Model and Hypotheses ...... 38 2.5. Hypothesis Development ...... 41 2.5.1. Effect of Information Type ...... 41 2.5.2. Effect of Emotional Appeal (Happiness vs. Sadness) ...... 45

Chapter 3: Pilot Study and Main Study ...... 50

3.1. Pilot Study ...... 52 3.1.2.. Method ...... 52 3.1.2. Analysis and Results ...... 63 3.2. Main Study ...... 71 3.2.1. Method ...... 71

Chapter 4: Data Analyses ...... 82

4.1. Data Analyses and Results ...... 84

Chapter 5: Discussion and Conclusion ...... 105

5.1. Discussion of Findings from Main study ...... 106

5.2. Conclusions and Implications ...... 113

vii

5.2.1. Theoretical Implications ...... 113 5.2.2. Managerial Implications ...... 115

5.3. Limiations of the Study ...... 118

5.4. Future Research ...... 120

References ...... 121 Appendix A: A List of Countries and Their Fair Trade Marks ...... 132 Appendix B: IRB Human Subject Review Exemption Approval for Pretests ...... 134 Appendix C: IRB Human Subject Review Exemption Approval for Main Study ...... 136 Appendix D: Pilot Study Consent Form ...... 138 Appendix E: Pilot Study Survey ...... 140 Appendix F: Pilot Study Stimuli ...... 146 Appendix G: Main Study Consent From ...... 150 Appendix H: Main Study Survey Example ...... 152 Appendix I: Main Study Questionnaire ...... 155 Appendix J: Main Study Stimuli ...... 163 Appendix I: Results of t-tests for Fair trade Shopping Experience ...... 165

viii

List of Tables

Table 2.1. Concrete vs. Abstract research ...... 29

Table 2.2. Summary of Hypotheses ...... 40

Table 3.1. Instruments for Pilot Test ...... 61

Table 3.2. Pilot Test Sample Profile: Demographic Characteristics ...... 64

Table 3.3. Results of Reliability Tests for Information Type and Emotional Appeal ..... 65

Table 3.4. Item Total Statstistics Reliability Tests for Information Type and Emotional

Appeal Type Minipulation Check ...... 66

Table 3.5. Results of a Serise of T-tests for Information Type Minipuation Check ...... 68

Table 3.6. Image Rating of Happiness and Sadness Appeal Type ...... 69

Table 3.7. Correlation Results for Impression about Artisan Pilot Test ...... 70

Table 3.8. Means Results for Impression about Artisan Pilot Test ...... 70

Table 3.9. Previous Studies with Stimulus-Organism-Response Model ...... 74

Table 3.10. Items for Dependent Variables ...... 79

Table 4.1. Summary of Analyses ...... 83

Table 4.2. Main Study Sample Profile: Demographic Characteristics ...... 85

Table 4.3. Online Fair Trade Shopping Experience ...... 86

Table 4.4. Results of a Series of T-tests for the Information Type Manipulation Check . 87

Table 4.5. Levene’s Test of Equality of Error Variances ...... 89

Table 4.6. Skewness and Kurtosis Values ...... 89

Table 4.7. Results of ANCOVA Analysis for the Main Study ...... 92

Table 4.8. Results of ANCOVA Analysis for Hypotheses 3a and 3b ...... 94

Table 4.9. Model for Multiple Regression Analysis for Hypotheses 4a and 4b ...... 96

Table 4.10. Coefficients for First Stage Multiple Regression Analysis for Hypotheses 4a and 4b ...... 98

Table 4.11. Coefficients for Second Stage Multiple Regression Analysis for Hypotheses

4a and 4b ...... 99

Table 4.12. Model for Multiple Regression Analysis for Hypotheses 5, 6a, and 6b ...... 100

Table 4.13. Coefficients for First Stage Multiple Regression Analysis for Hypotheses

5, and 6b ...... 100

Table 4.14. Coefficients for Second Stage Multiple Regression Analysis for Hypotheses

5, and 6b ...... 100

Table 4.15. Summary of Results of the Hypothesis Testing ...... 102

List of Figures

Figure 2.1. The Mehrabian – Russell Model ...... 36

Figure 2.2. Proposed Model for Main Study ...... 39

Figure 3.1. Summary of Pilot Test ...... 51

Figure 3.2. Information Type Minipulations Used in Pilot Test ...... 54

Figure 3.3. Emotional Appeal Type Minipulations Used in Pilot Test ...... 56

Figure 3.4. Information and Emotional Appeal Manipulations ...... 72

Figure 3.5. The Order of Survey Pages ...... 81

Figure 4.1. The Results of Hypotheses 1, 2a, and 2b ...... 92

Figure 4.2. The Results of Hypotheses 3a and 3b ...... 94

Figure 4.3. The Results of Hypotheses 4a and 4b ...... 97

Figure 4.4. The Results of Hypotheses 5,6a and 6b ...... 101

Figure 4.5. Summary of Results of the Hypothesis Testing ...... 103

Chapter 1: Introduction

1.1. Introduction

Fair trade is a system of voluntary certification and labeling of products that are obtained from producers who meet the international standards of fair trade principles.

The goals of fair trade principles encompass: 1) ensuring fair prices and a stable income for producers, 2) improving the working conditions of the workers, and 3) building long- term trade relations that allow proper production planning and practices for marginalized producers (Strong, 1997). As such, the overall concept implies that fair trade is concerned with improving the trading relationships for producers, and it serves as a marketing function for changing consumer behavior.

Today, fair trade marks exist in 24 countries across Europe, Asia, and the

Americas (FLO, 2014) (See Appendix A). After success with coffee, fair trade organizations have expanded the product mix to include grocery items, apparel, crafts and some textiles. Before a product can be labeled and sold as a fair trade product in the marketplace, it receives an evaluation of its compliance with fair trade principles through a certification system. Consumers learn that products are produced using fair trade principles via the use of certification labels; Fair Trade International (FLO) is an organization that sets fair trade standards and certifies consumer labels at an international

level (Moor, 2004). The FAIRTRADE mark (certified by Fair Trade International) is an independent guarantee that ensures consumers that fair trade products they consume will provide economic and societal benefits to the producers in the developing world (FLO,

2014).

For a number of years, consumers have been expressing their concerns about societal issues by means of their consumer behavior. Their actions demonstrate that they value social responsibility; marketers have thus created niche markets for various types of ethical goods such as fair trade products (Nicholls, 2002; Goig, 2007). These social movements are evidenced in the dramatic rise in demand for fair trade products

(Ozcaglar-Toulouse, Shiu, & Shaw, 2006). In recent years, sales of fair trade products have experienced exponential growth, even when all business sectors were hit hard by the recession. Global sales for fair trade products were up 15% between 2008 and 2009. In

2011, fair trade sales reached $6.6 billion, which represented a 590% increase over 2004

(FLO, 2014).

Over the past decade, the Internet has become an effective business channel for selling products. According to the U.S. Census Bureau, total online sales were 4.7 % of retail sales in 2011, up from 4.3 % in 2010. U.S. online sales for 2013 totaled $ 69. 2 billion and the growth rate reached 276% over 2004 (U.S. Census Bureau, 2012). To capitalize on these promising trends, it has been suggested that online retailing is a new venue for promoting and purchasing fair trade products, and for fair trade retailers who turn to the web to sell, it is important that they understand the key website elements that facilitate the satisfaction of consumer shopping needs (Halepete & Park, 2006).

Previous research has shown that information is an integral part of the success of a website’s business. Through their websites, retailers can create, communicate, and deliver value to consumers (Maignan & David, 2002). A unique characteristic of e- commerce environments is that they allow retailers to provide consistent content and high-quality information to consumers (Liu & Arnett, 2000). In an online shopping environment, the perceived quality of the information offered by a company can influence an individual’s evaluation of a product, increase consumer satisfaction and purchase intention, and reinforce the company’s ongoing relationship with its customers

(Hoffman & Novak, 1996; Kim & Lennon, 2009; Shim, Yong, & Nottingham, 2002).

Moreover, information about ethical issues (e.g., human rights, labor conditions, animal well-being, or environmentally friendly products) and the quality of the information provided have been highlighted as drivers that influence consumption behaviors (Martin & Simintiras, 1995). Scholars have found evidence that information is an antecedent of ethical beliefs and a determining factor in consumers’ attitudes and behavior formation (Shaw & Clarke, 1999). For instance, Pelsmacker and Janssens

(2006) found that the quality of ethical information affects attitudes towards fair trade and, thus, indirectly, buying behavior. The findings suggest that the provision of ethical information impacts purchasing decisions and behaviors.

Reflecting these insights, developing a socially oriented attitude among consumers has been emphasized as an important strategic objective of fair trade communication. The prime goal of fair trade is to raise awareness about social ideas in ways that will change individual behavior and thus benefit marginalized workers in developing worlds; the goal is not to benefit the sponsoring organization (Andreasen, 3

2002; Kotler & Zaltman, 1971). Promotions appeal to consumers’ values and their desire to support better conditions for workers and encourage consumers to use their purchasing power (Renard, 2003). On fair trade websites, artisan stories communicate the quality-of- life changes resulting from their participation in fair trade commerce (Littrell, Ma, &

Halepete, 2005). Therefore, the intent of providing the social dimension of fair trade information is ultimately to change consumer behaviors by evoking consumer social consciousness as a worthwhile outcome.

However, how to translate fair trade principles into purchase behaviors has been a major concern for fair trade retailers. Thus, research has stressed the importance of offering complete information, visual images, and an increased marketing voice for the artisans/producers of fair trade goods in order to persuade shoppers to buy (Lee &

Littrell, 2003). Shaylor (2001) urged the Fair Trade Foundation to be clear about how it communicates the benefits of fairly traded products.

In the commercial marketing context, emotional appeals (i.e., happiness, sadness) and information characteristics (i.e., concreteness, abstractness) have been recognized as effective persuasion techniques. Emotional appeals focus on the emotions evoked when consumers see an ad for the advertised product (Bagozzi, Gopinath, & Nyer, 1999). The goal is to elicit either negative or positive emotions that lead to purchase motivation through the use of different emotional execution styles (e.g., happiness, sadness). With respect to information characteristics, information concreteness is the degree of precision and specificity provided in a communication (Mackenzie, 1986). In contrast, information abstractness refers to broad or vague ideas or concepts; there are no physical referents involved (Gorman, 1961). Research suggests that emotional appeals and the 4

characteristics of the information provided influence the consumers’ responses to promotional communications, their attitudes, and their purchase behaviors (Ahearne,

Gruen, & Kim, 2000; Sawyer & Howard, 2000; Yana, Hyllegard, & Blaesi, 2012).

In recent years, many researchers have employed emotional execution styles (e.g., happiness, sadness) and information characteristics (e.g., abstractness, concreteness) to induce consumer responses. Based on the logic of the Stimulus-Organism-Response model (Mehrabian & Russell, 1974), the current study suggests that the information provided by the fair trade store has a different effect depending upon the type of persuasive techniques used (i.e., information characteristics and emotional appeals), the impact of the emotional responses, the cognitive reactions, and the purchase behaviors.

Although much research has discussed the effectiveness of the impact that emotional appeals and information characteristics have on consumer responses, the results about which types of information characteristics and emotional appeals are most effective in increasing consumer responses are inconclusive.

1.2. Problem Statement

Despite the growing sales of fair trade products, research suggests that fair trade retailers face difficulties in translating fair trade principles into messages that increase consumption behavior. Many studies have documented the important role of information in promoting ethical products, however, little research has empirically examined how different types of fair trade store information can persuade consumers to buy more fair trade products. Past studies noted that mistrust about the fair trade concept, due to 5

incorrect information, a lack of high-quality information or insufficient available information for consumers can undermine consumer interest (Carrigan & Attalla, 2001;

Maignan & Ferrell, 2004). Carrigan and Attalla (2001) and Titus and Bradford (1966) found that the inappropriately communicated information may fail to attract consumers to purchase ethical products. Lee and Littrell (2003) suggested that providing cultural information related to fair trade artisans in addition to product descriptions with greater appeal would produce an effective promotional strategy. However, despite the important implications for retailers to effectively communicate societal benefits of fair trade principles to consumers, little attention has been focused on how information characteristics used on fair trade websites can influence purchase decisions. Thus, there is a need to examine behavioral outcomes that are a consequence of fair trade information provided on retail websites.

Research on commercial marketing has focused on the effects of emotional appeals. Various emotional appeal styles have been examined, as crucial cues affect consumers’ emotional and/or cognitive states and consequently influence consumers’ shopping behaviors. The current literature reveals mixed results for the effectiveness of negative emotional appeals (e.g., sympathy) (Cialdini & Fultz, 1990; Wang, 2008) and positively-valenced emotional appeals (e.g., happiness) in encouraging altruistic behaviors. While emotions play an important role in the formation of attitudes and judgments (Edell & Burke, 1987; Royo-Vela, 2005; Faseur & Geuens, 2006), clearer findings are needed on which emotion helps consumers to make a purchase decision regarding fair trade products. In spite of the significance of identifying and understanding consumers’ behavioral outcomes resulting from exposure to distinct emotional execution 6

styles, the effects of emotional appeals on consumers’ emotional reactions and behavioral responses have not been studied much, and little research has examined the context represented by fair trade shopping. Therefore, it would be valuable to investigate the effects of specific types of emotional appeals on consumers’ emotional-cognitive states and consequently on positive shopping outcomes.

Concerning information characteristics (i.e., concrete and abstract information), early scholars unequivocally agreed that concrete information has a more positive impact on individual responses than does abstract information (Holmes & Langford, 1976;

Dawn, 1974). Nevertheless, subsequent evidence has suggested that the effectiveness of concreteness may vary depending on conditions such as frequency of words, the order of representation and the availability of contextual information (James, 1975; Johnson,

Bransford, Nyberg, & Cleary, 1972; Kroll & Merves, 1986). The inconclusive results from more recent studies indicate that there is a need to reevaluate generalizability of the effects of concrete and abstract information. Moreover, the effect of information concreteness and abstractedness on judgment has received much attention, but little consideration has been given to empirical research on attitudes and behaviors. To address this state of affairs, a major aspect of our study investigates the effects of information concreteness and abstractness on the generalizability of concreteness effects on consumption behavior. Further examination will be valuable in enhancing our understanding of consumer behavior in relation to the possibility that such information can be used as a means to increase purchase intent.

1.3. Purpose of the Study

This study will identify communication strategies that induce positive attitudes towards shopping for fair trade products on the Internet. The purpose of the study is (1) to test the roles of information type (i.e., concrete, abstract) and emotional appeals (i.e., happiness, sadness) on cognitive-affective reactions; (2) to assess the influence of cognitive states (perceived information quality) on consumer response behaviors

(purchase intention, willingness to buy, patronage behavior); (3) to assess the influence of emotional states (pleasure, arousal) on consumer response behaviors (purchase intention,); and (4) to assess the effects of cognitive and emotional states on response behaviors (purchase intention).

1.4. Significance of the Study

This study is expected to make several contributions to the practice of business, and to academia. First, it will test a comprehensive purchase decision model that explains and predicts the behavioral outcomes that are affected by fair trade store information. The present research will articulate the behavioral outcomes resulting from exposure to two information characteristics and two emotional execution styles. A higher quality of information perceived by consumers, and greater pleasure and arousal are expected to influence the likelihood of purchase (Park & Stoel, 2002) in the fair trade context.

Second, based on the results, this study will provide guidance on the characteristics of online store information that attracts fair trade consumers. Of particular 8

significance are findings that lend insight into how the benefits of fair trade products should be communicated and which communication type may influence the decision- making process. The results of our study can also contribute to the fair trade industry through its marketing implications and its recommendations for fair trade website content.

1.5. Definition of Terms

The following terms are used in this study

1. Abstract information: information abstractness refers to broad or vague ideas or

concepts; there are no physical referents involved (Gorman, 1961).

2. Approach behavior: approach behavior is defined as all positive final actions leading

to a particular purchase setting (e.g., intention to stay, explore, and affiliate).

3. Arousal: arousal refers to an emotional state, which explains the degree of stimulation

and excitement of individual feelings.

4. Avoidance behavior: avoidance behavior refers to an unfavorable behavioral

tendency toward particular purchase situation (e.g., intention to leave the store).

5. Concrete information: information concreteness is the degree of precision and

specificity provided in a communication (Mackenzie, 1986).

6. Emotion: emotion is defined as affective states involving feelings and mood. In this

study, pleasure and arousal are expected emotional reactions toward environmental

stimuli that consequently influence consumer response behaviors.

7. Emotional appeal: emotional appeal is a specific persuasion technique to create

emotional responses.

8. Information quality: unformation quality refers to accuracy, reliability, and

completeness of information that is specific to the problem under consideration

(Bailey & Pearson, 1983; DeLone & McLean, 1992; Zmud, 1978).

9. Organism: organism represents affective and cognitive states that intervene in the

relationship between the stimulus and individuals’ responses.

10. Pleasure: pleasure is an emotional state that reflects the degree to happy, contented

and satisfied individual feelings.

11. Stimulus: stimulus represents factors in the environment to which the individual

responds and that influence individual internal cognitive and emotional states.

12. Purchase intention: purchase intention is the extent to which a consumer’s willingness

to buy products and willingness to buy products from a visited website.

Chapter 2: Literature Review

This chapter contains four sections that build the theoretical and conceptual foundations for this study. The literature was reviewed as follows: 1) background literature, 2) literature review in relation to key concepts, 3) theoretical framework, and

4) hypothesis development.

The first section offers an overview of the literature about fair trade. It discusses

1) fair trade principles, 2) fair trade certification, 3) the fair trade retailers’ approaches to marketing, 4) the problems associated with applying fair trade principles to consumer consumption behavior, and 5) online fair trade store information strategies. In the second section, information type and emotional appeals are presented in regards to practical perspectives that can change consumer behavior. It covers 1) literature on information concreteness and abstractness and 2) emotional appeals of happiness and sadness. The third section discusses the S-O-R model and its applications in marketing and consumer research and explains how the S-O-R model is used as a framework for this study. The last section develops the research hypotheses.

2.1. Fair Trade

2.1.1. Overview of Fair Trade

The most widely accepted definition of fair trade is the following:

Fair trade is a trading partnership, based on dialogue, transparency and respect that seeks greater equality in international trade. It contributes to sustainable development by offering better trading conditions to and securing the rights of, marginalized producers and workers- especially in the South. Fair trade organizations are engaged actively in supporting producers, awareness raising and in campaigning for changes in the rules and practice of conventional international trade (FINE, 2001).

It is important to note that the concept of ‘fair trade’ must be clearly differentiated from that of ‘ethical trade’. The latter is a corporate policy that is concerned with the conditions of labor in mainstream production and distribution networks (Clarke, Barnettb,

Cloke, & Malpass, 2007; Nicholls, 2002), while the vision of fair trade suggests a working model of trade relationships that make a difference to the producers and consumers that engage in such trade. Fair trade builds mutual relationships between buyers and producers by generating considerable publicity for the fair trade cause to develop consumer consciousness (Moor, 2004). The other vision of fair trade proposes the modification of a traditional economic model that is focused on concern for the sustainability of workers (Brown & Tiffen, 1992): it is an unusual business practice in that producers are the primary stakeholders, which emphasizes empowerment of artisans through education (e.g., designs and skills), fosters sustainability of workers (e.g., a clean 12

and healthy working environment), and provides financial benefits (e.g., pre-financing to the producers), with the ultimate goals of establishing political and social justice, and developing equitable trading relationships.

2.1.2. Fair Trade Certification

The world’s first fair-trade label, Max Havelaar, was created via collaboration between the Max Havelaar Foundation (Dutch NGO Solidaridad) and a farmer’s organization (UCIRI) in 1988. The Max Havelaar Foundation was launched to foster fair trade principles. A key goal was improvement of the living conditions of small coffee producers in the third world. The Foundation increased demand for fair trade coffee by placing the product in conventional markets; this was done to help the producers cope with a decline in coffee prices in the late 1980s. As a result of this effort, the Max

Havelaar Foundation began labeling coffee with its quality label, Max Havelaar, to generate sustained sales to European consumers. The Max Havelaar label was initially placed on Mexican-grown coffee and was sold in mainstream supermarkets in the

Netherlands (Renard, 2003). The introduction of Max Havelaar enabled many fair trade product types to be sold in conventional stores. Within a few years, similar fair-trade labeling was independently developed from national initiatives across Europe and North

America. European Free Trade Association, the alternative trade organization (ATO), adopted their-own label, which is known as TransFair, and the FairTrade label was born in Great Britain. However, the fair trade movement has encouraged Max Havelaar

Foundation to unite diverse labeling initiatives under one umbrella (now known as Fair 13

Trade Labeling Organization International) to improve truthfulness in marketing and to avoid competition among the labels.

The process of fair trade certification requires a third party certifier to (1) audit the whole supply chain of a product, from initial on-site inspection to the point of consumer purchase, (2) certify that the products meet relevant environmental, labor, and developmental standards of fair trade norms, (3) ensure that products are coming from fair trade certified producers and traders who follow the requirements of fair trade standards, and (4) reassure that consumer purchases are socially and environmentally responsible. There are several fair trade labor certification systems, including Fairtrade

International (FLO), IMO Fair for Life, and Transfair USA.

Fairtrade International (FLO)

In 1997, certification among labeling initiatives was formalized

under the name of Fairtrade International (FLO); three producer

networks and 25 fair trade organizations currently form the FLO.

FLO, a non-profit organization, coordinates fair trade labeling at an

international level, makes fair trade standards uniform, licenses the

use of their fair trade label (FAIRTRADE Certification), and

promotes fair trade justice. The FAIRTRADE Certification Mark is

recognized as being highly credible, reaching the highest standards

for defining ethical trade. The certification mark is found on

bananas, cocoa, coffee, cotton, flowers, fresh fruit, gold, honey,

juices, rice, tea, wine and sports balls (FLO, 2014).

IMO Fair for Life

Fair for Life is a brand neutral third party certification, established by the Swiss Bio-Foundation and the Institute for Marketecology

(IMO) in 2006. IMO is a certifier of the Fair for Life label and is responsible for product inspection, certification and quality assurance. The Fair for Life certification program was created to expand the fair trade market internationally and to increase the types of fair trade products sold. The Fair for Life certification program combines social accountability and fair trade standards.

The Fair for Life label is applied to more than 500 products across food (honey, wine, and crops) and non-food commodities

(cosmetics, textiles, forest, wood, and paper), as well as tourist services (FairforLife, 2014).

FairTrade USA (formally TransFair)

FairTrade USA, a nonprofit organization, is the leading third-party certifier of fair trade products in the U.S., and a member of

Fairtrade International (FLO). FairTrade USA audits transactions

between U.S. corporations offering certified fair trade products and their international suppliers, educates consumers, helps retailers to gain the fair trade mark, and provides a better standard of living for farmers (FairTradeUSA, 2014).

2.1.3. Fair Trade Marketing Principles

Considerable attempts have been made to translate fair trade principles into consumer purchasing behavior. These efforts are centered on promoting fair trade principles (e.g., a marketing campaign) to persuade consumers to make purchases. In terms of strategic insights, it has been recommend that marketers increase the amount of information they provide about fair trade principles in order to raise awareness about and understanding of fair trade issues. In this promotional approach, marketers induce consumer moral duty to share responsibility in providing for the needs of producers

(Weber, 2007). For instance, a fair trade retailer may appeal to the consumer’s social conscience by communicating that consumer purchases will contribute to the well-being of disadvantaged producers in the developing world (Clarke, Barnett, Cloke, & Malpass,

2007).

There is some evidence to suggest that raising awareness about fair trade issues may motivate consumers to buy. Studies in the context of consumer ethical behavior have examined the important role that ethical information has on consumer responses (Shaw &

Clarke, 1999). These studies have found evidence that consumers process information they are exposed to and this may affect their beliefs, attitudes, and feelings, and eventually their intention and behaviors (e.g., Chan, 2002; Shaw & Clark, 1999; Shaw &

Shiu, 2002, 2003; Vermeir & Verbeke, 2006). Shaw and Clark (1999) identified the causal relationships among information, ethical belief formation, and behavioral intention. Their results showed that the use of ethical information that is embedded in trustworthy labels and advertising increased an individual’s belief about ethical issues, 16

which then influenced behavioral intention. Moreover, the study found that ethical information evoked feelings in some consumers. Thus, prior research finds evidence that information about ethical issues can influence consumer attitudes and behaviors.

However, consumers also consider the quality of the information being presented before or at the time of purchase. Pelsmacker and Janssens (2006) found that quality of information is a critical factor affecting the fair trade buying process. They suggested that the provision of more sufficient, controllable, and credible information about the fair trade issue influenced consumer attitude about the fair trade concept, and indirectly affected buying behavior.

A major element of “fairness” in the fair trade concept, that consumers in developed economies pay a premium price to support the artisans in developing economies, is not an attribute that consumers can experience directly through consumption (Potts, 2004; Weber, 2007). Therefore, it has been suggested that marketers provide higher quality information in order to reduce the information asymmetry that can occur between sellers and buyers; information asymmetry might result in a failure to act sustainably (Goworek, Fisher, Cooper, Woodward, & Hiller, 2012), thus, decreasing asymmetry is desirable.

2.1.4. Problems Related to Using Persuasive Fair Trade Principles to Influence

Purchasing Behaviors

Some studies have suggested that marketers face difficulties when trying to translate fair trade principles into their promotional materials in order to influence consumption behavior. The most commonly identified problems are the consumers’ lack of understanding about the concept of fair trade and consumer mistrust.

A lack of understanding about of the concept of fair trade has been found to be a barrier to consumption behavior in the fair trade market (Giovannucci & Koekoek, 2003).

Several studies have found that consumers do not understand the concept of fair trade

(Castaldo, Perrini, Misani, & Tencati, 2009; Potts, 2004; Weber, 2007). According to reports, 84% of consumers did not understand the meaning of the term fair trade that was found on a fair trade foundation certification (Fair-trade Foundation/MORI, 2000). A more recent study conducted in 2007 has suggested that the second most common reason that people do not buy fair trade products was because they do not understand the fair trade system (Gebben & Gitsham, 2007). Presumable explanations for this lack of understanding could be the consumers’ confusion about the complexity of the labels as well as the presence of incorrect information and the lack of high quality information

(Carrigan & Attalla, 2001; Maignan & Ferrell, 2004). In their study of ethical issues influencing consumer purchase behaviors, Carrigan and Attalla (2001) emphasized in the implication section that future ethical marketing information has to be conveyed in a manner that does not confuse or alienate future consumers.

There is an evolving concern that consumer mistrust can undermine interest in fair trade products, ultimately affecting buying behavior. This consumer mistrust is related to fair trade accountability and transparency issues. For example, in a study that investigated consumer perspectives on sustainable clothing, Goworek et al. (2012) noted that some participants expressed their mistrust towards fair trade retailers’ motives and their honesty in offering fair trade clothing. In the same study, consumers also revealed that a label did not prove if the fair trade practice promoted by the retailers was accurate.

Taken together, information about fair trade issues must be delivered to consumers accurately and clearly in an easily communicable form. The available study results indicated that incomplete information and information that is not easy to understand are perceived to be barriers to purchasing ethical products (Shaw & Clarke,

1999). Therefore, the quality of the information is thought to be very important to consumers in determining their attitudes and behavior (Pelsmacker & Janssens, 2006;

Strong, 1997).

2.1.5. The Impact of Fair Trade Website Attributes on Consumers

Research on website design suggests that the information offered by Internet retailers can play a critical role in a consumer’s decision-making process. The successful provision of information on a website can satisfy the consumers’ need for adequate information as they make purchasing decisions (Szymanski & Hise, 2000) and, consequently, this affects the consumers’ behavioral intentions (e.g., intent to use, intent to recommend, and intent to prefer that website over other websites). 19

For fair trade-related websites, several studies examined the use and the significance of the information included on the website. Lee and Littrell (2003) and

Halepete and Park (2006) investigated the specific nature of information presented on fair trade-related websites. Based on a content analysis, Lee and Littrell (2003) identified 101 fair trade retail websites that provided information comprising five categories. The elements include: information about company (e.g., company location), product information (e.g., the number of products offered, product image), cultural information about artisan, craft media (e.g., wood, fabric), types of crafts (e.g., home decoration items, clothing), and transaction and fulfillment information (e.g., price, shipping method). The study suggested that multiple facets of fair trade store information need to be improved: product information such as, size, weight, handmade, price, and care requirements; cultural information about production processes; and artisan information about the country, the artisan’s family and their daily lives. Lee and Littrell (2003) examined only fair-trade related websites and their suggestions were based on subjective evaluations of good website practice. Their findings are echoed in Halapete and Park’s

(2006) later study comparing fair-trade to traditional, commercial websites.

Using a benchmarking approach, Halepete and Park (2006) conducted research on information availability and website management. The study compared 28 fair trade websites and 28 commercial websites based on performance measures developed from benchmarking studies of successful online commercial retailers. The authors suggested that there are five information dimensions that form the core of fair trade website management: company information, product information, distribution channels, customer service, and website structure and media service. They found that fair trade retailers 20

presented a limited amount of information online in comparison to commercial retailers.

The information that was not readily available included company profile information; some product information about fiber content, color description, and product care requirements; and service options, such as shipment and order processing information.

The findings of their study showed that deficiencies exist in the fair-trade websites in the particular areas of the store information compared to commercial retailers’ websites.

Lee and Littrell (2005) followed up their 2003 study with another that identified the determinants of purchase intention for fair trade-related products in an online shopping context. Their results showed that information quality was an important website attribute for selling fair trade-related products on online shopping sites. In this study, information quality was measured followed by: information about the country and culture, information about where the products are made, information about the artisans, pictorial ideas for how the products can be used or worn, company information, and product relevant information. However, the results indicated that information quality did not significantly influence the consumers’ intention to shop for fair trade-related (e.g., cultural) products on the Internet. The results of the study differed from the common belief found in the fair trade literature, which posits that insufficient amounts of higher quality information may result in the consumers’ failure make a purchase (Lee & Littrell,

2005). Therefore, mixed findings on the effectiveness of high quality website information highlight the need for additional examination.

In summary, previous studies have identified product, store, or other information that is important for fair trade retailers to provide to their target customers. Research has suggested that retailers accommodate such attributes in order to foster favorable 21

behavioral outcomes. However, relatively little research was found that empirically examines the impact that attributes of fair trade information has on consumer behavior.

This gap suggests a need to investigate the characteristics of the information that contributes to the formation of a positive attitude in consumers and impacts their behavior, especially for fair trade products. The following section provides more details about characteristics of information and explains the logic of the information content properties that are used to persuade consumer behavior.

2.2. Concrete Information and Abstract Information

2.2.1. Overview of Concrete Information and Abstract Information

For several decades, social sciences research has focused on the comparison of the effects of information presented as concrete language versus information presented using abstract language. Concrete information refers to the use of details and specificity of objects, actions, outcomes, and situational contexts (Mackenzie, 1986). In contrast, abstract information refers to broad or vague ideas or concepts; there are no physical referents involved (Gorman, 1961). The concepts of “concrete” and “abstract” exist on opposite ends of a continuum that has a varying level of directness or specificity in the meaning of the words. The distinction between the effects of concrete and abstract information on individual behaviors is well documented. A sufficient number of studies have identified the relative effects of concrete and abstract information on cognitive domains: learning (Epstein, 1962), recall (Stokea, 1929), memory recognition (Gorman, 22

1961; Marschark & Paivio, 1977), readability (Borgida & Nisbett, 1977), comprehension, and decision-making (Pettusa & Diener, 1977).

Prior literature has shown that performance is consistently better for concrete information compared with abstract information. Traditionally, dual-coding theory (DCT;

Paivio, 1971a; 1991b) has been used to account for concreteness effects. According to this theory, concreteness has a natural advantage over abstract language because of the richer visual representation (or imagery) of concrete words (Begg & Paivio, 1969). The superiority of concreteness effects is explained by the fact that concrete language, whereby information is stored mentally in two codes (verbally and visually) should be better comprehended than abstract language, whereby information is stored in only one code (verbally).

A review of the literature that compared the effects of concrete and abstract information has revealed that concrete information has a more profound influence on memorability (Begg & Paivio, 1969; Bower, 1972; Paivio, 1971; Yuille & Paivio, 1969) and comprehension (Dawn, 1974; Holmes & Langford, 1976; Schwanenflugel & Shoben,

1983) than does abstract information. In an earlier study, Paivio (1965) found that concreteness, imagery, and meaningfulness were significantly related to recall ability.

The words pairs that cued recall (of the second word, cued by the first) decreased in effectiveness to do so in the following order: (1) concrete- concrete (CC), (2) concrete- abstract (CA), (3) abstract-abstract (AA), and (4) abstract-concrete (AC), suggesting that concrete language was a stronger predictor of recall compared to abstract language. This provided the theoretical foundation to establish that concrete language that readily evokes stronger mental images can serve to organize and unify information to facilitate recall in 23

memory. Using a meaningfulness task, Holmes and Langford (1976) examined the distinction between concrete and abstract sentences with regard to ease of comprehension.

They found that concrete sentences were comprehended significantly faster than abstract sentences. This study first showed the existence of concreteness effects in comprehension

(cf. Paivio and Begg’s conclusion).

Other research has attempted to identify how other factors, including familiarity, interestedness, and comprehension, are recalled in concrete and abstract information.

Across empirical results, Sadoski, Goetz and Fritz (1993) found that concrete paragraphs were rated as more interesting, more comprehensible, and were remembered better than abstract paragraphs. Although no difference was found with regard to familiarity, the study suggests that interest can serve as a mediator between concrete information

(independent variable) and comprehension and recall (dependent variables).

A second line of research has suggested that the effects of concrete information are problematic (Marschark, Richman, Yuille, & Hunt, 1987; Ransdell & Fischler, 1989;

Wattenmaker & Shoben, 1987). This line of thought is critical in that it asserts that concreteness effects have been confounded with other variables such as 1) inadequate comprehension of the abstract sentences (Johnson, Bransford, Nyberg, & Cleary, 1972),

2) frequency of words (James, 1975), 3) the order of representation (Kroll & Merves,

1986); and 4) the availability of contextual information. This suggests that when the properties of concrete information are properly manipulated, they have little or no difference between concrete and abstract information.

In treatment effects, Johnson, Bransford, Nyberg and Cleary (1972) found that the abstract sentences used in the previous studies (Begg & Paivio, 1969) were far more 24

difficult to comprehend than the concrete sentences. They argued that differential levels of comprehensibility have resulted in comparatively less meaning for abstract sentences than for concreteness. They concluded that without differentiating between the initial comprehension difficulty in the abstract and concrete sentences, the dual-coding hypothesis fails to explain the concreteness effects.

Others studies have shown that the effect of concreteness in lexical decisions is extremely sensitive to the word-use frequency and order of presentation. James (1975) found that response times were faster for concrete than for abstract low-frequency nouns.

However, no effect was found regarding concreteness for high-frequency nouns.

Similarly, Kroll and Merves (1986) found that only in conditions in which abstract words were followed by concrete words were lexical decisions for abstract words significantly longer than those for concrete words. When abstract words preceded concrete words, there was not a reliable difference between the two word types in lexical decision time.

The context-availability theory (Kiera, 1978) provides a general explanation for the lack of a concreteness advantage in comprehension (Ransdell & Fischler, 1989;

Wattenmaker & Shoben, 1987; Einstein, McDaniel, Bowers, & Stevens, 1984). This theory states that concreteness effects are minimized when the information is presented in a context, because concrete language has contextual information that is readily available.

Conversely, comprehension in the abstract language is improved in the presence of contextual information, because contextual information is not readily available in abstract language. For example, Pezdek and Royer (1974) found a clear effect of contextual- availability in recognition for abstract sentences. When subjects received a prior presentation of context paragraphs with abstract sentences, recognition memory for 25

meaning changes increased. However, the context did not exert these effects for concrete sentences. Similar results have also obtained by Schwanenflugel and Shoben (1983).

Reading time for the concrete sentence (i.e., concrete, abstract) was only higher in the absence of context condition. However, reading time did not differ when a preceding context was available.

2.2.2. Effect of Concrete and Abstract Information in Consumer Behavior

In consumer research, several studies have discussed the distinction between concrete and abstract information in the decision-making process. According to Wright

(1979), concrete information increased consumers’ planning behavior (or active action- planning). In a field experiment, consumers were shown a brief TV advertisement regarding purchasing drugs (antacids), which contained two information types: It contained either accurate information that was combined with concrete language and a verbal action recommendation, or information from which concrete language was excluded. The results indicated that the first condition encouraged shoppers more: 1) in tendencies to read in-store warning signs, and 2) total time spent on package inspection time. However, purchase behavior (i.e., willingness to buy antacids) was not reflected in the effects on package inspection times. In a fair-trade consumption situation, d’Astous and Mathieu (2008) measured recall and amount of money spent after providing concrete and abstract information to consumers. They found that the difference between these two types of information was statistically significant. Although consumer recall of the concrete information was significantly higher relative to recall of the abstract 26

information, consumers unexpectedly spent less money when influenced by concrete information than when affected by abstract information. The findings of this study provide important support for the concrete effect on cognitive state (recall); however, d’Astous and Mathieu (2008) failed to maintain the generality in the concrete and abstract information. First, the properties of concrete and abstract information in terms of the amount of information, and the credibility of information were not matched. This failure in controlling such factors may account for the lack of a clear relationship between cognitive state (recall) and behavioral response (actual money spent).

Based on the above two study results, the concrete and abstract nature of information can effectively change some types of consumer behavior. However, concrete effect had little or no impact on actual purchasing behavior in either case (d'Astous &

Mathieu, 2008; Wright, 1979). Interestingly, information abstractness was recognized as a more influential predictor in purchasing behavior (d'Astous & Mathieu, 2008). The results of the studies discussed indicate that there is a need for additional examination of concreteness and abstractness effects. First, it is not clear whether the differences between concrete and abstract information, with regard to effects on consumer behaviors, exist more generally because concrete language was only used in the manipulations in the study by Wright (1979). Second, the concrete-abstract dimension affecting the behavioral influences must be re-evaluated in terms of methodological grounds. In the study by d’Astous and Mathieu (2008), the properties of information, the amount of information, and the credibility of information were not fully controlled.

In summary, prior research indicates that there appears to be a discrepancy regarding the effects of concrete versus abstract information. Major limitations in the 27

previous studies examining the effects of concreteness appear to be related to research methodology in manipulating the effects, and less generalizable empirical results regarding behavioral responses. Therefore, there is a need to reevaluate the effects of concrete and abstract information with a robust study of consumer behavior.

Discrepancy between Context dependency between Concreteness Cognitive Concreteness and Abstractness and Abstractness

Recall Stoke, 1929; Dukes & Bastian, 1966 Ransdell & Fischler, 1989; Marschark & Hunt, 1989; Wattenmaker & Shoben, 1987; Keiras, 1978

Memory recognition Gorman, 1961; Sadoski, Goetz, & Fritz, Marschark & Hunt, 1989; Einstein, 1993; Paivio, Walsh, & Bons, 1994; Begg & McDaniel, Bower & Steven, 1984 Paivio, 1969; Bower, 1972; Paivio, 1971; Yuille & Paivio, 1969 Recognition speed Borkowski, Spreen, & Stutz, 1965; Riegel & Riegel, 1961;

Readability Borgida & Nisbett, 1977 Keiras, 1978

Comprehension Holmes & Langford, 1976; Dawn, 1974; Schwanenflugel & Shoben, 1983

Learning Epstein, 1962; Paivio, 1963 Marschark & Hunt, 1989

Lexical decisions Pettus & Diener, 1977 James, 1975; Kroll & Merves, 1986

Table 2.1. Concrete vs. Abstract Research 2.3. Emotional Appeals

2.3.1. Overview of Emotional Appeals

Emotional appeals are defined as specific persuasion tools used to elicit either negative or positive emotions leading to purchase motivation (Sciulli & Bebko, 2005;

Lerner & Ketlner, 2000; Kempa, Kennett-Hensela, & Kees, 2013). The effects of emotional appeals have been found to change consumer attitude and behavior (Edell &

Burke, 1987; Rossiter, Percy, & Donovan, 1991). While the impact that emotional appeals have on persuasion is well established in advertising and consumer behavior literature (Aaker & Williams, 1998; Burke & Edell, 1986; Edell & Burke, 1987;

Holbrook & Batra, 1987), the key question has been whether their relative importance depends upon the type of emotional appeal used (Panda, Panda, & Mishra, 2013).

The two most distinct types of emotional appeals that can be found in previous literature are: (a) positive emotional appeals, which are designed to create positive feelings that the consumer experiences when using the product (e.g., happiness, love, and joy) and negative emotional appeals, which are designed to generate anxiety in people so that they believe that if they do not change their behavior something terrible will happen

(e.g., sadness, sympathy, fear, guilt) (Puto & Wells, 1984).

2.3.2. Emotional Appeals (Happiness vs. Sadness appeals)

The happiness appeal is one of the positive emotions that is most frequently used by advertisers (Mogilner, Kamvar, & Aaker, 2013). The prime goal of the happiness appeal is to cultivate pleasure by creating a joyful or cheerful image of the product in the mind of the consumer (Isen, Labroo, & Durlach, 2004; Mogilner & Aaker, 2009). The term ‘happiness’ is defined as “a state of well-being and contentment; a pleasurable or satisfying experience” (Merriam-Webster Dictionary, 2009). Happiness appeals involve eliciting in consumers the feeling that product consumption will have an immediate effect on enhancing their pleasure and enjoyment. The examples of embedded happiness appeals in commercial marketing are numerous. For instance, Nivea clams that its body lotion provides a “happy sensation”.

Another type of emotional appeal, the sadness appeal, is becoming more popular as a persuasion technique. Previous literature has noted that sadness appeals play an important role in eliciting a feeling of ‘sympathy’ (Small & Verrochi, 2009). Sadness is defined as a feeling of loss, which occurs when an individual recognizes that he/she has low coping power in relation to a particular situation (Bagozzi, Gopinath, & Nyer, 1999).

On the other hand, sympathy encompasses concern for others, fostering feelings of affiliation and connectedness (Oveis, Horberg, & Keltner; Wispé, 1986). Sympathy can also fall into the category of negative emotions, like sadness, which can directly or indirectly have an influence on choices and decision-making.

Some studies have examined sadness appeals that lead to the feeling of sympathy and to positive attitudes towards pro-social behaviors (Cialdini & Kenrick, 1976). In such 31

cases, sadness appeals attempt to depict people in a miserable condition to arouse the audiences’ sympathy in order to stimulate them to take an action to help others, improve human suffering, or engaging in a charitable response. For instance, using sadness appeals in a charity advertisement, Wang (2008) investigated the influence of sadness appeals on consumer helping behaviors and found that people that experienced sympathetic emotions evoked by ads that use sadness appeals were more likely to make donations. Simply put, sadness appeals that generated sympathy for a needy recipient who appeared in the ad increased the audience responses to the call for help (Thornton,

Kirchne, & Jacobs, 1991).

Lastly, happiness appeals and sadness appeals lead either to purchasing behavior or to rendering support. Although the evidence indicates that the increased use of these appeals induces favorable behavioral consequences, most research regarding sadness appeals has been conducted in the context of a charity setting. Furthermore, given the significant impact that emotional appeals have on consumer responses, it is imperative to investigate which type of appeal is related to fair trade consumer responses in order to better understand the determinants that may contribute to successful website management.

2.4. Theoretical Framework

2.4.1 The S-O-R Model

The Stimulus- Organism-Response (SOR) model (Mehrabian & Russell, 1974) explains the effect that external stimuli have on consumer responses (See Figure 1 for the

Mehrabian – Russell model). In this model, three major components describe the consumer decision-making process: the stimuli (e.g., elements in the physical environment), the organism, which consists of emotional states (e.g., pleasure, arousal, and dominance), and the response (e.g., purchase intention, and approach-avoidance behavior) (Wundt, 1905).

The term, stimuli, refers to things that are “external to the person” and they include both marketing mix variables and other attributes of the environment (Bagozzi,

1986, p. 46). Among studies in environmental psychology, stimuli are further defined as a design of the buying environment that produces emotional states in the consumer to increase purchase probability (Kotler, 1973). In the offline shopping context, research has focused on sensory variables (e.g., music, color, scent, and lighting) as effective stimuli related to consumer responses (Donovan & Rossiter, 1982; Mattila & Wirtz, 2001). In the online shopping environment, verbal content (e.g., product relevant information, delivery, and return policy), visual content (e.g., product image), and shopping enhancement tools

(e.g., search navigation) have been examined as key environmental stimuli affecting consumers.

The organism is understood as being individual emotional and cognitive states that mediate the relationship between the stimuli and the final actions that are elicited.

Mehrabian and Russell (1974) defined emotional states as the three-dimensional schema of pleasure, arousal and dominance (PAD). Emotional responses (emotional states elicited by stimuli) can be measured using pleasure, arousal, and dominance dimensions

(Russel, 1978). Previous studies (e.g., Donovan, Rossiter, Marcoolyn, & Nesdale, 1992) have revealed that emotional state is an important determinant of a variety of consumer behaviors. For instance, pleasure is related to preference for the store and the likelihood of overspending and arousal is associated with amount of money spent in the store, time spent in the store, and the number of products purchase in the store. Along with emotional state, cognitive state is a strong predictor of various dimensions of consumer behaviors. Cognitive state refers to an internal mental process of acquisition, retention, and retrieval of information (Eroglu, Machleit, & Davis, 2001). Cognitive states evoked by stimuli have resulting cognitive responses such as attitude, beliefs, attention, comprehension, memory, and knowledge.

The response in the S-O-R paradigm represents the final actions or outcomes, and includes the approach to or avoidance of the stimulus environment; thus responses are behavioral reactions (Donovan & Rossiter, 1982; Sherman & Smith, 1986; Sherman et al., 1997). Approach behavior is defined as all positive final actions leading to a particular purchase setting (e.g., intention to stay, explore, and affiliate) while avoidance behavior refers to the opposite behavioral response of approach behavior and encompasses actions such as intention to leave to setting. As a consequence of individual cognitive and emotional responses, behavioral response is represented in the form of 34

helping behavior (Wang, 2008), efficient decision-making (Schwarz, 1990; Sinclair &

Mark, 1995), approach behavior (e.g., affinity for the store, time spent in the store), purchase intention (Fiore & Kim, 1997) and actual purchase behavior (e.g., the amount of money spent).

Following the S-O-R model, numerous researchers in consumer behavior have examined the impacts of retail environments (including online stores or physical brick- and-mortar stores) on consumer emotions (e.g., pleasure, arousal, and dominance) and their resulting influence on behavioral outcomes (e.g., satisfaction, purchase intention, revisit, patronage decision). Among the studies examining the traditional retail environment, research has focused on in-store elements that affect consumer decision- making. Many of these studies have examined sensory variables such as music, (Dubé,

Labatt, Chebat, & Morin, 1995; Mattila & Wirtz, 2001), color (Bellizzi & McVey, 1983), scent (Donovan & Rossiter, 1982; Mattila & Wirtz, 2001) and lighting (Baker, Levy, &

Grewal, 1992). Such store elements have been found to lead to purchase intention (Babin

& Babin, 2001; Fiore, Jin, & Kim, 2005), actual money spent (Sherman, Smith, &

Mansfield, 1986), judgments about product quality (Baker, Grewal, & Parasuraman,

1994), other types of approach (e.g., desire to stay) and in-store behavior (e.g., interaction with store personal).

Applying the Mehrabian and Russell (1974) framework (S-O-R) to the online retail environment, Eroglu, Machleit and Davis (2001) expanded the original S-O-R model to include a cognitive dimension in the organism concept; cognitive states are described as consumer’s internal mental processes including attitudes, comprehension, beliefs, attention, memory, and knowledge (Eroglu, Machleit, & Davis, 2001). In a later 35

study using the S-O-R framework, Huang (2003) found evidence that atmospheric web cues (information availability) enhanced consumer emotional responses (arousal, pleasure, dominance), and in turn, affected approach behavior (exploration decision, shopping decision).

Behavioral Emotional Responses Environmental (Pleasure, Arousal, Dominance) Response Stimuli Approach-avoidance

Figure 2.1. The Mehrabian – Russell Model

2.4.2. Application to the Fair Trade Context

Based on the literature, the current study examines the impact that fair trade store information has on consumer purchase decisions in an Internet shopping context.

Previous studies conducted by Eroglu, Machleit and Davis (2001) and Huang (2003) have provided useful insights for model development; however, we also recognize the need to expand the atmospheric web cues to include those found in the existing literature reviewed above and further examine how these cues affect cognitive and emotional states, and behavior outcomes.

Applying the S-O-R model to a fair trade shopping context, this study postulates that the environmental cues of fair trade store information (stimuli) elicit intervening effects, specifically the consumers’ cognitive and affective states (organism), leading to purchase intention (behavioral response). And consistent with later S-O-R research by

Eroglu, Machleit and Davis (2001), our model hypothesizes that both cognition (C) and affect (A) are ways in which a consumer organism (O) can respond to fair trade store information and generate a behavioral response (R). Thus, this study proposes that the two stimuli of information type and emotional appeal type influence consumer cognitions of information quality (C) and affective states of pleasure and arousal (A), which subsequently influence shopping behaviors as represented by purchase intent (R).

2.4.3. Proposed Model and Hypotheses

The S-O-R model (Mehrabian & Russell, 1974) forms the theoretical background we used to explain our hypothesis that the stimuli of online store information influences consumers’ cognitive and emotional states, which in turn affects the consumers’ response behavior. In this study, two persuasive techniques are expected to have varying levels of ability to increase the cognitive states (perceived information quality) and emotional states (pleasure, arousal) that influence consumer behavior (purchase intention). Thus, this study hypothesizes that a cognitive state (i.e., information quality) and emotional states (i.e., pleasure, arousal) are evoked differentially in response to fair trade store information, which may or may not lead to purchase intent.

The proposed study anticipates: 1) the effect of fair trade store information depending on information type (concrete vs. abstract) on cognitive states (perceived information quality) and emotions (pleasure, arousal); 2) the effect of fair trade store information depending on emotional appeal type (happiness vs. sadness) on cognitive states (perceived information quality) and emotions (pleasure, arousal); and 3) the effect of consumer cognitive and emotional states on response behaviors (purchase intention)

(See Figure 2 for the proposed model for main study).

Stimulus Organism Response

Figure 2.2. Proposed model for main study

Hypothesis

H1 Perceptions of information quality will be higher for concrete information than for abstract information

H2a As compared to those exposed to abstract information, those exposed to concrete information will experience more pleasure

H2b As compared to those exposed to abstract information, those exposed to concrete information will experience less arousal

H3a As compared to those exposed to sadness appeals; those exposed to happiness appeals will feel more pleasure

H3b As compared to those exposed to sadness appeals; those exposed to happiness appeals will feel more arousal

H4a Pleasure will influence consumers’ cognitive appraisal of information quality

H4b Arousal will influence consumers’ cognitive appraisal of information quality

H5 Perceptions of information quality will be positively related to purchase intention

H6a Pleasure will be positively related to purchase intention

H6b Arousal will be related to purchase intention

Table 2.2. Summary of Hypotheses

2.5. Hypotheses Development

2.5.1. Effects of Information Type

The Effects of Information Type on Cognitive States

Concrete information refers to the degree of detail and specificity of information about events and participants in the events; whereas abstract information refers to information which describes little contextual information (Gorman, 1961). Following the logic of the S-O-R model, information characteristics serve as stimuli that influence consumers’ cognitive states (Eroglu, Machleit, & Davis, 2001; Huang, 2003).

Previous studies that compared the relative effect of concrete versus abstract information have shown that information presented using concrete language has a more powerful influence on cognitive responses (e.g., comprehension, judgments) than information that is presented using abstract language (Dawn, 1974, Holmes & Langford,

1976). Theoretical accounts of this effect emphasize the role of concreteness in imageability. To support this notion, Paivio (1971) empirically demonstrated that image ability of concrete information evokes greater imagery and makes it possible to more easily perceive ideas and form images than abstract information. In his earlier study,

Paivio, Yuille and Madigan (1968) found the grounds to support a dual coding system

(verbal, non-verbal) where concrete information is highly correlated with both imagery

(non-verbal) and meaningfulness (verbal); on the other hand, abstract information is only

associated with meaningfulness (verbal). According to the study results, abstract information derives its meaning mainly from verbal associations with surrounding words, which is reflected in meaningfulness, rather than imageability (e.g., Paivio, Yuille, &

Madigan, 1968). Thus, these findings indicate that the use of concrete information may reduce the consumers’ need to engage in additional cognitive effort in processing information. Together, these studies suggest this relationship may account for the inferior performance of abstract information in a variety of cognitive tasks (e.g., memory, comprehension, recall).

In the current study, fair trade store information, which contains concrete information, is therefore hypothesized to be of higher quality than information presented using abstract information. The richer imagery of concrete information (Begg & Paivio,

1969) allows a decision-maker to perceive that that information has greater clarity (Paivio

& Desrochers, 1980) and thus is easier to comprehend (Holmes & Langford, 1976;

Dawn, 1974; Schwanenflugel & Shoben, 1983), and is faster to access. Therefore, concrete information is likely to be perceived as having a higher quality compared to abstract information.

H1. Perceptions of information quality will be higher for concrete information

than for abstract information.

The Effect of Information Type on Emotional States

Further, this study proposes that information type will affect consumers’ emotional states. In a test of the S-O-R model, stimuli induce pleasure and arousal states in consumers (Eroglu, Machleit, & Davis, 2001; Huang, 2003). In the Internet shopping context, information characteristics such as amount, detail, and load of information can contribute to consumer emotional responses (e.g., pleasure, satisfaction). Szymanski and

Hise (2001) showed that the amount of information positively influenced emotional satisfaction felt from an online shopping experience. In their work, the authors concluded that richer and more extensive product information increased the degree of satisfaction with perceptions of merchandising. However, Huang (2003) provides empirical evidence that a low information load of a website is positively associated with individual feelings of pleasure. The results show that information intensive websites were found to be less pleasurable since they can distract consumers from relevant information. Therefore, the findings suggest that providing specific information that is relevant to consumer’s needs was more important to consumer shopping decisions than simply providing a large amount of information on the website.

In online retail shopping, consumer exposure to information characteristics such as those mentioned above, may induce arousal states. Mehrabian and Russell (1974) suggest that information load is an arousing environment quality that increases perceptions of novelty, complexity, intensity, unfamiliarity, improbability, change or uncertainty of the setting. Using two dimensions of information load (i.e., novelty, variety), Donovan and Rossiter (1982) identified the influence of the information load on arousal in the conventional retail environment. The authors found that subjects 43

experiencing novelty perceived increased levels of arousal, but that variety decreased perceived arousal.

From the above evidence, logic suggests that the characteristics of information provided on a website can influence both pleasure and arousal. In regards to pleasure, in an online shopping environment shoppers are attracted by detailed information; however, as Huang (2003) indicates, the information needs to be easily interpreted by the consumer. Given the concreteness effect, concrete information is perceived to have greater clarity than abstract information due to its rich, contextual, specific information.

Consequently, it is predicted that concrete information may increase pleasure of consumers more than abstract information (Eroglu, Machleit, & Davis, 2001; Huang,

2003). However, the relationship with arousal is different; although no studies specifically examined the effect of concrete information on arousal, as the above discussion suggests, a greater information load (amount of information to be processed) makes consumers more distracted and decreases perceived arousal (Donovan & Rossiter,

1982; Huang, 2003). Thus, concrete information, which provides specificity and many details, may be perceived as complicated and challenging to process, accordingly it may decrease arousal of the consumer more than abstract information (Eroglu, Machleit, &

Davis, 2001; Huang, 2003). Therefore, the study hypothesis is that information type

(concrete, abstract) has different effects on the emotional states of pleasure and arousal.

H2a. As compared to those exposed to abstract information, those exposed to

concrete information will experience more pleasure.

H2b. As compared to those exposed to abstract information, those exposed to

concrete information will experience less arousal.

2.5.2. Effect of Emotional Appeal

The Effect of Emotional Appeals on Emotional States

Emotion refers to a subjective felt tendency produced by environmental stimuli and mediated by physiological variables. To evoke emotional responses, emotional appeal, a specific persuasion tool is used to create either negative or positive emotions

(e.g., Aaker & Williams, 1998; Burke & Edell, 1986; Edell & Burke, 1987; Holbrook &

Batra, 1987). According to prior research in the advertising literature, Edell and Burke

(1986) provide empirical evidence showing that ad characteristics (e.g., interesting, humorous, tender) may induce affective responses including upbeat states (e.g., cheerful, delighted, liberated), negative responses (e.g., disgusted, remorse, irritated) and feelings of warm (e.g., caring, sentimental, contemplative). This suggests that various emotional responses (e.g., happiness, sadness, and sympathy) are evoked by the stimuli of emotional appeals (i.e., sadness and happiness).

Emotional appeals conveying happiness and sadness should generate emotional reactions. Such emotional responses may vary depending on the appeal style. Happiness is a positively-valenced emotion referring to state of well-being and contentment; it is a pleasurable or satisfying experience (Merriam-Webster Dictionary, 2009). On the other hand, sadness is a negatively-valenced emotion referring to an unpleasureable emotional state or a feeling of loss (Bagozzi, Gopinath, & Nyer, 1999). Applying the S-O-R 45

paradigm (Mehrabian & Russell, 1974), the two emotional stimuli (happiness and sadness) will influence consumers in terms of pleasure and arousal. Specifically, pleasure refers to the degree of happiness, contentment and satisfaction, and arousal refers to the degree of stimulation and excitement. Therefore, the study expects that happiness appeals would be more likely to increase pleasure and arousal compared to sadness appeals.

Thus, the following hypothesis 3 is developed.

H3a. As compared to those exposed to sadness appeals; those exposed to

happiness appeals will feel more pleasure.

H3b. As compared to those exposed to sadness appeals; those exposed to

happiness appeals will feel more arousal.

Effect of Emotional States on Cognitive States

Previous studies have documented different effects with regard to positive versus negative emotions on judgments. Research using the valance approach suggests that positive feelings lead to optimistic judgments; whereas negative feelings lead to pessimistic judgments (Adaval, 2003; Forgas, 1990; Isen & Shalker, 1982; Meloy, 2000).

For instance, in the study conducted by Lerner and Keltner (2000), participants in a positive mood more favorably rated their life satisfaction than participants in a negative mood. One theoretical explanation for this phenomenon is that people tend to use current feelings to solve problems or make complex judgments (Clore, 1992; Schwarz, 1990;

Schwarz & Clore, 1983). Since judgment quality can be influenced by individual current

emotional states, the study hypothesis is that feelings of pleasure have an effect on judgment; thus, the following hypothesis is developed.

H4a. Pleasure will influence consumers’ cognitive appraisal of information

quality.

H4b. Arousal will influence consumers’ cognitive appraisal of information

quality.

The Effect of Cognitive States on Response Behaviors

Information quality has been defined as accurate, reliable and complete information that is relevant or specific to the problem under consideration (Bailey &

Pearson, 1983; DeLone & McLean, 1992; Zmud, 1978). Researchers have discussed the important roles of information quality on the decision-making process. Higher quality information offered by Internet retailers plays a critical role in consumer’s judgments about products/service value (e.g. Park & Stoel, 2005; Teo, 2002), increasing consumer confidence about a purchase/patronage decision and consumers’ behavioral intentions

(e.g., intent to use, intent to recommend, and intent to prefer over other websites) (e.g.,

Kim & Niehm; 2009; Park & Stoel, 2005). In studies examining online apparel retailing,

Spiller and Lohse (1998) found evidence that low-quality information about company, product, and service had a negative influence on consumer product purchase. For fair trade related websites, the significance of the information quality of the website was found in a previous study by Lee and Littrell (2005). According to this study, information quality was an important website attribute that influenced consumer attitude towards fair 47

trade products. The findings indicated for consumers seeking utilitarian shopping value, as opposed to hedonic value, information quality led to a more favorable attitude toward online fair trade shopping. This suggests that for some consumers, specific information

(e.g., product, company) depicted on the fair trade retailers’ website plays an important role in consumer’s behavior in shopping.

Based on the literature review, it is expected that perceptions of information quality, a cognitive state, may play a key role in forming consumer’s purchase intention.

In particular, customers who perceive higher quality fair trade store information are more likely to intend to purchase. Therefore, hypothesis 5 is developed as follows.

H5. Perceptions of information quality will be positively related to purchase intention.

The Effect of Emotional States on Response Behavior

The S-O-R framework (Mehrabian & Russell, 1974) suggests that the effect of consumers’ emotional states (pleasure, arousal) influence consumer response behaviors.

Pleasure and arousal are independent emotional constructs and the former is the extent to which one experiences happy, contented and satisfied feelings, and the latter is the extent to which one feels stimulated and excited. Previous studies have shown that pleasure and arousal independently impact approach – avoidance behavior and actual shopping behavior. Research has found evidence that higher levels of pleasure produce favorable attitudes toward visiting more websites (e.g., Menon & Kahn, 2002), increase actual time spent in store (e.g., Donovan & Rossiter, 1992) and increase unplanned spending (e.g.,

Donovan & Rossiter, 1992). On the other hand, arousal is inversely associated with

consumer behaviors. For instance, extremely high or very low levels of arousal are not highly correlated to spending time in store and spending money (Foxall & Greenley,

1998; Greenaland & McGoldrick., 1994).

Applying the S-O-R model, thus, the levels of pleasure and arousal felt by online fair trade shoppers will be related to purchase intention.

H6a. Pleasure will be positively related to purchase intention.

H6b. Arousal will be related to purchase intention.

Chapter 3: Method

This research consists of a pilot test and the main study. This chapter presents the methodology for the study (i.e., pilot test, main study) and describes the SPSS analysis and the results of the pilot test. The method section includes information about the pilot test to develop the stimuli for the manipulations, information about research design, experimental manipulations, data collection procedure, and dependent variables for the main study. Both elements of this research, both the pilot test and the main study, were determined to be exempt from IRB review by The Office of Responsible Research

Practices. Protocol number 2014E0303 was assigned for the pilot test (See Appendix B) and 2014E0337 for the main study (See Appendix C).

Pilot Study 1

Develop Manipulations for Information type and Emotional appeal type

! Develop information type manipulation based on previous research (Klee & Legge, 1976; Pavio, Yuille, Stephen, & Madigan, 1968; Wright, 1979; Anderson, Goetz, Pichert, & Halff, 1977) and check concrete effectiveness

! Select two emotional appeal pictures for the Main study and check a completion of target emotion

Figure 3.1. Summary of pilot test

3.1. Pilot Study

The objective of the Pilot Study is to select appropriate stimuli that will be used to manipulate information type (concrete, abstract) and emotional appeal type (happy and sad condition), which are the two main effects in the experiment for the overall study.

For emotional appeal type stimuli, the study purposely did not control the styles and traditional garments of fair trade artisans in order to increase potential real-world validation (Garner, 1985).

3.1.1. Method

Stimulus 1: Information Type Materials

The construction of stimuli was conducted in several stages. First, fair trade store information was obtained from an actual fair trade online retailer’s website

(www.globalgoodspartners.org) to increase reality. To prevent the effects of potential bias from previous exposure to that brand, a fictitious fair trade brand name (i.e., Fair

Trade Store) was created for the manipulation stimuli. Second, target sentences were identified and evaluated for level of concreteness/abstractness using prior literature to guide the process; the statements were then modified to create a concrete paragraph and an abstract paragraph following the past literature (Klee & Legge, 1976; Paivio, Yuille,

Stephen, & Madigan, 1968). To maximize the treatment effect, concrete and abstract verbs, nouns and subjects were used. Anderson, Goetz, Pichert and Halff (1977) proposed that sentences with more concrete subjects increased the recognition and probability of

recalling. Third, the store information (i.e., concrete, abstract) was analyzed to ensure that there was no overall difference in terms of amount, credibility and comprehensibility of information. Previous studies found evidence that the superiority of concrete information may be due to other variables such as amount, credibility and comprehension of information (Pezdek & Royer, 1974). For instance, Johnson, Bransford, Nyberg, and

Cleary (1972) have argued that Begg and Paivio (1969)’s study is problematic in that the existing meaning differences between abstract and concrete information are due to differences in the above three variables. The failure to control these potentially confounding variables may contribute to the lack of a clear relationship between concrete and abstract effects and consumer behavior. Therefore, the amount of information were controlled by using similar word count and word length in both the concrete and abstract paragraphs.

The concrete and abstract statements were equated in word count, by using the same number of words in each sentence, and in overall word length, as determined by the number of letters: 1) average # of letters per word for abstract: M=4.3, SD=1.24, average

# of letters per word for concrete: M=4.3, SD=1.24, and 2) average # of words for three abstract statements: M=21.67, SD=6.43, average # of words for three concrete statements:

M=22, SD=5.29. Aside from the amount of information, credibility of information and comprehension of information were checked during the pretest using measurement scales from prior research (Flanagin, & Metzger, 2000; Paivio, 1968).

Information type

Concrete World-Fair-Partners is a nonprofit organization that retails handmade

fair trade garments on our online store to improve the economic status of

women in poor communities in poverty-stricken countries. World-Fair-

Partners works with 40 artisan groups that collectively employ over

3,000 women in 20 countries around the world. World-Fair-Partners’

work to build sustainable livelihoods has been recognized by CNN,

Marie Claire, Daily Candy, CBS News, and O Magazine.

Abstract Our company is a nonprofit entity that handles handmade fair trade

items on our e-commerce store to improve the economic status of

women in poor communities in developing places. Our company works

with many artisan groups that collectively employ thousands of women

in many countries around the world. Our company’s work to model

sustainable livelihoods has been mentioned in national news outlets and

fashion magazines.

Figure 3.2. Information type manipulations used in pilot test

Stimulus 2: Emotional Appeal

Images were selected to develop emotional appeal stimuli. According to Staats and Lohr (1979), images can elicit emotional responses, thus affect behavioral responses.

Such emotions guide individuals’ evaluations, decisions, and behavior toward objects

(Hsee & Kunreuther, 2000; Pham, 1998; Pham, Cohen, Pracejus, & Hughes, 2001).

Pictures of ten (five each for happiness appeals and sadness appeals) fair trade artisans were downloaded from existing fair trade websites. In the happiness appeal condition, a smiling face of an artisan was used to generate happiness feelings; and in the sadness appeal condition, a neutral or subtle crying face of an artisan was used to generate sadness feelings.

Consistency across pictures was maintained with the assistance of Adobe

Photoshop in terms of resolution, angle, background, brightness, and contrast. All artisan pictures were presented on a white background without a body form and the resolution of pictures was maintained to be 300 X 550 pixels. To avoid potentially reducing real-world validation (Garner, 1985), other factors such as styles or traditional garments of fair trade artisans were purposely not controlled.

Emotion appeal Happy Sad type Image1

Image2

Image3

Continued

Figure 3.3. Emotional appeal manipulations used in pilot test

Figure 3.3 continued

Emotion appeal Happy Sad type Image4

Image5

Sample

Participants in this study were recruited from Amazon Mechanical Turk

(www.MTurk.com). Amazon’s Mechanical Turk (MTurk) is an open online marketplace that attracts participants based on a compensation system. Participants were recruited from MTurk because it offers a convenient way to obtain high-quality data rapidly and inexpensively. MTurk participants tend to be demographically diverse than standard

Internet samples and typical American college samples (Buhrmester, Kwang, & Gosling,

2011). Only adults 18 years of age or older were sought for this study. A sample size of

80 was deemed adequate given six variables to be included in the analysis (Park &

Lennon, 2008)

Procedure

The pilot test was designed to select materials for 2 manipulations: information type: (concrete vs. abstract) and emotional appeal type (happiness vs. sadness). A Web- based survey was used for the pretest. Eighty subjects recruited from Amazon

Mechanical Turk participated in the Pilot test for monetary compensation. One US dollar was offered as an exchange for their participation.

In the pilot test, the web survey contained two main sections. In the first section, participants were randomly assigned to one of the two information types (concrete, abstract). After viewing the information, participants were asked to evaluate the information type to which they were exposed (concrete, abstract) in terms of 1) the concreteness, 2) credibility, and 3) comprehensibility of the information (question content provided below). 58

In the survey, the first page included the purpose of the study and description of the procedure. On the second page, fair trade store information was presented on a white background. After reading the three statements to be included in the fair trade paragraph, subjects were asked to rate each statement for level of concreteness on 7-point bi-polar scales. In measuring concreteness effects, a bi-polar scale has been frequently used (e.g.,

Sadoski, Goetz, & Fritz, 1993). The two items comprising the concreteness dimension scale, adopted from previous studies (developed by Sadoski, Goetz, & Fritz,1993; Spreen

& Schulz, 1966), contain bipolar adjectives with anchors of 1 and 7. The ratings, with the scale ranges, were: 1 = “very abstract, hard for me to form mental images of this” and

“information may not be experienced by our senses of persons, places and things that can be seen, heard, felt, smelled or tasted” to 7 = “to very concrete, easy for me to form mental images of this” and “information may be experienced by our senses of persons, places and things that can be seen, heard, felt, smelled or tasted”. To ensure that the modification of information type was not confounded with other variables, subjects were asked to rate the credibility and comprehensibility of the information. Credibility of information was assessed across seven dimensions including accuracy, believability, timeliness, relevance, understandability, detail, and appropriate format. These 7 items were measured on a 7-point Likert-type scale ranging from 1= “not at all” to 7=

“extremely” (Flanagin & Metzger, 2000). Comprehensibility of information was measured using two items, which were assessed on a 7-point scale ranging from 1= “very hard” and “not comprehensible” to 7= “very easy” and “quite comprehensible” (Carrell,

1983; Schwanenflugel & Shoben, 1983).

In the second section of the survey, participants were randomly assigned to one fair trade artisan image from a set of five different images. Participants then provided their 1) impressions about the fair trade artisan image, and 2) their current emotional feelings. Two items were used to measure impression about the fair trade artisan.

Subjects were asked to indicate the degree to which they thought “the artisan in this picture is from a developing country” and “the artisan in this picture is from a poor country”. Using a 7-point Likert scale, the ratings ranged from (1) strongly disagree to (7) strongly agree. With regards to emotion, the following question was given: “please indicate how much the following emotions describe how you feel right now” and complete the 5-item emotion survey (Larsen, McGraw, & Cacioppo, 2001). This procedure (i.e., the procedure for rating the image) was repeated once more to evaluate the second group of five images (ten images were evaluated in total in the pilot test, using two groups of five images in each group). On the last section of the survey, two simple demographic questions (i.e., age, gender) were asked of participants. A promo code with a thank you message was given to each survey participant upon the completion of the survey to award the promised salary after their participation. All measures used in the pilot test are listed in Table 3.1.

Variables Measurement Reliability

Concreteness: Sadoski, Please indicate your overall evaluation of the fair trade store information on (=.66) Goetz, & Fritz (1993); each of the 2 questions below.. Spreen & Schulz 1= “very abstract, hard for me to form mental images of this” to (1966) 7= “very concrete, easy for me to form mental images of this”; 1= “information may not be experienced by our senses of person, places and things that can be seen, heard, felt, smelled or tasted” to 7= “information may be experienced by our senses of persons, places and things that can be seen, heard, felt, smelled or tasted”

Information 1. It provides accurate information (=.89) 61

Credibility: Barnes & Not at all (1) ------(7) Extremely Vidgen (2003) 2. It provides believable information Not at all (1) ------(7) Extremely 3. It provides timely information Not at all (1) ------(7) Extremely 4. It provides relevant information Not at all (1) ------(7) Extremely 5. It provides easy to understand information Not at all (1) ------(7) Extremely 6. It provides information at the right level of detail Not at all (1) ------(7) Extremely 7. It presents the information in an appropriate format Not at all (1) ------(7) Extremely

Continued

Table 3.1. Instruments for Pilot Test Table 3.1 continued

Variables Measurement Reliability

Comprehension: Carrell, How easy or difficult was it for you to understand and comprehend the (=.95) (1983); information.. Schwanenflugel & 1. Very hard (1) ------(7) Very easy Shoben (1983) 2. Not comprehensible (1)------(7) Quite comprehensible

Fair trade artisan image 1= “strongly disagree that artisan from a developing country” to 7= “strongly agree that artisan from a developing country” 1= “strongly disagree that artisan from a poor country” to 7= “strongly agree that artisan from a poor country”

Positive/negative 1. Calm (1) ------(7) Tense (=.90) emotion: Larsen, 2. Relaxed (1) ------(7) Stressed McGraw, & Cacioppo 3. Pleased (1) ------(7) Displeased (2001) 4. Happy (1) ------(7) Sad 5. Excited (1)------(7) Depressed

3.1.2. Analysis and Results

In the Pilot Test, a total of four manipulation treatments were tested: two for information type (concrete vs. abstract) and two for emotional appeal type (happiness vs. sadness). To analyze the information type, a series of three independent sample t-tests were used to compare across the three dimensions (i.e., concreteness, credibility, comprehensibility) of concrete and abstract information. These manipulation checks were performed to determine if participants would perceive 1) difference in the level of concreteness of information, 2) no difference in credibility of information, and 3) no difference in comprehensibility of information.

For the test for emotional appeal type, descriptive statistics were used to select the final happiness and sadness appeals using the following steps. The emotional appeal manipulation was assessed using the five questions measuring positive and negative emotion (lower score indicates positive emotion; higher score indicates negative emotion on a bi-polar scale). The five scores for emotion were averaged to select the final happiness and sadness appeal for the Main study (lowest mean was selected for positive emotion appeal; highest mean was selected for negative emotion appeal). An additional manipulation check for the impression about fair trade artisan was assessed to ensure that participants would perceive that the artisan in the picture is from a developing/poor country. Impression about the fair trade artisan is expected to show higher score on a developing/poor country.

Sample Characteristics

Table 3.2 summarizes demographic information of the respondents. The mean age of participants (N=80) was 37 years, with ages ranging from 20 to 62. Approximately 70

% of the respondents were between 25 and 44 years of age. Male participants (N=40) accounted for 50% of the total respondents and the other half (N=40) were female participants.

Demographic Categories Frequency Percent (N=80) Age 18 to 24 years 7 9% Mean= 37.07 25 to 34 years 32 40% SD= 10.66 35 to 44 years 23 29% 45 to 54 years 13 16% 55 to 64 years 5 6% Gender Male 40 50% Female 40 50

Table 3.2. Pilot Test Sample Profile: Demographic Characteristics

Reliability Analysis

Prior to the manipulation check, reliabilities for the research variables were tested.

Cronbach’s alpha (α) coefficient is commonly used as an index for reliability assessment.

The results of reliability analysis (i.e., Cronbach’s alpha) show that the concrete scale, which consisted of 2 items, produced an alpha of 0.66, the credibility of information scale consisted of 7 items and produced an alpha of 0.89 (see Table 3.3). The comprehensibility of information was found to be highly reliable (2 items; α= .95).

Reliability for the five-item emotion scale was 0.90 (see Table 3.4). While a value of .80 or higher is considered to be a strong composite reliability (Grefen, 2003), Bagozzi & Yi

(1988) indicated that 0.6 is an acceptable reliability coefficient.

Cronbach’s Alpha N of Items Status

Concreteness 0.66 2 Accepted Credibility 0.89 7 Accepted Comprehensibility 0.95 2 Accepted Positive and negative 0.90 5 Accepted emotion

Table 3.3. Results of Reliability Tests for Information Type and Emotional Appeal Manipulation Check

Scale Mean Scale Corrected Squared Cronbach’s if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted

Concreteness1 4.860 2.019 .491 .241 . Concreteness2 5.088 1.851 .491 .241 .

Scale Mean Scale Corrected Squared Cronbach’s if Variance if Item-Total Multiple Alpha if Item Deleted Item Deleted Correlation Correlation Item Deleted

Credibility 1 33.050 30.356 .756 .643 .865 Credibility 2 32.506 31.792 .644 .592 .878 Credibility 3 33.468 30.432 .616 .442 .882 Credibility 4 32.557 31.147 .732 .563 .868 Credibility 5 32.405 31.962 .630 .492 .879 Credibility 6 33.379 29.033 .646 .611 .881 Credibility 7 32.759 29.364 .808 .721 .858

Scale Scale Corrected Squared Cronbach’s Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted

Comprehensiblity1 6.087 .942 .917 .842 . Comprehensiblity1 6.087 1.119 .917 .842 .

Scale Mean Scale Corrected Squared Cronbach’s if Variance if Item-Total Multiple Alpha if Item Deleted Item Deleted Correlation Correlation Item Deleted

Emotion 1 13.168 21.361 .738 .832 .869 Emotion 2 13.043 20.067 .775 .846 .862 Emotion 3 12.525 22.125 .756 .684 .865 Emotion 4 12.431 20.851 .786 .752 .858 Emotion 5 11.856 24.828 .653 .526 .888 Table 3.4. Item Total Statistics Reliability Tests for Information Type and Emotional

Appeal Manipulation Check

Manipulation Check

Information type. Participants were asked to assess their perception of the level of concreteness of the information about the fair trade retailer. Results show a statistically significant mean difference between concrete information (M=5.44, SD=.97, n=35) and abstract information (M=4.63, SD=1.24, n=45) at the .05 level of significance (t=3.16, df= 78, p<.02). On average participants who read the concrete information tended to perceive a higher level of concreteness than those exposed to abstract information (see

Table 3.5).

As expected, the results for credibility of information revealed (t=.87, df=78, p<.39) that there was no significant difference in perceptions of credibility between concrete and abstract information. This indicates that participants exposed to the concrete info condition perceived the credibility of the information to be quite similar to that perceived by participants who were exposed to the abstract info condition (for concrete information, M=5.60, SD=.96; for abstract information, M=5.42, SD=.90) (see Table 3.5).

Similarly, there was no significant difference in comprehensibility of information

(t=1.12, df=78, p<1.12), between participants who read the concrete information as compared to those who read the abstract information. The average comprehensibility score for participants who read concrete information (M = 6.23, SD = .85) was very similar to the score for participants who read the abstract information (M= 5.98, SD =

1.09) (see Table 3.5). Therefore, based on results of these three t-tests, information type was successfully manipulated.

Measure Concrete Abstract Information Information

M(SD) n M(SD) n T df p Concreteness 5.44 (.97) 35 4.63 (1.24) 45 3.16 78 .02

Credibility 5.60 (.96) 35 5.42 (.90) 45 .87 78 .39 Comprehensibility 6.23 (.85) 35 5.98 (1.09) 45 1.12 78 .26

Table 3.5. Results of t-tests for Information Type Manipulation Check

Emotional appeal type. A measure of emotion was obtained using five items.

This five item scales asks participants to rate their current emotional state, after looking at the image of an artisan, on a seven point bi-polar scale (see Table 3.1). Ratings are averaged across all five items to select the final happiness appeal and sadness appeal images. On the bi-polar scale, a lower score indicates participants felt more positive emotion, thus, happiness picture 1 is selected because it has the lowest mean. For the sadness appeal, picture 1 is selected since it has the highest mean on the emotional scale

(see Table 3.6).

Happiness Appeal Sadness Appeal

N Min. Max. Mean SD N Min. Max. Mean SD Image1 18 1.00 5.00 2.64 1.00 15 2.60 6.00 3.87 1.10 Image2 15 2.00 4.60 3.13 .74 14 1.20 5.80 3.27 1.38 Image3 18 1.00 5.00 2.74 1.13 15 1.40 6.00 3.45 1.38 Image4 14 1.20 5.00 2.90 1.12 16 1.00 5.40 3.54 1.40 Image5 16 1.60 4.40 2.77 .81 18 1.40 5.40 3.31 .99 Note. The emotion appeal type was rated with a 7-point rating scale, where 1 = happy and 7 = sad.

Table 3.6. Image Rating of Happiness and Sadness Appeal Type

Impression about fair trade artisans. The construct for impression about fair trade artisans included two items measures of the participants’ perception of impression of the fair trade artisan shown in the two different photo images (“the artisan in this picture is from a developing country”, “the artisan in this picture is from a poor country”). Using a 7-point Likert scale, the ratings ranged from (1) strongly disagree to (7) strongly agree. Correlation tests (Pearson correlation) were used to examine the strength and direction of the relationship between the two artisan impression items. There was a significant correlation of .79, p < .001 between the two items in impression about fair trade artisans construct (see Table. 3.7). The mean score for perception that the artisan is from a developing country was 3.97 (SD= 1.95) where the score ranges between 1 and 7

(see Table 3.8). The mean for perceived impression about artisan being from a poor country was 4.24 (SD=2.10). The two results indicate that participants perceived that the artisan is from a developing/poor country (see Table 3.8). 69

Subscale Impression about fair trade artisan

1 2 1. Artisan from a developing country -- .79* 2. Artisan from a poor country -- Note. Correlations marked with an asterisk (*) were significant at p<.001 (2-tailed).

Table 3.7. Correlation Results for Impression about Artisan

Subscale Impression about fair trade artisan

M SD N 1. Artisan from a developing 3.968 1.950 160 country 2. Artisan from a poor country 4.243 2.014 160 Note. The Impression about fair trade artisans was rated with a 7-point Likert scale, where 1 = strongly disagree and 7 = strongly agree.

Table 3.8. Means Results of Impression about Artisan

3.2. Main Study

3.2.1. Method: Main Study

Research Experimental Design

The design of the Main Study is a 2 (information type: concrete vs. abstract) X 2

(emotional appeal type: happiness vs. sadness) between-subjects factorial design. This study included four treatment conditions (1) concrete information and a happiness appeal,

(2) concrete information and a sadness appeal, (3) abstract information and a happiness appeal and (4) abstract information and a sadness appeal.

Stimuli

The display of the stimuli contained store information text and an artisan image that were carefully selected in the prior Pilot Test. The presentation medium for the stimuli was an image of a fair trade website. A British fair trade website

(http://www.samatoa.com/PrestaShop/) was captured via screenshot and a fictitious fair trade apparel brand name (i.e., Fair Trade Store) was created and appended to the screenshot to control potential bias effects from previous experiences with fair trade websites. To control potential confounding effects of other variables on the stimuli, the manipulations were restricted to those for the information type and the emotional appeals.

All other conditions were kept equal, by displaying the same website design. The four created webpage images were inserted into a web survey system.

CONCRETE /HAPPY ABSTRACT/HAPPY

CONCRETE/SAD ABSTRACT/ SAD

Figure 3.4. Information and emotional appeal manipulations

Subjects

A web-based experiment was conducted as the method of the main study.

Research participants (N=274), recruited from Amazon Mechanical Turk, participated in this study for monetary compensation. In determining the sample size, several research studies (e.g., Ha & Im, 2011; Kim & Lennon, 2009; Park & Lennon, 2008) were reviewed for stimulus organism response models having more than five variables (see

Table 3.9). Based on Park and Lennon (2008), a sample size of approximately 250 was considered adequate in the main study.

The population from the main study does not overlap with the pilot study. For the main study, Mturk masters were invited whereas the responses from regular Mturk were collected in the pilot test. To increase the overall quality of the survey responses, the data was obtained from Mturk masters, since they tend to demonstrate specific types of HITs with a highly satisfactory accuracy on the Mechanical Turk marketplace than regular turks.

Number of Number of Number of Sample Authors Theory Latent items sample characteristics variable Ha & Im Stimulus- 6 20 804 Female (2011) Organism- college Response student Kim & Stimulus- 5 10 392 Female Lennon Organism- college (2009) Response student Park & Stimulus- 7 36 230 Female Lennon Organism- college (2008) Response student

Table 3.9. Previous Studies with Stimulus-Organism-Response Model

Procedure

In conducting the main study, effort has been made to avoid contamination of the treatment effects. Thus, the examination of pre-mood and post mood of participants was performed to remove any effects of pre-existing mood on the results. This is important to consider because differences in individuals’ pre-existing mood has been found to influence judgment quality and behavioral intentions (Pham, 1998).

For the experiments, participants were randomly assigned to one of the four treatment groups. Two information types (concrete, abstract) were fully crossed with two emotional appeal types (happiness, sadness). In the main study, subjects followed six steps to complete the survey. When participants arrived at the survey site, they were instructed to (1) read an intro page containing a welcome message and instructions for the study, (2) answer questions to rate their current mood (called pre-mood) before viewing the fair trade store website image (mood rating will be repeated in the sixth step as well),

(3) view the fair trade store webpage presenting store information and an artisan image,

(4) answer multiple questions measuring information quality, emotional state, and purchase intention (5) rate their current mood at the end of the survey (post-mood), and

(6) respond to simple demographic questions, including age, gender, ethnic background, education, income and real experiences with fair trade shopping. Once participants finished the survey, a thank you message appeared on the last page.

Dependent Measures

The dependent variables for the study are (a) cognitive dimension: perceptions of information quality; (b) emotional dimension: pleasure, arousal; and (c) response dimension: purchase intention (see Table 4.5).

Information quality. Information quality refers to accurate, sufficient, and complete information that is reliable and easy to comprehend relative to the situation

(Bailey & Pearson; 1983; Delong & McLean, 1992; Wang & Strong, 1996; Zmud, 1978).

Information quality was measured using seven items developed by Barnes and Vidgen

(2003). Responses were recorded using a 7-point Likert rating scale with endpoints of 1

(strongly disagree) and 7 (strongly agree). Although the reliability for information quality was not reported in Barnes and Vidgen (2003)’s study, the items have been empirically tested in various online shopping contexts such as bookstore and auction sites (e.g.,

Barnes & Vidgen, 2002).

Emotions. Emotion refers to a subjective felt tendency produced by exposure to environmental stimuli. Mehrabian and Russell (1974) identified three dimensions of

emotion (i.e., PAD: pleasure, arousal, and dominance). Affective states are assessed using twelve items from the PAD scale (Mehrabian & Russell, 1974): six items for pleasure and six items for arousal factors. The dominance dimension is not relevant to this study; therefore, it was not included. All emotional dimension scales comprise sets of bipolar adjectives with anchors of 1 and 7 (see Table 5). The reliability of the scale was established in previous studies (Mehrabian & Russell, 1974) (α = .93 for pleasure- displeasure, α = .88 for arousal-non-arousal).

Purchase behavior. Behavioral intention was measured using two items, which are modifications of two items used in previous literature (Dodds, Monroe, & Grewal,

1991; Grewal, Baker, Levy, &, Voss, 2003). The first uses a 7-point Likert response scale ranging from 1 (strongly disagree) to 7 (strongly agree) for the statement “I would be willing to buy fair trade product at this store.” The second question, “My willingness to buy fair trade product is:” is answered on a 7-point scale ranging from 7 (very high) to 1

(very low). The reliability of the scale was established in previous studies (α = .88,

Grewal, Baker, Levy, &, Voss, 2003).

Measures for manipulation check. The manipulation of the fair trade information

(i.e., concreteness of information, comprehensibility of information, and impression about fair trade artisan) was measured with the index used in the pilot study. Subjects were asked to provide their opinions about the level of concreteness and comprehensibility of information shown in the text of the fair trade website images. The two items on the concreteness dimension scale contain bipolar adjectives with anchors of

1 and 7. The ratings have the following scale ranges: 1 = “very abstract, hard for me to 76

form mental images of this” and “information may not be experienced by our senses of persons, places, and things that can be seen, heard, felt, smelled or tasted” to 7 = “very concrete, easy for me to form mental images of this” and “information may be experienced by our senses of persons, places, and things that can be seen, heard, felt, smelled or tasted.” Comprehensibility of information was assessed on a 7-point scale ranging from 1 = “very hard” and “not comprehensible” to 7 = “very easy” and “quite comprehensible” (Carrell, 1983; Schwanenflugel & Shoben, 1983). Subjects were asked to provide their impression about the fair trade artisan pictured in the image on two items using a 7-point bipolar scale: “the artisan in this picture is from a developing country” and “the artisan in this picture is from a poor country.”

Control variable

Mood was assessed and used as a control variable since pre-existing mood might influence consumers’ emotional states that consequently affect their shopping outcomes

(Donovan R. J., Rossiter, Marcoolyn, & Nesdale, 1994). Thus, pre-mood was considered for the current study prior to investigate the relationship among cognitive response (i.e., information quality), emotional response (i.e., pleasure and arousal), and behavioral response (i.e., purchase intention) in our model.

Mood. Mood is defined as a general and pervasive affective state that is subjectively perceived in specific times and situations (Gardner, 1985). To measure the emotional states, a six-item mood scale consisting of discouraged, happy, sad, delighted, downhearted, and joyful was used. All mood items were answered on a 7-point Likert

scale ranging from 1 (strongly disagree) to 7 (strongly agree). The reliability of the mood scale was established in previous studies (Park, Lennon, & Stoel, 2002) for pre-mood (α

= .81) and for post-mood (α = .76) (see Table 3.10).

78 Variables Items Cronbach’s alpha

Information quality: Barnes & 1. It provides accurate information Vidgen (2003) Not at all (1) ------(7) Extremely 2. It provides believable information Not at all (1) ------(7) Extremely 3. It provides timely information Not at all (1) ------(7) Extremely 4. It provides relevant information Not at all (1) ------(7) Extremely 5. It provides easy to understand information Not at all (1) ------(7) Extremely 6. It provides information at the right level of detail Not at all (1) ------(7) Extremely 7. It presents the information in an appropriate format Not at all (1) ------(7) Extremely 79

Pleasure: Mehrabian & Russell 1. Unhappy (1) ------(7) Happy Pleasure-displeasure (= .93) (1974) 2. Bored (1) ------(7) Relaxed 3. Annoyed (1) ------(7) Pleased 4. Unsatisfied (1) ------(7) Satisfied 5. Despairing (1) ------(7) hopeful 6. Melancholic (1) ------(7) Contended Arousal: Mehrabian & Russell 1. Unaroused (1) ------(7) Aroused Arousal-non-arousal (=. 88) (1974) 2. Relaxed (1) ------(7) Stimulated 3. Dull (1) ------(7) Jittery 4. Sleepy (1) ------(7) Wide awake 5. Calm (1) ------(7) Excited 6. Sluggish (1) ------(7) Frenzied Continued

Table 3.10. Items for Dependent Variables Table 3.10 continued

Purchase behavior: Grewal et al. 1. The likelihood that I would shop in this fair (2003) trade store is very high. Dodds et al. (1991) 2. My willingness to buy fair trade products is: (very high to very low)

Mood 1. Discouraged Pre mood (=. 81) 2. Happy Post mood (=. 76) 3. Sad 4. Delighted 5. Downhearted 6. Joyful

Concreteness: Sadoski, Goetz, & 1. Very abstract, hard for me to form mental Fritz (1993); Spreen & Schulz, images of this 80 (1966) 2. Very concrete, easy for me to form mental

images of this 3. Information may not be experienced by our senses of persons, places and things that can be seen, heard, felt, smelled or tasted 4. Information may be experienced by our senses of persons, places and things that can be seen, heard, felt, smelled or tasted

Table. 3.10. Items for Dependent Variables

Welcome message & Instruction

⇓

Survey Section 1 (Pre-mood)

⇓

Stimulus (Info & Emotion)

⇓

Survey Section 2

⇓

Survey Section 3 (Post-mood)

⇓

Survey Section 4

⇓

Thank you & Promotion code

Figure 3.5. The order of survey pages

Chapter 4: Data Analysis

The analyses and results sections describe the demographics of participants, manipulation checks, preliminary analyses, and hypotheses testing. Data were analyzed using SPSS 20.

Descriptive statistics, analysis of covariance (ANCOVA), and multiple regression were employed. Descriptive statistics were used to describe demographic characteristics. Statistical analyses (i.e., ANOCVA, multiple regression) were used to test the effects of information type and emotion appeal type on information quality, pleasure, arousal that lead to purchase intent

(see Table 4.1).

H Hypotheses Analyses

H1 Perceptions of information quality will be higher ANCOVA for concrete information than for abstract information H2a As compared to those exposed to abstract ANCOVA information, those exposed to concrete information will experience more pleasure H2b As compared to those exposed to abstract ANCOVA information, those exposed to concrete information will experience less arousal H3a As compared to those exposed to sadness ANCOVA appeals, those exposed to happiness appeals will feel more pleasure H3b As compared to those exposed to sadness ANCOVA appeals, those exposed to happiness appeals will feel more arousal H4a Pleasure will influence consumers’ cognitive Multiple appraisal of information quality regression

H4b Arousal will influence consumers’ cognitive Multiple appraisal of information quality regression

H5 Perceptions of information quality will be Multiple positively related to purchase intention regression

H6a Pleasure will be positively related to purchase Multiple intention Regression

H6b Arousal will be related to purchase intention Multiple regression

Table. 4.1. Summary of Analyses

4.1. Data Analysis and Results

Sample Size

An online survey link was activated on Amazon Mturk in July 2014 and returned

274 surveys. Of those respondents, 56 submitted surveys containing incomplete responses or significant missing data (approximately 20%) and were thus eliminated from the final analysis. As a result, 218 individuals participated in this study.

Demographic Characteristics

Table 4.2 summarizes participants’ demographic information. The 218 participants in this study consisted of 107 (49.1%) males and 110 (50.5%) females. The mean age of participants was 36.53 (SD= 10.88) with ages ranging from 20 to 70. More than 80% were 25–54 years old. In terms of their ethnic background, the majority of participants were Caucasian American (70%). Regarding their level of education, most of the participants had a college degree (91%); of these 16% had a graduate or professional degree. Based on their personal annual income, approximately 23% of the participants indicated they earned between $15,000 and $24,999. See Table 4.2 for more detailed information.

Demographic Frequency Category Percent Information (N=218) Age 18 to 24 years 21 10.0 Mean = 36.53 25 to 34 years 88 40.0 SD = 10.88 35 to 44 years 57 26.0 Missing data = 1 45 to 54 years 36 17.0 55 to 64 years 12 6.0 65 and older 3 1.0

Gender Male 107 49.1 Missing data = 1 Female 110 50.5 Other 1 0.5

Ethnic background African American 10 4.6 Caucasian American 161 73.9 Hispanic/Hispanic American 9 4.1 Native American 4 1.8 Asian/Asian American 27 12.4 Multicultural 5 2.3 Others 2 0.9

Education Less than 9th grade 1 0.5 9th to 12th grade, no diploma 0 0.0 High school graduate 18 8.3 Some college, no degree 74 33.9 Associate’s degree 20 9.2 Bachelor’s degree 73 33.5 Graduate or professional degree 32 14.7

Income $1 to $9,999 or loss 33 15.1 $10,000 to $14,999 34 15.6 $15,000 to $24,999 50 22.9 $25,000 to $34,999 26 11.9 $35,000 to $49,999 36 16.5 $50,000 to $64,999 23 10.6 $65,000 to $74,999 6 2.8 $75,000 to $99,999 7 3.2 $100,000 or more 3 1.4

Table 4.2. Main Study Sample Profile: Demographic Characteristics

Online Fair Trade Shopping Experience

Participants’ fair trade shopping experience was also assessed; 28% of the participants have purchased fair trade products online before. Coffee, apparel and tea were the most commonly purchased fair trade products (see Table 4.3).

Frequency Fair trade shopping experience Percent (N=218) Online shopping experience for fair trade products Yes 61 28.0 No 157 72.0

If yes, kind of fair trade product purchase Coffee 21 9.6 Chocolate 5 2.3 Apparel 15 6.9 Jewelry 5 2.3 Tea 10 4.6 Wine 1 0.5 Other products 4 1.8

Table 4.3. Online Fair Trade Shopping Experience

Previous experience with online fair trade shopping

Additional t-tests were performed to determine whether or not differences existed between shoppers who have purchased fair trade products online and shoppers who have not purchased fair trade products online in terms of information quality, pleasure, arousal and purchase intention. The test results indicated that the prior experience group and the non-prior experience group did not differ significantly on the tests of information quality, pleasure, and arousal (see Appendix I). However, subjects having prior experience 86

(M=5.64, SD=1.05) in online fair products shopping had a higher purchase intention than did those who did not have prior purchase experience (M=4.97, SD=1.23), t(216)=4.00 p=.000.

Manipulation Check

Information type. In the main study, information type was manipulated with two levels (concrete vs. abstract). A manipulation check was performed to determine whether participants would perceive a difference in the level of concreteness and comprehensibility of information manipulated in the website images. Randomly assigned participants in each treatment group browsed only one of the two types of information

(concrete or abstract store information). Participants were then asked to assess their perception of the levels of concreteness and comprehensibility of information. Each construct (i.e., concreteness and comprehensibility of information) contains two items and was measured on a 7-point bipolar scale.

To test the information manipulation, two t-tests were performed with information type as the independent variable and level of concreteness as the dependent variable in the first test and comprehensibility of information (dependent variable) in the second test.

As expected, the t-test results indicated a significant difference for the level of concreteness between the concrete information (M = 5.26, SD = .99) and abstract information (M = 4.84, SD = 1.24) condition, t(216) = 2.72, p= . 007. With respect to the comprehensibility of information, there was not a significant difference in the scores for concrete information (M = 6.35, SD = .87) and abstract information (M = 6.20, SD = .84)

conditions, t(216) = 1.32, p = .187. This confirms that information type was successfully manipulated in the main study (see Table 4.4).

Concrete Abstract Measure information information M(SD) n M(SD) n T df p Perceived level of 5.26(.99) 112 4.84(1.24) 106 2.72 216 .00 concreteness Comprehensibility 6.35(.87) 112 6.20(.84) 106 1.32 216 .19 of information

Table 4.4. Results of a Series of t-tests for the Information Type Manipulation Check

Design

Ten hypotheses were tested in four treatment groups (concrete/happy, abstract/happy, concrete/sad, and abstract/sad). To test the hypotheses, a 2 (information type) X 2 (emotional appeal type) between-subjects factorial design was employed. Two independent variables include the information type with two levels (concrete vs. abstract) and emotional appeal type with two levels (happy vs. sad). Prior to hypothesis testing, assumptions for the analysis methods were checked.

Pre-examine data

Prior to conducting the analysis of covariance (ANCOVA) to test hypotheses 1 through 3b, a preliminary analysis evaluating the homogeneity of variances was tested.

This determines whether the assumptions of ANCOVA were met. Levene’s Test for 88

Equality of Variances allows researchers to assess if the two groups have approximately equal variances for a specific variable. The results of the homogeneity of variance tests indicated that none of the p-values are significant at the 0.05 significance level, suggesting violation of the assumptions (see Table 4.5). However, analysis of variance is robust to violations of its assumption if the sample sizes are equal or close to equal (i.e., the sample size in the largest group should not be greater than 1/2 times the sample size in the smallest group) across experimental conditions (Leech, Barrett, & Morgan, 2005).

Thus, further analyses were continued because similar sample sizes were observed across the four treatment groups (112 in the concrete information condition, 106 in the abstract information condition, 110 in the happiness appeal condition, and 108 in the sadness appeal condition).

Before starting the regression analysis to test hypotheses, normality and linearity were assessed to check for violations of the basic assumptions needed for valid multiple regression analyses. As shown in Table 4.6, skewness and kurtosis values for every variable were between -5 to 5, so the assumptions for multiple regressions were met.

Dependent F df1 df2 Sig. Variable Information .011 1 216 .918 quality Pleasure .898 1 216 .344 Arousal .159 1 216 .691 Note. Test the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design: Intercept + Pre mood+ Information type

Table 4.5. Levene’s Test of Equality of Error Variances

Dependent variable N Mean SD Skewness Kurtosis Information quality 218 4.98 1.44 -.909 -1.18 Pleasure 218 3.82 .997 3.30 1.81 Arousal 218 5.16 1.22 -2.63 .274 Purchase intention 218 5.70 .806 -1.55 -1.90

Table 4.6. Skewness and Kurtosis Value

Hypothesis Testing

Analysis of covariance (ANCOVA) was employed to test Hypotheses 1 to 3b to predict the effects of information type (concrete vs. abstract) and emotional appeal type

(happiness vs. sadness) on information quality, pleasure and arousal. Pre-mood was treated as a covariate.

H1. Perceptions of information quality will be higher for concrete information

than for abstract information.

Hypothesis 1 predicted that information quality presented in the website text would be higher for concrete information than for abstract information. The results of the analysis of covariance (ANCOVA) [between-subjects factor: information type (concrete, abstract); covariate: pre-mood] revealed main effects of information type, F (1, 215) =

6.40, p = .012, and pre-mood, F (1, 215) = 10.8, p = .001 (see Table 4.9). The test results indicated that perceptions of information quality differed significantly between the concrete and abstract information groups when controlling for pre-mood. Subjects in the concrete information group (N = 112, M = 5.83, SD = .81) perceived store information presented on websites to be of higher quality than those in the abstract information group

(N = 106, M = 5.56, SD = .78) (see Table 4.7). Thus, Hypothesis 1 was supported.

H2a. Compared to those exposed to abstract information, those exposed to

concrete information will experience more pleasure.

Hypothesis 2a proposed that pleasure induced by website images will be higher for concrete information than for abstract information. The independent variable was information type, the dependent variable was pleasure, and pre-mood was treated as a covariate. The results of the analysis of covariance (ANCOVA) showed no significant difference in pleasure by information type, F (1,215) = .51, p = .476 (see Table 4.7).

Thus, Hypothesis 2a was not supported.

H2b. Compared to those exposed to abstract information, those exposed to

concrete information will experience less arousal.

Hypothesis 2b stated that arousal induced by website images will be lower for concrete information compared to abstract information. The independent variable was information type, the dependent variable was arousal, and pre-mood was treated as a covariate. The test results for the analysis of covariance (ANCOVA) indicated no significant difference in arousal by information type, F (1,215) = 2.40, p = .123 (see

Table 4.7). Thus, Hypothesis 2b was not supported.

Dependent Information type group variable Concrete Abstract F P Information 5.83 5.56 6.40 .012* quality (.81) (.78) Pleasure 5.03 4.92 .51 .476 (1.16) (1.13) Arousal 3.91 3.72 2.40 .123 (.97) (1.02) Note. * p < .05, ***p < .001. Standard deviations appear in parentheses below means.

Table 4.7. Results of ANCOVA Analysis for the Main Study

ANCOVA for information quality F (1,215) = 6.16 p < .012* ANCOVA for pleasure F (1,215) = .45, p < .476 ANCOVA for arousal F (1,215) = 2.10, p < .123

Figure 4.1. The results of hypotheses 1, 2a, and 2b

H3a. Compared to those exposed to sadness appeals, those exposed to happiness

appeals will feel more pleasure.

Hypothesis 3a proposed that participants exposed to website images with happiness appeals will experience more pleasure than those exposed to website images with sadness appeals. The analysis of covariance (ANCOVA) treating emotional appeal type (happiness, sadness) as between-subject factors and pre-mood as a covariate showed main effects of emotional appeal type and pre-mood, F (1,215) = 8.86, p = .003 and F

(1,215) = 35.4, p = .000, respectively. The results revealed that there was a significant difference in perceptions of pleasure between the concrete and abstract information groups (see Table 4.8) when controlling for pre-mood. People in the happiness appeal condition (N = 110, M = 5.22, SD = 1.14) exhibited greater pleasure than people in the sadness appeal condition (N = 108, M = 4.74, SD = 1.09) (see Table 4.8). Therefore,

Hypothesis 3a was supported.

H3b. Compared to those exposed to sadness appeals, those exposed to happiness

appeals will feel more arousal.

The ANCOVA results indicate no significant difference between sadness and happiness appeals in terms of arousal, F (1,215) = .503, p = .479), suggesting that

Hypothesis 3b was not supported (see Table 4.8).

Dependent Emotional appeal type group variable Happiness Sadness F P Pleasure 5.22 4.74 8.86 .003* (1.14) (1.09) Arousal 3.89 3.72 .503 .123 (1.05) (.93) Note. *p < .05, ***p < .001. Standard deviations appear in parentheses below means.

Table 4.8. Results of ANCOVA Analysis for Hypotheses 3a and 3b

ANCOVA for pleasure F (1,215) = 8.86, p < .003* ANCOVA for arousal F (1,215) = .503, p < .479

Figure 4.2. The results of hypotheses 3a and 3b

A two stage hierarchical multiple linear regression analysis (see Table 4.9) was employed to predict the effects of pleasure and arousal on information quality

(Hypothesis 4a and 4b). The independent variables are pleasure and arousal, and pre- mood functioned as a covariate. On the first step, pre-mood was entered into the model. It was significantly correlated with information quality F (1, 216)= 6.08, p <.015 and accounted for 4.7% of variation in information quality (see Table 4.10). On the second step, when all independent variables were included in the regression model, pre-mood was not a significant predictor of the variance in information quality (see Table 4.11).

Together, the two independent variables accounted for 18.5% of the variance in information quality.

H4a. Pleasure will influence consumers’ cognitive appraisal of information

quality.

Hypothesis 4a stated that pleasure would directly influence the perception of information quality. Per the above information, the test results showed that pleasure was significantly related to information quality (β = .144, t = 2.94, p = .004). Thus,

Hypothesis 4a was supported (see Table 4.11).

H4b. Arousal will influence consumers’ cognitive appraisal of information

quality.

Hypothesis 4b predicted that arousal relates to the perception of information

quality. The results of hierarchical multiple linear regression demonstrated a significant

positive effect of arousal on information quality (β = .250, t = 4.46, p = .000). Thus,

Hypothesis 4b was supported (see Table 4.12).

Model SS df MS F P

1 Regression 6.62 1 6.62 10.6 .001b Residual 134 216 .622 Total 141 217 2 Regression 27.6 3 9.22 17.4 .000c Residual 113 214 .530 Total 141 217 Note. R2 = .217 b, Adjusted R2 = .047 p < .05, *** p < .001, R2 = .443 c, Adjusted R2 = .185 p < .05, *** p < .001 a. Dependent Variable: Information quality b. Predictors: (Constant), Pre-mood c. Predictors: (Constant), Pre-mood, Arousal, Pleasure

Table 4.9. Model for Multiple Regression Analysis for Hypotheses 4a and 4b

Independent β b t p variable 1Premood .217 .145 3.26 .001* Note. *p < .05, *** p <. 001 β: Standardized regression coefficient, b: Unstandardized regression coefficient a. Dependent Variable: information quality

Table 4.10. Coefficients for First Stage Multiple Regression Analysis for Hypotheses 4a and 4b

Independent β b t p variable 2Premood .324 .022 .324 .746 Pleasure .204 .144 2.94 .004* Arousal .309 .250 4.46 .000*** Note. *p < .05, *** p <. 001 β: Standardized regression coefficient, b: Unstandardized regression coefficient a. Dependent Variable: information quality

Table. 4.11. Coefficients for Second Stage Multiple Regression Analysis for Hypotheses 4a and 4b

Multiple linear regressions for pleasure β = .204, t = 2.94, p = .004* Multiple linear regressions for arousal β = .309, t = 4.46, p = .000***

Figure 4.3. The results of hypotheses 4a and 4b

A two stage hierarchical multiple linear regression analysis was conducted with purchase intention as the dependent variable (Hypothesis 5, 6a and 6b). Pre-mood was entered at stage one of the regression to control for pre-mood effects. Pre-mood was significantly correlated with purchase intention, F (1,216)=.608, P<.015 and accounted for 2.3% of the variation in information quality (see Table 4.12). On the second step, information quality, pleasure and arousal (independent variables) were entered, resulting in a significant increase in R2=.287, F (4,213)=21.4, P<.000 (see Table 4.12). Pre-mood did not have a significant partial effect in the full model at the second stage (β = -.023, t

= -3411, p = .733) (see Table 4.14).

H5. Perceptions of information quality will be positively related to purchase

intention.

Hypothesis 5 suggested that consumers’ perception of information quality would positively influence purchase intention. The independent variable was information quality, the dependent variable was purchase intention, and pre-mood was treated as a covariate. Per the paragraph above, the results showed a significant positive relationship between information quality and purchase intention (β = .404, t = 6.25, p = .000), supporting Hypothesis 5 (see Table 4.14).

H6a. Pleasure will be positively related to purchase intention.

Hypothesis 6a predicted that consumers’ feelings of pleasure are positively related to their purchase intention. The independent variable was pleasure, the dependent variable was purchase intention, and pre-mood was treated as a covariate. The results demonstrated no significant positive effect of pleasure on purchase intention (β = .078, t

= 1.16, p = .247). Thus Hypothesis 6a was not supported (see Table 4.14).

H6b. Arousal will be related to purchase intention.

Hypothesis 6b predicted that arousal would be related to consumer purchase intention. The independent variable was arousal, the dependent variable was purchase intention, and pre-mood was treated as a covariate. The results showed a significant positive influence of arousal on purchase intention (β = .184, t = 2.70, p = .007), supporting Hypothesis 6b (see Table 4.13).

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Model SS df MS F P 1 Regression 8.81 1 8.81 6.00 .015b Residual 316 216 1.46 Total 325 217 2 Regression 93.4 4 23.3 21.4 .000c Residual 232 213 1.09 Total 325 217 Note. R2 = .165 b, Adjusted R2 = .027 p < .05, *** p < .001, R2 = .536 c, Adjusted R2 = .287 p < .05, *** p < .001. a. Dependent Variable: Purchase intention b. Predictors: (Constant), Pre-mood c. Predictors: (Constant), Pre-mood, Information quality, Arousal, Pleasure

Table 4.12. Model for Multiple Regression Analysis for Hypotheses 5, 6a, and 6b

Independent β B t p variable 1Premood .165 .167 2.45 .015 Note. *p < .05, *** p < .001 β: Standardized regression coefficient, b: Unstandardized regression coefficient.

Table 4.13. Coefficients for First Stage Multiple Regression Analysis for Hypotheses 5, 6a, and 6b

Independent β b t p variable 2Premood -0.22 -.023 .341 .733 Information .404 .613 6.25 .000*** quality Pleasure .078 .083 1.16 .247 Arousal .184 .227 2.70 .007* Note. *p < .05, *** p < .001 β: Standardized regression coefficient, b: Unstandardized regression coefficient.

Table 4.14. Coefficients for Second Stage Multiple Regression Analysis for Hypotheses 5, 6a, and 6b

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Multiple linear regressions for pleasure β = .078, t = 0.83, p = .000*** Multiple linear regressions for pleasure β = .184, t = 2.27, p = .247 Multiple linear regressions for arousal β =.404, t = 2.70, p = .007*

Figure 4.4. The results of hypotheses 5, 6a, and 6b

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H Hypotheses Result

H1 Perceptions of information quality will be higher Supported for concrete information than for abstract information

H2a As compared to those exposed to abstract Not supported information, those exposed to concrete information will experience more pleasure

H2b As compared to those exposed to abstract Not supported information, those exposed to concrete information will experience less arousal

H3a As compared to those exposed to sadness Supported appeals, those exposed to happiness appeals will feel more pleasure

H3b As compared to those exposed to sadness Not supported appeals, those exposed to happiness appeals will feel more arousal

H4a Pleasure will influence consumers’ cognitive Supported appraisal of information quality

H4b Arousal will influence consumers’ cognitive Supported appraisal of information quality

H5 Perceptions of information quality will be Supported positively related to purchase intention

H6a Pleasure will be positively related to purchase Not Supported intention

H6b Arousal will be related to purchase intention Supported

Table 4.15. Summary of Results of the Hypothesis Testing

103

Figure 4.5. Summary of results of the hypothesis testing

104

Chapter 5: Discussion and Conclusion

The purpose of the research was to examine the impact of fair trade store information on consumers’ cognitive perceptions of information quality and on affective emotions, both of which influence purchase intention based on the Stimulus-Organism-

Response model. The main study investigated the following: (a) the effect of information type on information quality, pleasure, and arousal (Hypotheses 1, 2a, and 2b); (b) the effect of emotional appeal type on pleasure and arousal (Hypotheses 3a and 3b); (c) the effects of pleasure and arousal on information quality (Hypotheses 4a and 4b); and (d) the effects of information quality, pleasure, and arousal on purchase intention (Hypotheses

5a, 5b, and 6). Table 4.15 shows the results of hypotheses tests. This chapter summarizes the findings of the study, the conclusions and implications, and presents limitations of the study and recommendations for future research.

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5.1. Discussion of Findings from the Main Study

(1) The effect of information type on information quality, pleasure, and arousal

(Hypotheses1, 2a, and 2b)

The present study investigated the relationships among two levels of information type (concrete vs. abstract) and three dependent variables (information quality, pleasure, and arousal), controlling for pre-mood (treated as a covariate). Analysis of covariance

(ANCOVA) was used to test the relationships. The results revealed that there was a significant main effect of information type on perceptions of information quality. The effects of information type on pleasure and arousal were not supported.

Information quality. Hypothesis 1 results revealed that information type

(concrete versus abstract) has a significant main effect on the perception of information quality. That is, participants exposed to website text composed of concrete information perceived the information to be of higher quality than those exposed to website text composed of abstract information. This suggests that when concrete information is presented on fair trade websites, people will rate the quality of the target information higher than when abstract information is presented on such websites. These findings are congruent with the S-O-R paradigm (Mehrabian & Russell, 1974) and prior research, which indicated the superiority of concreteness effects; concrete information has a more profound influence on cognitive responses (e.g., memorability, comprehension) than does abstract information (Begg & Paivio, 1969; Dawn, 1974; Holmes & Langford, 1976;

Paivio, 1971; Schwanenflugel & Shoben, 1983; Yuille & Paivio, 1969).

106

Pleasure. The focus of Hypothesis 2a was to assess the effect of information type

(concrete versus abstract) on pleasure. Contradicting the predictions, information type had no effect on pleasure. Although the pleasure experienced by shoppers was higher in the concrete than the abstract information group, the difference was not statistically significant. This is inconsistent with the S-O-R paradigm (Mehrabian & Russell, 1974) and previous research suggesting that information type is positively related to emotions such as pleasure and satisfaction (Szymanski & Hise, 2001; Huang, 2003).

A likely explanation can be found in prior research on store environments. Oh,

Fiorito, Cho, and Hofacker (2008) indicated that a text-based website design has less impact on consumer perceptions about shopping enjoyment than a picture-based design.

A consumer’s perception that a website is information-intensive may reduce consumer shopping enjoyment regardless of the type of information the website conveys. As a result, the effectiveness of the information communicated may weaken the pleasure that the consumer experiences while visiting the website.

Arousal. Hypothesis 2b assessed the effects of information type on arousal. The results showed that no significant effect of information type on arousal was found. That is, the arousal experienced when participants were exposed to website text comprising concrete information was not significantly different from those exposed to website text comprising abstract information. Although Donovan and Rossiter (1982) found that information type affects perceived arousal levels, no relationship was found in the current study.

This can be explained by arousing environment quality. According to Mehrabian and Russell (1974), consumers are aroused when exposed to an environment that 107

increases their perceptions of novelty, complexity, intensity, unfamiliarity, improbability, change or uncertainty of the setting. Donovan and Rossiter (1982) found evidence that perceived novelty has a positive influence on arousal. The recent dramatic rise in demand for fair trade products (Ozcaglar-Toulouse, Shiu, & Shaw, 2006) suggests that more consumers are aware of fair trade, and thus, information about fair trade may not be an unfamiliar attribute for consumers. Therefore, the effectiveness of the information communicated may weaken the arousal that the consumer experiences while visiting the website.

(2) The effect of emotion type on pleasure and arousal (Hypotheses 3a and 3b)

Two major findings in the main study included the effect of emotional appeal type on pleasure and on arousal. Pre-mood was controlled in the ANCOVA. The results revealed that there was a significant main effect for emotional appeal type on pleasure

(Hypothesis 3a), while no effect on arousal was observed (Hypothesis 3b).

Pleasure. The focus of Hypothesis 3a was to assess the effect of emotional appeal type (happiness versus sadness) on consumers’ emotional state of pleasure. As expected, the results revealed that participants exposed to website images with a happiness appeal exhibited greater pleasure than those exposed to website images with a sadness appeal.

This suggests that when a happiness appeal is presented on fair trade websites, people are more likely to feel pleased than when a sadness appeal is provided. These findings are consistent with previous research indicating a positive relationship between happiness appeals and positive emotions (e.g., Edell & Burke, 1986).

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Arousal. Hypothesis 3b focused on the effect of emotion appeal type (happiness versus sadness) on arousal experienced by shoppers. In contrast with the prediction, emotional appeal type had no significant impact on participants’ emotional state of arousal. This result is inconsistent with the S-O-R paradigm (Mehrabian & Russell, 1974) that the two emotional stimuli (happiness and sadness) influence consumers in terms of pleasure and arousal.

The possible reason for these results might be the experimental environments and manipulations. In the present study, extreme sadness or happiness in artisans’ faces was excluded from the prior selection of pictures because it potentially diminished the real- world setting (Garner, 1985). As shown in Table 4.8, the average score for arousal among participants in happiness was 3.89, and the average score for arousal among participants in sadness appeal was 3.74, with a range of 1 to 7. These scores indicate no significant mean difference between the two groups. Thus, it is possible that, even in the treatment conditions where an artisan picture stimulated some degree of participant arousal, the absence of extreme sadness or happiness may deflate the magnitude of emotional appeal on perceived arousal.

(3) The effects of pleasure and arousal on information quality (Hypotheses 4a and 4b)

Hypotheses 4a and 4b predicted that consumers’ emotions (pleasure and arousal) affect their perceptions of information quality. Hypotheses 4a and 4b were supported by the results in the analysis of multiple linear regressions.

Pleasure. The findings confirmed that pleasure had a direct effect on cognitive state, as measured by information quality (Hypothesis 4a). People who experienced more

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pleasure while browsing fair trade website images were likely to perceive the information as being of higher quality. This suggests that when experiencing pleasure, a consumer perceives information to be of higher quality. Consistent with previous studies indicating the role of positive feelings on optimistic judgments (Lerner & Keltner, 2000), this study showed that pleasure does lead individuals to perceive information to be of higher quality.

Arousal. The analysis results indicated that arousal significantly influenced information quality. That is, people who experienced more arousal when viewing website images tended to have higher perceptions of information quality. This result is consistent with previous research indicating that judgment quality can be influenced by individuals’ current emotional state (Clore, 1992; Schwarz, 1990; Schwarz & Clore, 1983).

(4) The effect of information quality, pleasure, and arousal on purchase intention

(Hypotheses 5, 6a, and 6b)

Hypotheses 5, 6a, and 6b predicted that consumers’ cognitive state (information quality) and emotions (pleasure and arousal) influence their purchase intention. The test results revealed that Hypotheses 5 and 6b were supported, while Hypothesis 6a was not supported.

Information quality. Hypothesis 5 explored the effect of the perception of information quality on purchase intention. The results showed that there was a positive relationship between the perception of information quality and purchase intention. People who perceived higher quality information while browsing fair trade websites were likely to have greater intention to purchase fair trade products online. This is congruent with prior research indicating the critical role of information quality in terms of shopping 110 decisions, such as purchase/patronage decisions and consumers’ behavioral intentions

(e.g., intent to use, intent to recommend, and intent to prefer over other websites) (e.g.,

Kim & Niehm; 2009; Park & Stoel, 2005).

Pleasure. The effect of pleasure on purchase intention was not statistically significant in this study (Hypothesis 6a was not supported). Participants’ pleasure experienced while browsing websites did not have a significant effect on their intent to buy fair trade products. This research is inconsistent with prior research indicating a positive relationship between pleasure and purchase intent (Donovan & Rossiter, 1992).

One possible explanation for the different result is that pleasure might not be a strong factor leading to purchase behavior in fair trade shopping. In researching consumer ethical behavior in the fair trade context, Castaldo, Perrini, Misani, and Tencati

(2009) suggested that trust plays a crucial role in determining consumer choices. The authors revealed that trust in fair trade significantly influenced consumers’ brand loyalty and willingness to pay a premium price toward fair trade products. The attribute of fairness in the fair trade concept cannot be experienced at the time of purchase (Potts,

2004; Weber, 2007), so it is reasonable to assume that other factors, such as trust, may be more important considerations than feelings of pleasure in shopping for fair trade products.

Arousal. Arousal had a significant effect on purchase intention. People who experienced more arousal while browsing websites tended to have greater purchase intention than those who felt less arousal. Arousal was positively related to the likelihood of purchasing fair trade products. The results are consistent with the S-O-R paradigm,

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suggesting the significant effects of emotion on consumers’ response behavior

(Mehrabian & Russell, 1974).

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5.2.Conclusions and Implications

5.2.1. Theoretical implications

In spite of a large body of literature on ethical consumption, relatively piecemeal attention has been paid to fair-trade consumer behavior in the online shopping environment. The objective of the current research was to investigate consumer reactions to environmental cues provided in online fair-trade store information. Of particular interest were the relationships between information quality, pleasure, arousal, and purchase intention, as affected by the type of information (i.e., concrete or abstract) and the type of emotional appeal (i.e., happiness or sadness).

One major contribution of this study is to provide empirical support for the hypothesis that information type and emotional-appeal type have significant influences on the consumer decision-making process. As predicted, the results showed that concrete information strengthened the perception that information is of higher quality compared to abstract information. This effect led to stronger purchase intent towards fair-trade products. Similarly, the findings indicated that a happiness appeal increased the consumer pleasure experienced, while a sadness appeal did not. Together, perceived information quality and experienced arousal had significant impacts on overall purchase intention.

The current study also makes contributions in methodology. Concerning information type, the study re-evaluated the discrepancy regarding the different effects of concrete and abstract information with a robust study measure. The properties of 113

information (i.e., amount, credibility, and comprehensibility) were controlled using the two information conditions, which enabled the reduction of confounding effects

(Marschark, Richman, Yuille, & Hunt, 1987; Ransdell & Fischler, 1989). In previous research, d’Astous and Mathieu (2008) and Wright (1979) examined the effects of concrete information on actual purchasing behavior. However, in manipulating the effects, confounding effects were not considered in both research methodologies. As such, one contribution of this research is to deepen the methodology by expanding knowledge about the effects of information type on consumption behavior.

Another significant result of this study is to resolve the mixed findings in previous studies regarding the effectiveness of different types of emotional appeals. Studies that looked at which types of emotional appeals are more effective in increasing consumer responses have been inconclusive in the context of pro-social consumer behavior

(Cialdini & Kenrick, 1976). In this study it was found that the happiness appeal was a more effective means of creating pleasure responses than was the sadness appeal. Thus, the results provide partial empirical support for the hypothesis that happiness appeals have a greater effect in facilitating positive consumer responses.

The results of the current study also have theoretical implications. A significant gap in fair-trade research is the lack of a theoretical approach to explain consumer reactions toward fair-trade information. The S-O-R (Stimuli-Organism-Response) paradigm suggests that environmental cues (S) elicit intervening effects, including the consumers’ cognitive and affective states (O), which consequently lead to purchase intention (R) (Mehrabian & Russell, 1974). Applying the S-O-R model to a fair-trade shopping context, the research here empirically showed how consumers’ cognitive 114

responses to information quality and emotional states are evoked (O), and ultimately behavioral outcomes of the purchase intention (R) are generated, through the effects of the environmental cues of fair-trade store information (S). Thus, the study revealed the S-

O-R model to be applicable in explaining consumer responses toward fair-trade store information.

5.2.2. Managerial implications

Fair trade retailers have emphasized that it is important to find ways to translate fair trade principles into messages that increase consumption behavior in order to benefit fair trade producers. By demonstrating the significant effects of information and emotional appeal type on consumer response behaviors, the current study’s findings provide valuable managerial insights to online fair trade retailers regarding the development and management of effective communication strategies.

The major conclusion of the main study concerns the direct effect of information type on consumers’ perception of information quality. The findings showed that consumers who browsed website text that included concrete information perceived information to be of higher quality than those who browsed website text composed of abstract information. This suggests that the use of concrete information on fair trade websites may play a significant role in enhancing the information quality perceived by online shoppers. Accordingly, the use of concrete information might be an effective form to present store information in online fair trade store environments. Research findings suggest that providing concrete information on fair trade websites may allow consumers

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to perceive that such information has greater clarity (Paivio & Desrochers, 1980), may increase consumers’ understanding of and processing of information (Dawn, 1974;

Holmes & Langford, 1976; Schwanenflugel & Shoben, 1983), and help consumers to perceive information as being of higher quality.

The findings of this research revealed that emotional appeals significantly impact the pleasure felt by online consumers. Particularly, when happiness appeals are offered on a website, consumers experienced greater feelings of pleasure. According to the results, a happiness execution style may enhance consumer’s pleasure and enjoyment while shopping on the Internet. This allows online fair trade retailers to cultivate pleasure in the mind of the consumer. For instance, happiness appeals (i.e., smiling pictures of fair trade artisans) presented on fair trade websites increased consumers’ feelings of pleasure.

Thus, the results of this study suggest that happiness appeals should be used on online fair trade retailers’ websites to facilitate positive shopping experiences for consumers.

The main study revealed that information quality is an important factor influencing purchase intention. Consumers who perceive information as being of higher quality tend to have a favorable intention to purchase fair trade products. In addition, this empirical evidence suggests an indirect effect of concrete information, through information quality, on purchase intention. Therefore, fair trade online retailers should consider using concrete information as a new method of store information presentation.

Utilizing concrete information in online store communication may lead consumers to purchase fair trade products due to the informativeness of the information and consequently enhance the competitiveness of fair trade online retailers.

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The findings of the main study also revealed a significant effect of arousal on purchase intention. People who experienced more arousal while browsing fair trade website page were more likely to have greater purchase intention. In the online shopping context, higher level of arousal is positively associated with consumer behaviors such as spending time in store and spending money (Foxall & Greenley, 1998; Greenaland &

McGoldrick., 1994). Hence, it is important for fair trade retailers to build arousing online atmosphere to enhance consumer’s purchase intention.

5.3. Limitations of the Study

While offering valuable insights into how consumers respond to online fair trade

store information, several limitations were evident in this research: (a) the

generalizability of the findings; (b) the moderating variables; and (c) the analysis

techniques.

First, the present study is based on a single context of online retailing, which is

the fair trade shopping situation. This particular context was chosen to be the

experimental condition since relatively less is known about fair-trade consumer

behavior in the online shopping environment. Therefore, the results of this study

should be interpreted with caution when being applied to other types of online

retailing.

Second, this study’s generalizability is limited due to the use of ANCOVA and multiple linear regression analysis. Although indirect relationships among store information (Stimulus), responses (Organism), and behavior (Response) have been 117

suggested in the present study, the statistical tests used cannot verify the precise mediation role of cognitive and emotional responses in the model. Therefore, to fully examine indirect effects suggested by the Stimulus-Organism-Response, a more powerful analysis technique such as mediated regression or structural equation modeling is required in future research.

Third, the lack of support for the relationship between emotional appeal type and arousal can be a major obstacle in finding a meaningful relationship between emotional appeal type and purchase intention. Particularly, this study failed to demonstrate the effects of emotional appeal type on arousal experienced (Hypothesis 3b). However, as

Hypothesis 6b reveals, arousal was a significant factor that influenced purchase intention.

The failure in testing Hypothesis 3b may thus limit the scope of the research analysis.

Therefore, further study should continue to explore how arousal can be effectively manipulated.

Lastly, consumer fair trade knowledge and previous purchase experience with fair trade shopping may influence variables tested in this research, but these constructs were not included in the proposed model. Several researchers have found that knowledge and previous shopping experiences are major antecedents of attitudes that lead to fair trade buying (Hunt & Vitell, 1986, 1993; Shaw & Clarke, 1999; Shaw & Shiu, 2002, 2003).

Therefore, future researchers should examine the moderating role of such constructs.

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5.4 Future Research

The following future research ideas are recommended. The current study examined the influence of information type and emotional appeal type on shopping outcomes for fair trade websites. However, the effects such environmental cues have on consumer responses may vary across different types of retail business (e.g., commercial apparel, organic food). Thus, to confirm the interpretations of the research findings, this experiment should be replicated across diverse business types. A study on how consumers react toward environmental cues in various business settings would confirm the present findings and could provide practical insights about the issue.

As discussed previously, additional research should be conducted to evaluate the proposed model in this study and causal relationships among the variables in this model to fully understand consumers’ shopping behavior. Application of structural equation modeling (SEM) could be an appropriate statistical technique for future study. SEM is a powerful data analysis technique that allows researchers to investigate comprehensive theoretical frameworks in a straightforward fashion (Baumgartner & Homburg, 1996).

Thus, in future research, direct and indirect paths of influence among constructs in the proposed model should be reexamined with a stronger analysis technique.

Prior research exploring consumer behaviors in the fair trade shopping context has indicated that purchase behavior is directly or indirectly affected by fair trade knowledge and prior experience with fair trade products (Hunt & Vitell, 1986, 1993;

Shaw & Clarke, 1999; Shaw & Shiu, 2002, 2003). Thus, further examination of possible 119

moderating effects will enable researchers to articulate the effects of information type and emotion appeal type on consumer responses. Therefore, the need for considering moderating effects is recommended in future research.

Additionally, the effects of manipulations used for emotional appeal type may have varying effects on consumer responses due to artisan’s ethnicity. Therefore, testing artisan images portraying diverse ethnicities may provide a more robust prediction of the relationship between emotional appeal and consumer purchase behavior and would be an interesting topic to discuss in future research.

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Appendix A: A List of Countries and Their Fair Trade Marks

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Country National FairTrade Organizations

Australia Fairtrade Australia and New Zealand Austria Fairtrade Austria Belgium Max Havelaar Belgium Canada Fairtrade Canada Denmark Fairtrade Maerket Danmark Estonia Fairtrade Estonia Finland Fairtrade Finland France Max Havelaar France Germany Fairtrade Deutschland Ireland Fairtrade Mark Ireland Italy Fairtrade TransFair Italy Japan Fairtrade Label Japan Latvia Fairtrade Latvia Lithuania Fairtrade Lithuania Luxembourg Fairtrade Lëtzebuerg The Netherlands Stichting Max Havelaar Netherlands New Zealand Fairtrade Labelling Australia and New Zealand Norway Fairtrade Max Havelaar Norway Portugal Fairtrade Ibérica Spain Fairtrade Ibérica Sweden Fairtrade Sweden Switzerland Fairtrade Max Havelaar Switzerland South Africa Fairtrade Label South Africa UK The Fairtrade Foundation Note. Fairtrade International (2014). Retrieved from http://www.fairtrade.net/fairtrade- organizations.html

133

Appendix B:IRB Human Subject Review Exemption Approval for Pretests

(Protocol No.201E0303)

134

Office of Research Office of Responsible Research Practices

Protocol Title: FAIR TRADE WEBSITE CONTENT: EFFECTS OF INFORMATION TYPE AND EMOTIONAL APPEAL TYPE: PILOT TEST

Protocol Number: 2014E0303

Principal Investigator: Leslie Stoel

Date of Determination: 06/11/2014

Qualifying Category: 02

Attachments: None

Dear Investigators, The Office of Responsible Research Practices has determined the above referenced project exempt from IRBIreview.

Please note the following: • Retain a copy of this correspondence for your records. • Only the OSU staff and students named on the application are approved as OSU investigators and/or key personnel for this study. • No changes may be made to exempt research (e.g., personnel, recruitment procedures, advertisements, instruments, etc.). If changes are need, a new application for exemption must be submitted for review and approval prior to implementing the changes. • Per university requirements, all research-related records (e.g., application materials, letters of support, signed consent forms, etc.) must be retained and available for audit for a period of at least three years after the research has ended. • It is the responsibility of the investigators to promptly report events that may represent unanticipated problems involving risks to subjects or others.

This determination is issued under The Ohio State University’s OHRP Federalwide Assurance #00006378. All forms and procedures can be found on the ORRP website: www.orrp.osu.edu.

Please feel free to contact the Office of Responsible Research Practices with any questions or concerns. Thanks, Cheri ! Cheri Pettey Quality Improvement Specialist | Regulatory & Exempt Determinations Office of Responsible Research Practices | The Ohio State University T: 614.688.0389 F: 614.688.0366 E: [email protected] W: www.orrp.osu.edu

135

Appendix C: IRB Human Subject Review Exemption Approval for Main Study

(Protocol No. 2014E0337)

136

Office of Research Office of Responsible Research Practices

Protocol Title: Fair Trade Website Content: Effects Of Information Type And Emotional Appeal Type: Main Test

Protocol Number: 2014E0337 Principal Leslie Stoel Investigator: Date of Determination: 07/09/2014 Qualifying Category: 2 Attachments: None

Dear Investigators,

The Office of Responsible Research Practices has determined the above referenced project exempt from IRB review.

Please note the following: Retain a copy of this correspondence for your records. Only the OSU staff and students named on the application are approved as OSU investigators and/or key personnel for this study. No changes may be made to exempt research (e.g., personnel, recruitment procedures, advertisements, instruments, etc.). If changes are need, a new application for exemption must be submitted for review and approval prior to implementing the changes. Per university requirements, all research-related records (e.g., application materials, letters of support, signed consent forms, etc.) must be retained and available for audit for a period of at least three years after the research has ended. It is the responsibility of the investigators to promptly report events that may represent unanticipated problems involving risks to subjects or others.

This determination is issued under The Ohio State University’s OHRP Federalwide Assurance #00006378. All forms and procedures can be found on the ORRP website: www.orrp.osu.edu.

Please feel free to contact the Office of Responsible Research Practices with any questions or concerns. Thank You, Ellen

Ellen Patricia, MS, CIP Program Director HRPP Quality Improvement Office of Research Office of Responsible Research Practices 307 Research Administration Building , 1960 Kenny Road, Columbus, OH 43210 614-688-5556 Office / 614-688-0366 Fax [email protected] www.orrp.osu.edu

137

Appendix D: Pilot Study Consent Form

138

Dear Participants,

Thank you for taking time to participate in this consumer research study! This study is conducted by researchers in Human Science department at the Ohio State University. In this study, we are interested in evaluating fair trade store information. The following questionnaire will require approximately 7 - 10 minutes to complete.

We award $ 1.00 for your participation in this survey. By clicking on the continue bottom below, you are consenting to be a participant in this research study. If you choose to participate in this project, please answer all questions as honestly as possible. Participation is strictly voluntary and you may stop participating any time. Please be assured that there is no foreseeable risk or discomfort associated with this research. Your data will not be associated with your name or with any other identifiable information (e.g., your Mechanical Turk account information will only be connected to your payment, not to any of your responses). All data will be held and protected by Qualtrics (a survey research company) using their online security features. Although every effort to protect confidentiality will be made, no guarantee of Internet survey security can be given as, although unlikely, transmissions can be intercepted and IP addresses can be identified. if you have any questions about the research, or in the extremely unlikely event of a research- related injury, please contact Ms. Songyee Hur ([email protected]) or Dr. Leslie Stoel ([email protected], phone 1-614-688-8594, fax 1-614-688-8133) at the Ohio State University, 1787 Neil Ave., Suite Number 265, Columbus, OH 43210). For questions about your rights as a participant in this study or to discuss other study-related concerns or complaints with someone who is not part of the research team, you may contact Mr. Sandra Meadows at The Ohio State University Office of Responsible Research Practices, 300 Research Administration Building, 1960 Kenny Road, Columbus, OH 43210-1063 (phone 1-800-678-6521).

Thank you for taking the time to assist me in my educational endeavors.

Sincerely,

Songyee Hur, Masters candidate Dr. Leslie Stoel, Professor Dept. of Consumer Sciences Dept. of Consumer Sciences 265 Cambell Hall, 1787 Neil Avenue 265 Cambell Hall, 1787 Neil Avenue The Ohio State University The Ohio State University Columbus, OH 43210-1295 Columbus, OH 43210-1295 Phone. 1-206-735-1562 Phone. 1-614-688-8594 Email. [email protected] Email. [email protected]

139

Appendix E: Pilot Study Survey

140

Q1 Please indicate your overall evaluation of the fair trade store information on each of the 2 questions below.

1! 2! 3! 4! 5! 6! 7 Very Very abstract, concrete, hard for me easy for me to form ! ! ! ! ! ! ! to form mental mental images of images of this this The The information information may not be may be experienced experienced by our by our senses of senses of persons, persons, ! ! ! ! ! ! ! places, and places, and things that things that can be can be seen, heard, seen, heard, felt, felt, smelled or smelled or tasted tasted

141

Q2 Please evaluate the fair trade store information presented in the paragraph on the last page.

Strongly0 Strongly0 disagree! agree! 1 2! 3! 4! 5! 6! 7

It provides accurate ! ! ! ! ! ! ! information It provides believable ! ! ! ! ! ! ! information It provides timely ! ! ! ! ! ! ! information It provides relevant ! ! ! ! ! ! ! information It provides easy to ! ! ! ! ! ! ! understand information It provides the information ! ! ! ! ! ! ! at the right level of detail It presents the information ! ! ! ! ! ! ! in an appropriate format

142

Q3 How easy or difficult was it for you to understand and comprehend the information? (for example, ‘1’ for ‘very hard’ and ‘7’ for ‘very easy’)

1! 2! 3! 4! 5! 6! 7! Very easy Very hard to ! ! ! ! ! ! ! to understand understand Very easy Very hard to to ! ! ! ! ! ! ! comprehen comprehend d

Q4 Please indicate the number that best indicates the degree to which you agree or disagree with the following statement (for example, '1' 'for strongly disagree' and '7' for 'strongly agree').

Strongly0 Strongly0 disagree0! 0 agree0! 1! 2! 3! 4! 5! 6! 7! The artisan in this picture is ! ! ! ! ! ! ! from a developing country

Q5 Please indicate the number that best indicates the degree to which you agree or disagree with the following statement (for example, '1' for 'strongly disagree' and '7' for 'strongly agree').

Strongly0 Strongly0 disagree0! agree0! 1! 2! 3! 4! 5 6! 7! The artisan in this picture is ! ! ! ! ! ! ! from a poor country

143

Q6 Please indicate how much the following emotions describe how you feel right now (for example, '1' for 'calm' and '7' for 'tense').

1! 2! 30 4! 5! 6! 7! Calm ! ! ! ! ! ! ! Tense Relaxed ! ! ! ! ! ! ! Stressed Displease Pleased ! ! ! ! ! ! ! d Happy ! ! ! ! ! ! ! Sad Excited ! ! ! ! ! ! ! Depressed

144

Q7 The following words describe moods. Please indicate the extent to which you agree or disagree that the following statements describe your mood right now.

Strongly0 Strongly0 disagree! agree! 1 2! 3! 4! 5! 6! 7

Upset ! ! ! ! ! ! ! Distressed ! ! ! ! ! ! ! Sympathetic ! ! ! ! ! ! ! Alarmed ! ! ! ! ! ! ! Grieved ! ! ! ! ! ! ! Troubled ! ! ! ! ! ! ! Compassionate ! ! ! ! ! ! ! Perturbed ! ! ! ! ! ! ! Worried ! ! ! ! ! ! ! Disrupted ! ! ! ! ! ! !

The following set of statements asks some general questions about you. Please answer the following questions.

Q11 What is your age? (Type your age in the box)

Q12 What is your gender?

! Male ! Female ! Other ______

145

Appendix F: Pilot Study Stimuli

146

Information type

Concrete World-Fair-Partners is a nonprofit organization that retails handmade fair trade

garments on our online store to improve the economic status of women in poor

communities in poverty-stricken countries. World-Fair-Partners works with 40

artisan groups that collectively employ over 3,000 women in 20 countries

around the world. World-Fair-Partners’ work to build sustainable livelihoods

has been recognized by CNN, Marie Claire, Daily Candy, CBS News, and O

Magazine.

Abstract Our company is a nonprofit entity that handles handmade fair trade items on

our e-commerce store to improve the economic status of women in poor

communities in developing places. Our company works with many artisan

groups that collectively employ thousands of women in many countries around

the world. Our company’s work to model sustainable livelihoods has been

mentioned in national news outlets and fashion magazines.

Information type manipulations used in Pilot Test.

147

Emotion appeal type Happy Sad

Image1

Image2

Image3

Emotion appeal manipulation used in Pilot Test

148

Image4

Image5

Emotion appeal manipulation used in Pilot Test

149

Appendix G: Main Study Consent From

150

Dear Participants,

Thank you for taking time to participate in this marketing research study. This study is conducted by researchers in Human Science department at The Ohio State University. In this study, we are interested in evaluating fair trade store information. You will be asked to answer a series of questions. The total duration will take around 15-20 minutes.

We award $ 1.75 for your participation in the survey. By clicking on the survey link below, you are consenting to be a participant in this research study. Although there are no immediate benefits to you for participating, we hope the research conducted in this study will contribute to our understanding of how individuals evaluate fair trade websites. There are no foreseeable risks or discomforts associated with this research. You may complete the current survey and receive the associated payment, or seek other task on Mechanical Turk at your discretion. Your data will not be associated with your name or with any other identifiable information (e.g., your Mechanical Turk account information will only be connected to your payment, not to any of your responses). All data will be held and protected by Qualtrics (a survey research company) using their online security features. Although every effort to protect confidentiality will be made, no grantee of Internet survey security can be given as, although unlikely, transmissions can be intercepted and IP addresses can be identified.

If you have questions about the research, or in the extremely unlikely event of a research-related injury, please contact Ms. Songyee Hur ([email protected]) or Dr. Leslie Stoel ([email protected], phone 1-614-688-8594, fax 1-614-688-8113) at The Ohio State University, 1787 Neil Ave., Suite Number 265, Columbus, OH 43210). For questions about your rights as a participant in this study or to discuss other study-related concerns or complaints with someone who is not part of the research team, you may contact Ms. Sandra Meadows at The Ohio State University Office of Responsible Research Practices, 300 Research Administration Building, 1960 Kenny Road, Columbus, Ohio 43210-1063 (phone 1-800-678-6251).

Your participation is completely voluntary. If you encounter a question that is uncomfortable to answer, you may skip the question without penalty. Refusing to participate or quitting the survey in the middle will involve no penalty (although you will not receive payment if you do not progress to the end of the survey, your account will not be debited and there will not be any other penalty).

Sincerely,

Songyee Hur, Master's student Dr. Leslie Stoel, Professor Dept. of Human Sciences Dept. of Consumer Sciences 265 Cambell Hall, 1787 Neil Avenue 265 Cambell Hall, 1787 Neil Avenue The Ohio State University The Ohio State University Columbus, OH 43210-1295 Columbus, OH 43210-1295 Phone. 1-206-735-1562 Phone. 1-614-688-8594 Email. [email protected] Email. [email protected]

151

Appendix H: Main Study Survey Example

152

153

154

Appendix I: Main Study Questionnaire

155

Q1 The following words describe moods. Please indicate the extent to which you agree or disagree that the following statements describe your mood right now.

Strongly0 Strongly0 disagree01! agree0! 2! 3! 4! 5! 6! 7! Discouraged ! ! ! ! ! ! ! Happy ! ! ! ! ! ! ! Sad ! ! ! ! ! ! ! Delighted ! ! ! ! ! ! ! Downhearted ! ! ! ! ! ! ! Joyful ! ! ! ! ! ! !

Q2 Please indicate the number that best indicates the degree to which you agree or disagree with the following statement (for example, '1' 'for strongly disagree' and '7' for 'strongly agree').

Strongly0 Strongly0 disagree0! agree0! 1! 2! 3! 4! 5! 6! 7! The artisan in this picture is ! ! ! ! ! ! ! from a developing country

Q3 Please indicate the number that best indicates the degree to which you agree or disagree with the following statement (for example, '1' for 'strongly disagree' and '7' for 'strongly agree').

Strongly0 Strongly0 disagree0! agree0! 1 2! 3! 4 5! 6! 7! The artisan in this picture is ! ! ! ! ! ! ! from a poor country

156

Q4 Please evaluate fair trade store information provided in the paragraph on the above web-page.

Strongly0 Strongly0 disagree00 agree0! 1! 2! 3! 4! 5! 6! 7! It provides accurate ! ! ! ! ! ! ! information It provides believable ! ! ! ! ! ! ! information It provides timely ! ! ! ! ! ! ! information It provides relevant ! ! ! ! ! ! ! information It provides easy to ! ! ! ! ! ! ! understand information It provides the information ! ! ! ! ! ! ! at the right level of detail It presents the information ! ! ! ! ! ! ! in an appropriate format

157

Q5 We would like to know how you feel after looking at the fair trade store web-page. Each line below lists a feeling, with a positive extreme on one side and a negative extreme on the other side. Please click the button under the number between each pair of words that best describes your current feelings.

1! 2! 3! 4! 5! 6! 7! Unhappy ! ! ! ! ! ! ! Happy Bored ! ! ! ! ! ! ! Relaxed Annoyed ! ! ! ! ! ! ! Pleased Unsatisfied ! ! ! ! ! ! ! Satisfied Despairing ! ! ! ! ! ! ! Hopeful Melancholic ! ! ! ! ! ! ! Contented

Q6 Below are some additional feelings. Each line lists a feeling, with a positive extreme on one side and a negative extreme on the other side. Please click the button under the number between each pair of words that best describes your current feelings.

1! 2! 3! 4! 5! 6! 7! Unaroused ! ! ! ! ! ! ! Aroused Relaxed ! ! ! ! ! ! ! Stimulated Dull ! ! ! ! ! ! ! Jittery Wide Sleepy ! ! ! ! ! ! ! awake Calm ! ! ! ! ! ! ! Excited Sluggish ! ! ! ! ! ! ! Frenzied

158

Q7 Assume that Fair-Trade-Store is a real, fair trade, online store. Now that you have viewed the store's web-page, please click the button for the number that best reflects your thoughts about purchasing?

Very0low0 Very0high0 1! 2! 3! 4! 5! 6! 7! My willingness to buy fair ! ! ! ! ! ! ! trade products at this store is My willingness to buy fair ! ! ! ! ! ! ! trade product is

Q8 The following words describe moods. Please indicate the extent to which you agree or disagree that the following statements describe your mood right now.

Strongly0 Strongly0 disagree0! agree0! 1! 2! 3! 4! 5! 6! 7! Discouraged ! ! ! ! ! ! ! Happy ! ! ! ! ! ! ! Sad ! ! ! ! ! ! ! Delighted ! ! ! ! ! ! ! Downhearted ! ! ! ! ! ! ! Joyful ! ! ! ! ! ! !

159

Q9 Please indicate your overall evaluation of the fair trade store information provided in the paragraph on the above web-page on each of the 2 questions below.

1! 2! 3! 4! 5! 6! 7! Very Very abstract, concrete, hard for me easy for me to form ! ! ! ! ! ! ! to form mental mental images of images of this: this Information Information may not be may be experienced experienced by our by our senses of senses of persons, persons, ! ! ! ! ! ! ! places, and places, and things that things that can be seen, can be seen, heard, felt, heard, felt, smelled or smelled or tasted tasted

Q10 How easy or difficult was it for you to understand and comprehend the information provided in the paragraph on the above web-page? (for example, '1' for 'very hard' and '7' for 'very easy')

1! 2! 3! 4! 5! 6! 7! Very easy Very hard to ! ! ! ! ! ! ! to understand understand Very easy Very hard to ! ! ! ! ! ! ! to comprehend comprehend

160

The following set of statements asks some general questions about you. Please answer the following questions.

Q11 What is your age? (Type your age in the box)

Q12 What is your gender?

! Male ! Female ! Other ______

Q13 What is the highest degree of level of school you have completed? If currently enrolled, mark the previous grade or highest degree received.

! Less than 9th grade ! 9th to 12th grade, no diploma ! High school graduate (includes equivalency) ! Some college, no degree ! Associate's degree ! Bachelor's degree ! Graduate or professional degree

Q14 How would you classify your self?

! African American ! Caucasian American ! Hispanic/Hispanic American ! Native American ! Asian/Asian American ! Multicultural ! Other ______

161

Q15 What is your personal income?

! $1 to $9,999 or loss ! $10,000 to 14,999 ! $15,000 to $24,999 ! $25,000 to $34,999 ! $35,000 to $49,999 ! $50,000 to $64,999 ! $65,000 to $74,999 ! $75,000 to $99,999 ! $100,000 or more

We would like to know about your real experiences with fair trade shopping. Please answer the following questions based on your own experience.

Q16 Have you ever purchased any fair trade products online before?

! Yes ! No

Answer If Have you ever purchased any fair trade products online before? Yes Is Selected

Q17 If yes, what kind of fair trade products did you purchase?

! Coffee ! Chocolate ! Apparel ! Jewelry ! Tea ! Wine/beer ! Others ______

Q18 Do fair trade principles impact your shopping activities?

! Yes ! No

162

Appendix J: Main Study Stimuli

163

CONCRETE/HAPPY ABSTRACT/HAPPY

CONCRETE/SAD ABSTRACT/SAD

164

Appendix I: Results of t-tests for Fair trade Shopping Experience

165

Measure Prior Non-prior experience experience M(SD) n M(SD) n T df p Information 5.80 61 5.66 157 1.24 114 .216 quality Pleasure 4.91 61 5.00 157 1.65 97.7 .100 Arousal 4.00 61 3.74 157 -.545 108 .587 Purchase 5.64 61 4.97 157 4.00 127 .000 Intention

Results of t-tests for Fair trade Shopping Experience

166