A Comparative Study of the Effect of Virtual Intensity and Work Location Preference

REMOTE AND ON-SITE KNOWLEDGE WORKER PRODUCTIVITY AND ENGAGEMENT: A COMPARATIVE STUDY OF THE EFFECT OF VIRTUAL INTENSITY AND WORK LOCATION PREFERENCE

JOSEFINA MARTINEZ-AMADOR

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Weatherhead School of Management

Designing Sustainable Systems

CASE WESTERN RESERVE UNIVERSITY

May, 2016

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of

Josefina Martinez-Amador

candidate for the degree of Doctor of Philosophy*.

Committee Chair

Kalle Lyytinen, Ph.D., Case Western Reserve University

Committee Member

Diane Bailey, Ph.D., University of Texas at Austin

Committee Member

Kathleen Buse, Ph.D., Case Western Reserve University

Committee Member

Roger Saillant, Ph.D., Case Western Reserve University

Date of Defense

March 2, 2016

*We also certify that written approval has been obtained

for any proprietary material contained therein.

Dedication

This work is dedicated to Betty, my loving partner in life and best friend. Without your support, this will not be possible…just like many other things in my life. Thanks for all your love, patience, understanding, and kindness, for being by my side in the good and the bad times….and for being such an inspiration for the human kind...I love you.

I also dedicate this to my mother (RIP) and my father (RIP), who always encouraged me to work hard, be a good human being, and never to give up. Their values have been the pillars of my success. And to all my siblings, for their moral support and for always believing in me. And lastly, to all my friends, for their words of encouragement, and unconditional support during these four years.

More importantly, I dedicate this work to God, who has given me the strength, the wisdom and the faith to accomplish this personal mission…(“Commit your work to the Lord, and then your plans will succeed” – Proverbs 11:10).

Table of Contents

List of Tables ...... x! List of Figures ...... xi! Abstract ...... xiii! CHAPTER I: INTRODUCTION ...... 1! Personal Research Motivation ...... 1! Problem of Practice ...... 2! Gaps in the Research, Study Goals, and Research Questions ...... 5! Gaps in the Research ...... 8! Study Goals and Research Questions ...... 9! CHAPTER II: LITERATURE REVIEW, THEORETICAL FRAMEWORK & RESEARCH METHODS ...... 13! Remote Work Literature & Previous Research Review ...... 13! Knowledge Worker ...... 13! The Beginning of Remote Work ...... 15! Defining Remote Work ...... 16! The Positive Organizational Impact of Remote Worker ...... 17! The Negative Organizational Impact of Remote Worker ...... 20! Remote Work in Virtual Teams ...... 21! Distributed Work Arrangements ...... 23! Nomadic Work ...... 25! Research Literature Review & Socio-Technical Systems ...... 36! Socio-Technical Systems ...... 36! Technical Subsystem ...... 38! ICT utilization ...... 39! Personnel Subsystem ...... 41! Personality and Traits ...... 41! Intrinsic Motivation ...... 43! Self-Efficacy ...... 45! Theories of Employee engagement ...... 47! Work System/Organizational Structure Subsystem ...... 51! Leadership Exchange (Leader-Member Exchange LMX) ...... 52! Virtual Work Intensity ...... 53!

Environmental Systems ...... 54! Remote Workforce productivity ...... 55! Research Questions ...... 58! Research Design ...... 59! Research Framework ...... 61! Phase 1 – Research Question and Methodological Approach ...... 66! Phase 2 – Research Question and Methodological Approach ...... 67! Phase 3 – Research Question and Methodological Approach ...... 69! Discussion Section ...... 72! Remaining Chapters ...... 72! CHAPTER III: DRIVERS OF PRODUCTIVITY AND ENGAGEMENT ...... 73! Introduction ...... 73! Literature Review ...... 77! Employee engagement ...... 77! Employee engagement Related & Different Theories ...... 80! Knowledge Worker productivity ...... 82! Research Design ...... 84! Methodology ...... 84! Sample ...... 86! Data Collection ...... 87! Data Analysis ...... 89! Findings ...... 91! Nature of engagement ...... 92! Nature of Disengagement ...... 92! New Workplace Practices - Drivers of engagement ...... 92! New Workplace Practices - Drivers of Disengagement ...... 93! Other New Workplace Dynamics ...... 93! Discussion ...... 101! Limitations ...... 108! Implications for Practice and Future Research ...... 109! CHAPTER IV: THE IMPACT OF VIRTUAL INTENSITY AND WORK LOCATION PREFERENCE ...... 111! Introduction ...... 111! Theoretical Foundation, Hypothesis Development, and Conceptual Model ...... 113! vi

Remote Work productivity ...... 115! Job Engagement ...... 117! ICT Utilization ...... 121! Intrinsic Motivation ...... 123! Leader–Member Exchange Theory (LMX) ...... 125! Virtual Work Intensity ...... 126! Theoretical Model ...... 128! Hypothesis Development, Research Design and Methods ...... 129! Mediation ...... 131! Moderation ...... 134! Measurement Model ...... 136! Construct Operationalization ...... 136! Worker productivity ...... 137! Job engagement ...... 137! ICT utilization ...... 138! Intrinsic Motivation (work location enjoyment/work location stress) ...... 138! Leadership ...... 138! Virtual Intensity ...... 139! Pre-Testing (Q-Sort & Pilot Test) ...... 142! Controls and Demographics ...... 143! Data Collection and Sample ...... 144! Data Analysis ...... 145! Data Screening ...... 147! Measurement Model Analysis ...... 147! Exploratory Factor Analysis ...... 148! Confirmatory Factor Analysis ...... 150! Convergent and Discriminant Validity ...... 152! Common Method Bias ...... 154! Measurement Model Invariance ...... 155! Structural Equation Modeling ...... 157! Hypothesis Testing - Mediation ...... 158! Hypothesis Testing – Moderation ...... 162! Interaction Effects ...... 166! Findings ...... 167! vii

Discussion ...... 169! Limitations and Future Research ...... 174! Conclusion and Implications for Practice & Academia ...... 176! CHAPTER V: THE IMPACT OF THE BLENDED WORK ALTERNATIVE AND WORKER SELF-EFFICACY ...... 179! Introduction ...... 179! Theoretical Foundation, Hypothesis Development, and Conceptual Model ...... 181! Virtual Work Intensity: Extended Review ...... 182! Self-Efficacy ...... 186! Theoretical Model - Extension ...... 188! Hypothesis Development, Research Design and Methods ...... 189! Hypothesis Development ...... 190! Measurement Development ...... 197! Construct Operationalization ...... 198! Virtual Intensity ...... 198! Self-Efficacy ...... 198! Controls and Demographics ...... 199! Data Collection and Sample ...... 199! Methods...... 200! Hypothesis Testing ...... 202! Findings ...... 206! Discussion ...... 207! Limitations and Future Research ...... 212! Conclusion and Implications for Practice & Academia ...... 213! CHAPTER VI: INTEGRATED FINDINGS AND DISCUSSION ...... 215! Summary of Integrated Findings ...... 215! Work Location Preference, Remote Work & Work Location Flexibility ...... 216! ICT utilization ...... 220! Leadership Support ...... 222! Worker Self-Efficacy ...... 224! Contributions to Theory ...... 225! Socio-Technical Framework – Workforce Productivity and Engagement ...... 226! Implications for Practice ...... 228! Limitations ...... 229! viii

Future Research ...... 230! Appendix A: Interview Protocol for Qualitative Study ...... 231! Appendix B: Survey Instrument for Quantitative Study One ...... 232! Appendix C: Survey Instrument – Quantitative Study Two ...... 236! REFERENCES ...... 241!

List of Tables

Table 1. Meta-List of Existent Remote Work Research ...... 28! Table 2. Sample Demographics ...... 87! Table 3. Main Themes Coded ...... 90! Table 4. Construct Definition Table ...... 141! Table 5. Participants' Demographics ...... 145! Table 6. Pattern Matrix (EFA) & Cronbach Alpha by Factor ...... 149! Table 7. EFA Measurement Model Results ...... 150! Table 8. CFA Model Fit Indices ...... 151! Table 9. Convergent & Discriminant Validity Results ...... 153! Table 10. Common Method Bias (Common Latent Factor Method) Results ...... 155! Table 11. Metric Invariance across Groups Results ...... 156! Table 12. Mediation & Direct Effects Results by Worker Location* ...... 160! Table 13. Mediation & Direct Effect w/o the Mediator ...... 162! Table 14. Estimates from Study Model 2 Part 2: Moderation Effect of Days Working Remote ...... 164! Table 15. Summary of Hypotheses ...... 165! Table 16. Participants' Demographics ...... 200! Table 17. Direct Effects on Productivity (Comparative Results) ...... 203! Table 18. Direct Effects on Engagement (Comparative Results) ...... 206! Table 19. Hypothesis Testing Summary ...... 207! Table 20. Integrated Findings ...... 216!

List of Figures

Figure 1. Research Questions Addressed in Each Phase of the Study ...... 12! Figure 2. Sequential Mixed Method Research Approach ...... 61! Figure 3. Theoretical Research Framework ...... 63! Figure 4. Three Phase Sequential Study Approach ...... 64! Figure 5. Proposed Research Design ...... 65! Figure 6. Proposed Model – Quantitative Study One ...... 68! Figure 7. Conceptual Models Approach - Quantitative Study Two ...... 71! Figure 8. Key Quotes Supporting Finding #1 – Leadership as a Driver of Engagement ...... 95! Figure 9. Key Quotes Supporting Finding #2 – Worker Job Engagement & Self-Efficacy as a Driver of engagement ...... 97! Figure 10. Key Quotes Supporting Finding #3 – Technology ...... 99! Figure 11. Key Quotes Supporting Finding #4– Acceptance of Remote Work in the Workplace ...... 101! Figure 12. Theoretical Framework – Quantitative Research Study One ...... 115! Figure 13. Research Model and Summary of Hypotheses ...... 131! Figure 14. Data Analysis Flow Chart ...... 146! Figure 15. Hypothesis Results (Mediation and Direct Effects) ...... 159! Figure 16. Moderation Effect of Virtual Intensity on Work Location Enjoyment and Productivity ...... 166! Figure 17. Moderation Effect of Virtual Intensity on Work Location Stress and Engagement ...... 167! Figure 18. Theoretical Framework – Quantitative Research (EXPANSION) ...... 182! Figure 19. Research Model Extensions & Hypothesis ...... 190! Figure 20. Comparative Analysis (On-site, Blended, and Remote Worker) ...... 212! Figure 21. Socio-Technical Framework – Workforce Productivity & Engagement ...... 228!

Acknowledgements

A special thank you to the honorable members of my Dissertation Committee, who despite the personal challenges I encountered while writing this dissertation, never gave up on me, and never hesitated to challenge me to think beyond what I though was possible. Diane, Kathy, Kalle, and Roger, I am forever in debt with all you.

Many thanks to Kalle Lyytinen, our faculty director, who has challenged me in a positive and productive manner. His insight related to theoretical development and thinking beyond the obvious has aided in transforming my work and has transformed me to a focused researcher.

Many thanks to all the program faculty, for teaching us what teamwork, partnership, caring and support for each other means in academia. And from which now we are a team that will stay together in all the still unwritten research and books that are ahead of us.

Many thanks to Sue Nartker and Marilyn Chorman have facilitated this journey for me, their countless examples of support are immeasurable, and I am forever thankful that they run this program.

Many thanks to Alexis Antes, my editor, for walking this journey with me, and polishing my ideas into impeccable paragraphs that make this dissertation more amenable for readers.

Lastly, thanks to all my class members and all my professors especially Yohannes, Kris, Carol, Branka, Lori, Arlonda, Angela, Jimeka—for the countless hours of moral support, the great discoveries (now we know that we really don’t know!), the tears, the laughs, the disappointments, and the delightful dinner nights – full of Dewey, Axelrod, Percy, Boland, Hardin, Doherty, Kalle, ….and how can I forget…the Bluefin Tuna! that kept us all together and coming back every residency! Thanks for making this such a fulfilling and rewarding journey.

xii

Remote and On-Site Workers Knowledge Worker Productivity and Engagement: A

Comparative Study of the Effect of Virtual Intensity and Work Location Preference

Abstract

JOSEFINA MARTINEZ-AMADOR

Information technology now impacts the way people work (anytime, anywhere) as it creates new emerging forms of work involving an almost equal distribution of virtual

(remote) and on-site (traditional office setting) knowledge workers. Organizations are presented with challenges on how to achieve productivity and engagement with this new blended workforce. This research focuses on developing new knowledge in three areas associated with this challenge. First, it seeks to understand how knowledge workers become engaged and how the mode and drivers of engagement differ between remote and on-site workers. Second, anchoring in socio-technical systems theory, it studies the factors that influence productivity and engagement in knowledge workers (i.e., work location preference, leadership, ICT utilization) and the moderation and interaction effects of virtual intensity, and how those factors compare between remote and on-site knowledge workers. Third, this research further compares how the socio-technical factors

(ICT utilization, work location preference, leadership support, self-efficacy) impact the full-time on-site, full-time remote and blended workers. We employ a three-part, sequential, mixed methods study to define the factors that influence productivity and engagement. We find that work location enjoyment has a positive effect on productivity, xiii

and this has a more significant effect on remote workers. We also find that virtual intensity moderates the effect of work location enjoyment on productivity, where at higher levels of enjoyment of the location, high virtual intensity workers (remote) are more productive than low virtual intensity (on-site). Conversely, virtual intensity moderates the effect of work location tension and engagement, where at a higher level of stress, high virtual intensity workers (remote) are less engaged than low virtual intensity workers (on-site). Lastly, new insights on key differences among full-time on-site, full- time remote and blended workers were found. Overall, the study makes novel theoretical, methodological, and practical contributions to theories of remote workforce management.

Keywords: productivity; engagement; remote work; work location enjoyment; virtual intensity; ICT utilization; leadership; self-efficacy!

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CHAPTER I: INTRODUCTION

Personal Research Motivation

This research is motivated through personal experience gained in my last 8–10 years as a human resources practitioner, where I have had the responsibility to lead with management the implementation and design of remote workforce policies and practices.

Often, we have failed to provide the best strategies, mainly due to the lack of understanding of the implications that such a complex undertaking has in the workforce and the enterprise overall. This lack of understanding is exacerbated by the information that management gathers from non-academic journals, which are confusing and abundant.

And in some case, by the knowledge gathering from academic literature, which is conflicting and complex to understand, given the divergent schools of thought in the field. This, in addition to the unprecedented invasion of remote work technologies that create a rapidly evolving workplace, makes it more difficult for managers to implement these remote workforce programs.

The fact is that the workplace is changing dramatically, and work is no longer restrained by physical locations or time, mainly driven by the pervasive introduction of

ICT remote work technology. This has resulted in the introduction of new work arrangements where employees are presented with a variety of alternatives to work from home or any place outside of their traditional office settings. Even more recently, managers are seeing remote work arrangements that vary in terms of the virtual intensity

(the degree or amount of time that the employee works virtually or remotely), adding chaos to a system that is far away from being under reasonable control. This dynamic is changing the paradigms in the way that leaders and HR think about managing and leading

this new workplace and its workforce. And while employees have been provided with new tools and work policies to be able to execute their work under the new virtual environment, and managers have been given new guidance as far as how to manage these new work arrangements, my view is that little consideration has been given to understand what are the implication of these changes, associated with a worker’s ability to maintain productivity and engagement levels (i.e., pervasive utilization of ICT technology, working effectively from a distance, and without the physical presence of supervision or the presence of their co-workers).

In order to maximize the productivity and engagement of the workforce, management and HR need to recognize whether the workplace is truly different from the one bound by physical location and fixed time, where employees have access to various kinds of technology to support their jobs, and where they can conduct their work across different locations anywhere, anytime—the possibility exists that the nature and level of productivity and engagement will evolve. My intent of this research is to bring new knowledge to this field. I seek to augment theory to current conceptual models of how organizations should approach the implementation of remote work practices in the workplace, and what considerations should be given to the utilization of technology, leadership, individual motivations like the workers’ work preferences, as well as remote work policy design.

Problem of Practice

The U.S. workplace is different than it was ten or even five years ago. The changes are countless: economic changes, globalization, technology evolution, and the unstoppable impact of a multigenerational workforce, to name a few. One of the most

overwhelming and complex changes of all the time is the entrance of remote workers in the workplace. Most current research data shows that remote workers would grow to 63 million (43% of the U.S. knowledge worker population) by 2016 (Deloitte Development

LLC, 2015 ). Research shows that flexible (ICT) tools, leadership, and virtual work alternatives play a major role in employee satisfaction and retention (Deloitte Digital

Workplace Report, 2015). Forty-six percent of companies that allow telework say it has reduced attrition and improved productivity which is creating job satisfaction in employees (Global Workplace Analytics, 2012). According to Telework Research

Network (Lister & Harnish, 2010), across all age groups, flexible work ranked third, as

“important for happiness on the job” (p. 5).

While this new type of work arrangement has created many benefits that researchers seem to recognize, it has also created a challenge for management and human resources, who are left with the task to create new ways to manage this workforce so that they can sustain the demands to continuously improve productivity, while at the same time maintaining the workforce engagement. This seems to be a top priority for corporations given the huge impact that the effective management of the workforce has in the financial performance of the organization. It is also a theme that is constantly in the center of attention for many companies like Yahoo, Cisco, IBM, and other recognized firms (Deloitte Digital Workplace Report, 2015). And while many of them continue to drive acceptance of remote work practices, there are many that are retracting themselves from these programs given the concerns of not being able to maintain the same level of productivity in the remote workforce in a comparative way to the on-site workforce. On the other hand, work engagement, defined as “a positive, fulfilling work-related state of

mind that is characterized by vigor, dedication and absorption” (Schaufeli, Salanova,

González-Romá, & Bakker, 2002: 74) seems to be the second concern from management

(Bersin & Associates, 2012). Most recent data shows that workforce engagement in the

U.S. has remained unchanged since 2013 (Hewitt, 2015), and they also report that, especially in the remote workforce, drivers of engagement are under-researched. In addition, a more recent concern has been the appearance of more diversified types of virtual intensity, the amount of time the employee spends working from a remote location

(Wiesenfeld, Raghuram, & Garud, 1999) and different types of remote work arrangements that are entering the workplace (Global Workplace Analytics, 2010). For example, workers now are sometimes working from home, or sometimes from the office, and mixing it more at their own judgment rather than this being dictated by their employer; hence, creating a bigger challenge to understand what this does to productivity and engagement. All of these current workplace dynamics may be an indication that the drivers of productivity and engagement could be evolving.

Given the most recent workplace dynamics (different levels of virtual intensity in remote work, the increase in terms of the utilization of remote work technology, the employee’s expectations to work in a location of preference as a factor of attraction and retention), there is confusion in the workplace as far as what are the implications of these dynamics for productivity and engagement of the workforce. In this study, I examine the factors that drive productivity and engagement for knowledge workers, and how these factors compare between remote and on-site workers. Specifically, I will explore the impact of workers’ location preference, leadership, ICT utilization, and self-efficacy to

productivity and engagement as well as what happens to productivity and engagement in the presence of different levels of virtual intensity of remote work.

Gaps in the Research, Study Goals, and Research Questions

The literature in the field of remote work is very extensive, and it has been studied from the multi-level perspective (Mathieu & Chen, 2010; Rousseau, 1985). For example, some researchers have studied remote work impact to the productivity of the organization. In a review of current literature in remote work, Shin, El Sawy, Sheng, and

Higa (2000) reported that changes in teleworker productivity were consistently of interest to a number of studies (Di Martino & Wirth, 1990; DuBrin, 1991; Olson, 1989). Others have studied the reasons why organizations are opting to introduce remote work (Huws,

1992), and they found indications for improved productivity, reliability and work quality among teleworkers (Shamir & Salomon, 1985).

At the individual level, related individual characteristics have been studied.

O'Neill, Hambley, Greidanus, MacDonnell, and Kline (2009) found that there are differences in certain personality and motivational traits related to teleworker and non- teleworker effectiveness. In the context of communication and worker isolation, research shows that communication among teleworkers is reduced with better collaborative technologies (Bélanger & Allport, 2008). But at the individual level, professional isolation among teleworkers is negatively associated with job performance and increases with time spent on teleworking (Golden, Veiga, & Dino, 2008). In the context of job satisfaction, Golden and Veiga (2005) investigated inconsistent findings in research on telecommuter job satisfaction. They found a curvilinear U-shaped relationship between the extent of telecommuting per week and job satisfaction, suggesting that certain jobs

may become more difficult to perform effectively when frequently telecommuting.

Golden (2007) found that prevalence of telecommuting is negatively associated with non- telecommuting co-worker satisfaction; this relationship is influenced by the amount of time co-workers telecommute, the extent of face-to-face interactions, and job autonomy.

Golden, Veiga, and Simsek (2006) found that the relationship between the extent of telecommuting and job satisfaction was mediated by the quality of interactions in work- oriented and family-oriented relationships.

Productivity, at the individual level, is regularly reported as a perceived benefit of telework for organizations (Callentine, 1995; Hill, Ferris, & Märtinson, 2003; Pitt-

Catsouphes & Marchetta, 1991). Reasons cited include working at peak efficiency hours, reducing distractions and interruptions, being in an environment conducive to increased concentration, and reducing incidental absence (Baruch, 2000; Bélanger, 1999). Others like Shin et al. (2000), have found that the productivity is associated more with the engagement of the employee that tends to put more hours into working and, therefore, produces more.

As it relates to the limited research related to the engagement of remote workers, most of it seems to be focusing on understanding “who” is more or less engaged, but less focus on understanding the factors associated with it and the differences between remote and on-site. Researchers have reported that organizations are implementing teleworker programs because of the demand from employees for more flexible work environments and autonomy in their work (Bailey & Kurland, 2002; WorldatWork, 2011). Other researchers’ focus has been on the impact on the employee and their research suggests that the issues related to perceptions of workplace isolation and disengagement may also

increase (Gajendran & Harrison, 2007; WorldatWork, 2011). According to Thompson and Caputo (2009), disengaged virtual workers are often a cited concern. WorldatWork

(2011) posits that engagement factors among teleworkers are under-researched.

In other studies focusing on the impact of remote work and remote workers, some researchers have found that remote work was perceived to increase productivity (Harker

Martin & MacDonnell, 2012) as well as being positively related to higher levels of perceived autonomy of employees (Harker Martin & MacDonnell, 2012). Gajendran and

Harrison (2007) found that remote work positively affects the relationship with supervisors and negatively affects the relationship with peers. They found that remote work lowers the worker risk to have work/family conflicts, increases job satisfaction, reduces the turnover intent, and surprisingly, does not affect career prospects for employees. As it relates to the impact of virtual intensity of the remote work, they found that high-intensity telecommuting negatively impacts relationships with colleagues and that there are some moderation effects between high-intensity telecommuter and co- worker relationships. In other research, data shows that different remote alternatives have proven to drive higher organizational commitment (Hunton & Norman, 2010). From the perspective of the impact of self-efficacy on remote work, Raghuram, Garud, Wiesenfeld, and Gupta (2001) report that self-efficacy has a significant influence in determining telecommuter adjustment to remote working and confirmed moderation effects of the extent of telecommuting between telecommuter self-efficacy and adjustment to telecommuting, as well as confirmed moderation effects of the extent of telecommuting between telecommuter self-efficacy and structural behavior. Because self-efficacy is being confirmed to be a good predictor of employee performance, this has been studied

more closely in the remote work field research. Most recently, Staples developed a self- efficacy construct to measure the worker ICT Self-Efficacy Construct (Staples, Hulland,

& Higgins, 1999).

Gaps in the Research

Despite the amount of research that exists in the field, there are limited comparative studies that address the most current factors that drive or limit productivity and engagement. In fact, there are no empirical studies that address the impact, in an integrated manner of the use of technology that exists today in the workplace, workers’ work location preference, the impact of leadership support and how worker self-efficacy influences productivity and engagement in light of this new and different workplace dynamic. And, there is no study that actually provides answers to the question on the implications of the different levels of virtual intensity to productivity and engagement of the workforce.

While there is research that confirms that ICT utilization is an enabler of remote work, there is no updated research that has evaluated the impact given most current technologies (video, intranet document sharing, and internal employee chats) on productivity and engagement. From the perspective of work location preference, there is a lack of explanation on what is the impact, either positively or negative, of the employee’s experience of enjoyment or tension of the work location. And more importantly, the hidden impact on the worker’s productivity and engagement. In fact, to- date, no construct exists that can help to measure this impact. As it relates to the impact of virtual intensity, there is a lack of explanation in regards the impact of the different levels of virtual intensity, in the presence of technology, leadership, the employee’s work

location preference, and the worker’s self-efficacy, and how organizations can take advantage of the joint optimization of the socio-technical organizational resources. In summary, there are limited comparative studies that address the factors that drive or limit productivity and engagement with an integrated approach (socio-technical theory) as the kind that I will conduct in this research.

In this study, I seek to build an encompassing model based on how human resource practitioners can improve the management and implementation of remote work practices. I anchor the framework of my research on Trist and Bamforth (1951) and Katz and Kahn’s (1966) classical work of socio-technical systems theory. Socio-technical systems theory incorporates four elements that are critical to transforming work systems into outputs: technology, task, social structure, and people-related factors. The theory posits that these subsystems continually and jointly interact with each other to produce work systems outcomes (Trist & Bamforth, 1951). For the purposes of this research, I limit the focus to the key dimensions that prevail in the workplace today, using as our unit of analysis—the knowledge worker. We look into related theories and uses of technology, dimensions of personality, and personnel characteristics (intrinsic motivation, self-efficacy and job engagement), and organizational factors (leadership) as major antecedents to productivity. We also look into the effect of some organizational/ work design factors like the virtual intensity of the work and the impact of the knowledge worker self-efficacy. We further describe our research focus below.

Study Goals and Research Questions

Given the entrance in the workplace of new emerging forms of work involving an almost equal distribution of remote (virtual environment) and on-site (traditional office

setting) knowledge workers, organizations are presented with challenges on how to achieve productivity and engagement with this new blended workforce. Our research focuses on developing new knowledge in three areas associated with this challenge. First,

I seek to understand how knowledge workers become engaged and how the mode and drivers of engagement differ between remote and on-site workers. Second, anchored in socio-technical systems theory, I study the factors that influence productivity and engagement in knowledge workers (i.e., work location preference, leadership, ICT utilization) and the moderation and interaction effects of high and low levels of virtual intensity. Third, I expand the study with a comparative analysis to further understand the impact of the drivers of productivity and engagement, using a different types of worker categories: full-time on-site, full-time remote, and blended workers. In addition, given the changes that workers are experiencing and coping with, working either remotely, on-site or in a blended category, we explored the impact of the worker self-efficacy on productivity and engagement. Last, I integrate the findings and elaborate on the contributions, evaluating the academic and practical implications, as well as highlighting the limitations of the thesis.

To advance the understanding of these phenomena, the research questions that I address in this thesis are:

1)! How do remote workers become engaged/productive and are the mode and

drivers different from on-site workers?

2)! What factors influence productivity and engagement for on-site and remote

workers?

a.! What happens when the employee experiences enjoyment or stress/tension

in the work location? Does it matter?

b.! What is the impact of ICT remote work tools/ utilization and leadership?

c.! What is the impact of virtual intensity?

3)! Given the different categories, the full-time remote, full-time on-site, and

blended workers entering the workplace?

a.! What are the differences of the degree of impact of the factors that impact

productivity and engagement, amongst the three different categories?

b.! Given the changes that workers are experiencing and coping with,

working either remotely, on-site or in a blended category, what is the

impact of the worker self-efficacy on productivity and engagement?

This thesis employs a three-part, sequential, mixed methods study approach to defining the factors that influence productivity and engagement. Research questions addressed in each phase are shown in Figure 1.

With all the new dynamics emerging in the workplace, it is imperative that integrated research is carried out to further inform how organizations should evolve their human resource practices to create value and increase competitiveness. In this research, I seek to understand the changes in the nature of the engagement of the workforce, and what factors are more critical given the entrance of new forms of work, where some knowledge workers will continue to work in an office, but others will be working remotely. Just as technologies invite us to be more innovative in our approaches to customers and business processes, new frontiers have been erected regarding how to reimagine the workplace. These new workforce dynamics provide organizations with

opportunities to rethink productivity, engagement, leadership, the impact of the use of technology, work processes, and communication practices. The challenge for management is to work out how to best match employees, work processes, human resource practices, and technologies to create value in the new era. It is my intent that this research will open such new vistas for academia, general management, and human resources professionals by augmenting and discovering new knowledge that can enable organizations to advance their strategies to confront this major workplace dynamic.

Figure 1. Research Questions Addressed in Each Phase of the Study

CHAPTER II: LITERATURE REVIEW, THEORETICAL FRAMEWORK & RESEARCH METHODS

In this chapter, I examine the key theoretical underpinnings informing this thesis and the different studies, the theoretical framework, and research methods. First, I provide a theoretical definition of the unit of analysis, the knowledge worker. Second, I provide a review of remote workforce theories and its evolution as well as some informative related theory in the field such as nomadic work, virtual work, etc. Because the theoretical framework is anchored in socio-technical systems theory, a specific section is dedicated to the introduction of this theory. Third, a review follows, of the most relevant theories that are associated with productivity and engagement, and more specifically, the theories that are part of the theoretical conceptual models of the two quantitative studies of this research. Lastly, I will close with a description of the research methods. Next, I review selective literature of previous research in remote work.

Remote Work Literature & Previous Research Review

This section provides an overview of selective literature and theories related to remote work. This includes a review of the unit of analysis and the definition of knowledge workers is provided, a historical review of remote work and an overview of the advantages and disadvantages existent today in current literature. And lastly, a meta- list of the key approaches and dimensions on how remote work has been studied before

(see Table 1).

Knowledge Worker

Knowledge workers are those employees who have a responsibility for exploring and generating ideas and concepts rather than concentrating solely on implementing or managing existing processes or operations within the organization. The term was first 13

coined by Peter Drucker in 1957. In 1999, he suggested, “the most valuable asset of a

21st-century institution, whether business or non-business, will be its knowledge workers and their productivity” (Drucker, 1999: 79). Generally speaking, knowledge workers have high degrees of expertise, education, or experience, and the primary purpose of their jobs involves the creation, distribution, or application of knowledge. Sunassee and Sewry

(2002) said, “knowledge grows like organisms, with data serving as food to be assimilated rather than merely stored” (as cited in Terry, 2007: 39). In 1963, Popper stated there is always an increasing need for knowledge to grow and progress continually, whether tacit (as described by Polanyi) or explicit. Toffler (1990) observed that typical knowledge workers (especially Research and Development (R&D) scientists and engineers) in the age of knowledge economy must have some system at their disposal to create, process, and enhance their own knowledge. In some cases, they would also need to manage the knowledge of their co-workers. Nonaka (1991) described knowledge as the fuel for innovation but was concerned that many managers failed to understand how knowledge could be leveraged. Companies are more like living organisms than machines, he argued, and most viewed knowledge as a static input to the corporate machine. He advocated a view of knowledge as renewable and changing, and that knowledge workers were the agents for that change. He believed knowledge-creating companies should be focused primarily on the task of innovation.

Given the definition of knowledge workers, this worker category seems to be more suitable for the purposes of our research. In addition, I found support for this decision during the literature review. Based on recent research from The Work

Foundation Organization (2009: http://www.theworkfoundation.com), their findings

show that those with more knowledge-based jobs have greater flexibility than those in less knowledge-based jobs, at least as far as a choice over hours is concerned and the ability (whether willing or not) to work at home. Therefore, I chose to focus on the knowledge worker as the main unit of analysis across the three studies.

The Beginning of Remote Work

Although telework (or remote work) was foreseen as early as 1950, it did not become possible until the entrance of personal computers and portable modems in the early 1970s. In 1973, the term “telecommuting” was introduced to emphasize that telework could replace the daily commute (Nilles, 1994). In the beginning, corporations considered telework as a way to make them less dependent on fuel and oil, especially in the fuel crisis in the early and mid-1970s (Tolbert & Simons, 1994). The number of teleworkers then grew to more than ten-fold in a decade, to about 11.1 million

(Shellenbarger, 1994, Jan 1).

Several factors have contributed to the emergence of telecommuting. First, numerous companies are trying to lower the costs of office space. Second, faced with increased competition, many companies adopt extended workdays and flexible work schedules to respond better to customer needs and to retain and attract skilled employees.

Third, computer and telecommunications technologies are becoming increasingly affordable and cost-effective, which enables a strong penetration of ICT in the organization (Brimsek & Bender, 1995). Remote working (also known as telework) has grown from its modest beginnings in the early 1970s to achieve an unprecedented level today. Most recently in the U.S., while many conjectured that telecommuting would

decline during the recession, it actually grew by nearly 16% from 2008 to 2012 (Global

Workplace Analytics, 2012).

Defining Remote Work

The terms remote work, telework, and telecommuting are used interchangeably, and it is a quite accepted practice in this area of research. In 1975, Jack Nilles (1975) was the first scholar to coin the term “telecommuting”. In 2002, Bailey and Kurland (2002) defined it as, “working outside the conventional workplace and communicating with it by way of telecommunications or computer-based technology” (p. 384). In another recent literature review (Bailey & Kurland, 2002), and meta-analysis (Gajendran & Harrison,

2007), these authors further expanded their definition where telework is defined as the substitution of communication technology for work-related travel, and can include paid work from home, a satellite office, a telework center or any other workstation outside of the main office for at least one day per work week (Verbeke, Schulz, Greidanus, &

Hambley, 2008).

Remote working or telecommuting can be defined as a “work arrangement in which employees perform their regular work at a site other than the ordinary workplace, supported by technological connections” (Fitzer, 1997: 65). It can be performed on a full- or part-time basis and on a permanent or temporary basis (e.g. for one or two months or for the duration of a specific project). Telecommuting represents an expansion of the places and times considered auspicious for work. Three principal components of telecommuting can be identified as utilization of ICT, link with an organization, and delocalization of work (Pinsonneault & Boisvert, 1996).

First, telecommuting depends on the processing, manipulation, and transformation of information. Thus, ICT represents one of the major components of telecommuting, because it enables workers to be in constant communication with their organization and their colleagues. Second, contrary to independent workers, telecommuters have ties with an organization (Bailyn, 1994). Telecommuting is not limited to permanent workers, as employees working on a contractual basis may also telecommute. However, distinctions exist between the types of telecommuting that is prevalent in each group. Authors generally recognize that telecommuting by contractual employees engenders a greater number of difficulties (Huws, 1992; Ramsower, 1985). This type of telecommuting usually includes clerical work where employees are remunerated on a piecemeal basis or, less frequently, on an hourly basis. Third, telecommuting is not constrained by time and space (Nilles, 1994; Olson, 1988). The delocalization of work takes four main forms: telecommuting from home (home-based), satellite offices, neighborhood work centers, and mobile work. Home-based telecommuting is usually performed in a dedicated area of the worker’s place of residence. Satellite offices take the form of small organizational affiliates generally located in proximity to residential areas where a telecommunications link with headquarters is permanently maintained (Doswell, 1992; Nilles, 1994).

Neighborhood work centers are private information centers that possess telecommunication tools and that are generally shared by employees from various enterprises (Di Martino & Wirth, 1990; Nilles, 1994; Olson, 1987b).

The Positive Organizational Impact of Remote Worker

Benefits from remote working appeared early despite the limited technology as compared to today. Solomon and Templer (1993) found that 75% of companies that have

implemented telecommuting were satisfied or very satisfied with the experience, and only

8% were dissatisfied. Remote working has been found to positively impact important human resources (HR) indicators in the workforce. As an example, it has been shown to reduce absenteeism and increase employee loyalty to the organization. In a study of 20 employees across 20 organizations that had either adopted telecommuting or had started pilot projects, Olson (1987a) found that telecommuting reinforced the existing relationship between workers and their organizations. Moreover, telecommuting was found to allow organizations to retain employees that might otherwise have left and to attract skilled employees who were unwilling to relocate and for whom flexibility was important (Davenport & Pearlson, 1998; Di Martino & Wirth, 1990; Piskurich, 1996b).

Another benefit found for organizations is the improvement of productivity and quality of work associated with telecommuting, which is probably the most cited organizational benefit in the literature. Some telecommuting specialists evaluate the increase in productivity to be between 15% and 50% (Alvi & McIntyre, 1993; Barthel,

1995; Baruch & Nicholson, 1997; Côté-O’Hara, 1993; Gordon & Kelly, 1986; Kirkley,

1994; Langhoff, 1996). For instance, Huws (1993) reported that telecommuters were 47% more productive (based on evaluations from their supervisors) than their colleagues working in the office. Furthermore, this study indicated that 25% of the work performed by telecommuters was of higher quality than comparable work performed by “traditional” workers. Similarly, New York Telephone reports a 43% average increase in productivity associated with telecommuting, while Control Data Corporation estimates the productivity gain at about 20% (Clutterbuck, 2005). A survey conducted at Northern

Telecom (Nortel) indicated that 88% of its telecommuters reported increased productivity

ranging from 10% to 22% (Froggatt, 1998). Baruch and Nicholson (1997) found that over

70% of the 62 telecommuters (managers and professionals) they studied perceived themselves as working more effectively than in a traditional work arrangement. Several factors can explain the increase in productivity of telecommuters: lower levels of interference and interruptions, a work environment better tailored to specific individual and task needs, the possibility of choosing more convenient working hours, less time wasted commuting (Eldib & Minoli, 1995), and a stronger focus on achieving the required results rather than simply being physically present at work (Guimaraes &

Dallow, 1999).

Studies from ten years ago show that telecommuting allows organizations to reduce certain expenses. Typically, lower costs can be realized by reducing office space, energy consumption, parking spaces, and overcrowding of offices. For example, in the

U.S., IBM reported saving US$75 million by selling buildings and reducing its leased office space (McCune, 1998). Ernst & Young was able to save US$25 million annually by reducing office space by two million square feet (Monnette, 1998). At AT&T, the alternative work initiative is estimated to have saved the company US$460,000 annually

(Apgar, 1997).

Telecommuting also allows greater organizational flexibility and a better capacity to respond quickly to unexpected events. In fact, several organizations use telecommuting to decentralize their operations and to organize them into networks. Among the several benefits identified in a survey of 252 IS department heads (Ruppel & Harrington, 1995) was the organization’s ability to continue operating in emergency situations (Fitzer,

1997). For instance, following a series of earthquakes, many Californian companies

relied on telecommuting to continue their daily operations and have made these work arrangements permanent due to their initial success (Eldib & Minoli, 1995; Fitzer, 1997).

Organizations may also increase their flexibility by hiring workers under various contractual arrangements (e.g. on a temporary basis). Furthermore, telecommuting enables an organization to provide flexible working hours for its employees. Finally, telecommuting allows for a more efficient usage of the organization’s information system, particularly during non-office hours (e.g. at night and on weekends) (Gordon &

Kelly, 1986; Hamilton, 1987).

The Negative Organizational Impact of Remote Worker

Remote working can have a negative impact on organizations. Often, the employees who are better suited for telecommuting (i.e., motivated, well organized, and requiring little supervision) are those that companies would rather retain on-site (Johnson,

1997). In addition, telecommuting can reduce organizational synergy. Coordination and motivation of employees, valorization of a common culture, and feelings of belonging are much more difficult to sustain in a telecommuting context (Davenport & Pearlson, 1998).

In a study aimed at identifying the factors underlying corporate resistance to telecommuting in the United Kingdom, Gray, Hudson, and Gordon (1993) found that over 35% of the 115 senior personnel managers surveyed believed that telecommuting threatened corporate structure and identity. A second negative impact is the discontentment of managers in charge of telecommuters. This often arises due to managers’ difficulty in adapting their management styles to the new reality imposed by telecommuting (Christensen, 1992). Third, the security of transmitting corporate data via telecommunication networks concerns managers (Gray et al., 1993; Greenstein &

Feinman, 1999; Katz, 1987). Finally, it is often difficult to objectively evaluate the financial benefits of telecommuting programs (Alvi & McIntyre, 1993; Doswell, 1992).

Remote Work in Virtual Teams

Virtual teamwork is one current topic in the literature review and has been studied from many different angles, starting with virtual team antecedents to performance and team characteristics. In particular, the performance of virtual teams has become an important factor for determining satisfaction of team members. Studies have also linked virtual team attributes, like team cohesion, to objective measures of team performance

(e.g. Beal, Cohen, Burke, & McLendon (2003); Evans & Dion (1991); Gully, Devine, &

Whitney (1995); Copper & Mullen (1994); Prapavessis & Carron (1997); and Wech,

Mossholder, Steel, & Bennett (1998)), though in most cases, the relationship reported was weaker than expected. Indeed, researchers have suggested that there may be important moderators of the relationship between cohesion and objective performance

(e.g., norms regarding task focus; see Seashore, 1954). Despite the lack of theoretical and empirical support for the connection between team cohesion and objective performance, teams that are more cohesive are more likely to believe that they are performing better.

Recent research has demonstrated a strong relationship between team cohesion and a team’s perception of its own performance (Chang & Bordia, 2001; Jung & Sosik, 2002), although the cross-sectional design of both studies makes it impossible to tease out the true nature of the relationship between the variables.

The availability of a flexible and configurable base infrastructure is one of the main advantages of virtual teams. Anderson, Lau, Segal, and Bishop (2007) suggested that the effective use of communication, especially during the early stages of the team’s

development, plays an equally important role in gaining and maintaining trust. Virtual

R&D teams which members do not work at the same time or place (Stoker, Looise,

Fisscher, & Jong, 2001) often face tight schedules and a need to start quickly and perform instantly (Munkvold & Zigurs, 2007). To this end, virtual teams reduce time-to-market

(May & Carter, 2001). Lead-time or time-to-market has generally been admitted to be one of the most important keys to success in manufacturing companies (Sorli, Stokic,

Gorostiza, & Campos, 2006). Clearly, the rise of network technologies has made the use of virtual teams feasible (Beranek & Martz, 2005), which is clearly one of the advantages that enables the performance at the individual level when working remotely.

While some authors recognize that virtual teams may allow people to collaborate more productively at a distance, others claim that there are still serious disadvantages that need to be addressed. One of them is the lack of personal interaction. Some researchers claim that there is no substitute for personal interaction to establish trust between colleagues. These researchers go further to show that personal interaction was still the most reliable and effective way to review and revise a new idea (Gassmann & Von

Zedtwitz, 2003). As a drawback, virtual teams are particularly vulnerable to mistrust, communication breakdowns, conflicts, and power struggles (Rosen, Furst, & Blackburn,

2007).

Finally, organizational and cultural barriers are another serious impediment to the effectiveness of virtual teams. Many managers are uncomfortable with the concept of a virtual team because successful management of virtual teams may require new methods of supervision (Jarvenpaa & Leidner, 1999).

Distributed Work Arrangements

Many large organizations have adopted DWA (distributed work arrangements) as a new option to increase workers’ productivity while reducing costs. Due to the high demand in the workplace, there have been several research efforts in defining relationships among factors of distributed work arrangements.

There are many different forms of alternative workplaces, as discussed by

Gilleard and Rees (1998). They outline two basic types of alternative workplaces: on-site workplaces and off-site workplaces. Within on-site workplaces, there are four main types, including free address, hoteling, group address, and project team environments. For off- site working, there are four main types: telecommuting, satellite officing, remote telecenters, and virtual officing.

Various authors have defined, at the organizational level, the multiple types of on- and off-site work settings. They define a workforce as “distributed” if it meets any of the following three conditions: (1) individual workers operate in different physical locations;

(2) asynchronous communications and interactions are utilized for most normal interchanges, even with colleagues in the next office; and (3) individual workers are not all employed by the same firm/organization or are working within distinctively different parts of the same parent organization (Ware & Grantham, 2003: 3).

Another definition of DWA by Venkatesh and Vitalari (1992) is an arrangement of “decentralized organizational structure where the core organization distributes a portion of its functions to a remote site” (p. 2). Various work settings in which workers do not have a permanent workspace on an organization’s premises are also called

“distributed work arrangements.” Distributed work arrangements include various types of

alternative working, rather than traditional work at an assigned workstation within the office.

With the creation of the computer in the mid-twentieth century, work processes have evolved from production to knowledge focus. This subtle change is not fully appreciated by workers and managers who still utilize production management methodologies. New management skills and expectations are needed for knowledge age workers and work processes. Hinds (2002) mentioned that “in distributed work, there is considerable uncertainty about others’ behaviors” (p. 311). In distributed work settings,

Pancucci (1995) suggested that managers had to be available for coaching remote workers and had to hold regular meetings and review sessions with distributed workers.

Workspace decision-making should contribute to the strategic and financial concerns of an organization and provide a productive work environment across the organizational network.

The unexplored linkages and trade-offs between conventional workspaces and information and communication technology (ICT) create several risks in the process of workspace decision-making. These are summarized as several key risk issues involved in the implementation of ICT in distributed workspaces: maintaining organizational culture; keeping the work-life balance; workforce management; brands building; and security.

Harrison (2002) indicated that today’s enterprises needed assistance in developing and implementing a distributed work strategy, even down to the level of workplace design and management. The challenge of workspace allocation in a distributed setting is to provide and maintain productive work environments for people across the whole organizational network. People are the most important assets of any enterprise. Hence,

distributed workspace decision making should be human-centered, in order to satisfy people’s social and psychological needs. It is now critical to come up with comprehensive solutions that can help organizations create high-value and effective distributed work arrangements supporting the organization’s needs in the knowledge age.

Nomadic Work

Over the past number of years, new forms of work have emerged and developed, particularly in the service and education sector, and, more broadly, with regards to information work. One of the key characteristics of such forms of work, and particularly information work, is the potential (and often the need) for nomadic practices since workers mainly deal with something that can be represented digitally and taken to or accessed from different locations. In other words, work activities in certain professional contexts can and often must be detached from stable premises, and performed when and where it suits the workers’ needs (Davis 2002). In fact, increasing attention is being paid to what is here called modern nomadicity, and that involves engaging with work activities across different locations based on the availability of the resources that are necessary for accomplishing such activities (de Carvalho et al. 2011).

Several studies have addressed issues related to nomadic practices and the design of technologies to support those involved with them, addressing a broad range of issues varying according to the particular field of inquiry within which they were conducted.

For instance, in Ubiquitous Computing and Business Information Systems, researchers have been addressing the development of mobile and pervasive technologies and technological affordances, which can be translated into specific performances when used in individual and organizational practices (Gorlenko & Merrick, 2003; Kleinrock, 1996;

Sørensen, 2011; Weiser, 1993). In turn, researchers have been concerned with the usability of portable devices and the development of methods for accurately assessing it

(Coursaris & Kim, 2006; Johansson, Karlsson, & Stough, 2006; Weiss, 2002). In CSCW, the focus has been directed towards the use of computing technologies to mediate social and collaborative activities in and across different locations and towards an understanding of how different spaces are inhabited and transformed in places as work gets accomplished, i.e., a concern with issues to do with articulation and mobilization work as well as with place-making activities (Rossitto, 2009).

Different frames may be applied to understanding nomadicity (Rossitto, 2009).

Four prominent perspectives can be found in the literature, and they have to do with a technology-centered, practice-centered, place-centered, and work-life boundary-centered approach to define, explore, and understand nomadicity and the issues surrounding it

(Ciolfi & Pinatti de Carvalho, 2014). These frames are not mutually exclusive. There are overlaps between them and studies that prioritize one of these views may address issues of the other three. However, most of them address nomadicity in the same sense, i.e., that of accomplishing work in and across different locations with the help of computing technologies, even though not all of them use “nomadicity” as the overall term to describe this. Nevertheless, frames identified by the authors are practical categories that represent a variety of concerns that characterize the study of nomadicity and the issues that have been privileged in particular subsets of the literature.

From our literature in nomadic work, considered by many as an extreme form of mobile work, is becoming increasingly prevalent in organizations. Yet so far there has

not been enough research attention on the particular challenges that nomadic workers face in order to design support for their work practices.

In Table 1, I present a sample of studies conducted from 1975 and onward. The table reveals a number of studies that address remote work. Our list is presented chronologically, then by authorship, type of study, key focus area and findings. While in earlier studies researchers seemed more concerned about the rate of innovation and adoption, gradually they became more interested in the larger implications to the worker evaluating advantages and disadvantages of telework, individual characteristics impact, type of work more suited for telework. Work today draws upon a wide range of theories and levels of analysis, addressing phenomena that are becoming more critical in the field, as the impact of the intensity of the remote work and the need to move from individual remote workers towards understanding the larger effects of remote workforce management.

Table 1. Meta-List of Existent Remote Work Research

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Communication 2015 Smith, Patmos, Communication and Quantitative Study This study examines teleworkers’ job satisfaction related to Communication channel

and Teleworking and Pitts (2015) Teleworking: A Study the use of and satisfaction with a variety of communication satisfaction, job of Communication channels and workers’ personality type. U.S. teleworkers (N satisfaction, and Channel Satisfaction, = 384) completed an online survey and self-reported on personality (extraversion, Personality, and Job dimensions of communication channel satisfaction, job openness, agreeableness, Satisfaction for satisfaction, and personality. Results indicated that and conscientiousness ) Teleworking extraversion, openness, agreeableness, and Employees conscientiousness are positively correlated with job satisfaction. Additionally, significant moderating effects were found for the relationship between openness and phone and video communication, and agreeableness and phone communication on job satisfaction. Findings from this study yield important practical implications for organizations including suggestions for optimizing communication satisfaction for employees of differing personality types and recommendations to help organizations effectively hire and retain teleworkers. Technology as a 2014 Ciolfi and Pinatti Work Practices, Literature Review and Extends the understanding of nomadicity and goes beyond Nomadic Work, Modern Mediator de Carvalho Nomadicity and the Recommendations the categorization of studies of nomadicity. It supports the Nomadicity, Nomadic

(2014) Mediational Role of view on the need to see nomadicity as a dynamic and Computing (all Technology emergent concept, where technologies, infrastructures, technologies that enable locations, organizational needs and constraints and personal mobile work) strategies are intertwined in complex ecologies of practice. Multilevel 2013 Bélanger, Watson- Multi-level socio- Multilevel Framework Building on STS theory, a multi-level telecommuting Socio-Technical Systems Framework Manheim, and technical systems Using Literature Review framework was developed, which proposes theoretical Theory, multi-level

Swan (2013) telecommuting relationships that address the conceptualization issues found analysis, different aspects framework in telecommuting research: namely, (1) telecommuting and of remote work theory its ICT artefacts as the context or environment in which work is performed instead of just as an aspect of the actual work itself; (2) telecommuting as a multi-level concept whose impacts are often realized at the individual level of analysis but also have influences and outcomes across levels of analysis and (3) telecommuting as a concept whose antecedents and outcomes are affected by the passing of time. The theoretical framework can be used as a lens for evaluating past telecommuting research, and developing new areas of inquiries in telecommuting. 28

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Intensity of 2008 Gajendran and The Good, the Bad, Meta-Analysis A theoretical framework and meta-analysis of 46 studies in Remote Work Intensity,

Remote Work, Harrison (2007) and the Unknown Quantitative-study natural settings involving 12,883 employees. Perceived Autonomy, psychological About Telecommuting: Telecommuting had small but mainly beneficial effects on Work-Family Conflict, effects to Meta-Analysis proximal outcomes, such as perceived autonomy and (lower) Job Satisfaction, Turn workers of Psychological work–family conflict. Importantly, telecommuting had no Over Intent, Role Stress, Mediators and generally detrimental effects on the quality of workplace Relationship Quality Individual relationships. Telecommuting also had beneficial effects on Consequences more distal outcomes, such as job satisfaction, performance, turnover intent, and role stress. These beneficial consequences appeared to be at least partially mediated by perceived autonomy. Also, high-intensity telecommuting (more than 2.5 days a week) accentuated telecommuting’s beneficial effects on work–family conflict but harmed relationships with coworkers. Results provide building blocks for a more complete theoretical and practical treatment of telecommuting. Virtual Teams 2013 Leonardi, Bailey, The digital Article Discusses different types of virtual work arrangements that Impact of Virtual work,

and Barley (2013) organization: How exist today and the impact that various types of virtual work virtual teams, virtuality impacts the can have on organizational processes and structures. Taking organization structure way teams work. an example from the U.S. automobile industry, the authors highlight how a traditional industry must come to terms with the new organizational challenges occasioned by today's digitally mediated relationships. This case offers cautionary advice and lessons for other organizations that would turn to the virtual in the hope of reducing costs by replacing humans and objects with data and representations. Telecommuting 2012 Noonan and Glass The hard truth about Quantitative Study Telecommuting has not permeated the American workplace, No theory but a series of

Effectiveness/pro (2012) telecommuting using Longitudinal data and where it has become commonly used, it is not helpful in methods applied to ductivity and US Census Current reducing work-family conflicts; telecommuting appears, analyze remote worker Population. /use of instead, to have become instrumental in the general working hours descriptive statistics expansion of work hours, facilitating workers’ needs for additional work time beyond the standard workweek and/or the ability of employers to increase or intensify work demands among their salaried employees. 29

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Telecommuting 2012 Harker Martin and Is telework effective Meta- Review and meta-analysis of 32 correlations from empirical Organizational Effectiveness/pro MacDonnell for organizations? A Analysis/Quantitative studies find that there is a small but positive relationship Commitment,

ductivity (2012) meta-analysis of Study between telework and organizational outcomes. Telework is Performance, Retention, empirical research on perceived to increase productivity, secure retention, Intention to Leave perceptions of strengthen organizational commitment, and to improve telework and performance within the organization. In other words, it is organizational indeed beneficial for organizations. outcomes Multilevel 2004 Swan, Belanger, Theoretical Conceptual Framework These applications of incentive theory revealed three Incentive-related theories Framework and Watson- Foundations for important findings: (1) distributed work dilemmas can that can be used as

Manheim (2004) Distributed Work: benefit by applying incentive theories; (2) the conceptual possible foundations for Multilevel, Incentive framework’s multilevel perspective revealed that, over time, future distributed work Theories to Address incentives at one level of analysis can be outweighed by research include Current Dilemmas disincentives from other levels of analysis; and (3) incentive Hertzberg’s theory strategies, such as reward and compensation plans, need to explicit and implicit be studied in the context of distributed work environments. motivation, expectancy theory, managerial control theory, group process theories, socio- technical systems theory, and resource allocation theories.

productivity 2004 Westfall (2004) Does Telecommuting Article Analyzes telecommuting and its impact on productivity. Different related theories Really Increase Concludes that telecommuting does not deliver, claiming and studies in telework productivity? that otherwise many organizations would have increased the number of teleworkers already. 30

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Telework 2002 Bailey and A review of telework Literature Review and Authors review current research and recommend future Grounded theory,

Research Review Kurland (2002) research: findings, new Recommendations research. The recommendations are 1) Scholars should Telework Research, Job directions, and lessons expand the research lens beyond individual teleworkers. Suitability for Telework, for the study of Doing so would shift attention from the question of who Advantages of Telework, modern work teleworks to the larger question of whom the practice of Profile of Who telework affects. 2) Given the reality of how people Teleworks, Reasons on telework, scholars should reconsider why employees work Why Individuals Work, away from the office. Under a new conceptualization, What happens when telework might come to be seen as one of many mechanisms people telework. individuals enact to cope with the demands of the modern workplace. Additionally, a new conceptualization of telework could prompt scholars to recognize potential outcomes overlooked in the current literature. 3) New studies should emphasize theory-building and forge links to existing organizational theories. Such efforts would be instrumental in sorting out what happens when people telework. Technology 2001 Raghuram et al. Technology enabled Quantitative Study The results of this study suggest that telecommuter self- Self-Efficacy, Newcomer

Impact (2001) work: The role of self- efficacy is positively associated with telecommuters reported Adjustment Theory efficacy in determining adjustment and structuring behavior. Furthermore, the (Newcomer adjustment telecommuter positive relationship between telecommuter self-efficacy and (adapted from Feldman, adjustment and the two dependent variables is stronger among those who 1981), Structuring structuring behavior telecommute more extensively. Their findings regarding the Behavior Theory interactive relationship between self-efficacy and extent of telecommuting reinforce the critical role that self-efficacy plays as a means of aiding individuals in coping with the most challenging telecommuting arrangements—those that involve full-time telecommuting. Furthermore, the results draw attention to the relevance and importance of structuring behavior as a manifestation of employee proactivity in the increasingly prevalent context of remote work. Telework 2001 Daniels, Lamond, Teleworking: Conceptual Framework Integrates current literature of remote work, and links this to Neo-Institutional Theory, Adoption and Standen Frameworks for a theoretical framework using Neo-Institutional Theory Mental Models, Culture,

(2001) Organizational Organizational Change, Research Adoption as a process in support of innovation, Principal-agent Theory, Organizational Design, ICT utilization, Institutional Strategy, Conceptualization of

31 Telework

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Adoption (using 2000 Ruppel and Telework: An Literature Review and Authors argue that organizations don't see telework as an Innovation Theory

Innovation Harrington (1995) innovation where Recommendation innovations strategy. As an administrative innovation and a Theory) nobody is getting on process-oriented change that responds to internal or external the bandwagon organization stresses or opportunities, telework requires an effort to react to a qualitatively different, postindustrial environment that forces frequent and faster organizational innovations [9]. Taking an innovation approach permits gaining an understanding of relations between organizational factors and telework. It also offers rich process theories that facilitate the cumulative progress of telework research based on different research streams.

Telework 2000 Shin et al. (2000) Telework: Existing Literature Review and A review of the relevant literature and a characterization of Theories of freedom and Research Review Research and Future Recommendations telework were conducted from 3 different angles: the control, role conflict Directions research methodology, the focus of existing telework models and human studies, and the research paradigm. First, an overall lack of motivation, the attitude– robust research methodology was found in many studies. behavior relation, and a Second, although telework is an organizational phenomenon, job characteristics model, disproportionate attention has been given to teleworker- Contingency Theory, related personal issues. Finally, the current telework Agency Theory, paradigm was discovered to be characterized by suitability- Innovation Theory, based planning that selects appropriate persons and tasks and by ad-hoc implementation in response to local needs. Authors suggest that future research could be enriched with more rigorous research methodology, more balanced focus for studies, and more flexible perspectives in the research paradigm. Innovation 1999 Reichwald and Telework Strategies: Results of the conceptual study conclude that an innovation Practicability term,

Möslein (1999) The Diffusion of a path starting from workplace-oriented telework pilots, Innovation Theory, The Workplace Innovation through process redesign and overall organizational Innovation Curve (Multilevel Strategy innovation to long-term visions of societal changes in the Applied to Telework Model For The organization. Discusses 3 Levels of Implementation: Diffusion Of Telework Workplace Innovation, Process Innovation, and As Workplace Organizational Innovation. Innovation) 32

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Telework 1999 Guimaraes and Empirically testing the Quantitative This research examines what organizations should do to Success factors of program success, Dallow (1999) benefits, problems, and accomplish success with their telework programs. A telecommuting: Individual success factors for thorough survey of the relevant literature was undertaken to Autonomy, Self- Characteristics telecommuting exhaustively identify the many potential benefits, problems, discipline, Highly skilled programs. and the proposed success factors for telecommuting (self-efficacy), ICT programs in practice. Using the benefits derived from Skills, Positive Attitudes telecommuting programs and their impact on company to Telecommuting, performance as the measures of program success, six main Prefers Little social success factors were empirically tested with a sample of 316 interaction telecommuters from eighteen companies. The results corroborate at least partially the importance of carefully considering the characteristics of supervisors, employees, tasks, and work environments, as well as management support and problems encountered, for the success of telecommuting programs.

Advantages and 1999 Kurland and Telework: The Compilation of Theory Kurland, N.B., and Bailey, D.E. 1999. Telework: The Work and family/ life Challenges Bailey (2000) advantages and advantages and challenges of working here, there, anywhere, balance of the employees challenges of working and anytime. Organizational Dynamics, 28(2): 53-68. or family conflicts, longer here, there, anywhere, working hours, and and anytime. feelings of social isolation. Individual 1998 Baruch and Home, sweet work: Qualitative Study Qualitative interviews with forty-one male and twenty-one Autonomy, supervision, Characteristics Nicholson (1997); Requirements for female professional and sales teleworkers examined their motivation, self- Davenport and effective home experiences of telework. Family, personal, organization and discipline, trusting Pearlson (1998); working. work type contexts that contributed to effective teleworking relationship with manager Fitzer (1997); were identified. Most were satisfied with teleworking, felt their work performance improved but were spending the same or greater number of hours working. Teleworkers reported improved family relations but increase in family stress. Space and intrusion into the family were identified as sources of stress. Self-management and less interest in socialization were requisite personal qualities. Work type was either highly controlled or autonomous. The authors conclude there were few occupations that are suited to telecommuting. Management should be aware of problems that employees experience with social isolation and the potential for discrimination in the selection of employees for 33 telecommuting

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Individual 1998 Staples, Hulland, A self-efficacy theory Quantitative Study This study investigates how virtual organizations can Self-Efficacy Theory, Characteristics and Higgins explanation for the manage remote employees effectively. The research used Remote Workforce

(1998) management of remote self-efficacy theory to build a model that predicts Management, Training in workers in virtual relationships between antecedents to employees' remote Remote Workers and organizations. work self-efficacy assessments and their behavioral and Experience attitudinal consequences. The model was tested using responses from 376 remotely-managed employees in 18 diverse organizations. Overall, the results indicated that remote employees' self-efficacy assessments play a critical role in influencing their remote work effectiveness, perceived productivity, job satisfaction and ability to cope. Furthermore, strong relationships were observed between employees' remote work self-efficacy judgments and several antecedents, including remote work experience and training, best practices modeling by management, computer anxiety, and ICT capabilities. Because many of these antecedents can be controlled managerially, these findings suggest important ways in which a remote employee's work performance can be enhanced, through the intermediary effect of improved remote work self-efficacy. The current study also provides a basis for future research in the remote work area through its development and testing of a remote management framework. 34

Linked Theories Key Areas Year Author Title Type of Study Results/findings in the Research/Key of Focus Themes Adopting and 1993 Tomaskovic- Telecommuting Academic Journal This article focuses on a "contingency theory" of Contingency Theory, Designing Devey and innovation and technological work reorganization that addresses Technology Impact, Job

Telecommuting Risman (1993) organization: A organizational, managerial, and job characteristic Characteristics Jobs contingency theory of contingencies in the reorganization of the work process. The labor process change,” focus is on the rationales of top decision-makers in a sample Social Science of firms for adopting and designing telecommuting jobs. Quarterly Telecommuting, employees working from home using computer technology, is made possible by technological innovations that reduce the cost and increase the efficiency and power of microcomputers and information transmission systems. Effects of technology on work organization will be "contingent" upon organizational processes. Unlike, classic contingency theory, technology has not been stressed as decisive. Instead, technology is seen as embedded in social choices made by managers and workers in an organizational context. Managers do not simply have goals which define subordinates' work activity. Instead managerial goals and methods react to the power of employees as well as to organizational constraints.

Telecommunicati 1975 Nilles (1975) Telecommunications Literature Review This paper describes research at the University of Southern Diverse theories reviewed ons Impact to and Organizational California that estimates the magnitude, direction, and rate Decentralization Decentralization of innovation of telecommunications-augmented of Work decentralization of “information industry” organizations. 35

Research Literature Review & Socio-Technical Systems

Socio-Technical Systems

Remote working is a complex and dynamic phenomenon that is continuously influencing in many different levels modern organizations, the structure of the labor markets, as well as firm’s competitiveness and productivity advantages of this new work practice. Recent research shows that in addition to be a response to organizational needs to increase internal efficiencies and create competitive advantage, remote work is a phenomenon that is highly linked to the advancement of ICT mobile and virtual technologies (Hill, Erickson, Holmes, & Ferris, 2010; Pearce, 2009), the impact of the global economy that has resulted in the globalization of work, and the need for employees to often perform work during non-traditional working hours (Kumar, van

Fenema, & Von Glinow, 2009). Implementing remote work is a strategic decision that can impact the firm’s competitiveness, and consequently, this decision can impact the organization in different dimensions.

Since remote workforce programs involve complex inter-relationships between work environments, work practices, individual motivations, management, and technology

(Bélanger et al., 2013), evaluating the different implications in isolation may hinder its strategic impact. As other authors have argued before, telecommuting is a technological change whose effects cannot be understood without considering the social system within which is embedded (Passmore & Sherwood, 1978). Approaching our research with a comprehensive understanding of the socio-technical implications of remote work will enable a more effective outcome. Therefore, I anchor our theoretical framework in socio- technical systems theory.

Socio-technical systems theory (STS) has its roots in the socio-technical systems view of organizations (Katz & Kahn, 1966; Trist & Bamforth, 1951). The theory has evolved over time as it has been used and tested by researchers from various fields (e.g.

(Hendrick & Kleiner, 2001; Holden & Karsh, 2009; Markham, 1988). From a STS view, organizations are open work systems that transform inputs to desired outputs (Hendrick

& Kleiner, 2002; Morrison, Cordery, Girardi, & Payne, 2005; Pasmore, 1988; Trist &

Bamforth, 1951). A work system consists of two or more persons interacting using some form of job design, hardware, and/or software machine(s) or tool(s), and information/knowledge within a structure(s) or process(es) in both internal and external environments. Work systems and organizations are considered open in that their boundaries are permeable, allowing interactions with their environment across levels of analysis (Katz & Kahn, 1966). Thus, socio-technical systems can be as simple as a person performing a task with a simple tool or as complex as a large number of individuals in a multinational enterprise working together using advanced ICT. Examples of applications of STS in the IS literature include investigations of ICT investment decision processes and user acceptance of ERP systems (Lim, Pan, & Tan, 2005). Socio-technical systems theory incorporates factors from four elements critical to transforming work system inputs to outputs: technology-related factors included in the technical subsystem, social and people-related factors included in the personnel subsystem, organizational structures, and work processes included in the organizational structure or work/job design subsystem, and the environment external to the work system. These subsystems characterize the internal and external contexts in which people perform their work.

These subsystems continually and jointly interact with each other and both internal and external organizational environments to produce work system outcomes and organizational survival (DeGreene, 1973; Hendrick & Kleiner, 2002; Pasmore, 1988;

Trist & Bamforth, 1951). While all relevant, for the purposes of developing our theoretical framework, and given the fact that I are using the individual knowledge worker as our unit of analysis, I focus on the internal subsystem environments (factored at the individual level): personnel, technical and organizational structure/work design flexibility) and avoid any discussions of environmental conditions (internal and external).

Next, I describe each one of the subsystem and a brief description anchoring in the theories of our key constructs.

Technical Subsystem

The technical subsystem includes factors representing technologies, policies and practices that describe the modes of production (e.g., the type and level of ICT support for the work), the actions individuals take on an object when performing work (e.g., the tasks themselves), the strategy for reducing uncertainty in the process (e.g., policies or practices whether supported by ICT or not), the degree of process/workflow integration

(e.g., the degree of automation or workflow rigidity), etc. (e.g., Brown, 2002). Related to telecommuting, the technical subsystem describes such factors as the types of ICT used when telecommuting, the facilities available to telecommute from, the reward and compensation plans of the organization, task/work design when telecommuting, etc.

Next, I provide the literature review on ICT utilization

ICT utilization

The increasing attention to the concept of mobility is not a new phenomenon. The period we are in is a precondition for the emerging discourses on mobile technology and the newly incoming digital solutions entering the workplace environment. It is widely recognized that our time can be characterized as the “post-industrial society” (Bell,

1976), the “post-capitalist society” (Drucker, 1993), or the “information age” (Toffler,

1990).

Research on the role of ICT in telework may be especially significant because the most notable negative effect of telework results from the deterioration of internal processes such as organizational communication and control. At the organizational level, concern about the degradation of internal processes is, in fact, the primary reason for the scarcity of employer-initiated telework programs (Olson, 1987b). As research and technology have evolved, some authors argue the contrary and claim that state-of-the-art

ICT offers an effective mechanism for addressing this concern in ways that include information management, monitoring capability, and communication and collaboration support (Shin et al., 2000). In particular, the revolution in data communication technology enables any worker to belong to the virtual network of a company regardless of his/her geographical location. At the team level, Hertel, Geister, and Konradt (2005) found that computer-mediated teams (virtual teams) performed at a higher level during group work than did traditional face to face teams. Further, Hambley, O’Neill, and Kline

(2007) found that team interactions and cohesiveness are influenced by the perceived accessibility and ease of collaboration and sharing of information via the selected

communication medium is the use of project websites or web portals to foster a sense of community

At the individual level for remote workers, the enhanced quality of internal processes is expected to reduce frequently discussed side effects of telework such as isolation, role conflict and ambiguity, and at the team/management level, to address difficulties in coordination with peers and supervision of teleworkers (Shin et al., 2000).

A variety of available technologies includes e-mail, voice mail, audio and video conferencing, fax, and the World Wide Web. These tools are vastly different in their information carrying ability, provision of accessibility to information and data, portability, transportability of work (e.g., workflow management), and collaboration support. Mokhtarian and Sato (1994) suggested that commonly available ICT can have a marked effect on organizational processes. It appears that general-purpose ICT applications (e.g., e-mail and the Web) have a larger impact than specialized ICT (e.g., video conferencing) in reducing task uncertainties and facilitating better coordination between an organization and its teleworkers. It was also shown that effective adoption of a general-purpose communication medium (e.g., e-mail) gave teleworkers an information-rich tool that enhanced their work productivity (Shin et al., 2000). Overall, more empirical investigations in this field should improve our understanding of the effect of ICT on telework effectiveness and teleworkers’ performance (Shin et al., 2000).

Most recently, many technology companies, like Accenture, CSC Corporation,

HPE Enterprise, are developing integrated solutions to improve the digital workplace, and as technology evolves, both the integration of the tools and the workforce are evolving as well (Gartner Report, 2015). Recent research shows that employees are

upbeat about the anticipated advancements from digital technologies, with 71% identifying the team benefits of innovation, agility (69%) and productivity (68%)

(Kleynhans & Fiering, 2015). Unsurprisingly, the young, better educated and those with higher level occupations are more positively disposed to digital technologies in the workplace. Almost two-thirds (64%) said they are proactively learning new digital tools and technical skills to prepare them to adapt to digital advances.

Personnel Subsystem

The personnel subsystem includes at least three types of factors: demographic characteristics of the workforce, psychosocial aspects of the workforce (e.g., dimensions of personality, attitudes towards the work environment or the work itself, individual motivations, etc.) and the degree of professionalism required to perform the work (e.g., values, norms or expected behavior patterns of the job, team and/or organization).

Outcomes of the personnel subsystem primarily describe the way tasks are performed.

Related to telecommuting, factors that are considered part of the personnel subsystem can include workers’ motivations to telecommute, attitudes towards the work while telecommuting, beliefs or expectations for reward, compensation, and/or communication when telecommuting, personality preferences for working alone or in collaboration with others, telecommuter work/life balance issues, and telecommuter demographics.

Personality and Traits

Personality has been considered an important factor in the personality related studies specifically for predicting job performance. It is a behavior, which differentiates one person from another (Beer & Brooks, 2011) and provides acumen whether a person will do some specific job, in comparison to others (Sackett, Gruys, & Ellingson, 1998).

Moreover, the traits, relevant to personality, are considered to be stable and steady throughout the work life in a personality behavior model (Denissen, van Aken, &

Roberts, 2011; Gerber et al., 2011; Myers, 1997). Hogan and Shelton (1998) pointed out that the various personality theories examine the variances and similarities in a person.

The similarities can be used to predict one’s performance and behavior, as they provide the collective attributes of human nature. Whereas, the variances provide the measures of individual’s performance and are used to describe human performances and behaviors.

Experts in the field of personality are of the view that the individuals, in fact, have stable and long-term traits that affect behaviors at work (Denissen et al., 2011; Gerber et al., 2011). With reference to research on personality, some scholars captured that personality is the most effective tool to predict job performance (Ozer & Benet-Martinez,

2006; Schulman, 2011). Techniques to identify personality traits are mostly adopted at the time of personnel selection procedure (Barrick & Mount, 1991). Studies on personality and organizational outcomes have received enormous attention by researchers in the organizational behavior research. The latest studies illustrate that personality affects the environments in which individuals are living (Barrick, Mount, & Judge, 2001;

Chen, 2004; Judge & Cable, 1997; Schneider, Smith, Taylor, & Fleenor, 1998) and plays a significant role in individuals’ selection of the situation in which they decide to remain in. According to Barrick and Mount (1991), the preference for organizational environments, the cycle of individuals that a person chooses to interact with, and the kind of activities that a person enjoys strongly relies on personality.

Intrinsic Motivation

Intrinsic motivation is a characteristic that comes from within an individual, out of will and interest for the activity at hand. No external rewards are required to incite the intrinsically motivated person into action. The reward is the behavior itself. Logically, this seems like an ideal, for people to act as “origins” of their behavior rather than

“pawns” (De Charms, 1968). However, it is certainly not the case that every real world behavior stems from an intrinsic energy. In management, there is particular interest when it comes to intrinsic versus extrinsic motivation, particularly because of the different outcomes that researchers have shown to result from intrinsic motivation: more interest, excitement, confidence, enhanced performance, persistence, creativity, self-esteem and general well-being (Deci & Ryan, 1991; Ryan & Deci, 2000; Ryan, Deci, & Grolnick,

1995; Sheldon, Ryan, Rawsthorne, & Ilardi, 1997). Over the years, several theorists have offered insights into the phenomenon through their conceptions of intrinsic motivation.

Intrinsic motivation comes from inside a person: it is a sense of achievement, responsibility, job satisfaction, purpose, involvement, empowerment, and ownership—all the things that make employees feel that what they are doing make a big difference in their lives and in the organization itself. If employees feel that what they are doing is insignificant, they will feel insignificant; if, in turn, they feel their work is valued, they feel valued.

Ryan and Deci (2000) acknowledged that, even when work environment supports autonomy and competence, if a person is simply not interested in a particular activity, he or she would not be intrinsically motivated to engage. Rather, he or she will be motivated by external factors like compensation or bonuses. However, the authors stipulated that

external motivations could be internalized. Despite a lack of interest, a person can still be self-determined if the activity can be integrated into a sense of self. For example, a worker may find working remotely uninteresting and therefore not be intrinsically motivated to work as a remote worker. However, if this person can come to understand how such activity can be valuable and important as a means of work-life balance, personal growth and skill enhancement, the person will internalize the extrinsic motivation. Through this process, the worker can now approach the activity with a sense of will rather than pressure.

Intrinsic motivation is a very important factor to consider in our search since the reactions to working remotely may trigger certain changes in human behavior. And while some research exists, very little empirical research exists today addressing the uniqueness of working remotely.

From the management and peers’ perspective, some researchers have reported that due to the natural lack of observability of the day to day work, it is difficult for supervisors to understand and measure the productivity or remote workers versus the employee on-site. In a survey conducted in 2007 by CCH Incorporated

(https://www.cchgroup.com), 37% of the managers felt that remote workers spend too much time carrying out personal activities. In contrast, 41% of the employees surveyed felt that remote workers were more productive. In addition, most research has focused on the leader’s role in motivating teleworkers, rather than on the unique needs of the remote workers (Connaughton & Daly, 2004).

From the employee perspective, some authors have reported motivational issues associated with teleworking (feelings of isolation, lack of opportunity for employees to

interact and be creative as an example). Nilles (1994), found that teleworkers often complain about feeling a loss of visibility and belonging. Many remote workers have reported as well that being out of the visible site of their supervisor is a detriment to the assignment, and even in some case marking them as top candidates for staff reduction

(Piskurich, 1996a), which all can trigger very different behaviors in the workforce, and as result create either high or low levels of intrinsic motivation.

Self-Efficacy

Over the past 20 years, self-efficacy has become one of the most widely studied variables in the educational, psychological, and organizational sciences. Self-efficacy is an individual’s belief in his or her capacity to muster the cognitive, motivational, and behavioral resources required to perform in a given situation (Bandura, 1997). That is, self-efficacy is a situation-specific competence belief. Its popularity rests on the research that has found that self-efficacy is related to a number of educationally and organizationally relevant variables (e.g., academic and job performance; Robbins et al.,

2004; Stajkovic & Luthans, 1998). Despite its popularity, the history of self-efficacy has been marked by numerous controversies and debates (e.g., Bandura, 1984; Bandura &

Locke, 2003; Eastman & Marzillier, 1984; Kirsch,1985; Vancouver, 2005). In recent years, a derivative of self-efficacy called general self-efficacy (GSE) has been developed.

The extension of task-specific self-efficacy to GSE has become an issue of contention among researchers (e.g., Bandura, 1997; Cervone, 1997; Stajkovic & Luthans, 1998).

GSE is “individuals’ perception of their ability to perform across a variety of different situations” (Judge, Erez, & Bono, 1998: 170). That is, GSE is a situation-independent competence belief. Although there are several criticisms of GSE (e.g., Bandura, 1997),

one of the most critical issues is related to its measurement. This issue is critical because the measurement of self-efficacy can impact conclusions about its relationships with other variables (Lee & Bobko, 1994). Admittedly, the measurement criticisms are somewhat reasonable. In particular, the evidence of the reliability of the responses to the items on GSE measures is not overly impressive (Chen, Gully, & Eden, 2001). Given that to establish the construct validity of the scores on a measure rests to a great degree on the reliability, item parameters, and the factor structure (Guilford, 1954), efforts to address the criticisms about the construct validity of GSE are hindered by the weak evidence concerning the other measurement properties of GSE. Researchers have begun to tackle this criticism via new scale development and rigorous psychometric studies (e.g.,

Bosscher & Smit, 1998; Chen et al., 2001; Scholz, Gutiérrez Doña, Sud, & Schwarzer,

2002). This research has clearly put GSE on more solid psychometric footing.

For this research, I choose to use the GSE construct. The construct of perceived self-efficacy reflects an optimistic self-belief (Olson, 1989; Schwarzer, 1992). This is the belief that one can perform a novel or difficult tasks, or cope with adversity in various domains of human functioning. Perceived self-efficacy facilitates goal-setting, effort investment, persistence in the face of barriers, and recovery from setbacks. It can be regarded as a positive resistance resource factor. Ten items are designed to tap this construct. Each item refers to successful coping and implies an internal–stable attribution of success (Schwarzer & Jerusalem, 1995). The theory appears to be particularly well suited to studying virtual organizations. The remote employees in such organizations typically work with minimal supervision and rely heavily on their own abilities and initiative to perform their job tasks. Information technology is the typical medium used to

communicate with management since face-to-face interaction is rare or infrequent. Often the employee works in a location with few or no co-workers, so the potential for isolation can be high and the availability of co-worker advice is often low. Since remote employees enjoy considerable work autonomy, the potential impact that their own motivation and beliefs in their abilities (i.e., self-efficacy judgments) can have on their outcomes may be considerably more than for employees whose behaviors are under tighter supervision. Therefore, virtual organizations that learn how to maximize employees' self-efficacy with respect to working remotely may reap greater benefits from a virtual working environment.

Theories of Employee engagement

Various authors have recognized the inconsistencies and differing interpretations of the construct “employee engagement.” For example, Simpson (2009) stated that although employee engagement has emerged as an important work-related concept, the definitions and measurement of engagement at work are poorly understood.

Practitioner literature focuses on employee engagement as a positive work-related outcome that an organization needs to employ in order for them to reap the benefits of decreased turnover, increased commitment, and retention and increased productivity

(Ketter, 2008; Seijts & Crim, 2006). As an example, The Gallup Organization uses the term “employee engagement.” Gallup researchers, Harter, Schmidt, and Hayes (2002) define employee engagement as “the individual’s involvement and satisfaction with as well as enthusiasm for work” (p. 269). This model of employee engagement identifies four antecedents that are necessary for employee engagement to occur: clarity of expectations and basic materials and equipment that is provided; feelings of contribution

to the organization; feeling a sense of belonging to something other than oneself; and feeling as though there are opportunities to discuss progress and growth (Simpson, 2009:

9). These antecedents are reflected in the Gallup Organization’s measure of employee engagement, namely the Gallup Workplace Audit (GWA). In recent research provided by

Watson’s (2012) Global Workforces Study, he found that the top five drivers of employee engagement are: effective leadership, stress/balance of the workload, having clarity of goals and objectives, the involvement of supervisors, and lastly the image of the organization.

Researchers from the Institute of Employment Studies (Robinson, Perryman, &

Hayday, 2004) define employee engagement as “a positive attitude held by the employee towards the organization and its values” (p. ix). An engaged employee is aware of business context and works to improve performance for the benefit of the organization.

The organization must work to develop and nurture engagement, which requires a two- way relationship between employer and employee (Robinson et al., 2004). This definition emphasizes a positive attitude towards the organization and the important aspect of engagement as a two-way process.

Hewitt Associates’ definition of employee engagement, “the state of emotional and intellectual commitment to the organization or group producing behavior that will help fulfill an organization’s promise to customers—and in so doing, improve business results” (Vance, 2006: 3) also refers to the connection to the organization rather than one’s work. This definition also uses a related concept, commitment, which could further confuse engagement with other well-validated constructs. Other authors also use the term

“commitment” when referring to employee engagement. For example, Fleming,

Coffman, and Harter (2005) used the term “committed employees” as a synonym for

“engaged employees.”

Macey and Schneider (2008) noted commonalities to various definitions of employee engagement. These include it being a desirable condition; it has an organizational purpose; and it includes involvement, commitment, passion, enthusiasm, focused effort and energy. Macey and Schneider (2008) highlight that employee engagement encompassed both attitudinal and behavioral components. They also advocated that the antecedents of such behavior lay in the conditions of one's work and the consequences of such behaviors are thought to be of value to organizational effectiveness. Macey and Schneider (2008) noted that this lack of precision with regards to employee engagement does not mean that the construct lacks practical or conceptual utility, but suggested that it would benefit from being framed as a model that simultaneously embraces both the psychological state and the behavior it implies.

Kahn (1990) introduced the term “personal engagement and disengagement.” He provided a theoretical conceptualization of engagement in a study which focused on psychological presence during particular moments of role performances. Kahn (1990) defines personal engagement as the “simultaneous employment and expression of a person’s preferred self in task behaviors that promote connections to work and to others, personal presence and active, full role performances” (p. 700). Whilst in engagement, employees will express themselves cognitively, physically and emotionally (Kahn, 1990).

When people are engaged, they are understood to be physically involved, cognitively vigilant and emotionally connected (Simpson, 2009).

Personal disengagement is the withdrawal and defense of the employee’s preferred self, which results in a lack of connections, personal absence and incomplete role performances (Kahn, 1990). Whilst in disengagement, employees will withdraw and defend themselves physically, cognitively and emotionally during role performances

(Kahn, 1990). Kahn’s (1990) premise was that employees will express their preferred selves on the basis of their past psychological experiences of self-in-role. Kahn (1990) proposed that three psychological conditions would determine the extent to which individuals will express their preferred self in a performance role. These three psychological conditions are psychological meaningfulness, psychological safety, and psychological availability. Psychological meaningfulness is a feeling that one is receiving a return on investments of self-given in their work role performances (Kahn, 1992).

Psychological safety refers to a feeling that one is able to show one’s self without fear of negative consequences to their self-image, status or career (Kahn, 1992). Psychological availability refers to a sense of having the physical, emotional or psychological resources to engage personally at a particular moment in time. Kahn’s definition, therefore, focuses on one’s physical, emotional and cognitive involvement and connection to their work role, which is determined by the three psychological conditions.

Bakker, Schaufeli, Leiter, and Taris (2008) also provided an attempt to consolidate the meaning of employee engagement. They discussed three approaches.

First, it is conceived as a set of motivating resources such as recognition, support, feedback, and opportunities for skill development and learning, which is consistent with

Macey and Schneider’s (2008) antecedents of engagement; second, it is conceived in terms of commitment and extra-role behavior; and third, engagement is defined

independently from job resources and positive organizational outcomes as a “positive, fulfilling, affective-motivational state of work-related well-being that is the antipode of job burnout” (Bakker et al., 2008: 187–188).

Work System/Organizational Structure Subsystem

The organizational structure subsystem is typically characterized in terms of centralization, formalization, and complexity. Centralization refers to the level and degree of formal decision-making in a work system (e.g., strategic, tactical or operational). Formalization refers to the degree to which jobs or tasks within a work system are standardized. There are two types of complexity assessed in relation to the

STS work system: differentiation and integration. Differentiation complexity takes three forms—vertical, horizontal, or spatial—and refers to the degree to which a work system or organization is segmented into parts. Integration complexity refers to the type and number of mechanisms that are required in the work system to ensure communication, collaboration, and control of the differentiated elements in a work system. In general, the need for integrating mechanisms goes up as the degree of differentiation increases.

Related to telecommuting, the organizational structure subsystem characterizes such aspects of the work system describing the number and degree of differentiation complexity existing in the organization when telecommuting, the degree to which work tasks are standardized or ad-hoc (formalization) when telecommuting and the location and degree of formal decision-making in the work system, (centralization) when telecommuting.

Leadership Exchange (Leader-Member Exchange LMX)

Telecommuting and the inevitable absence of an office bring about unique challenges regarding leadership and monitoring employee effectiveness. Leaders may not have the same opportunity to get to know the employee as remote access via telephone or e-mail does not always result in building high-quality interpersonal relationships as direct contact does. For this reason, leadership exchange has been included as it focuses on relationship building between leaders and employees.

Unlike many other prominent leadership theories, leader–member exchange

(LMX)—formally known as Leader Member Exchange Theory (Graen & Uhl-Bien,

1995)—does not focus on the specific characteristics of an effective organizational leader. Rather, LMX focuses on the nature and quality of the relationships between a leader and the individual subordinates. The ideal is for a leader to develop as many high- quality relationships as possible. This will lead to increases in subordinates’ sense of job satisfaction and organizational citizenship, as well as to increased productivity and attainment of organizational goals. LMX has been criticized for its potential to alienate some subordinates, failing to account for the effects of group dynamics and social identity, and failing to provide specific advice on how leaders can develop high-quality relationships. However, LMX has been heralded as an important leadership theory in higher and distance educational contexts, because of its emphasis on promoting autonomy and citizenship, as well as its ability to complement and mediate transformational leadership styles.

Literature shows that leadership has a positive effect on employee engagement.

Most recent research shows that having a good relationship with immediate supervisors

and believing in senior leadership (Carnegie, 2012) is a top driver of engagement. In this study, based on previous research and the findings shown in our recent qualitative study,

I theorize that leadership has a higher impact for high-intensity workers than it does for low-intensity remote workers, that is to say that the less that the employee works remotely, the less the importance of leadership, but the more that the employee works remotely, the higher the importance of leadership.

Virtual Work Intensity

By treating telecommuting as a single, undifferentiated program, researchers tend to overlook potentially important structural distinctions among work arrangements. The main structural distinction made by previous investigators deals with what they refer to as virtual work intensity: the extent or amount of scheduled time that remote workers spend doing tasks away from a central work location. This idea has been referred to as “virtual status” by Wiesenfeld et al. (1999: 782), “virtuality” by Scott and Timmerman (1999:

242), and as “home-centered versus office-centered” telework by Konradt, Hertel, and

Schmook (2003: 62), among other terms (Hill et al., 2003). An emerging perspective on telecommuting intensity in the literature is that when telecommuters spend the majority, versus a minority, of their scheduled time away from a central location, it crosses a psychological threshold - in a sense, creating two classes, of employees in telecommuting arrangements (Meehl, 1992). High-intensity telecommuters spend the majority or all of their workdays away from a central location. Low-intensity telecommuters spend the majority of their workdays at a central (conventional) location, working remotely for only one or two days a week. Konradt et al. (2003) found that telecommuters who spent more than 50% of their time away from the office (home-centered) had different motivations

for telecommuting than those who spent less than 50% of their time away (office- centered). Home-centered or high-intensity telecommuters sought to balance their work and family demands while office-centered or low-intensity telecommuters sought freedom from interruptions. Similarly, Wiesenfeld et al. (1999) found that high virtual status employees (those who work three or more days per week away from a central work location, usually home) had different communication patterns as opposed to low virtual status employees (those who work three or more days a week at a central location).

Coveyduck (1997), De Lay (1995), Mackie-Lewis (1998), Schneider-Borowicz (2003), and Taveras (1998) also used similar splits of scheduled work time at work and at home as an indicator of behavioral immersion in telecommuting.

Environmental Systems

The work environment describes the relevant characteristics of the context within which the work system operates (both internal and external at whatever level of analysis).

It is critical that work systems and organizations are able to adapt to relevant factors in their environment. Environmental factors that positively or negatively affect work systems in organizations can be socioeconomic, educational, political, cultural, or legal.

For each organization and work system, these factors will differ in type, quality, and importance. For example, the external environment of the telecommuting work system may describe the political climate in relation to stakeholders external to the telecommuting work system being analyzed, the regional or national culture in relation to trust and work, the workgroup, team, or organizational traditions for collaborating face- to-face vs. virtual, and the legal requirements for transacting business, protecting

proprietary information or providing secure ICT infrastructure when telecommuting.

Others can be internally focused as financial metrics and productivity outcomes.

Remote Workforce productivity

Productivity is the amount of output produced in a specific amount of time

(Miller, 2008). productivity measurement is a quantifiable measure calculated as the ratio of what is produced to what is required to produce it (Miller, 2008). Previous research from Butler, Aasheim, and Williams (2007) included a case study on an organization to see if telecommuting really does improve a company’s productivity. The authors stated that many studies on this topic had been short term and based on self-reported data instead of actual measurements. Because of this, the authors believe that previous findings could be called into question. To make their work more credible, they performed a longitudinal test over nearly five years on the Kentucky American Water Company

(KAWC).

During this time, the researchers studied the cost over time and the productivity of both telecommuter employees and regular office employees. The findings of their research were paramount to the field. As the first finding, they claimed that telecommuting did increase employee’s productivity. Thirteen months after the implementation of the system, the average telecommuter productivity increased by 154%.

In the following year, productivity increased from 9.4 calls per hour to 10.5 calls per hour but dropped to 10.2 calls per hour in the third year. It then increased to 11.1 calls per hours in the final three months of operation. They also found that telecommuters worked

3.98 hours more per month than non-telecommuters. In their conclusion, they believed that telecommuting did increase overall productivity for the company, but did not want to

make a generalization, as there had not been similar test cases to date, and that this might only be applicable to the KAWC. As in this case, research about remote workforce productivity is controversial, so it is important to understand the different lenses that authors have as it refers to productivity at the individual and organizational level.

Productivity, at the individual level, has been reported as a perceived benefit of telework for organizations (Callentine, 1995; Hill et al., 2003; Pitt-Catsouphes &

Marchetta, 1991). Reasons cited include working at peak efficiency hours, reducing distractions and interruptions, being in an environment conducive to increased concentration, and reducing incidental absence (Baruch, 2000; Bélanger, 1999). Huws

(1992) found indications for improved productivity, reliability and work quality among teleworkers (see also Salomon & Shamir, 1985). They were perceived more loyal, less likely to avoid work (i.e., be “absent” from home during work time) and had lower tendency to change employer.

At the organizational level, though, productivity has frequently been contradictory. Higa, Sheng, Shin, and Figueredo (2000) reported that changes in teleworker productivity were consistently of interest to a number of studies (Di Martino

& Wirth, 1990; DuBrin, 1991; Olson, 1989). These studies were remarkably alike in pointing out the positive effect of telework in enhancing individual productivity.

However, except for a few state-commissioned studies such as the Evaluation Audit and

methodological details. The authors also noted that there were methodological and conceptual weaknesses in the existing literature.

First, the measurement of productivity change depended primarily on bias-prone self-reports. Telecommuters’ and managers’ different evaluations of productivity gains appear to exemplify perception bias (Westfall, 2004). Second, sample populations selected under particular personality and task criteria, in general, could be expected to have higher work motivation and, therefore, contribute to increased productivity. Third, the relegation of other tasks to on-site workers may have contributed to increased telework productivity. Finally, higher productivity might have been the consequence of increased working hours. Motivated workers may have invested the commuting time saved in extra work. In such a case, if the productivity is measured as the ratio between inputs and outputs, an increased amount of work done during extended working hours does not necessarily mean increased productivity (Shin et al., 2000).

Some authors support that future research on productivity may be facilitated if subcomponents (or alternative measures) of productivity were identified. The effect of telework on each subcomponent (or alternative measure) and on the dynamics among subcomponents could then be examined. For instance, Gordon (1997) proposed an effectiveness concept that covers multidimensional aspects of work characteristics in terms of quantity, quality, timeliness, and multiple priorities. Multiple priorities represent

“how many things can be done simultaneously” (Gordon, 1997) by a teleworker.

Similarly, the EAR Group investigated teleworker performance from perspectives of overall work productivity, quantity of work, quality of work, the ability to meet deadlines, absenteeism, punctuality, and overtime work. On the positive side, Di Martino

and Wirth (1990) and Caudron (1992) indicated benefits gained by home working for employers in productivity terms (see also Metzger & Von Glinow, 1988). Along the same line, Stavrinidis (1991) provides financial analysis, claiming significant savings for companies in terms of office space, cars, and use of time.

Research Questions

As previously introduced, this research focuses on developing new knowledge in three areas associated with the challenge on how to achieve productivity and engagement in the remote and on-site workforce. First, I seek to understand how knowledge workers become engaged and how the mode and drivers of engagement differ between remote and on-site workers. Second, anchored in socio-technical systems theory, I study the factors that influence productivity and engagement in knowledge workers (i.e., work location preference, leadership, ICT utilization) and the moderation and interaction effects of virtual intensity, as well as how those factors compare between remote and on-site knowledge workers. Third, I expand our study to understand the impact of worker self- efficacy in both categories. Last, I integrate the research findings and elaborate on the contributions, evaluating the academic and practical implications, as well as highlighting the limitations of our study.

To advance the understanding of these phenomena, the research questions that I address in this thesis are:

1)! How do remote workers become engaged/productive and are the mode and

drivers different from on-site workers?

2)! What factors influence productivity and engagement for on-site and remote

workers?

a.! What happens when the employee experiences enjoyment or stress/tension

in the work location? Does it matter?

b.! What is the impact of ICT remote work tools/ utilization and leadership?

c.! What is the impact of virtual intensity?

3)! Given the different categories, the full-time remote, full-time on-site, and

blended workers entering the workplace?

a.! What are the differences of the degree of impact of the factors that impact

productivity and engagement, amongst the three different categories?

b.! Given the changes that workers are experiencing and coping with,

working either remotely, on-site or in a blended category, what is the

impact of the worker self-efficacy on productivity and engagement?

We employ a three-phases, sequential, mixed methods study to define the factors that influence productivity and engagement.

Research Design

In this research, an exploratory sequential mixed methods approach is employed.

A QUAL → QUAN → QUAN mixed-methods approach with an exploratory sequential design (Plano Clark & Creswell, 2011) was employed. The main purpose of this method is to generalize qualitative findings based on a few individuals from the first phase to a larger sample gathered during the second phase (Plano Clark & Creswell, 2011). This design is based on the premise that an exploratory step is needed, either because 1) measures or variables are not available; 2) the variables are unknown; or 3) because there is no guiding framework or theory. This mixed method research method was perfectly suited to address the research questions and goals of our study. Taken together, the

research questions were mostly exploratory but also confirmatory in nature. One of the primary advantages of a mixed methods approach is that it facilitates addressing both kinds of questions in the same study (Teddlie & Tashakkori, 2009). This method allowed examination of the research questions in greater breadth and depth than would have been possible using any single method as it facilitated both discovery and explanation.

The first qualitative phase uses grounded theory to understand the differences between drivers of productivity and engagement for knowledge workers. The findings from this first phase informed the development of the second and third quantitative phases, including a narrowing of the scope of analysis to the factors that work within the confines of productivity and engagement. The quantitative phases used factor analysis, structural equation modeling, and simple linear regression techniques to identify relationships and test the strength and generalizability of the factors identified. Results were triangulated in the interpretation phase providing stronger support for the study’s conclusions and development of theory than would have been not possible in a single methods study. Not only did the qualitative study provide the grist for the development of the quantitative phases, but it also suggested rich explanations for the relationships discovered in those studies.

Our thesis is composed of three research phases and a discussion section. The result of our phase, the qualitative study is partially the foundation of our theoretical framework for study two and three. Figure 2 provides an integrated view on the sequential phases/studies, highlighting the type of research methods as well as the main focus each one of the studies. The ultimate goal of the research is to build an integrated framework on how to improve the management and implementation of remote work

practices. As previously described, the research was completed in three different sequential studies, which then were consolidated, analyzed and integrated, ending with the creation of a framework that illustrates the key aspects that should be considering by management and human resources when implementing remote work practices.

Figure 2. Sequential Mixed Method Research Approach

Next, we discuss our theoretical model and its foundation.

Research Framework

The structure of the research is anchored in four sub-systems of the socio- technical systems theory—Trist and Bamforth (1951) and Katz and Kahn’s (1966) socio- technical systems theory. The four elements or sub-systems that are critical to transforming work systems into outputs are technology, personnel, organization & work- organization design, internal and external environment. Socio-technical systems theory posits that these subsystems continually and jointly interact with each other to produce 61

work systems outcomes (Trist & Bamforth, 1951). From the technology sub-system perspective, the utilization of technology was incorporated as a potential key driver of productivity and engagement; this was also informed by both, the findings of the qualitative study and current literature in the field related to the impact of technology on remote work. From the personnel subsystem, intrinsic motivation was adapted to explore the impact of work location preference, and the impact of enjoyment or stress given the preference of the employee to a particular type of work alternative. Self-efficacy was also incorporated to understand, how the workers are coping with the different type of working conditions given the entrance of different remote work alternatives in the workplace. This was also informed by both the findings of our qualitative study and current theory in the field. From organizational/work sub-system, leadership support was incorporated to understand the degree of influence that leadership has in workers and how this impact may be vary depending on the type of work alternative. In addition, virtual intensity was incorporated to understand the implications to productivity and engagement effects of high and low levels of virtual intensity. Figure 3 shows an integrated view of the theoretical research framework.

In study one, the aim was to understand how remote knowledge workers become engaged, and how the mode and drivers of engagement differ from remote versus on-site workers. To complete this study, a qualitative methods approach was employed. In the second study, the focus was to measure and confirm the factors that drive productivity and engagement, as well as to understand the key differences on the impact of this factors among the remote and on-site workers and the implications of the virtual intensity. In the third study, the focus was to expand the research to understand, given the different types

of work alternatives: the full-time remote, full-time on-site, and blended workers, entering the workplace, what are the differences of the degree of impact of the factors that impact productivity and engagement, amongst the three different categories. And lastly, given the changes that workers are experiencing and coping with, working either remotely, on-site or in a blended category, what is the impact of the worker self-efficacy on productivity and engagement. For the second and third studies, a quantitative methods approach was employed. Next, we explain the sequential mixed methods approach in detail, as well as we provide a deeper account of specific theoretical framing, as it relates to each one the phases of this research.

Figure 3. Theoretical Research Framework

The sequential research design is shown in Figure 4.

Figure 4. Three Phase Sequential Study Approach

Next, we review the research methodology proposed which is divided into three separate but connected studies. We briefly list each study and then discuss why the approach is relevant and how each contributes the overall study as shown in Figure 5.

Figure 5. Proposed Research Design

Phase 1 – Research Question and Methodological Approach

The research question: What is the nature of employee engagement?

•! Are these factors different between remote and on-site workers?

•! What new trends are emerging in HR practices?

In this study, we seek to discern new insights about employee engagement and how the nature of engagement is changing. We extend our research to understand if there are any particular differentiators in the nature of engagement for remote versus on-site workers. Phenomenological, semi-structured interviews, informed by the grounded theory principles of (Corbin & Strauss, 1990) were conducted with 20 individuals currently active in the workforce, either working remotely or in a traditional on-site setting. The semi-structured interviews allowed for structure and uniformity in the collection of data but preserved flexibility and opportunity for the emergence of novel contributions from respondents.

As described by Corbin and Strauss (1990), the qualitative method “…allows researchers to get at the inner experience of participants, to determine how meanings are formed through and in culture” (p. 12). Our research problem was well suited for such an approach as it involved individual’s attitudes and behavior. Furthermore, understanding employees’ experiences that are very personal and evoke strong emotional reactions not easily extracted or measured by quantitative methods.

Our data was interpreted using analytical methods recommended by Corbin and

Strauss (1990) that included constant comparison and theoretical sampling. Emergent themes and concepts directed forward sampling, which continued until no more themes, or concepts could be identified, signaling theoretical saturation.

Sample, data collection, and data analysis will be further explained in the qualitative study section of our thesis.

Phase 2 – Research Question and Methodological Approach

The second study is a sequential study of our first study. Using the key findings as a reference, we formulated the following questions:

1.! What factors influence productivity and engagement for on-site and remote workers? How do they compare

a.! What happens when the employee experiences enjoyment or stress/tension in the work location? Does it matter?

b.! What is the impact of ICT Remote Work Tools/ Utilization and leadership?

c.! What is the impact of virtual intensity?

To empirically test the proposed model, we had already surveyed 304 employees

This model was developed using our findings in the first phase of this study, and was complemented with relevant theories extensively studied related to engagement and remote worker productivity. Details on the theory behind the model will be explained.

Figure 6. Proposed Model – Quantitative Study One

Personnel) Subsystem Job) Engagement

Technical) Subsystem Moderator Virtual-Intensity-(High/Low) IT)Utilization

Personnel) Subsystem Enjoyment) Work) Location Organizational) Outcomes

Stress/Tension) Worker Work)Location Productivity

Organization)Subsystem

Leadership) Direct-effects Exchange Mediation Moderation Controls • Gender • Experience Mediation,-Moderation-&-Group-Comparison-(Remote-and-Onsite)

Our analysis consisted of eight major steps that we briefly describe as follows.

Several statistical techniques were employed to ensure validity, reliability, and adequacy of the data and to create appropriate model specification prior to the testing the hypotheses. After basic data analysis and model specification, we proceeded to prepare to test our hypothesis. The first step involved validating the reflective measurement model using an exploratory factor analysis in SPSS and then a confirmatory factor analysis in

AMOS. The second step involved creating composite variables from latent variable scores in AMOS. This step reduces the fully latent model into just one composite variable per factor. The new composite variables then account for the factor weights of the latent variables, just as in the latent model. With only one variable per factor, the testing of the structural model is greatly simplified. The third step of our analysis included the testing

of the structural model in AMOS. To test for mediation, we employed the Baron (1986) and Sobel (1982) approaches.

The research analysis is further detailed in this study section.

Phase 3 – Research Question and Methodological Approach

This third study is a sequential study of our first and second studies (Pitt-

Catsouphes & Marchetta, 1991; Scott & Timmerman, 1999). Using the key findings as a reference, we expand the research to understand, given the different types of work alternatives: the full-time remote, full-time on-site, and blended workers, entering the workplace, what are the differences of the degree of impact of the factors that impact productivity and engagement, amongst the three different categories. And lastly, given the changes that workers are experiencing and coping with, working either remotely, on- site or in a blended category, what is the impact of the worker self-efficacy on productivity and engagement. We formulated the following questions:

1.! Given the different categories, the full-time remote, full-time on-site, and

blended workers entering the workplace?

a.! What are the differences of the degree of impact of the factors that impact

productivity and engagement, amongst the three different categories?

b.! Given the changes that workers are experiencing and coping with,

working either remotely, on-site or in a blended category, what is the

impact of the worker self-efficacy on productivity and engagement?

To empirically test the proposed model, we use the results of our cross-sectional study, the same data used for the study, but augment the construct of self-efficacy to measure the impact of self-efficacy on productivity and engagement. We also clarify and

confirm previously tested hypotheses as presented in Study 2. More importantly, as shown below expanded the model to measure the impact of the factors that drive productivity and engagement, and compared them against the different types of work alternatives entering the workplace, but only the most critical factors founded in the previous quantitative study, impacting productivity and engagement were considered.

The model measuring productivity considers ICT utilization, work location enjoyment and leadership support, with gender and experience as moderators. The model measuring the impact to engagement, considers self-efficacy, with gender and experience as moderators (Figure 7). This model was developed using the findings of both the qualitative study and the previous quantitative study of this research; therefore, the measurement characteristics (survey, scales, controls) and conditions of the model are the same, except the addition of self-efficacy theory. The analytical approach was the same, with the addition of a comparative method approach that was applied to analyze the key differences amongst the three groups’ findings in the first phase of this study, and was complemented with relevant theories extensively studied related to productivity and engagement of remote worker productivity. Details on the application of the theory behind the model are further explained in each study.

Figure 7. Conceptual Models Approach - Quantitative Study Two

Our analysis consisted of eight major steps that we briefly described above in phase 2 of this research. The same methodology was applied to ensure the validity of our model but including self-efficacy. In addition, we applied diverse statistical techniques to validate the extended hypothesis. To conduct this comparative analysis, direct effects analysis was conducted using path analysis in SPSS Amos version 23, as well as multi- group analysis will be applied. Given the nature of multiple group comparison, this statistical strategy is best suited to our analysis. For the multi-group analysis, a chi-square test was conducted to understand the differences at the model level (Byrne, 2004).

Because the χ2 diff test for the null and free model was found to be statistically significant, a pair-wise parameter comparison was used (Arbuckle & Wothke, 1999) to determine which pairs of parameters were significantly different at the individual factor level. For the pair-wise parameter comparison test, critical ratios for differences between

two parameters in question were calculated by dividing the difference between the parameter estimates by an estimate of the standard error of the difference (Arbuckle &

Wothke, 1999).

The research analysis is further detailed in this study section.

Discussion Section

Remaining Chapters

The remaining chapters focus on the results of the qualitative and quantitative studies. First, we discuss the qualitative study, and then we review both quantitative studies. Once all three studies have been reviewed, we integrate the results before concluding.

CHAPTER III: DRIVERS OF PRODUCTIVITY AND ENGAGEMENT

Introduction

Research accumulated in the last ten years shows that engaged employees are more productive employees, and companies with high levels of employee engagement generate better financial results. Companies with high levels of engagement are more profitable, more customer-focused. In fact, Bersin & Associates (2012) reports that corporations currently invest $720 million annually in engagement programs. Companies in which 60% (or more) of the workforce is engaged have average five-year total returns to shareholders (TSR) of more than 20%. That compares to companies where only 40% or 60% of the employees are engaged which have an average TSR of about 6%

(Baumruk, 2006).

Despite the incremental efforts and investments to increase levels of employee engagement, current data shows that the indexes around the world have remained pretty steady, putting pressure in corporations to re-evaluate and re-think their employee engagement models (Bersin & Associates, 2012). The Gallup organization has reported that only 31% of employees are engaged, 2% lower than the 33% reported in 2010

(Gallup, 2011). Previous years support a similar trend, 2009 showing 30% engagement, and 31% reported in 2008. In 2010, at least 46% of companies participating in the Aon

Hewitt survey reported a decrease in engagement levels of minus 4%, where in past years, the percentage was much lower, 25%, by 2008 and 31% in 2009 (Aon Hewitt,

2011). Most recent data as of October 2015 reports that the percentage of U.S. workers that Gallup considers engaged in their jobs averaged 32.1%. In conclusion, employee engagement levels have been nearly unchanged since March of this year.

This information then poses the question of what factors are changing in the environment that could be affecting the lack of engagement, and as consequence limiting productivity in organizations. Some claim that managers and leaders play a critical role.

Gallup has found that managers who focus on their employees’ strengths can practically eliminate active disengagement and double the average of U.S. workers who are engaged worldwide. In their most recent Global Human Capital Trends Report 2015, Deloitte

Human Capital reported that culture and engagement were rated the most important issue for management overall slightly edging out leadership (the No. 1 issue last year). This challenge highlights the need for business and HR leaders to gain a clear understanding of their organization’s culture and reexamine every HR and talent program as a way to better engage and empower people.

While these are internal aspects of organizational effectiveness that for the most part are within the internal control of the organization, there are other macroeconomic, technology and workforce demographic factors influencing the state of the workplace.

Many argue that the economy is one big factor especially in the last 5–8 years, where massive layoffs, pay freezes, cuts in benefits, fewer people to do the work and general instability, all seem likely to affect employee’s morale (Cascio, 2002; Mishra et al.,

1998), making people less engaged in their work, increasing the challenge for organizations to drive performance.

On the other hand, the pervasive utilization of technology outside of the workplace is forcing organizations to implement tools and technology that can attract and retain professionals, who want to work more efficiently, in a more flexible environment where work can be performed anytime, and anywhere (Fulk & DeSanctis, 1995). This

change in technology has as a consequence created changes in workplace practices, such as remote working, distributed teaming environments (Aubert & Kelsey, 2003), increasing the difficulty for organizations to understand how to maintain employees engaged in a less restricted environment and at the same time drive the necessary productivity and performance (Ilgen & Pulakos, 1999).

The importance of ICT is increasing for organizations (Jamali & Hashemi, 2011).

Although this has not been explored as an enabler of employee engagement, recent research (McGrath & Freed, 2012) shows that companies’ social intranets and enterprise technology are becoming the online reflection of the company’s culture. At the same time, work productivity and social tools (video conferencing, telepresence, instant messaging, Facebook, LinkedIn, etc., are becoming the norm in the working place; creating virtual places that some would argue, give every employee a face and a voice, humanizing the company’s online environment and creating the foundation for engagement (McGrath & Freed, 2012).

From the workforce demographic perspective, Gallup research shows that different types of workers need different types of engagement strategies. The generations near the end of their careers tend to be more engaged than those at the beginning of their careers. Millennials are most likely of all generations to say they will leave their jobs in the next 12 months if the job market improves. Women have slightly higher overall engagement than men. Employees with a college degree are not as likely as those with less education to report having a positive, engaging workplace experience.

Clearly, given the impact of the economy, the accelerated introduction of technology as well as the new trends of practices in the workplace, it imperative for

organizations to make sense of the effects and level of impact this may have on employee productivity and engagement of the organization.

Though employee engagement has declined to its lowest point in eight years, leaders have huge opportunity to prevent further declines. Employees are individuals with individual talents, individual motivations, and individual challenges. Their perceptions of their workplaces are driven by many unique factors that come together to create one culture, and each organization’s culture is unique. This is why it’s vital for corporate leaders to understand what uniquely drives productivity and engagement. So then the question is, “Where can leaders take action within their organizations?”

In our research, we argue that the answer lies with first understanding what factors drive productivity and engagement in the workforce. And that is the main goal of our research. This research explores the factors explaining the nature of productivity and engagement of knowledge workers. And also provides insights to key prevailing differentiators in the nature of productivity and engagement between remote and on-site knowledge workers

We began this study by reviewing the academic and practitioner literature on productivity and engagement of knowledge workers, new workplace dynamics as practices of remote work and the impact of technology in the workplace. We discuss our methodology and then describe our key findings. Finally, we identify the limitations of this study and conclude with our thoughts on the implications for practitioners and for future research.

Literature Review

This section identifies key academic literature, theories, and practitioner articles that seeded our research and shaped the research question. Given the nature of this qualitative study, the theories described in this section underpins this research and the corresponding grounded questionnaire. This literature will include a review of employee engagement theories as well as research related to productivity; these are the pillars that shaped our research questions.

Employee engagement

The term “employee engagement”, in its present usage, was coined by the Gallup

Organization, as a result of 25 years of interviewing and surveying employees and managers. Their intent was to create a measure of workplaces that could be used for comparisons. Their research has been published in books, practitioner magazines, academic journals, and on websites. In First, Break all the Rules, the original book coming out of the Gallup research (Buckingham & Coffman, 1999) report that Gallup spent years refining a set of employee opinion questions that are related to organizational outcomes. The statistically derived items, called the Gallup Workplace Audit (GWA), that measure employee engagement are related to productivity, profitability, employee retention, and customer service at the business unit level (hospital, hotel, factory, etc.).

They report that employees who score high on the questions are “emotionally engaged” in the work and the organization. (See Appendix A for the questions.) In Follow This

Path, the second book coming out of the Gallup research, Coffman, Gonzalez-Molina, and Gopal (2002) say that engagement is not only about how people think but also about how they feel. They say that the engaged employees collectively are an “economic force

that fuels an organization’s profit growth” (p. 26). They group employees into three categories, the actively engaged, the non-engaged, and the actively disengaged employees. Most of the book is devoted to “how-to” chapters for managers.

(Ketter, 2008; Seijts & Crim, 2006). As an example, The Gallup Organization uses the term “employee engagement.” Gallup researchers, Harter et al. (2002) define employee engagement as the individual’s involvement and satisfaction with as well as enthusiasm for work. This definition focuses on the employees’ work and uses other related constructs to explain the concept. Harter, Schmidt and Hayes’ (2003, as cited in Simpson,

2009) model of employee engagement identifies four antecedents that are necessary for employee engagement to occur: clarity of expectations and basic materials and equipment that is provided; feelings of contribution to the organization; feeling a sense of belonging to something other than oneself; and feeling as though there are opportunities to discuss progress and growth (Simpson, 2009: 9). These antecedents are reflected in the Gallup

Organization’s measure of employee engagement, namely the Gallup Workplace Audit

(GWA). In recent research provided by Towers Watson (2012), they found that the top five drivers of employee engagement are: Effective leadership, Stress/Balanced of the workload, Having clarity of goals and objectives, involvement of supervisors, and lastly, the image of the organization

On the other hand, academic researchers from the Institute of Employment

Studies, Robinson, Perryman, and Hayday Robinson et al. (2004) define employee

engagement as a positive attitude held by the employee towards the organization and its values. An engaged employee is aware of business context and works to improve performance for the benefit of the organization. This definition emphasizes a positive attitude towards the organization and the important aspect of engagement as a two-way process. Other authors also use the term commitment when referring to employee engagement. For example, Fleming et al. (2005, as cited in Little & Little, 2006) used the term “committed employees” as a synonym for “engaged employees”.

Macey and Schneider (2008) note commonalities to various definitions of employee engagement. These include it being a desirable condition, having an organizational purpose, and it includes involvement, commitment, passion, enthusiasm, focused effort, and energy. Macey and Schneider (2008) highlight that it encompasses both attitudinal and behavioral components. The authors advocate that the antecedents of such behavior lie in the conditions of one's work and the consequences of such behaviors are thought to be of value to organizational effectiveness. Macey and Schneider (2008) note that this lack of precision with regards to employee engagement does not mean that the construct lacks practical or conceptual utility but suggest that it would benefit from being framed as a model that simultaneously embraces both the psychological state and the behavior it implies (Macey & Schneider, 2008).

Kahn (1990) introduced the term personal engagement and disengagement. He provided a theoretical conceptualization of engagement in a study—which focused on psychological presence during particular moments of role performances. Kahn (1990) defines personal engagement as the “simultaneous employment and expression of a person’s preferred self in task behaviors that promote connections to work and to others,

personal presence and active, full role performances” (p. 700). Whilst in engagement, employees will express themselves cognitively, physically and emotionally (Kahn, 1990).

When a person is engaged, they are understood to be physically involved, cognitively vigilant and emotionally connected (Simpson, 2009).

Schaufeli and Bakker (2008) also provide an attempt to consolidate the meaning of employee engagement. They discuss three approaches. First, it is conceived as a set of motivating resources such as recognition, support, feedback, and opportunities for skill development and learning, which is consistent with Macey and Schneider’s (2008) antecedents of engagement. Second, it is conceived in terms of commitment and extra- role behavior. Third, engagement is defined independently from job resources and positive organizational outcomes as a “positive, fulfilling, affective-motivational state of work-related well-being that is the antipode of job burnout.”

Employee engagement Related & Different Theories

Because many of the definitions of employee engagement invoke existing constructs, such as job satisfaction, organizational commitment, organizational citizenship behaviors and job involvement, it is important to understand the theories its relationship to employee engagement and how they differ from each other

Job satisfaction, a widely researched construct, is defined as a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences

(Locke & Henne, 1986). Harter et al. (2002) begin their discussion of engagement by using the term engagement-satisfaction, but drop the satisfaction from the term early in their article. Generalized job satisfaction has been shown to be related to other attitudes and behaviors. Positively, it is related to organizational commitment, job involvement,

organizational citizenship behaviors and mental health. Negatively, it is related to turnover, perceived stress and pro-union voting (Kreitner & Kinicki, 2004). It has been found that while the relationship between job satisfaction and performance is weak at the individual level, but is stronger at the aggregate level (Ostroff, 1992). In the engagement literature, Harter et al. (2002) invoke Ostroff’s research as a reason for studying employee engagement at the business unit level.

Job involvement is the degree to which one is cognitively preoccupied with, engaged in and concerned with one’s present job (Paullay, Alliger, & Stone-Romero,

1994). Pfeffer (1994) argues that individuals’ being immersed in their work is a primary determinant of organizational effectiveness. Job involvement has been shown to be related to OCBs and job performance (Diefendorff, Brown, Kamin, & Lord, 2002). In the employee engagement literature, Wellins and Concelman (2005) use the term job ownership as a synonym of engagement.

Organizational commitment is the degree to which an individual identifies with an organization and is committed to its goals. Commitment has been shown to be related to voluntary employee turnover. It is also seen as crucial to individual performance in modern organizations that require greater self-management than in the past (Dessler,

1999). In the engagement literature, several of the authors use terms such as commitment

(Fleming et al., 2005), an amalgam of commitment, loyalty, productivity and ownership

(Wellins & Concelman, 2005), and loyalty (DDI).

Organizational citizenship behaviors (OCBs) are discretionary behaviors that are beyond formal obligations. They “lubricate the social machinery of the organization, reducing friction and/or increasing efficiency” (Podsakoff & MacKenzie, 1997). These

desirable behaviors have been shown to be related to job satisfaction and organizational commitment and to be related more to work situation than dispositional factors

(Podsakoff, MacKenzie & Bommer, 1996). OCB, an outcome of the attitudes of job satisfaction and organizational commitment, is similar to the definitions in the engagement literature of being respectful of and helpful to colleagues and willingness to go the extra mile (Robinson et al., 2004), or working longer hours, trying harder, accomplishing more and speaking positively about the organization (Wellins &

Concelman, 2005).

After reviewing all these related theories, it is almost impossible to ignore the question as to whether employee engagement is a meaningful concept that management should continue to explore, or whether it is missing some other aspects that management should be looking at. We argue that employee engagement is an outcome and that there are other aspects related to human behavior, socio-technical factors that should be evaluated in other for management to create a more meaningful understanding of the aspects affecting the current state of the workplace. Therefore, our ground theory approach will allow us to apply an open-ended method that will allow us to uncover factors that may be hidden at this point.

Knowledge Worker Productivity

According to the OECD (Organization for Economic Cooperation and

Development), productivity is commonly defined as a ratio between the output volume and the volume of inputs. In other words, it measures how efficiently production inputs, such as labor and capital, are being used in an economy to produce a given level of output. There are different measures of productivity and the choice between them

depends either on the purpose of the productivity measurement and/or data availability.

At the organizational level, productivity is a performance measure encompassing both efficiency and effectiveness in an organization. Therefore, it is important to know not only who the productive workers are, but what drives it. Productivity is a performance measure encompassing both efficiency and effectiveness. High performing, effective organizations have a culture that encourages employee involvement. Therefore, employees are more willing to get involved in decision-making, goal setting or problem- solving activities, which subsequently result in higher employee performance raise employee productivity and satisfaction Hellriegel, Slocum, and Woodman (1998),

According to Miller and Monge (1986), job satisfaction increases productivity through bringing high-quality motivation and through increasing working capabilities at the time of implementation. There is evidence that participative climate has a more substantial effect on workers’ satisfaction than participation in specific decisions, and it appears that participation in goal setting does not have a strong effect on productivity. Participation has a strong effect on both job satisfaction and productivity, but its effect on satisfaction is somewhat stronger than on productivity.

At the individual level, and given the current newly workplace practices of remote work, employee productivity is being heavily scrutinized by academia and management.

Some agree that productivity at the individual level is regularly reported as a perceived benefit of telework for Organizations (Callentine, 1995; Hill et al., 1998; Pitt-Catsouphes

& Marchetta, 1991). Reasons cited include working at peak efficiency hours, reducing distractions and interruptions, being in an environment conducive to increased concentration, and reducing incidental absence (Baruch, 2000; Bélanger, 1999). Other

organizational level incentives for opting to introduce remote work, Huws (1992) found indications for improved productivity, reliability and work quality among teleworkers

(see also Salomon & Shamir, 1985). They were perceived as more loyal, less likely to avoid work (i.e., be “absent” from home during work time) and had lower tendency to change employer.

Productivity at the organizational level has frequently been contradictory. In a review of current literature in remote work, Shin et al. (2000) reported that changes in teleworker productivity were consistently of interest to a number of studies (Di Martino

& Wirth, 1990; DuBrin, 1991; Olson, 1989). These studies were remarkably alike in pointing out the positive effect of telework in enhancing individual productivity.

However, except for a few state-commissioned studies such as the Evaluation Audit and

Review (EAR) Group Report, research that systematically investigated productivity changes resulting from telework has been rare. Most studies merely quoted anecdotal program reports or referenced nominal discussions based on hearsay without mentioning methodological details. The authors also noted that there are methodological and conceptual weaknesses in existing literature as well.

Research Design

Methodology

This study used grounded theory as the primary research methodology, first outlined by Glaser and Strauss in 1967 (Charmaz, 2006). Grounded theory is an inductive methodology and the objective is to discover “theory from data” rather than “deducing testable hypotheses from existing theories” (Charmaz, 2006; Glaser & Strauss, 1967).

This method has been chosen because of its strength in helping the researcher discover

new ideas, theories and patterns in people, events and behavior. Grounded theory was selected for this inductive study because this method seeks to explain and describe while also uncovering relevant changing conditions; therefore, it captures how individuals respond and interpret the consequences of their actions (Corbin & Strauss, 1990). This approach facilitates posing open-ended questions and, through induction, discovers the concepts, categories, and themes, influencing employee productivity and engagement.

Constant comparison and contrast were used throughout, within and between the interview and analysis process to reveal emerging themes and concepts (Charmaz, 2006).

Using the grounded approach, we learned from the lived experiences of our research participants who experience the current state of the workplace. Grounded

Theory, as Charmaz outlined, allows us to compare data as the research is conducted through a process of initial, focused, axial and theoretical coding (Charmaz, 2006). We developed and compared codes as the research progressed and our theories started to formulate. Almost immediately, we found that one dynamic was obvious when interviewing knowledge workers, the location of where they worked was sometimes at home, and sometime in their office. It became clear that remote work and work flexibility were prevailing in the workplace. This initial insight gave us the advantage to create two groups of employees based on this characteristic, on-site and remote knowledge workers.

Based on this immediate finding, interviewees were chosen based on two determining factors, one being the employee either have to work on-site or remote and that the employee will have to be a knowledge worker. We report our findings using this distinction in a manner that this order can help us to better delineate if there were any differences in how they experience the workplace.

Next, we discuss the characteristics of our sample in more detail.

Sample

The sample obtained for this study included 20 working professionals from a variety of organizations and industries, with operations in the United States. Participants worked in a variety of areas, including human resources, infrastructure, engineering, program design/software development, operations, supply chain, and other management areas, no sales, traditional over the phone customer service professionals or consulting professionals were considered for the interviews, given the natural tendency for this jobs to either travel, work from home and travel constantly. As shown in Table 2, the gender representation was 60% females and 40% males. 55% of the participants were remote workers and the other 45% reported to work from a formal office. As far as role in the organization, 40% of the participants reported to have manager responsibilities and 60% reported to have individual contributor role. Managers were not primarily on-site, and individual contributors were not primarily remote. Our sample generational distribution included at least three commonly recognized generations. We coded them separately for easy identification (BB = baby boomers, X = Generation X or baby boosters, Y =

Millennials).

Table 2. Sample Demographics

Data Collection

We conducted semi-structured critical incident interviews. In most cases, the interviews lasted between 45 and 75 minutes. Interviews were done between September and November 2013. Ten interviews were conducted in person and ten over the telephone. Each interview was audio recorded by the principle researcher and transcribed by a professional transcription service. All interviewees and companies were guaranteed confidentiality and anonymity. All names are pseudonyms.

We began each interview asking the participant to share a brief summary about themselves and how they got to get to where they are in their careers. Following this

warm-up question allowing for the capturing of demographic data, we asked interviewees to share three very detailed experiences: (1) a time in their careers when they felt really excited about the work that they were doing, and a specific example of project/work they had executed in their careers when they felt they really exceeded performance at work or were more productive; (2) a time in their careers when they felt not as excited/demotivated about the work that they were doing, and a specific example of project they had executed in their careers when they did not feel good about their performance at work; and 3) a time when their companies introduced new tools/technologies, and how those new tools/technologies affected their work either positively or negatively. In some cases, we expanded questions in those areas where they were more willing to get deeper in their responses.

Using probes to elicit their thoughts and feelings during these interactions, we captured rich detail from these narratives. Emotions observed during the interviews were also noted, such as when an interviewee’s eyes welled up with emotion describing distress or excitement captured through expressive gestures, raised voices and shifts in vocal tone. Additional probes assisted in capturing how they experience the work environment around them, changes in the work environment, which became relevant to the research. Other questions such as why they come to work, what they like and dislike about the role, ideas they may have had to improve a positive experience in the workplace, were also used to gain a more robust understanding of their motivations and what drives engagement at a personal level (see Appendix A).

Data Analysis

Coding evolved through a systematic process using a grounded approach to data collection with validation from literature. Using initial coding technique, transcripts were coded which were used to compare the experiences not only to others but also the literature (Charmaz, 2006). Through the initial coding phase, 392 codes that were substantial in nature were identified at the beginning of the coding. During focused coding, the codes were grouped by recurring themes and concepts that started to take form. The iterative initial and focused coding processes required constant validation and

9 follow-up interviews with some of the participants of an average of 15 to 20 minutes call, to validate the concepts that emerged. Once the focused coding was completed, we began axial coding where the logical themes were formed. Through the process of axial coding (Strauss & Corbin, 1990), these codes were reduced to approximately 42 codes.

The reduction was a result of unifying codes that were considered to be similar or the same and eliminating those that were not relevant to this research. During this process, some codes were subsumed into others, and some categorized as “belonging” to more than one of several higher-level categories.

Table 3 provides a view of the coding. The left column provides a view of the primary categories identified; the second column provides the sub-categories. The third column provides the most common and similar statements. Lastly, column four provides the coding and summarizes at the bottom the 45 codes. The last columns provide the number of times the comment was mentioned as well as the distinction of the type of worker that accounted for the comment.

Table 3. Main Themes Coded

Findings

Our findings identified several overarching themes, highlighting the perceptions of the interview group as a whole. These findings also confirmed earlier research that exists today about the factors that drive productivity and engagement for knowledge workers. In addition, based on our initial learning from practice and through theory, the findings confirm that the factors that drive productivity and engagement are not only

internal management factors but also factors that are strongly related to the motivations and desires of the employee. The core findings are listed below:

Nature of engagement

1.! Leadership: Having the right leadership that is supportive, trusting, provides the right direction and provides opportunities for development is essential for both categories of employees, remote and on-site workers.

2.! Job Engagement & Self-Efficacy: Employees feel more engaged when they were successful at their work, especially when working in interesting and challenging work, that can provide the opportunity to solve problems, be empowered and create new meaningful things. This was a top driver of engagement in both categories

3.! ICT Tools Utilization and Availability: Having the right technology to work available and working for companies that have a strong culture of working with tools, was perceived as a key driver of engagement. This appears to be of higher importance for remote workers than for on-site workers

4.! Work Location Flexibility: Having the opportunity to work remotely, as well as having the opportunity to work on-site and being at the same time able to choose to work from home when needed. this opportunity appears to drive higher levels of commitments to the company and the supervisors, the employee seem to value that.

5.! Self-Efficacy: Having the opportunities to self-direct work, lead, demonstrate capabilities to drive complex and significant work career advancement and opportunity to learn new things and advance

Nature of Disengagement

1.! Ineffective leadership: Conversely, not having the right leadership that is supportive, trusting, was a big driver of disengagement in both categories.

2.! Lack of Project Success: Not being successful in projects or special initiatives, either because of lack of resources or support from their manager

3.! Lack of engagement with Work: Not being challenged in their work and not having the opportunity to utilize their skills to the maximum potential. This was more relevant for remote workers than for on-site workers.

New Workplace Practices - Drivers of engagement

1.! Availability of Work Flexibility Programs: This was found to be a top driver of engagement for those employees working on-site. 92

2.! Remote Work Availability: This was found to be a top driver of engagement for those employees working on-site.

3.! Availability of Remote Work Technology: Having tools like WebEx, IBM® Sametime® or other communication tool enables speed of communications and enhances productivity.

New Workplace Practices - Drivers of Disengagement

1.! Remote Work Experience/Absence of leadership: The lack of presence of leadership with remote workers is creating disengagement, mainly due to the lack of communication and attention to participate in critical work, career development, and learning opportunities compared to their on-site peers.

2.! Remote Work & Work Location Flexibility: The lack of alternatives to work remotely and the lack of opportunity to be on-site but still be able to work one or two because sometimes managers are not as flexible, even if the type of work that they perform allows them to.

Other New Workplace Dynamics

1.! Remote Work Experience is Becoming the Norm: Remote work is clearly becoming the norm in the workplace, showing signs of more acceptance by peers and management. This is coming in different types of intensity.

2.! Remote Workforce Technology Availability Increasing and Enabling Productivity: The availability of tools is a clearly driving productivity, collaboration and speed of the work, enabling both, remote and on-site workers to be more productive. It was previously noted that technology is also a top driver of engagement.

We next synthesize the findings in four key areas driving productivity and engagement, and provide detail for each one of them.

Finding 1: Leadership effectiveness arose as important amongst both worker categories. And as expected, the “lack of leadership effectiveness” is also the one driver of disengagement. This is common between remote and on-site employees.

The respondents (both remote and non-remote) in this study reported common experiences that suggest that Effective leadership is equally critical for both categories of

employees, which means leadership appears to be a factor that is independent of the location, with a total count of 49 positive comments. Paradoxically, ineffective leadership was also one of most mentioned drivers of disengagement in both categories, with a total count of 21negative comments. This first finding correlates with existent theory in the field. Harter, Schmidt and Hayes’ (2003, as cited in Simpson, 2009) model of employee engagement identifies four antecedents that are necessary for employee engagement to occur: clarity of expectations and basic materials and equipment that is provided; feelings of contribution to the organization; feeling a sense of belonging to something other than oneself; and feeling as though there are opportunities to discuss progress and grow

(Simpson, 2009: 9). At least three key items very similar to this assertion were noted during the interview process. Employees noted that a major driver of disengagement where cases, when the manager did not provide the right direction, kept changing the objectives, and did not provide opportunities for growth. Conversely, some employees expressed that the best times they felt engaged was because they felt strongly supported by their managers. Figure 8 provides key quotes supporting this finding.

Figure 8. Key Quotes Supporting Finding #1 – Leadership as a Driver of Engagement

“The number one thing that is, it can be a motivator or de-motivator, is my manager. So, if it’s a bad manager or a manager who micromanages or doesn’t align my skills and kind of give me the support that I need, um, that would be a negative part of my, um, experience, and I’ve had that in, you know, in, in a role before. Um, I’ve had a really bad, you know, director before and I’ve had, you know, good ones, and, and then I’ve had two great ones, and I’m, I’m reporting to a great one right now. And, so, I’d say the top satisfaction is my manager. “ – Remote Worker

“It was the connection and the support for my manager that was a key differentiator. Of course, I was up for the task, but he really made me felt he was behind me and that um, counts a lot you know. It’s good to know your manager is supportive, and it is there for you. Not only that, I think leadership has an effect at all levels. The manager of my manager was also a great supporter of my career, and as a consequence, I felt that I was super committed to them and the work that I was doing um, you know, I felt I should do my best!” – On-site Worker

“If you boiled it all down, it was a major mission that was really exciting like I mention um, unfortunately, did not have huge support from my manager. It was almost like my manager was not communicating, and kept changing the direction constantly, it was very confusing, um. And I felt I had put my career in jeopardy because we were not making any progress, I was lucky to find another job in another department; otherwise, I would have left the company”. – Remote Worker

“The one thing that really drives me crazy about my manager is the lack of communication and the constant change of plans, we can’t accomplish anything right because nothing is solid with him um…that makes me feel like we are not being successful. “ – Remote Worker

“I think the things that have always, seemed really bad are things that people ask me to do, tell, tell me to do, and suggest somehow that it’s mission critical, practicing vital information that needs to get done, and I work at it really hard, and then see it go absolutely nowhere. It’s, it just kills your soul to have somebody set a direction for you, and to have it go nowhere. And I can see that I’ve done that a lot, where somebody else has need me to do this., and I disagree, but I go off, and I do it because you know, you’re trying to be the good soldier, and it’s just so awful when nothing gets done with it”-On-site Worker

Finding 2: Engagement with the work, their jobs and the opportunity to enhance workers’ learning, enhancing worker self-efficacy. This is common among remote and on-site employees.

The respondents (both remote and non-remote) in this study reported common experiences that suggest that being engaged with the work is equally critical for both categories of employees, which means work engagement appears to be a factor that is independent of the location, with accounted for 35 comments. The opportunity to work in projects, products or work where the employee has a passion for, and where there is

excitement about the work that needs to be done was another are that accounted for 25 comments. The opportunity to learn and participate in challenging work, innovative work that can make them growth professionally through the experience. Other factors reported by employees included teamwork, having a sense of adding value and being empowered in the work are also important drivers of engagement. This second finding also correlates with existing proven theories related to productivity and engagement of employees.

Bandura (1978) defined self-efficacy as “a judgment of one's ability to execute a particular behavior pattern” (p. 240). Wood and Bandura (1989) expanded upon this definition by suggesting that self-efficacy beliefs form a central role in the regulatory process through which an individual's motivation and performance attainments are governed. Self-efficacy judgments also determine how much effort people will spend on a task and how long they will persist with it. People with strong self-efficacy beliefs exert greater efforts to master a challenge while those with weak self-efficacy beliefs are likely to reduce their efforts or even quit (Bandura & Schunk, 1981; Brown & Inouyne, 1978;

Schunk, 1981; Weinberg, Gould & Jackson, 1979). Our observation here is that when employees exhibit strong self-efficacy are more prone to be successful. The theory appears to be particularly well suited to studying remote work. The remote employees work with minimal supervision and rely heavily on their own abilities and initiative to perform their job tasks. Information technology is the typical medium used to communicate with management since face-to-face interaction is rare or infrequent. Often the employee works in a location with few or no co-workers, so the potential for isolation can be high and the availability of co-worker advice is often low. Since remote employees enjoy considerable work autonomy, the potential impact that their own

motivation and beliefs in their abilities (i.e., self-efficacy judgments) can have on their outcomes may be considerably more than for employees whose behaviors are under tighter supervision. Therefore, virtual organizations that learn how to maximize employees' self-efficacy with respect to working remotely may reap greater benefits from a new blended workforce. Figure 9 provides key quotes supporting this finding.

Figure 9. Key Quotes Supporting Finding #2 – Worker Job Engagement & Self- Efficacy as a Driver of engagement

“It was the connection. It was the connection because I think, um, you know, in each of our own world, what we are doing is very important, and we feel that, and we resonate with that, and we're … we have passion around that or, at least, those of us that are lucky enough to work within the area that are passionate.” – Remote Worker

“If you boiled it all down, it was a major mission that was really exciting, huge amounts of support, lots of structure, um, in the way we were working, lots of changes to address, my voice was being heard. I was learning, and learning, and then I was also learning hugely both in and outside of work and everything related in my personal life and how much I could do! It was really an incredible learning experience”, - Remote Worker

“I am still in touch with them to this day, and they have really been able to successfully implement a lot of the changes I recommended and have seen a lot of positive results. So, that was exciting. As I mentioned, this is my first career job, being able to have such a high impact like that and having it all work out is very exciting. “ – On-site Worker

“There are projects that come our way that I'm not crazy about, but, they must be done. Some of the things that I dislike is having really, really vague requests. Our marketing leads, for instance, wanted us to develop an assessment for something they couldn't even really exactly say what. The reason that turns me off is because the chance for failure or disappointment is so high. You cannot even articulate what it is that you're looking for, so, the chances of me delivering exactly what you're looking for are pretty low. So, that does happen from time to time. I am getting more used to it because I think that's not something that's going to change. So, I guess it's just learning how to sort of take the vague and make it into something that actually happens. “- On-site Worker

Finding 3: The entrance of new remote ICT tools, its utilization and availability is having a big impact in productivity, collaboration and improving the speed of communication. This appears to be more important for remote workers.

The respondents (both remote and non-remote) in this study reported common experiences that suggest the shift in the workplace where technology is becoming more pervasive in the workplace. Among this sample of participants, no obvious differences in

response types were noted between remote or non-remote workers. As perceived by the respondents, technology supports effective collaboration between locations, time zones across the world, making the organization most productive and efficient. Technology also has a positive impact on connectedness and information dissemination, supports the availability of communication and organizational information. Technology supports better collaboration and communication, especially when visual/video of the person is available. Technology is critical and has a high importance to facilitate Virtual Presence.

Participants described the use of technology tools such as web-conferencing or teleconferencing (VIDEO) capabilities, email, and instant messaging, in addition to more traditional methods of remote communications, such as the telephone, to enhance the personal connections and communications by supporting verbal and non-verbal communications and more personal relationships. These contacts, even via telephone, have advanced with the ubiquitous availability of cell phones and anytime/anywhere access via global networks. Because of the pervasiveness of the use of these technologies to connect business teams globally, both remote and non-remote workers report similar experiences with technology use and involvement with remote work, with some participants on the office side of the communications and others on the remote side of communications, but both communicating and working using advanced technologies in the workplace. Seventeen of the twenty participants noted the positive impact of technology on information dissemination and staying connected, supporting communication and more effective collaboration. This positive impact was seen as critical to business operations, particularly in terms of disseminating communications and information.

Figure 10. Key Quotes Supporting Finding #3 – Technology

“We use a lot of Google products, so it’s, uh, Google video conferencing, it’s Google Docs. Um, and go back and forth … uh, about Google Docs now because not all the features and capabilities are there. But, uh, you can collaboratively edit documents with other individuals, which is huge. “ – On-site Worker

"I had a Telepresence (High-Quality Video) meeting maybe once or twice a month, but generally, everything now is WebEx Video. So, um, that's a big change from even when I was back in [previous location] and meeting with people face-to-face." – Remote Worker

“Technology helps with productivity, helps with, communication that is missed sometimes. You are able to work across time zones and geographies…we are able to track information in aggregate." – Remote Worker

"That's why I like VIDEO so much because I can see the non-verbal...VIDEO is pretty pervasive in the company these days. And more importantly, the behavior of using these, you know, teleconferencing systems is very pervasive… much of my constituents, frankly speaking, never leave their offices to go to conference rooms anymore. They're all on Telepresence…One of the things that really makes me excited is the use technology and how our culture of using it makes it pretty transparent that allows people to contribute from anywhere in the globe," – Remote Worker

“I think technology has had a positive impact. I use everything! They are like my tools I need them… I need the computer. I need my phone to be connected. I need the IP phone, the video system. You need to have a good setup because your access is everything in the company. So, I think the technology helps me. I don't need the mail room, I need you know, the technology to get me in touch with people.”- On-site Worker

Finding 4: Remote Work and Work Flexibility practices are becoming the norm of the future, and employees like the flexibility and it’s perceived as a driver of productivity and engagement.

Remote work and work flexibility, appear to be generating a significant level of engagement on knowledge workers, and this appears to be of the utmost importance in both categories, with a total count of 28 positive comments. These new kinds of work arrangements appear to have a positive impact for those that work remotely and also for the ones that work in a traditional office environment, who at the same time are able to enjoy work flexibility given the interaction in the environment with their remote work colleagues. One noticeable finding is that, although they were asked to provide their worker category, and declared that their company have them formally as remote or on- 99

site workers, in addition, many participants reported different kinds of arrangements: some of them reported to work from home 100% of their time; some others reported working remotely three days a week; others reported one day a week. This indicates that the horizon of the virtual intensity of the remote work is expanding, and with that, the complexity for managers who need to ensure that this has no negative effect on performance or productivity of the workforce.

Remote work is perceived as a new and expanding benefit that is being made available to employees; this accounted for 26 positive comments. Besides providing more flexibility to employees to balance work and life, employees reported feeling happier, more engaged when having the choice to go to the office or work from home. Many of our participants expressed how much they value that their company have this kind of work alternative, a great majority of the interviews expressed that having this alternative work arrangement, has resulted in better relationships with their supervisors, where the employees are more willing to be flexible with an employer that's flexible with him or her. In return for a flexible schedule and freedom, an employee is more likely to work harder to repay the favor. Despite the positive comments, we also heard some concerns about isolation and potentially impacting negatively the career opportunities for employees to grow, this accounted for 10 negative comments in our coding.

Overall, participants reported that the culture of remote work was perceived by this sample to be generally accepted and becoming the norm, with perceptions of a portion of virtually any work environment working remotely not only because employees are working from home, but also resulting from business globalization. This acceptance is perceived to be dependent on the organizational culture and the company’s business

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model. The leadership culture described by participants in this study was reportedly generally accepting, accommodating, and supportive of remote work. Some employees noted that remote work was becoming the norm, and employees expect to see acceptance increasing, especially with managers being more accommodating and supportive.

However, the acceptance of remote work feels to be dependent on company model and business culture.

Figure 11. Key Quotes Supporting Finding #4– Acceptance of Remote Work in the Workplace

"I think remote work is becoming the new normal." – Remote Worker

"That's the number one most important thing for me, flexibility, and that is what I will always bet for any job in the future. It’s part of the billing.” – On-site Worker

“I think accepting a culture of remote work starts with your leader, I think my leader is accommodating and supportive of this notion of pulling the best talent regardless of their location." – Remote Worker

“We have come a long way in the past 6 years in this organization; you see now people working remotely in every meeting. I enjoy having the opportunity to at least once a week, work from home, how cool is that!” – On-site Worker

"We are going to see changes in how organizations operate; I think working remotely is going to become a way of life in the coming years." – Remote Worker

“For me having a choice to pick where I can work from is essential. I feel more motivated and engaged knowing that I have this choice.” – On-site Worker

Discussion

These findings validate some of the existing research and expand the body of knowledge around drivers of productivity and engagement in knowledge workers, as well as some of the key differences between remote and on-site workers. As is appropriate for inductive grounded theory qualitative research, the original research question we posed was broadly, “What is the nature of employee productivity and engagement? Is the nature

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of engagement different across remote and on-site workers? What new trends are emerging?

Our findings indicate that effective leadership presents the highest association to nature of productivity and engagement for both, remote and on-site workers. Based on coding results, leaders play a big role in the development of engagement by projecting behaviors that are tied to engagement drivers, such as being supportive, providing a vision to the employees, providing opportunities for career development, establishing clear objectives and challenging goals that can enable the worker to maintain the excitement about the contribution of their work and can keep them interested and engaged with the company and the manager as well. While this finding is not necessarily a new discovery, our interviews reveal new insights about the expectations of leaders, especially as it refers to those leaders that are exposed to manage a blended workforce of remote and on-site members. It almost appears that the more virtual the organization, the larger the need for effective leaders in the workplace. As an example, the traditional management mechanisms are changing given the entrance of new working ICT tools like

WebEx, video, conferencing, internal share documents. This means leaders have less face to face opportunities to interact and develop emotional links with the workforce.

Therefore, while in the previous role it was enough to schedule one performance review and possible a traditional one on one to catch up with the employee, in the new workplace managers have to be on “all the time”, continuously interacting virtually with their employees, available at their cellphones and potentially using video conferences to facilitate interactions with them and the rest of the internal organization membership

(peers).

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This finding leads us to conclude that organizations need to begin developing more advanced and sophisticated training programs for leaders, tailoring this to the new way of how leaders need to conduct themselves, and display behaviors that go beyond traditional communication, performance management tools that are more adequate for on-site management. A set comprehensive strategies for leaders to build trust, share their vision, and create open communication, in an environment where remote and on-site workers can blend its required. In addition, leaders will have to learn to adapt to use technology like video and other interactions tools like IBM® Sametime®, internal chats, which are mechanisms that are prevailing in the workforce. This new kind of leadership profile requires not only the ability to use the technology by itself, but it will require leaders to adjust their mental models to a new dynamic workplace, where employee’s expectations go beyond traditional leadership support. In exchange, if organizations can provide the right tools and proactively develop their leaders for the new workplace needs, this could increase the potential for remote and on-site workers to develop higher levels of organizational commitment and as a consequence, increase productivity levels.

These findings also indicate worker engagement and the opportunity to enhance worker self-efficacy are key drivers of productivity and engagement. The respondents

(both remote and non-remote) in this study reported common experiences that suggest that being engaged with the work is equally critical for both categories of employees, which means work engagement appears to be a factor that is independent of the location.

Equally important seems to be for the interviewers to have the opportunity to work on interesting and challenging projects, develop new products or work where the employee has a passion for, and where there is excitement about the work that needs to be done,

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where they have an opportunity to learn and participate in greater work across the organization, as well as drive innovative work that can make them growth professionally through the experience and learning

This second finding confirms existing proven theories related to productivity and engagement of employees. One is the impact of worker self-efficacy in productivity and engagement. Bandura (1978) defined self-efficacy as “a judgment of one's ability to execute a particular behavior pattern” (p. 240). According to Schwarzer, self-efficacy reflects an optimistic self-belief (Schwarzer, 1992). This is the belief that one can perform a novel or difficult tasks, or cope with adversity in various domains of human functioning. Perceived self-efficacy facilitates goal-setting, effort investment, persistence in the face of barriers and recovery from setbacks. It can be regarded as a positive resistance resource factor. Because of the new virtual setting of the work for remote workers, self-efficacy becomes even more important, especially for remote workers. As we learn from our ground research, the remote employees typically work with minimal supervision and rely heavily on their own abilities and initiative to perform their job tasks, and in most cases, ICT tools (WebEx, chats, video, conference calls) is the typical medium used to communicate with management since face-to-face interaction is rare or infrequent. Often the employee works in a location with few or no co-workers, so the potential for isolation can be high and the availability of co-worker advice is often low.

Since remote employees enjoy considerable work autonomy, the potential impact that their own motivation, beliefs in their abilities can have on their outcomes may be important more than for employees whose behaviors are under tighter supervision.

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We also can confirm a second theory related to this finding, worker job engagement. Our findings also provide indications that workers continue to be concerned about being engaged with challenging and meaningful work and believe that being exposed to different kinds of projects, challenging work will strengthen and enhance the opportunity for growth, independently of where work is performed, either in a tradition office setting or remotely. Many employees provided examples when they felt more enthusiastic about their jobs when they were involved in exciting projects and more meaningful work and also when they felt more positive about their managers and the company in general. From the theoretical perspective, this is being studied extensively.

Schaufeli et al. (2008), define work engagement as a positive, fulfilling, affective- motivational state of work-related wellbeing that can be seen as the antipode of job burnout. Engaged employees have high levels of energy, and are enthusiastically involved in their work (Bakker et al., 2008).

While this finding does not provide new knowledge, it provides new insights as it relates to the relevance of this source of worker productivity and engagement. Job engagement indicates to be at the top of the worker agenda as a source of engagement, and while in some cases, compensation or promotions have been more important, our data shows that the importance of work/job engagement moves to the top of the list in terms of areas that organizations should focus their efforts to drive productivity and engagement. Paradoxically, the complexity of contemporary work, where more rigid systems exist to define the worker roles and responsibilities, may work against this expectation. In an environment where employees learn faster, have access to unlimited information within and outside of the organization, and are eager to be part of greater

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things than their traditional roles and responsibilities, traditional rigid role definitions will not be suitable. As a consequence, organizations have to proactively create new ways to incent employees to go beyond the confines of traditional job design, while half of the responsibility is on the employee showing the desire to do more and contribute more, it is responsibility of the organization to create an environment, establishing development processes and creating a culture of learning and development, that allows employees to engage in broader work in any work location. Jointly, employees and managers can then enable more vibrant and engaging on-site and virtual work environments.

In our third finding, we confirmed that technology indeed is shaping the way people work. Participants described the use of technology as resources to enhance the personal connections and communications by supporting verbal and non-verbal communications and more personal relationships. Because of the pervasiveness of the use of these technologies to connect business teams globally, both remote and non-remote workers report similar experiences with technology, confirming that technology is becoming an enabler to better produce and deliver results an environment where remote and on-site employees have to collaborate, enabling communication anytime and anywhere, increasing the proximity to remote worker’s peers and those that work in global locations. Technology also appears to have a positive impact on connectedness and information dissemination, supports the availability of communication and organizational information, and from the perception of the employees, this is making the organization more productive overall. This finding is also congruent with current research. Shin et al. (2000), found that in fact technology, is considered a positive driving

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force behind remote work, and is perceived as an internal mechanism to drive collaboration and productivity for remote workers.

Lastly but also equally important as the importance of ICT remote working tools, is the overwhelming importance the workers appear to give to remote work and work flexibility. In addition to the most traditional remote work arrangements, we uncover that newer types of work arrangements and in some cases, now workers are part of the decision on how much they can work from home. In some cases, we found that employees now can choose to work at home some of the time, and then work in the office another part of the working week, enjoying a culture of great workplace freedom. This kind of new work arrangement, when happening, allows employees also to have the opportunity to draw the advantages of having the human interactions in the workplace.

This finding is one of the most interesting findings in the whole study for many different reasons. First, not only do we see a more aggressive presence of remote work in the workplace, but we are seeing it in many different variations. Secondly, where in the past the company was making the decision to work from home, in many examples we can see that now employees have an opinion and for the most part, are empowered to choose where they want to work, and even in some case, the decision to work from home is based on their own judgment, and how they manage their schedule to decide when and where to work virtually. From the management perspective, this can be a challenge, as the unknown implications to productivity and engagement of this new dynamic is at chance. While employees may perceive this as a good alternative for them, we don’t have evidence to support that this is actually a good thing for organizations, that ultimately, are

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challenged every day to optimize their resources overall, and one of them is workforce productivity.

From the literature review, we learned that remote work is increasing, and researchers predict that this trend will continue to grow in future years (Cisco, 2011).

Research shows that this practice is an effective means to reduce corporate spending and enhance productivity, morale, and work-life balance (Greengard, 1994; Henkoff, 1995;

Hequet, 1996; Shellenbarger, 1997). This research confirms that, in fact, remote work is becoming the norm, and leaders and employees are embracing these practices.

However, this also opens questions that leaders and academics have to further research. And that is, what are the implications of the different remote work arrangements in the workplace. How can leaders better optimize the utilization of the all the resources, either the remote work technology, flexible work policies, workforce, leadership in a way that they can maximize productivity and engagement. We suggest that given the dramatic changes happening in the workplace, further qualitative research should be pursued to confirm our findings and discover other potential pitfalls of implementing remote workforce management programs. We also recommend that resources should be jointly evaluated so that we can see how, where and what levers should managers explore more, as well as the level of the interrelatedness of social and technical aspects of an organization.

Limitations

This study has a number of limitations that must be acknowledged. While our research aims at theoretical or analytical generalizability rather than probabilistic or statistical generalizability, the characteristics of our data set imply certain limits on the

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generalizability of findings. Sample participation was limited to employees in two or three industries: high tech, industrial manufacturers, and other (mainly companies in

Silicon Valley). Overall, these industries compare to others have a workforce that has higher level of engagements versus other industries, driven mainly by innovation practices in the workplace as well as having the highest levels of compensation; hence, the outcomes may be biased by these employees all perceiving this factors as non- important since they are taken care of, and being more concerned with other aspects that drive engagement. Lastly, our sample included only knowledge workers, all interviewees had, at least, a bachelor degree and most had MBA and Ph.D. degrees.

Implications for Practice and Future Research

The workplace is constantly evolving, with new technology that changes the way people work and live, and with new work practices that a few years back where not as pervasive as they are today, being remote work one of them. The implications of this research require practitioners to evaluate the impact of this changes in employee engagement, the drivers and how this is impacting productivity in the workplace. First, management should pay specific attention to the hiring and training of leaders, and how are they educating them to lead a constantly changing workforce that expects to be lead effectively. Secondly, it is of the utmost importance that management makes extra efforts to design interesting work as well as provide opportunities for employees to advance, learn and growth professionally. This will entice employees to bring the best of themselves and demonstrate strong self-efficacy that then can lead to higher levels of employee productivity and engagement. Third, the pervasive utilization of ICT Tools and

Technology is invading the workplace; this is creating new alternatives to engage the

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workforce that need to be explored. Remote work and work flexibility practices are the ones that exist today, but management and HR should be open to other new upcoming alternative work practices. Leaders and HR practitioners should continue to understand the evolution of the workplace systematically so to address proactively those areas that may hinder positive productivity and engagement in the workforce. And given the accelerated speed of adoption of remote work, it is imperative that HR practitioners and those in management evaluate alternatives to ensure employees remote workers are managed systematically and not as an exception of the workforce.

Academically, new theoretical models inclusive of workforce characteristics, the

ICT utilization as well as other workplace dynamics are must be further studied, so to drive new contributions to this field. While many of the current existent theories were highly correlated with our findings, it is of the utmost importance that new and alternative models are created and evaluated comparing remote and on-site knowledge workers. This comparative analysis is essential so we can provide knowledge in the field that is desperately needed in order for management to manage the upcoming workforce and it next generations.

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CHAPTER IV: THE IMPACT OF VIRTUAL INTENSITY AND WORK LOCATION PREFERENCE

Introduction

Most current data shows that remote workers would grow to 63 million (43% of the U.S. knowledge worker population) by 2016 (Deloitte Digital Workplace Report,

2015). Research shows that flexible (ICT) tools, leadership and Virtual Work

Alternatives play a major role in employee satisfaction and retention (Deloitte Digital

Workplace Report, 2015). Forty-six percent of companies that allow telework say it has reduced attrition and improved productivity and this is creating job satisfaction in employees (Global Workplace Analytics, 2011). According to Telework Research

Network (2011), across all age groups, flexible work ranked third, as “important for happiness on the job” (Telework Research Network, 2010). While these new type of remote work alternatives have created many benefits for the employees and in general for employers, it is also one of the greatest challenges ever experienced by management and human resources, who are left with the task to create new ways to manage this workforce in a way that they can sustain the demands to continuously improve productivity, while at the same time maintaining the workforce engagement.

This seems to be a top priority for corporations given the huge impact that the effective management of the workforce has in the financial performance of the organization. It is also a theme that is constantly in the center of attention for many companies like Yahoo, Cisco, IBM, and other recognized firms (Deloitte Digital

Workplace Report, 2015). And while many of them continue to drive acceptance of remote work practices, there are many that are retracting themselves from these programs given the concerns of not being able to maintain the same level of productivity in the 111

remote workforce in a comparative way to the on-site workforce. On the other hand, engagement seems to be the second concern from management (Bersin & Associates,

2012). Most recent data shows that the workforce engagement in the US has remained unchanged since 2013 (Hewitt, 2015), and they also report that especially in the remote workforce, drivers of engagement are under-researched. In addition, a more recent concerned has been the appearance of a variety of diverse types of remote working practices that are entering the workplace (Global Workplace Analytics, 2010), driving a different type of virtual intensity, and hence creating a bigger challenge to understand what this does to productivity and engagement. All these current workplace dynamics may be an indication that the drivers of productivity and engagement may be evolving.

In our previous qualitative study we found 4 key dynamics in the workplace influencing worker productivity and engagement in knowledge worker: 1) effective leaders, 2) engagement with work and worker self-efficacy, 3) the utilization and availability of ICT remote working tools, and 4) the opportunity to work remotely and having work flexibility. While some of these findings seemed to be aligned with current theoretical findings and research in the field, are clearly some nuances that need to be reconsider to really understand the implications of this new workplace trends to the workforce productivity and engagement.

Our study focuses on developing new knowledge in three areas associated with this challenge. First, we seek to understand how knowledge workers become engaged and how the mode and drivers of engagement differ between remote and on-site workers.

Second, anchoring in socio-technical systems theory, we study the factors that influence productivity and engagement in knowledge workers (i.e., work location preference,

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leadership, ICT utilization) and the moderation and interaction effects of virtual intensity, and how those factors compare between remote and on-site knowledge workers.

The remaining sections of this study are organized as follows. Initially, we built a theoretical foundation by conducting a brief literature review and its application to this research, of the initial socio-technical factors that based on the findings of the qualitative and informed by current literature in the field, were considered more relevant to understand what factors impacting productivity and engagement of the knowledge workers, given the current workplace dynamics. We then followed this by describing our research model and its associated hypotheses, in which we tie the antecedents to outcomes and examine the effect of different alternatives of remote work (high or low virtual intensity) have on employee productivity and engagement in the presence of some critical socio-technical factors (ICT utilization, work location/enjoyment, work location stress/tension, and leadership support) as core theories in our model. Third, we discuss the research design and methods, develop and further explain the definition of our constructs, and report the statistical analysis methodology we employed. We conclude by summarizing our findings, discussing the limitations of the study and outlining implications of our results for practice and future research.

Theoretical Foundation, Hypothesis Development, and Conceptual Model

Next, we review research on: 1) productivity, 2) job engagement, 3) ICT utilization, 4) Intrinsic Motivation, 5) leadership, and 6) Virtual Work Intensity. The review is founded on library searches of several reference databases, academic search engines (including searches using EBSCO, IEEE Xplore, AMJ, First Search and Google

Scholar) and many different leading management journals. We used the following

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keywords and phrases in our search: “remote work”, “productivity”, “remote work engagement”, “drivers of productivity”, “ICT Impact in Remote Worker” We conducted a multileveled analysis of the literature by not only reviewing each article but also reviewing their citations. Overall, the search did not result in a large number of studies similar to ours. At the individual level, most literature focused on comparing who is more or less productive if remote or on-site workers, remote workforce management, virtual teamwork, the productivity of remote workers, motivational factors, engagement. At the organizational level, remote work has been studied from the organization design–work reorganization, telework productivity, telework success, ICT, and Cost. While similar studies existed, there was no study that integrates the different critical elements identified in our qualitative study as the most recent potential drivers of productivity and engagement, within the context of the current workplace, and more importantly, there was no study comparing remote versus on-site knowledge workers.

Since remote workforce programs involve complex inter-relationships between work environments, work practices, individual motivations, management and technology

(Bélanger et al., 2013), evaluating the different implications in isolation may hinder its strategic impact. As other authors have argued before, telecommuting is a technological change whose effects cannot be understood without considering the social system within which is embedded (Passmore & Sherwood, 1978). Since one of the main expected outcomes of this research is to develop an integrated remote management framework, approaching the research with a comprehensive understanding of the socio-technical implications of remote work enabled a more effective outcome. The theory posits that these subsystems continually and jointly interact with each other to produce work

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systems outcomes (Trist & Bamforth, 1951). Figure 12 depicts integrating the theoretical framework that incorporates four elements that are critical to transforming work systems into outputs: personnel, organization/work design and work policies, technology, and external forces.

Figure 12. Theoretical Framework – Quantitative Research Study One Theoretical Framework

Socio%Technical,Theory (Trist,,Eric;,Bamforth,,Ken;,1951) The#joint# optimization# and#interrelatedness# of social and technical aspects#of#an organization.

Personnel(Sub, Internal(Environment( Organization(&( system Sub,system Work(Design( IM#Enjoyment##Scale Sub,system Work##Location# Enjoyment Worker, (Deci & Ryan,#1991) Organization Productivity Leadership# (McLean#and#DeLone# Exchange IM#Stress#Scale ,1992)# &,Job, (Graen#&#UhlGBien,# 1995)# Work#Location# Stress Engagement (Deci &#Ryan,#1991) (Schaufeli#and#Bakker#,# 2008)# Work#Design Worker#Job# Virtual#Intensity Engagement (Wiesenfeld,# Raghuram,# (Schaufeli#&#Bakker#,#2008)# and#Garud#1999)

Technical(Subsystem Utilization#of#Remote#Work#Technology (Raghuram#et#al.,#2001)

Remote Work productivity

Simply put, productivity is the amount of output produced in a specific amount of time (Miller, 2008). Productivity measurement is a quantifiable measure calculated as the ratio of what is produced to what is required to produce it (Miller, 2008). productivity has been studied in the remote workforce field research from many different perspectives, at the organizational level and at the individual level, and while in many cases researchers

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do not seem to agree with the concept, a recent meta-analysis of studies that theorized on the positive impact of remote work to productivity, it is confirmed that in fact remote work positively impacts productivity (Gajendran & Harrison, 2007).

Productivity at the individual level is regularly reported as a perceived benefit of telework for organizations (Callentine, 1995; Hill, Miller, Weiner, & Colihan, 1998; Pitt-

(1992) found indications for improved productivity, reliability and work quality among teleworkers (see also Salomon & Shamir, 1985). They were perceived as more loyal, less likely to avoid work (i.e., be ‘absent’ from home during work time) and had lower tendency to change employer.

Productivity at the organizational level has frequently been contradictory. In a review of current literature in remote work, Higa et al. (2000) reported that changes in teleworker productivity were consistently of interest to a number of studies (Martino &

Wirth, 1990; Dubrin, 1991; Olson, 1989). These studies were remarkably alike in pointing out the positive effect of telework in enhancing individual productivity.

However, except for a few state-commissioned studies such as the Evaluation Audit and

Review (EAR) Group Report, research that systematically investigated productivity changes resulting from telework has been rare. Most studies merely quoted anecdotal program reports or referenced nominal discussions based on hearsay without mentioning methodological details. The authors also noted that there were methodological and conceptual weaknesses in existing literature.

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Despite the controversies surrounding the definition, we are opting to use productivity as the construct to measure the outcomes/individual impact in our model, given that our qualitative research and literature review, clearly suggest that this is the main are of concern from management.

To operationalize this construct, and measure the productivity of knowledge remote workers, we adapted the subscale originally developed by DeLone and McLean in

1993 as part of their Information Systems (IS) Model. As part of their model, DeLone and McLean identified different components such as market share, rate of return and productivity. We adapted their productivity scale to measure the perception of employees as it refers to how remote working has resulted in improved outcomes or outputs, how this has resulted in increasing individual capacity to manage a growing volume of activity

(transactions, serve more customers, complete more projects, etc.), how remote work has resulted in employees being able to improve business processes.

Job Engagement

As previously introduced, the empirical study of employee engagement is relatively new, resulting in a few disparate definitions for the construct (e.g., Saks, 2008;

Shuck, 2011). At least three models of employee engagement have been proposed, each specifying that employee engagement is a construct unique from other similar constructs

(i.e., satisfaction), and some have been supported by empirical evidence (Kahn, 1990;

Macey & Schneider, 2008; Maslach, Schaufeli, & Leiter, 2001; Saks, 2008; Schaufeli et al., 2002). However, only two conceptualizations appear to have taken hold in the literature and have associated measures. Despite the growing popularity of these two perspectives, the UWES (Utrecht Work Engagement Scale) is currently the most

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commonly used measure to assess work engagement (Shuck, 2011). Although the initial focus on studying the UWES mainly looked at stress-related outcomes, it has recently been used to examine the relationship between engagement and efficacy, and proactive behavior (Salanova & Schaufeli, 2008). Because our study focuses on examining the behavior under certain working conditions, we opted to apply UWES scale.

Work engagement is defined as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (Schaufeli, Salanova,

González-Romá & Bakker, 2001). Rather than a momentary and specific state, engagement refers to a more persistent and pervasive affective-cognitive state that is not focused on any particular object, event, individual, or behavior. Vigor is characterized by high levels of energy and mental resilience while working, the willingness to invest effort in one’s work, and persistence even in the face of difficulties. Dedication refers to being strongly involved in one's work and experiencing a sense of significance, enthusiasm, inspiration, pride, and challenge. Absorption is characterized by being fully concentrated and happily engrossed in one’s work, whereby time passes quickly, and one has difficulties with detaching oneself from work (Schaufeli, Salanova, González-Romá &

Bakker, 2001).

In the quantitative study of this research, it was found that one of the main drivers of engagement for both categories of workers, remote and on-site. Employees expressed that having the opportunity to engage in meaningful work, being exposed to different projects to enhance their self-efficacy at work was very important. Many employees indicated that when they had the opportunity to participate in exciting work, feeling that

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their work is adding value, and when they were experiencing success at work, they felt more positive about their jobs, their supervisors, and their companies in general.

As antecedent, work engagement has shown strong correlation to positive attitudes towards work and towards the organization, such as job satisfaction, organizational commitment, and low turnover intention (Demerouti et al., 2001; Salanova et al., 2000; Schaufeli & Bakker, in press; Schaufeli, Taris & Van Rhenen, 2003), but also to positive organizational behavior such as, personal initiative and learning motivation (Sonnentag, 2003), extra-role behavior (Salanova, Agut & Peiró, 2003), and proactive behavior (Salanova et al., 2003). Furthermore, there are some indications that engagement is positively related to health, that is, to lower levels of depression and distress (Schaufeli, Taris & Van Rhenen, 2003) and psychosomatic complaints

(Demerouti et al., 2001). Finally, and more importantly for our research, it seems that work engagement is positively related to job performance. For instance, a study among about one-hundred Spanish hotels and restaurants showed that employees’ levels of work engagement had a positive impact on the service climate of these hotels and restaurants, which, in its turn, predicted employees' extra-role behavior as well as customer satisfaction (Salanova, Agut, & Peiró, 2003).

The previous findings about possible causes and consequences suggest that work engagement may play a mediating role between job resources on the one hand and positive work attitudes and work behaviors on the other hand. In a recent study, Schaufeli and Bakker tested such a model among four samples from different types of service organizations. Their structural equation model also included job stressors, burnout, and health complaints. They found some evidence for the existence of two types of processes:

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(1) a process of health impairment or erosion in which job stressors and lacking job resources are associated with burnout, which, in its turn is related to health complaints and negative work attitudes; (2) a motivational process in which available job resources are associated with work engagement, which, in its turn, is associated with positive work attitudes. Also other studies confirmed the mediating role of work engagement.

Essentially, the results of Schaufeli and Bakker (in press) have been replicated by

Hakanen, Schaufeli and Bakker (2003) in a study among a large sample of Finnish teachers. Furthermore, the results of the study by Salanova, Agut and Peiró (2003) corroborate the model of Schaufeli and Bakker (in press): work engagement plays a mediating role between job resources (e.g., technical equipment, participation in decision making) and service climate and job performance (i.e., extra-role behavior and customer satisfaction) Moreover, in another study among over 500 ICT workers, Salanova et al.

(2003) observed that work engagement mediated the relationship between available resources (technology, performance feedback, task variety, and job control) and proactive organizational behavior.

Because our interest is to understand what is the impact of the level of the energy of the employee and its commitment with work, both as a mediator in our model but also as an antecedent to productivity, this definition seems well suited for our research. In addition, the literature suggests the Utrecht Work Engagement scale has been primarily used as a mediator and as an antecedent in similar research. This scale seems to be suitable to the characteristics of our model for both, antecedents (ICT utilization, work location enjoyment, work location stress, leadership support as available aspects of the resources available to employees) and outcomes of our model (productivity). Therefore,

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we used this 17-item scale to measure the impact on productivity, but also the mediation effects in our model.

ICT Utilization

In this study, we posit that ICT utilization is the driver of productivity, especially on remote workers. Our basic axiom is that technology enables virtual and remote work, enabling speed of communication and data between the employees and across the organization. Research on the role of ICT in telework may be especially significant because the most notable

negative effect of telework results from the deterioration of internal processes such as organizational communication and control. At the organizational level, concern about the degradation of internal processes is, in fact, the primary reason for the scarcity of employer-initiated telework programs (Olson, 1987b).

As research and technology have evolved, some authors argue the contrary and claim that state-of-the-art ICT offers an effective mechanism for addressing this concern in ways that include information management, monitoring capability, and communication and collaboration support (Shin et al., 2000). In particular, the revolution in data communication technology enables any worker to belong to the virtual network of a company regardless of his/her geographical location. At the individual level for remote workers, the enhanced quality of internal processes is expected to reduce frequently discussed side effects of telework such as isolation, role conflict and ambiguity, and at the team/management level, to address difficulties in coordination with peers and supervision of teleworkers (Shin et al., 2000).

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A variety of available technologies includes e-mail, voice mail, audio and video conferencing, fax, and the World Wide Web. These tools are vastly different in their information carrying ability, provision of accessibility to information and data, portability, transportability of work (e.g., workflow management), and collaboration support. Mokhtarian and Sato suggested that commonly available ICT can have a marked effect on organizational processes. It appears that general-purpose ICT applications (e.g., e-mail and the Web) have a larger impact than specialized ICT (e.g., video conferencing) in reducing task uncertainties and facilitating better coordination between an organization and its teleworkers. It was also shown that effective adoption of a general-purpose communication medium (e.g., e-mail) gave teleworkers an information-rich tool that enhanced their work productivity (Shin et al., 2000). Overall, more empirical investigations in this field should improve our understanding of the effect of ICT on telework effectiveness and teleworkers’ performance (Shin et al., 2000).

Based on preliminary research as well as the outcomes of our qualitative research, we found that employees, particularly those that work remotely seem to give more value to the utilization of technology. Accordingly, in our study, we mediated for the frequency of use of technology when working remotely. To that effect, and based on information of the most predominant and generic working tools like: email, home video conference

(skype, WebEx, other), texting/face time, internal collaboration tools (Google docs,

Dropbox, Doc Sharing), Internal Intranet and other employee chats (IM, Sametime, etc.), a list was created that became part of our selective tools. Our goal with this construct was to determine if, in fact, remote working technology, in fact, impacts productivity or engagement, and if so, are there any differences between remote and on-site workers.

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Intrinsic Motivation

“pawns” (deCharms, 1968). However, it is certainly not the case that every real world behavior stems from an intrinsic energy. In management, there is particular interest when it comes to intrinsic versus extrinsic motivation, particularly because of the different outcomes that researchers have shown to result from intrinsic motivation: more interest, excitement, confidence, enhanced performance, persistence, creativity, self-esteem and general well-being (Deci & Ryan, 1991; Ryan & Deci, 2000; Ryan et al., 1995; Sheldon et al., 1997). Over the years, several theorists have offered insights into the phenomenon through their conceptions of intrinsic motivation. Intrinsic motivation comes from inside a person: it is a sense of achievement, responsibility, job satisfaction, purpose, involvement, empowerment, and ownership—all the things that make employees feel that what they are doing make a big difference in their lives and in the organization itself. If employees feel that what they are doing is insignificant, they will feel insignificant; if, in turn, they feel their work is valued, they feel valued.

Deci and Ryan (2000) acknowledged that, even when work environment supports autonomy and competence, if a person is simply not interested in a particular activity, he or she would not be intrinsically motivated to engage. Rather, he or she will be motivated by external factors like compensation or bonuses. However, the authors stipulated that external motivations could be internalized. Despite a lack of interest, a person can still be

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self-determined if the activity can be integrated into a sense of self. For example, a worker may find working remotely uninteresting and therefore not be intrinsically motivated to work as a remote worker. However, if this person can come to understand how such an activity can be valuable and important as a means of work-life balance, personal growth and skill enhancement, the person will internalize the extrinsic motivation. Through this process, the worker can now approach the activity with a sense of will rather than pressure. People are intrinsically motivated for some activities/tasks and not others.

In our model, we adapted two subscales of the intrinsic motivation inventory

(enjoyment/interest and stress/tension) to measure the degree of enjoyment or tension that the worker experiences when working either remotely or on-site. The Intrinsic

Motivation Inventory (IMI) is multidimensional. It has been used in several experiments related to intrinsic motivation and self-regulation (e.g., Ryan, 1982; Ryan, Mims &

Koestner, 1983; Plant & Ryan, 1985; Ryan, Connell, & Plant, 1990; Ryan, Koestner &

Deci, 1991; Deci, Eghrari, Patrick, & Leone, 1994).

The instrument assesses participants’ interest/enjoyment, perceived competence, effort, value/usefulness, felt pressure and tension, and perceived choice while performing a given activity, thus yielding six subscale scores. The interest/enjoyment subscale is considered the self-report measure of intrinsic motivation; thus, although the overall questionnaire is called the Intrinsic Motivation Inventory, it is only the one subscale that assesses intrinsic motivation, per se. As a result, the interest/enjoyment subscale often has more items on it that do the other subscales. Pressure/tension is theorized to be a negative

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predictor of intrinsic motivation. We operationalize these constructs by tailoring the statements in the questionnaire reflecting our interest in remote and on-site workers.

Leader–Member Exchange Theory (LMX)

Telecommuting and the inevitable absence from office bring about unique challenges regarding leadership and monitoring employee effectiveness. Leaders may not have the same opportunity to get to know the employee as remote access via telephone or e-mail does not always result in building high-quality interpersonal relationships as direct contact does. For this reason, leadership exchange has been included as it focuses on relationship building between leaders and employees.

Unlike many other prominent leadership theories, leader-member exchange

(LMX)—formally known as Leader Member Exchange Theory (Graen & Uhl-Bien

1995)—does not focus on the specific characteristics of an effective organizational leader. Rather, LMX focuses on the nature and quality of the relationships between a leader and the individual subordinates. The ideal is for a leader to develop as many high- quality relationships as possible. This will lead to increases in subordinates’ sense of job satisfaction and organizational citizenship, as well as to increased productivity and attainment of organizational goals. LMX has been criticized for its potential to alienate some subordinates, failing to account for the effects of group dynamics and social identity, and failing to provide specific advice on how leaders can develop high-quality relationships. However, LMX has been heralded as an important leadership theory in higher and distance educational contexts because of its emphasis on promoting autonomy and citizenship, as well as its ability to complement and mediate transformational

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leadership styles. Literature shows that leadership has a positive effect on employee productivity and engagement.

Most recent research shows that having a good relationship with immediate supervisors and believing in senior leadership (Carnegie, 2012) is a top driver of engagement. Based on previous research and the findings shown in our recent qualitative study, we theorize that leadership has a higher impact for high-intensity workers than it does for low-intensity remote workers, that is to say that the less that the employee works remotely, the less the importance of leadership, but the more that the employee works remotely, the higher the importance of leadership.

Virtual Work Intensity

By treating telecommuting as a single, undifferentiated program, researchers tend to overlook potentially important structural distinctions among work arrangements. The main structural distinction made by previous investigators deals with what we refer to as virtual work intensity: the extent or amount of scheduled time that remote workers spend doing tasks away from a central work location. This idea has been referred to as “virtual status” by Wiesenfeld, Raghuram, and Garud (1999: 782), “virtuality” by Scott and

Timmerman (1999: 242), and as “home-centered versus office-centered” telework by

Konradt et al. (2003: 62), among other terms (Hill et al., 2003). An emerging perspective on telecommuting intensity in the literature is that when telecommuters spend the majority, versus a minority, of their scheduled time away from a central location, it crosses a psychological threshold—in a sense, creating two classes, of employees in telecommuting arrangements (Meehl, 1992). High-intensity telecommuters spend the majority or all of their workdays away from a central location. Low-intensity

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telecommuters spend the majority of their workdays at a central (conventional) location, working remotely for only one or two days a week. Konradt et al. (2003) found that telecommuters who spent more than 50% of their time away from the office (home centered) had different motivations for telecommuting than those who spent less than

50% of their time away (office centered). Home-centered or high-intensity telecommuters sought to balance their work and family demands while office-centered or low-intensity telecommuters sought freedom from interruptions. Similarly, Wiesenfeld et al. (1999) found that high virtual status employees (those who work three or more days per week away from a central work location, usually home) had different communication patterns as opposed to low virtual status employees (those who work three or more days a week at a central location). Coveyduck (1997), DeLay (1995), Mackie-Lewis (1998), Schneider-

Borowicz (2003), Scott and Timmerman (1999), and Taveras (1998) also used similar splits of scheduled work time at work and at home as an indicator of behavioral immersion in telecommuting.

Because the findings of our qualitative study one indicate that, despite workers having a category either as remote or on-site worker, operationally, many of them actually have different levels of virtual work. As an example, some of them, even when they categorize themselves as on-site worker, they also work remotely one or two days a week. Similarly, we found cases in which, even when the employees identified themselves as remote workers, they were working from an office setting one or two days a week. Therefore, in our research, we will theorize that the relationship between ICT utilization, intrinsic motivation, and leadership will be moderated by the intensity of work. This is to say, that the extent of association of this will vary depending on the

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intensity of the remote work. For the purposes of our research, high virtual intensity worker is operationalized as those employees working remotely 4–5 days per week. Low virtual intensity worker is defined as those employees working remotely 1–3 days per week.

Theoretical Model

As previously introduced, this study is sequential to our first study. Using as a reference our key findings in our qualitative study, in terms of the most current workplace dynamics, as well as informed by previous research in the field, we have purposely constructed our theoretical model integrating socio-technological antecedents impacting the workplace. As a result, the following model emerged that guided our research, which incorporates different dimensions that interact in a remote work environment: ICT utilization, enjoyment or stress in the work location, leadership and virtual intensity of the remote work), as major antecedents to productivity and engagement.

We expect this model will help us to address the following questions: What factors are associated with Job productivity and engagement of on-site and remote workers? And what are the variations of the relationship based on the level of intensity of remote work? We have selected to focus on some specific constructs for our model that better characterized the current workplace dynamics. Our model involves seven constructs, from which four are the independent variables (ICT utilization, Enjoyment of

Work Location, Stress/Tension of Work Location, leadership), one mediator (job engagement), one dependent variable (productivity), one moderator (Virtual Intensity)

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and two controls (gender and experience)—all of which were measured with reflective scales (Figure 13).

Our basic axiom is that ICT utilization, Enjoyment of Work Location,

Stress/Tension of Work Location of the knowledge worker as well as leadership are strongly associated with job productivity and engagement of the knowledge worker, whether it is a worker working on-site or remotely. The extent as to what that association exists for each category of employee, maybe different depending on virtual intensity degree of the remote work. Because remote working is one of the most radical departures from standard working conditions, productivity and engagement are the organizational outcomes that continue to be scrutinized by management, and hence, our decision to include this as part of our model.

Hypothesis Development, Research Design and Methods

Given the radical change in the workplace where remote working is becoming more predominant, it is critical that socio-technical aspects of the management systems are evaluated to better understand the impact to productivity and engagement. In our research, we argue that is not one aspect of the system, but it is rather the interaction of this factors that make the worker more productive and engage in the workplace. We also argue that because in a remote work environment the worker lacks the defined boundary that is provided by an office setting, the way remote workers perform their daily work may be significantly altered when compared to their on-site peers. This may cause different outcomes between remote and on-site workers. For example, if remote workers don’t use the technology to enable their work, productivity results may be lower, causing a decrease in productivity, so productivity depends on how much utilization of the ICT

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Technology the worker has. Moreover, if the employee increases or decreases the ICT utilization while working remote, this may create a much different effect. From the perspective of location, if the location of the employee (either remote or on-site) is not the one that the employee enjoys and instead is experiencing stress, this may cause its level of engagement increase or decrease. Thus, we argue that interactions between these social-technical subsystems may result in outcomes that are different between remote and on-site workers

Hence, we argue that:

1)! Knowledge workers should exercise the use of technology to enable their productivity. As technology enables communication, speed, accelerating individual productivity, the more the utilization of technology the higher their productivity. To that end and as a secondary effect, job engagement will improve as having the right ICT Technology to communicate across the organization and execute their work, may result in feelings of satisfaction with the work environment, allowing employees to feel more energized, vigorous and enthusiastic about their work.

2)! We found in our qualitative study that knowledge workers appear to value the flexibility of the different kind of work arrangements and we identified this as one of the most important drivers of productivity and engagement. Hence, we theorized that Knowledge workers level of enjoyment of the work location is one of the most critical factors related to productivity. We posit that if the enjoys the work location, whether it is remotely or on-site, their level of productivity will increase. We also pose that as a result, the worker’s enthusiasm, focus, inspiration, with their jobs will increase and therefore, their job engagement will increase.

3)! On the opposite side, we hypothesize the reverse effects when the employee experiences tension/stress due to the work location. Hence, we theorized that Knowledge workers level of tension/stress of the work location is one of the most critical factors that can negatively impact productivity and engagement. We posit that if the employee experiences tension in the work location, whether it is remotely or on-site, their level of productivity will increase. We also pose that as a result, the worker’s enthusiasm, focus, inspiration, with their jobs will decrease and therefore, their job engagement will decrease.

4)! The presence of effective leadership is important for employees to be able to be more productive in the workplace, whether to provide the right direction or 130

personal coaching to employees. This presence, given the radical change involved in working remotely, will have a bigger effect on remote workers. As a secondary effect, we posit that in the presence of effective and supportive leadership, employees will be more engaged with their jobs and, therefore, will be more productive.

5)! Lastly, as working remotely represents a very different work environment where employees are exposed to isolation, lack of physical presence and proximity with the rest of the organization, we posit that the level of virtual intensity (days working remotely) will moderate the impact that these factors have in the productivity of the employee

Therefore, we propose the research model in Figure 13 with seven hypotheses that are discussed next.

Figure 13. Research Model and Summary of Hypotheses

Personnel) Subsystem Job) Engagement Technical) Subsystem Moderator IT)Utilization Virtual-Intensity-(High/Low)

Personnel) Subsystem

Enjoyment) Work) Location Organizational) Outcomes

Stress/Tension) Worker Work)Location Productivity

Organization)Subsystem Direct-effects

Leadership) Mediation Exchange Moderation Controls • Gender • Experience Mediation,-Moderation-&-Group-Comparison-(Remote-and-Onsite)

Mediation

We first hypothesize that by using the working technology tools like email, home video conferencing, data share points, that can enable the interaction and communication within and outside the boundaries of the physical traditional work environment workers 131

can improve their productivity and as consequence, there is an effect to the worker job engagement and thus have a direct effect on productivity. Building on our findings from our qualitative study, we suggest that job engagement mediates the positive relationship between ICT utilization and productivity. Given the impact in remote work, we posit that this relationship will be stronger for remote workers. However, because job engagement is a behavior that can be generated due to other psychological aspects, experiences and individual employee motivation, we suggest that a second hypothesis be tested to understand the direct effects of ICT utilization to productivity. Therefore, we posit that:

Hypothesis 1a. Worker job engagement mediates the positive relationship between ICT utilization and productivity; this will be a stronger relationship for remote than for on-site knowledge workers.

Hypothesis 1b. ICT utilization is positively related to productivity; this will be a stronger relationship for remote than for on-site knowledge workers.

Secondly, we hypothesize that Enjoyment of Work Location (the enjoyment they experience while working, their ability to cope positively with the environment, either working on-site or remotely) is positively related to productivity and this as a consequence has a positive effect on their level of engagement with their jobs. However, because job engagement can be sourced due to other personnel (like the social support from leaders), technological or organizational factors, we suggest that a second hypothesis be tested to understand the direct effects of work location enjoyment to productivity. Therefore, we posit that:

Hypothesis 2a. Worker job engagement mediates the positive relationship between Work Location and productivity; this will be a stronger relationship for remote than for on-site knowledge workers.

Hypothesis 2b. Work location enjoyment is positively related to productivity; this will be a stronger relationship for remote than for on-site knowledge workers.

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Conversely, we hypothesize that Work Location/Stress (feelings of anxiety and stress working either on-site or remotely) is negatively related to productivity and engagement. This is to say that when employees feel stress when working either remotely or on-site because of their individual preferences, this may have a big negative effect on productivity. We also pose that this will not be different between remote or remote workers. Therefore, we posit that:

Hypothesis 2c. Worker job engagement mediates the negative relationship between work location stress and productivity; this effect will be similar between groups.

Hypothesis 2d. Work location stress is negatively related to productivity; this effect will be similar between groups.

Thirdly, Literature shows that leadership has a positive effect on employee productivity and engagement. Most recent research shows that having a good relationship with immediate supervisors and believing in senior leadership (Carnegie, 2012) is a top driver of engagement. With remote working, the inevitable absence from office brings about unique challenges regarding leadership and monitoring employee effectiveness.

Leaders may not have the same opportunity to get to know the employee as remote access via telephone or e-mail does not always result in building high-quality interpersonal relationships as direct contact does. For this reason, leadership exchange has been included as it focuses on relationship building between leaders and employees.

Based on previous research and the findings shown in our recent qualitative study, we theorize that leadership has an impact in productivity and engagement and that work engagement mediates this relationship. Therefore, we posit that:

Hypothesis 3a. Worker job engagement mediates the positive relationship between leadership and productivity; this relationship will be similar in both categories 133

Hypothesis 3b. Leadership is positively related to productivity; this relationship will be similar in both categories

Moderation

Our next set of hypotheses is moderation based. An emerging perspective on telecommuting intensity in the literature is that when telecommuters spend the majority, versus a minority, of their scheduled time away from a central location, it crosses a psychological threshold—in a sense, creating two classes, of employees in telecommuting arrangements (Meehl, 1992). High-intensity telecommuters spend the majority or all of their workdays away from a central location. Low-intensity telecommuters spend the majority of their workdays at a central (conventional) location, working remotely for only one or two days a week. Konradt et al. (2003) found that telecommuters who spent more than 50% of their time away from the office (home centered) had different motivations for telecommuting than those who spent less than

Borowicz (2003), Scott and Timmerman (1999), and Taveras (1998) also used similar splits of scheduled work time at work and at home as an indicator of behavioral immersion in telecommuting.

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In our research, we will theorize that the relationship between ICT utilization, work location enjoyment, work location stress, and leadership on productivity and engagement will be moderated by the virtual intensity of the work. This is to say, that the extent of association of this will vary depending on the intensity of the remote work.

Based on our qualitative study results, we will theorize that a higher impact for high- intensity workers than it does for low-intensity remote workers, that is to say that the less that the employee works remotely, the less the importance of leadership, but the more that the employee works remotely, the higher the importance of leadership. Therefore, we posit that:

Hypothesis 5a. The level of intensity of virtual work moderates the relationship between ICT utilization and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 5b. The level of intensity of virtual work moderates the relationship between ICT utilization and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 6a. The level of intensity of virtual work moderates the relationship between Intrinsic Motivation A and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 6b. The level of intensity of virtual work moderates the relationship between Intrinsic Motivation A and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 7a. The level of intensity of virtual work moderates the relationship between Intrinsic Motivation B and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 7b. The level of intensity of virtual work moderates the relationship between Intrinsic Motivation B and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers.

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Hypothesis 8a. The level of intensity of virtual work moderates the relationship between leadership and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 8b. The level of intensity of virtual work moderates the relationship between leadership and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers.

Hypothesis 9a. The level of intensity of virtual work moderates the relationship between productivity and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers.

Measurement Model

To empirically validate the proposed model, we surveyed 304 knowledge workers

(152 that work remotely and 152 that work in a traditional on-site setting) using a psychometric survey methodology that maps individual responses to the underlying constructs within our model. Our model involved six constructs (four independent variables, one mediator, one dependent variable, one moderator) and two controls—all of which were measured with reflective scales. This model was developed using our findings in the first phase of this study, and was complemented with relevant theories extensively studied related to engagement and remote worker productivity. Further detailed on the theory behind the model will be further explained. Next, we describe our research method.

Construct Operationalization

We searched the literature to find previously validated scales to operationalize the key constructs in our study. We followed the procedures outlined by DeVellis to develop constructs through an iterative process of informational interviews, academic reviews, Q- sorting and pre-and pilot tests (DeVellis, 2012). In most cases, we used the scales as we found them with modifications in wording to reflect either the work context of the 136

employee working remotely or on-site. We created the ICT utilization and Virtual

Intensity constructs using the literature and our findings from the qualitative study. Scales used to measure the constructs in our study are summarized in Table 4. All items had a response format using five-point Likert scales ranging from “strongly disagree” to

“strongly agree”, but some questions were designed for reverse coding. The only exception to the five-point Likert scale is a categorical variable of Virtual Intensity.

Worker productivity

The productivity scale measures the perception of employees as it refers to how remote work has resulted in improved outcomes or outputs, increased individual capacity to manage a growing volume of activity (transactions, serve more customers, complete more projects, etc.), and how remote work has improved business processes. This scale was adapted using a sub-scale, developed originally by McLean and DeLone as part of there IS Model (1992). Four items were adapted to our scale in order to measure productivity. The scale reliability (Cronbach α) was .859 and higher than .8 as recommended to meet scale reliability requirements.

Job engagement

An eight-item scale was adapted and modified from Utrecht Work Engagement

Scale (17-item version). The scale measures three dimensions of work engagement for remote workers: (1) Vigor; (2) Dedication; (3) Absorption. The scale reliability

(Cronbach α) was .852 and higher than .8 as recommended to meet scale reliability requirements. In total, four items were included in the final construct, three items from the vigor (items 1, 4, and 8), and one from dedication (Item 5)

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ICT utilization

A construct was developed to measure the frequency of utilization of tools that are more commonly use to facilitate remote work: email, home video (Skype/WebEx, etc.), document sharing (Google docs, intranet, etc.). The scale reliability (Cronbach α) was .812 and slightly above of .8 as recommended to meet scale reliability requirements.

Four items in total were included in the final construct

Intrinsic Motivation (work location enjoyment/work location stress)

These two scales were adapted and modified from Deci and Ryan (2001) Self-

Determination/Intrinsic Motivation Questionnaire. The overall IM scale is a multidimensional measurement of 6 subscales. Only two scales were used as a part of our measurement model. The interest/enjoyment subscale is considered the self-report measure of intrinsic motivation; thus, although the overall questionnaire is called the

Intrinsic Motivation Inventory, it is only the one subscale that assesses intrinsic motivation, per se. As a result, the interest/enjoyment subscale often has more items on it that do the other subscales. Pressure/tension is theorized to be a negative predictor of intrinsic motivation. We operationalize these constructs by tailoring the statements in the questionnaire reflecting our interest in remote and on-site workers. The scale’s reliability

(Cronbach α) for Work Location/Enjoyment was .914, and it included 10 items. The scale’s reliability (Cronbach α) for Work Location/Stress was .887, and it included 4 items. Both higher of .8 as recommended to meet scale reliability requirements.

Leadership

A 4 item, 5-point Likert scale was adapted and modified from Leader Member

Exchange Theory (Graen & Uhl-Bien 1995). The leader-member exchange (LMX) 138

theory is a relationship-based approach to leadership that focuses on the two-way relationship between leaders and followers. It suggests that leaders develop an exchange with each of their subordinates and that the quality of these leader–member exchange relationships influences subordinates' responsibility, decisions, and access to resources and performance. Relationships are based on trust and respect and are often emotional relationships that extend beyond the scope of employment. Leader–member exchange may promote positive employment experiences and augment organizational effectiveness. The scale included statements where participants provided their perceptions on the effectiveness of the relationship with their leader, support from their leader to solve problems, support when going through difficult times and confidence in leader overall. The scale reliability (Cronbach α) was .852 and higher of .8 as recommended to meet scale reliability

Virtual Intensity

For the basic model of this study, comparison of two groups: remote and on-site workers was used to identify key differences amongst the two populations. Participants were asked at the beginning of the survey if they were either remote workers (employees that worked from a remote location outside of their employer’s work location, and did not had assigned a work location or flex space in the premises of their corresponding employer) or on-site workers (employees that will be co-located on-site offices and that had been allocated to a work location or flex space on the premises of their corresponding employer). However, an emerging perspective on telecommuting intensity in the literature is that when telecommuters spend the majority, versus a minority, of their scheduled time away from a central location, it crosses a psychological threshold—in a

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sense, creating two classes, of employees in telecommuting arrangements (Meehl, 1992).

High-intensity telecommuters spend the majority or all of their workdays away from a central location. Low-intensity telecommuters spend the majority of their workdays at a central (conventional) location. We adapted the same logic to our study and adapted a categorical variable. Participants were asked how many days a week they work from home or on-site, given option to respond from one to five days a week. The model was run using z-scores from the derived factors. The moderator variable, the number of days remote, was dichotomized as 1–3 days (n=159) and 4–5 days (n=146).

Construction definitions are provided in Table 4 for all factors and dimensions.

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Table 4. Construct Definition Table

# Construct Definition Author/Source Cronbach name Alpha 1 Worker Measures the perception of employees as it refers to how remote McLean and DeLone .859 productivity work has resulted in improved outcomes or outputs, increased (1992) (Dependent individual capacity to manage a growing volume of activity Variable) (transactions, serve more customers, complete more projects, etc.), and how remote work has improved business processes. This scale was adapted using a sub-scale, developed originally by McLean and DeLone as part of there IS Model (1992). Four items were adapted to our scale. 2 job Measures the level of vigor and dedication with one’s work. Schaufeli et al. .851 engagement Developed by Schaufeli et al. the UWES (Utrecht Work (2002) (Mediating engagement Scale) scale assesses three dimensions of Variable) engagement: vigor, dedication, and absorption. Four items were considered in our model. 3 ICT Measures the frequency of utilization of tools that are more Qualitative Research .812 utilization commonly use to facilitate remote work like email, home video (Martinez-Amador, (Independent (Skype/WebEx, etc.), document sharing (google docs, intranet, 2015) Variable) etc.). Four items were considered in our model 4 work Scale was adapted and modified from Deci and Ryan (2001) Deci, E. L., & Ryan, .914/.887 location Self-Determination/Intrinsic Motivation Questionnaire. The scale R. M. (1991) enjoyment is a multidimensional measurement of 6 subscales. Only two work scales were used to measure. One to measure the degree of location enjoyment of work location (interested, enjoyment, positive stress feelings in working remotely or on-site) and a second one to measure feelings of pressure or stress while working remotely or on-site The scale wording was modified to reflect our interest in remote and on-site workers. Ten items were considered for enjoyment and four items were considered for stress 5 leadership A four-item, 5-point Likert scale was adapted and modified from Graen & Uhl-Bien .852 (Independent Leader Member Exchange Theory (Graen & Uhl-Bien 1995). (1995) Variable) The leader-member exchange (LMX) theory is a relationship- based approach to leadership that focuses on the two-way relationship between leaders and followers. It suggests that leaders develop an exchange with each of their subordinates and that the quality of these leader-member exchange relationships influences subordinates' responsibility, decisions, and access to resources and performance. Relationships are based on trust and respect and are often emotional relationships that extend beyond the scope of employment. 6 Virtual An emerging perspective on telecommuting intensity is that when Konradt et al. (2003) NA Intensity telecommuters spend the majority of their scheduled time away Wiesenfeld, (Moderator) from a central location, it crosses a psychological threshold - in a Raghuram, and sense, creating two classes of employees in telecommuting Garud (1999, p. 782), arrangements (Meehl, 1992). Our moderator variable was dichotomized as 1-3 days for low-intensity worker and 4-5 days for the high-intensity worker. High-intensity workers spend the majority or all of their workdays away from a central location. Low-intensity workers spend the majority of their workdays at a central location. Remote and on-site worker was also used as a baseline category. 7 Gender Captures gender information from participants NA NA (Control) 8 Experience Captures number of years in workforce NA NA (Control)

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Pre-Testing (Q-Sort & Pilot Test)

We used a Q-sort technique following guidelines suggested by Thomas and

Watson (2002) to detect if those who participated in the Q-sort would group the items in the ways implied by what they purportedly were intended to measure. Our initial Q-sort results showed a few cross-loadings of items and too general construct definitions.

Overall, we executed three rounds of Q-Sorting to ensure item loadings were acceptable at an 80% agreement level. We experienced some difficulty with several constructs but in particularly, the job engagement and intrinsic motivation tended to cross-load within each other. Some items were eliminated to avoid confusion and improve understanding of the survey. Uncertain construct items tended to cross-load onto system quality.

After the Q-sort, we formed an expert panel of knowledge workers (mixed in categories of remote and on-site) who had been involved in similar research and obtained their feedback. We performed deep reviews of the instrument with four participants who did not participate in the final survey. This deeper analysis resulted in some specific wording changes that added clarity to the established items and ensured that the statements truly related to our topic and that the differences were clear to ensure the correct wording for the right category of participants. Based on this deeper analysis, multiple items were also modified for improved comprehension. The analysis helped clarify questions and avoid duplication of items without leaving critical concepts outside of the survey.

We also reviewed our qualitative study as to ensure the constructs were addressing the areas that we wanted to explore in our study, based on our original qualitative study. We then used a think-aloud protocol with a sample of 8 participants in

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total: three workers, three human resources professionals, and two researchers to refine the questions and to ensure that they were comprehensible, accurate, and offered a basis for clear judgments (Bolton & Bronkhorst, 1996). During the think-aloud protocol, we found that two items that included the words industry and organization were confusing, and we replaced them with more appropriate terms for a broader audience.

Finally, we conducted a pilot survey with 300 respondents. The survey sought to ensure that the initial statistical results were in line with construct definitions. The analysis showed normality (Kolmogorov-Smirnov significance > 0.05), adequate dimensionality (Kaiser-Meyer-Olkin > 0.5, Chi-square =; df= p < .05), and item reliability (Cronbach’s Alpha > 0.5), and item reliability (Cronbach’s Alpha > 0.5) for all constructs. Exploratory factor analysis based on theoretical groupings demonstrated acceptable communalities. We did not make any changes to the survey instrument, and the final questionnaire consisted of 30 items with all scales defined as previously indicated. After completing our EFA, the final questionnaire ended in 24 items (see

Appendix B).

Controls and Demographics

Experience has often been considered as a control in remote work research. Pynes

(1997) recommends that only employees with experience in the particular job and with a satisfactory or higher performance rating be considered for telecommuting. Experience with the company may help to ensure that employees have developed a commitment to the organization (Hamilton, 1987). Gender was also considered as a control.

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Data Collection and Sample

Data was collected over a three-month period from early November 2014 to late

January 2015 using an online survey tool (Qualtrics), a popular survey research platform.

Participants for our research were directed to complete an identical survey. As shown in

Table 5, the total participants for the survey were 304, from which 152 were remote workers (50%) and 152 were on-site workers (50%). The survey was directed to the knowledge worker category only, meaning it was restricted to employees that have a minimum of an undergraduate degree and currently performed in a professional role. The knowledge workers sample were full-time workers selected from different firms and different functions. We intentionally excluded knowledge workers from functional organizations like sales, consulting work and customer service work, which traditionally are jobs that require traveling, different kind of management requirements, or work that has traditionally being structured to perform remotely, such as call center operators.

Contractual workers were not included in the survey either given the temporal natural of engagement that they may have with a determined organization.

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Table 5. Participants' Demographics

Data Analysis

A detailed explanation of our data analysis procedures follows. See Figure 14 for a flowchart of our data analysis process.

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Figure 14. Data Analysis Flow Chart

Data$Analysis$Flow$Chart

(1) Data'Collection (2) N'='304

(2)'Data'Screening

(4A)'Reliability,'Convergent' (5A)'Metric'and'Configural' Validity'and'Discriminant' (3)'Measurement'Model' Invariance,'Common' Validity Analysis'(SPSS''&'AMOS) Method'Bias,'Model'Fit

(4)'EFA'Exploratory'Factor' (5)'CFA'Confirmatory'Factor' Analysis'(SPSS)'N'='304' Analysis'(SPSS)'N'='304'

(6)'Structural'Equation'' Model'Analysis'(SEM)'in' Amos

(7)'Conduct'MultiSGroup' (7)'Conduct'Moderation'and' Comparison' Interaction'Effects'

(8)'Confirm'Hypothesis'&' Wrap'Up'Conclusions

Our process consisted of eight major steps that we briefly describe as follows.

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structural model is greatly simplified. The third step of our analysis included the testing of the structural model in AMOS. To test for mediation, we employed the Baron (1986) and Sobel (1982) approaches. We will follow up this with a bootstrapped analysis of indirect effects with 500 resamples. Next, we detail the analysis.

Data Screening

To ensure quality data, we assessed missing values, outliers, and normality. All usable responses were complete. We explored age, experience, functional area and we found no outliers. All of our variables were ordinal (Likert-scale), and, therefore, extreme value outliers do not exist. Due to using short interval ordinal scales, skewness is not a major issue, but kurtosis may affect our results due to insufficient variance. Therefore, we tested for kurtosis. Our test kurtosis revealed no major concerns as our values were smaller than 2.0. Therefore, we opted to retain all items for our factor analysis. As far as controls we did not find kurtosis in any of them.

Measurement Model Analysis

As a first step, we conducted exploratory (EFA) and confirmatory (CFA) factor analysis (using maximum likelihood) in order to establish the reliability and validity of our construct measurements. The pattern matrix of item loadings we kept was above the

0.400 threshold recommended by Hair et al. (2010) for sample sizes greater than 200

(most averaging above 0.700). Cronbach’s alphas values are also reported for each factor.

All Cronbach’s alphas resulted above the recommended threshold of 0.700 for factor reliability (Fornell & Larker, 1981). Table 6 shows our initial EFA Pattern Matrix.

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Exploratory Factor Analysis

We conducted exploratory (EFA) and confirmatory (CFA) factor analysis (using

Maximum Likelihood) in order to establish the reliability and validity of our construct measurements. The pattern matrix of item loadings is shown in Table 6. We kept only loadings that were above the 0.400 threshold recommended by Hair et al. (2010) for sample sizes greater than 200 (most averaging above 0.700). Cronbach’s alphas values are also reported for each factor in Table 6. All Cronbach’s alphas are above the recommended threshold of 0.700 for factor reliability (Fornell, 1981). Total Variance

Explained was 60% for the 7-factor model. During the EFA, some items were dropped due to poor loadings or failing to load with the expected factor.

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Table 6. Pattern Matrix (EFA) & Cronbach Alpha by Factor

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Several other statistics indicated the EFA solution was acceptable. We observed the Kaiser-Meyer-Olkin (KMO) statistic was 0.897 to predict the likelihood of the data to factor well; this result is above the expected range of .60. Second, Bartlett’s Test of

Sphericity was significant (χ2 = 6191.230, df=561, p< 0.000) indicating sufficient inter- correlations (Table 8). Third, the communalities were all above 0.30 further confirming that each item shared some common variance with other items. Fourth, all Measures of

Sampling Adequacy (MSAs) across the diagonal of the anti-image matrix were above

0.70, indicating that the data is appropriate for factoring. Fifth, an examination of the inter-item correlation matrix indicated approximately 80% of the correlations were over

0.30. Finally, an additional check for the appropriateness of the respective number of factors that were extracted was confirmed by examining reproduced correlation (and residuals). We found only 40 (7%) non-redundant residuals with absolute values greater than 0.05.

All indicators loaded on their hypothesized factors, and the details are shown in

Table 7.

Table 7. EFA Measurement Model Results

Confirmatory Factor Analysis

The CFA measurement model was estimated using AMOS (Analysis of Moment

Structures) software v23.0, a covariance-based structural equation modeling technique 150

using the maximum likelihood estimation approach. In this model, no unidirectional path was specified between any latent. Instead, a covariance model was estimated where each latent variable was correlated with every other latent variable.

The psychometric properties of the six latent constructs involving 30 items were evaluated simultaneously in one confirmatory factor analysis (CFA). Most factors except were trimmed due to their lack of sufficient reliability and validity, ending with a total of

24 items for the final model. Final factors presented sufficient reliability and validity. The sample size of 300 was deemed sufficient given low communalities (Hair et al., 2010) and acceptable values on the Hoelter’s Critical Test (see Table 8 for Model Fit Indices).

Consequently, the model was expected to converge using maximum likelihood estimation. Tests were conducted to evaluate the convergent and discriminant validity and the reliability of reflective measures.

Table 8. CFA Model Fit Indices

It is generally recommended that multiple indices be considered simultaneously when overall model fit is evaluated. Our Chi-square (χ2) was not significant (1.661; df=231; p= 0.000) suggesting that the model did fit the data well; this is, the observed

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covariance matrix did match the estimated covariance matrix within sampling variance

(Hair et al., 2010: 698). The root-mean-square error of approximation (RMSEA), known as the most sensitive index to models with misspecified factor loadings (Hu & Bentler,

1999) was .047 and the confidence interval was small between .055 and .066, suggesting a reasonable close fit model (Browne & Cudeck, 1993; Hu & Bentler, 1999; MacCallum,

Browne, & Sugawara, 1996). The proportionate improvement in fit of the measurement model over a more restricted baseline model was assessed by the comparative fit index

(CFI) and Bollen’s (1989) incremental fit index (IFI), which were .959 and .951. In addition, TLI was 0.951. All thresholds from Hu and Bentler (1999) are met, indicating we have sufficient model fit. No adjustments to the model (such as addressing issues indicated by the modification indices) were required in order to obtain adequate goodness of fit.

Convergent and Discriminant Validity

Convergent validity used the three standards recommended by Bagozzi and Yi

(1988) to assess the measuring model: (1) all indicator CFA factor loadings should exceed 0.5 (Hair et al., 2010); (2) CR should be above 0.7; and (3) the average variance extracted, AVE, of every construct should exceed 0.5 (Fornell & Larcker, 1981). Hair et al. (2010) suggested that an item is significant if its factor loading is greater than 0.50. As shown in the below Table 9, the factor loadings of all the items in the measure range from

0.744 to 0.837, thus meeting the threshold set by Hair et al., and demonstrating convergent validity at the item level. At the construct level, Hair et al. (2010) recommended that the composite reliability should be used in conjunction with SEM to address the tendency of the Cronbach’s alpha to understate reliability. For composite

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reliability to be adequate, a value of .70 and higher was recommended (Nunnally &

Bernstein, 1994). The final indicator of convergent validity is the average variance extracted, which measures the amount of variance captured by the construct in relation to the amount of variance attributable to measurement error (Fornell & Larcker, 1981).

Convergent validity is judged to be adequate when average variance extracted equals or exceeds 0.50 (i.e., when the variance captured by the construct exceeds the variance due to measurement error). As shown in Table 9, the convergent validity for the proposed constructs of this study is adequate.

Table 9. Convergent & Discriminant Validity Results

In discriminant validity, as Fornell and Larcker (1981) suggested, the AVE of the construct should exceed other correlation coefficients of the construct. Table 9 shows the matrix of correlations among the constructs in this research. Diagonal elements are the square roots of the average variance extracted (AVEs) for the constructs. The correlation coefficients between any two constructs are smaller than the square root of the average variance extracted for the constructs. Constructs in the measurement model of this research indeed are different from one another, indicating that all constructs in this research carry sufficient discriminant validity. Finally, to examine the discriminate 153

validity of the measurement model, the correlations among latent constructs were examined. High-value correlations exceeding 0.9 (Hair et al., 2010) or correlations exceeding 0.85 (Kline, 1998) should be noted as an indication of a problematic level of inter-correlated constructs. Our testing used this criterion demonstrated the discriminate validity of the seven factors that comprise our model. A summary of our test results and the correlation matrix are shown in Table 9.

Common Method Bias

The data collection instrument for this study was a self-reported survey, administered to a single reporting source (i.e., remote or on-site knowledge worker participants). Therefore, we also tested for Common Method Bias, using the common latent factor (CLF) method to capture the common variance among all observed variables in the model. As recommended for this method, we compared the standardized regression weights with the CLF model to the standardized regression weights of the model without the CLF. There were no large differences (like greater than 0.200); therefore, we decided not to retain CLF. Below the results of our comparison (Table 10).

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Table 10. Common Method Bias (Common Latent Factor Method) Results

Measurement Model Invariance

Before creating composite variables for a path analysis, configural and metric invariance was tested during the CFA to validate that the factor structure and loadings were sufficiently equivalent across groups, remote and on-site respondents. After creating some co-variances and adjusting some modification indices, we met configural invariance. For metric invariance, we found partial metric invariance since three loadings in the work location enjoyment (INTMotA) factor loaded with some significance. Table

11 shows the results

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Table 11. Metric Invariance across Groups Results

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Structural Equation Modeling

Mediation analyses were conducted using path analysis in SPSS Amos version 23.

The current state of the science for mediation analysis has advanced since the earlier recommendations of Baron and Kenny (1986) in two important ways. First, the total effect of an independent variable on the outcome (i.e., x → y) does not need to be statistically significant for mediation to occur (Hayes, 2009). Second, although significance of the mediation was formerly assessed based on whether a significant total effect became non-significant, or a test, such as Sobel’s test, of the indirect effect, extensive empirical work now demonstrates that the parameter distribution of the indirect effect, a * b, is not normally distributed (MacKinnon, Lockwood, & Williams, 2004), and, therefore, violates a basic assumption of Sobel’s test. Current research points to

Monte Carlo approaches (MacKinnon, Fritz, Williams, & Lockwood, 2007), Bayesian methods (Yuan & MacKinnon, 2009), or bootstrapping (Preacher & Hayes, 2008), which is also recommended for multiple mediator models (Preacher & Hayes, 2008) along with bootstrapped confidence intervals for the indirect effect (MacKinnon et al., 2004). For this analysis, bootstrapping was used, with 95% bias-corrected confidence intervals. To ensure the relative stability of the estimates, 5,000 bootstrap resamples were taken, and results are based on these estimates. As this was a multiple mediator model, note that the total effect (i.e., the effect of the independent variable on the outcome without mediators in the model) is equal to the sum of the direct (i.e., with mediators in the model) and indirect effects. That is, total effect = a1b1 + a2b2 + c’, the extension of simple mediation to multiple mediator models.

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Hypothesis Testing - Mediation

Figure 15 shows the proposed model, and the results obtained were engagement mediates the relationship between ICT utilization, intrinsic motivation and leadership and productivity. Two control variables, Gender and Experience, were added to the model. A multi-group analysis was conducted between remote and on-site workers.

The model fit was χ2(20)=33.69, p=0.03; the χ2 was not p>0.05 which indicates a poor fit. However, this metric is sensitive to sample size. Other fit indices were

CFI=0.98, TLI=0.94, IFI=0.98 and RMSEA=0.046, all of which indicate a good model fit. The CMIN/DF was 1.63, also indicating a good fit. In on-site workers, the r2 for productivity was 0.41 and for job engagement was 0.59; the r2 for remote workers was

0.43 for job engagement and 0.49 for productivity. That means that 41% and 49% of the variance for productivity and 59% and 43% of the variance for job engagement were explained by the variables in the model for on-site and remote workers, respectively.

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Figure 15. Hypothesis Results (Mediation and Direct Effects)

R2=) .43)(remote)/.59)(onsite)

Personnel) Subsystem Model)Fit:)CFI=0.98,)TLI=0.94,) IFI=0.98) and) Job) RMSEA=0.046.) The)CMIN/DF)was)1.63 Engagement

Technical) Subsystem Groups ) IT)Utilization R)=)Remote O) =)On8Site

Personnel) Subsystem R2=).49)remote/.41)(onsite) Enjoyment) Work) Location Organizational) Outcomes

Stress/Tension) Worker Work)Location Productivity

Organization)Subsystem

Leadership) Exchange

Direct)effects Controls Mediation • Gender • Experience

Key)Findings • No)mediation)effects)with)Job)Engagement.) • IT)Utilization)has)a)positive)impact)in)productivity)in)both)categoriesMediation)&)Group)Comparison • Enjoyment)of)work)location)has)a)positive)impact)in)Productivity,)but)this)is)more)significant)for)remote)workers.) • Leadership)has)effects)to)engagement)in)both)categories,)but)also)to)productivity)for)onsite)workers.)

Table 12 shows the estimates from the model, encompassing the mediation and direct effects model.

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Table 12. Mediation & Direct Effects Results by Worker Location*

Hypothesis Direct Indirect effect X on Y w/o Sobel test Result effect X on X on Y mediation Y 1a Remote -0.150 0.002 0.148 0.41 No X=ICT utilization (0.06) (0.51) (0.033) (0.68) mediation Y=productivity M=job engagement 1a On-Site -0.150 0.004 0.145 0.61 No X=ICT utilization (0.04) (0.34) (0.031) (0.54) mediation Y=productivity M=job engagement 2a Remote 0.530 -0.014 0.516 -0.51 No X=Intrinsic Motivation (0.001) (0.45) (0.001) (0.61) mediation A Y=productivity M=job engagement 2a On-Site 0.328 0.036 0.364 1.01 No X= Intrinsic Motivation (0.001) (0.30) (0.001) (0.03) mediation A Y=productivity M=job engagement 3a Remote -0.086 0.00 -0.087 -0.03 No X=Intrinsic Motivation (0.22) (1.00) (0.21) (0.98) mediation B Y=productivity M=job engagement 3a On-site -0.078 0.004 -0.073 0.61 No X= Intrinsic Motivation (0.26) (0.29) (0.27) (0.54) mediation B Y=productivity M=job engagement 4a Remote 0.128 -0.02 0.107 -1.16 No X=leadership (0.18) (0.53) (0.25) (0.24) mediation Y=productivity M=job engagement 4a On-site 0.297 0.051 0.348 1.02 No X= leadership (0.008) (0.31) (0.002) (0.31) mediation Y=productivity M=job engagement

Job engagement did not mediate the relationship between ICT utilization and productivity in remote (indirect effect: b=0.002, p>0.05) or on-site workers (indirect effect: b=0.004, p>0.05). Therefore, Hypothesis 1a was not supported. Nevertheless, the relationship between ICT utilization and productivity was positive for both remote (b=

0.148, p>0.05) and on-site (b= 0.145, p=0.05) workers. The relationship was marginally

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significant in both categories. Hypothesis 1b was not supported as there was not a significant group difference for this relationship. However, our finding indicates while no differences between groups, worker ICT utilization has a positive medium effect to productivity.

There was no evidence of mediation between job engagement and work location enjoyment in remote (b=-0.014, p>0.05) or on-site (b=0.036, p>0.05) workers.

Hypothesis 2a was not supported. The relationship between work location enjoyment and productivity without the mediator showed that there was a significant positive relationship in both remote (b=0.530, p<0.001) and on-site (b=0.328 p<0.001) workers.

And there was a significant difference between remote and on-site workers for work location enjoyment, and, therefore, Hypothesis 2b was supported.

Job engagement did not mediate the relationship between work location stress in either remote (b=0.00, p>0.05) or on-site (b=0.004, p>0.05) workers. Hypothesis 3a was not supported. There was no significant direct effect of work location stress on productivity (remote: b= -0.087, p>0.05, on-site: b=-0.073, p>0.05), but the relationships were negative and did not differ between groups. Hypothesis 3b was supported.

There was no evidence of mediation between job engagement and leadership and productivity for remote (b=-0.02, p>0.05) or on-site (b=0.051, p>0.05) workers.

Hypothesis 4a was not supported. The relationship between leadership and productivity without the mediator showed that there was a significant positive relationship in on-site workers (b=0.297, p<0.002) but not in remote workers (b=0.128, p>0.05). However, this difference was not statistically significant. Hypothesis 4b was partially supported. It is worth to note that while no mediation effects, the relationship between leadership and job

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engagement was significant in both cases, remote (b=0.453, p>0.001) or on-site

(b=0.526, p>0.001); and while we did not hypothesize this relationship, this finding supports our initial findings in study one.

Table 13. Mediation & Direct Effect w/o the Mediator

Mediation & Direct Relationship - Hypothesis Result H1.1a Worker job engagement mediates the positive Not supported (no mediation for remote or relationship between ICT utilization and productivity; on-site workers) this will be a stronger relationship for remote than for on-site knowledge workers H1.1b ICT utilization is positively related to productivity; this Partially supported. Positively related, but will be a stronger relationship for remote than for on- no significant differences site knowledge workers H1.2a Worker job engagement mediates the positive Not supported (no mediation for remote or relationship between work location enjoyment and on-site workers) productivity; this will be a stronger relationship for remote than for on-site knowledge workers H1.2b Work location enjoyment is positively related to Supported. Significant effects for both productivity; this will be a stronger relationship for categories and the relationship was stronger remote than for on-site knowledge workers for remote workers with significant effects H1.2c Worker job engagement mediates the negative Not supported (no mediation for remote or relationship between work location stress and on-site workers); however, significant productivity; this effect will not be significantly different negative impact to job engagement was between groups found H1.2d Work location stress is negatively related to Not supported, no significant impact found productivity; this effect will not be significantly different between groups H1.3a Worker job engagement mediates the positive Not supported. Engagement did not mediate relationship between leadership support and the relationship. However, a direct productivity; this will be a stronger relationship for on- significant effect was found on job site than for remote knowledge workers engagement for both, remote and on-site workers H1.3b Leadership is positively related to productivity; this Leadership positively related but only relationship will be stronger for on-site than for remote significant for on-site workers knowledge workers

Hypothesis Testing – Moderation

Table 14 shows the results of proposed model for the moderating effects of the amount of time spend working virtually on the relationship between ICT utilization, work location enjoyment (INTMotA), work location stress (INTMotB) and leadership on productivity and engagement. Table 15 shows the estimates from the model.

The level of intensity of virtual work did not moderate the relationship between

ICT utilization and engagement (Beta=0.00, p=0.95). Therefore, hypothesis 5a was not 162

supported. Furthermore, there was no evidence of moderation between virtual intensity and ICT utilization and productivity (Beta=-0.01, p=.82). Hypothesis 5b was not supported.

There was no evidence that virtual intensity moderated the relationship between work location enjoyment and engagement (Beta=0.02, p=.68), but there was moderation between work location enjoyment and productivity (Beta= 0.13, p=.03). Hypothesis 6a was not supported, but there was support for Hypothesis 6b.

There was moderation between virtual intensity and work location stress and job engagement (Beta=-0.08, p=0.05) but not between work location stress and productivity

(Beta=-0.01, p=0.78). Hypothesis 7a was supported, but Hypothesis 7b was not supported.

Virtual intensity did not moderate the relationship between leadership and engagement (Beta= -0.02, p=0.70). Hypothesis 8a was not supported. Additionally, there was not a significant moderating effect of virtual intensity on leadership and productivity

(Beta=-0.07, p=.28). Hypothesis 8b was not supported.

Finally, virtual intensity did not moderate the relationship between productivity and engagement (Beta=-0.09, p=.19). Hypothesis 9a was not supported.

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Table 14. Estimates from Study Model 2 Part 2: Moderation Effect of Days Working Remote

B S.E. P b JobEng <--- Lead 0.49 0.05 *** 0.49 JobEng <--- INTMot_B 0.03 0.04 0.54 0.03 JobEng <--- INTMot_A 0.60 0.08 *** 0.35 JobEng <--- ITUse 0.00 0.04 0.99 0.00 JobEng <--- ICT_x_VI 0.00 0.04 0.95 0.00 JobEng <--- IntA_x_VI 0.02 0.05 0.68 0.02 JobEng <--- IntB_x_VI -0.08 0.04 0.05 -0.08 JobEng <--- Lead_x_VI -0.02 0.05 0.70 -0.02 JobEng <--- VI 0.02 0.04 0.65 0.02 productivity <--- Lead 0.22 0.06 *** 0.22 productivity <--- INTMot_B -0.09 0.05 0.07 -0.09 productivity <--- INTMot_A 0.75 0.10 *** 0.44 productivity <--- ITUse -0.15 0.05 0.00 -0.15 productivity <--- ICT_x_VI -0.01 0.05 0.82 -0.01 productivity <--- IntA_x_VI 0.13 0.06 0.03 0.13 productivity <--- IntB_x_VI -0.01 0.05 0.78 -0.01 productivity <--- Lead_x_VI -0.07 0.06 0.28 -0.07 productivity <--- Q19 0.16 0.09 0.06 0.08 productivity <--- Q13 0.02 0.02 0.33 0.04 productivity <--- VI 0.00 0.04 0.93 0.00 productivity <--- JobEng 0.00 0.06 0.98 0.00 productivity <--- Engage_x_VI -0.09 0.07 0.19 -0.09 *Beta=unstandardized beta; beta=standardized beta

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Table 15. Summary of Hypotheses

Moderation - Hypothesis Result Hypothesis 5a. The level of intensity of virtual work Not supported moderates the relationship between ICT utilization and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers. Hypothesis 5b. The level of intensity of virtual work Not Supported moderates the relationship between ICT utilization and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers. Hypothesis 6a. The level of intensity of virtual work Not supported moderates the relationship between work location enjoyment and engagement for knowledge workers. This relationship is more significant for high- intensity remote workers. Hypothesis 6b. The level of intensity of virtual work Supported moderates the relationship between work location enjoyment and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers. Hypothesis 7a. The level of intensity of virtual work Supported moderates the relationship between work location stress and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers. Hypothesis 7b. The level of intensity of virtual work Not Supported moderates the relationship between work location stress and productivity for knowledge workers. This relationship is more significant for high- intensity remote workers. Hypothesis 8a. The level of intensity of virtual work Not supported moderates the relationship between leadership and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers. Hypothesis 8b. The level of intensity of virtual work Not Supported moderates the relationship between leadership and productivity for knowledge workers. This relationship is more significant for high-intensity remote workers. Hypothesis 9a. The level of intensity of virtual work Not Supported moderates the relationship between productivity and engagement for knowledge workers. This relationship is more significant for high-intensity remote workers.

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Interaction Effects

The significant moderating relationships were explored further. A moderating effect of virtual intensity on work location enjoyment and productivity occurred (Beta=-

0.130, p=0.03). Figure 16 shows the interaction effects of Virtual Intensity work location enjoyment and productivity. At low levels of work location enjoyment, low virtual intensity showed higher levels of productivity than high virtual intensity. However, this changes at high levels of work location enjoyment, where there were higher levels of productivity in high levels of virtual intensity than in low levels of virtual intensity.

Figure 16. Moderation Effect of Virtual Intensity on Work Location Enjoyment and Productivity

Virtual Intensity strengthens the relationship in between enjoyment of work location and productivity, specially in remote workers (high virtual intensity)

Findings

! At#low#levels#of#Work#Location# Enjoyment,#remote#workers(high# virtual#intensity)#are#less#engaged#than# employees#that#work#from#an#office# (low# virtual#intensity).#(! )

! Conversely,#at#high#levels#of#Work# Location#Enjoyment,#remote#workers# (high# virtual#intensity)#have#a#higher# level#of#productivity#than#onsite# workers#(low#levels#of#intensity)

A moderating effect of virtual intensity on work location stress and engagement occurred

(Beta=-0.08, p<0.05). Figure 17 shows the graph of this moderation effect. As seen in the graph, at low levels of work location stress, low virtual intensity workers showed much 166

lower levels of job engagement than high virtual intensity workers. However, this changes at high levels of work location stress, where the difference with job engagement is less between high and low virtual intensity workers and low virtual intensity workers show increasing job engagement and high virtual intensity workers show decreasing levels of job engagement.

Figure 17. Moderation Effect of Virtual Intensity on Work Location Stress and Engagement

Work location preference is a factor that drives engagement in remote workers

Findings ! At#low#levels#of#stress,#high#virtual#intensity# workers#(remote)#are#more#engaged#than# low#virtual#intensity#(onsite)#employees#that# work#from#an#office#1<3#days#a#week.# Employees#when#working#remotely#can#be# more#engaged.# ! As#the#stress#increases,#high#virtual#intensity# (remote#workers)#employees#become#less#

engaged#(! ).#This#effect#is#not#the#same#for# employees#working#in#an#office#setting (1<3# days#a#week),#where#engagement##has#a# small#uptake#when#the#stress#increases#(! )

Findings

For the first set of mediation hypotheses, we found support for two out of the seven hypotheses. We found that ICT utilization is positively related to productivity, and this is similar for both categories of workers. We also found that work location enjoyment is positively related to productivity and that this relationship is stronger for remote workers than for on-site workers. In addition, we found that leadership is

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positively related to job engagement, and this relationship is significant for both, remote and on-site workers. We found that leadership is positively related to productivity as well, but is only significant for on-site workers.

For the second set of hypotheses, we found virtual intensity moderates the relationship between work location enjoyment and productivity and confirms that this is more relevant for remote workers. We found that at low levels of work location enjoyment, remote workers (high virtual intensity) are less engaged than employees that work from an office (low virtual intensity). Conversely, at high levels of work location enjoyment, remote workers (high virtual intensity) have a higher level of productivity than on-site workers (low levels of intensity).

We also found that virtual intensity moderates the relationship between work location stress and engagement and confirm that this effect is more impactful in high- intensity workers. At low levels of stress, high virtual intensity workers (remote) are more engaged than low virtual intensity (on-site) employees that work from an office 1–3 days a week. Employees when working remotely can be more engaged. As the stress increases, high virtual intensity (remote workers) employees become less engaged. This effect is not the same for employees working in an office setting (1–3 days a week), where engagement has a small uptake when the stress increases.

Finally, it is important to highlight that while we expected job engagement to mediate the relationship to productivity, job engagement did not mediate any of this relationships.

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Discussion

We began our study based on the factors that theory and prior research, including our qualitative study, suggest influence productivity and engagement of remote and on- site knowledge workers. We theorize that ICT utilization, work location enjoyment, work location stress, as well as leadership where all drivers of productivity, and that engagement mediated this effects. We also theorized that Virtual Intensity had a moderation effect in ICT utilization, Intrinsic Motivation A/B as well as in leadership.

Our results are mixed in the sense that some of the aspects we theorized appear to be contradictory to other existent research. First, and surprisingly, job engagement did not have a direct effect on productivity and it did not mediate the relationship to productivity in any of the cases. This is an unexpected result given the outcomes of previous research and also our findings in our qualitative study. While within the engagement literature, the study of engagement’s consequences has received little attention, where most research has followed the pattern of seeing engagement is the outcome. Nevertheless, most recent research has confirmed that job engagement is positively related to outcomes at work like commitment, worker performance and turnover intention (Halbesleben & Wheeler, 2008). One explanation could be that work engagement is not only an individual phenomenon, but it also occurs in groups; that is, it seems that employees in some teams or parts of the organization are more engaged than in other teams or parts (Bakker, Demerouti, Taris, Schaufeli, & Schreurs, 2003; Salanova,

Agut, & Peiró, 2005). Obviously, engagement is not restricted to the individual employee, but groups of employees may differ in levels of engagement as well. Schaufeli and Bakker (2003) observed in a study that included 130 teams from different

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organizations that the collective level of engagement of the team is associated with the individual level of engagement of the team members: the more engaged the team, the more engaged its members. Moreover, it appeared that the “engaged” teams were able to acquire more job resources compared to the teams that were less “engaged”, which in its turn had a positive impact on the level of engagement of the individual team members.

We suggest that job engagement should continue to be explored with more cohesive teams and a group of employees that belong to the same organization.

Second, we found confirmation in alignment with previous research, that ICT utilization, the use of tools that are more pervasive in the current workplace, is a driver of productivity, and that the effect is not different between remote or on-site workers. While this finding by itself is not new, the construct, which included the most current technology in the workplace, allows us to confirm that from the perspective of the ICT remote working tools that exist today, they appear to be relevant for both remote and on- site workers. And the level of importance is not different between both categories. This may indicate that technology is not enabling only the productivity of remote work, but is enabling the productivity of a blended workforce that has the needs to communicate internally and externally despite the work location. This is one of the ultimate outcomes that organizations and leaders are concerned about—the joint optimization of the socio- technical resources across the enterprise.

Third, we found Virtual Intensity strengthens the relationship between enjoyment of work location and productivity, especially in remote workers (high virtual intensity).

At low levels of work location enjoyment, remote workers (high virtual intensity) are less engaged than employees that work from an office (low virtual intensity). Conversely, at

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high levels of work location enjoyment, remote workers (high virtual intensity) have a higher level of productivity than on-site workers (low levels of intensity). This is one of the most critical findings of this study. While not the full answer to management concerns about the implications of virtual intensity to productivity and engagement, this finding provides evidence that employees’ enjoyment of the work location is a critical factor that needs to be considered when making individual decisions of remote workforce arrangements. Managers should carefully evaluate the potential psychological effect that the different levels of virtual intensity have in employees, especially those working remotely. Especially given the most recent attention to the impact of remote work to productivity and engagement, where most organizations, fearing of potentially see their productivity reduced, have opted to retract themselves from having this programs in place. We argue that managers should reconsider this decision. Bringing back workers to the office may not be the solution to increase productive. Our findings are telling us that the decision should consider the individual worker’s location preference, especially in the category of high virtual intensity workers (remote workers). If the employee prefers and enjoy working remotely, and can be productive, then this is how the manager will maximize productivity in this employee. The right combination for this employee is to be working remotely because he/she enjoys working remotely. Conversely, if the employee enjoys working remotely but is forced to work at an on-site location, this could be in detriment of their own productivity because the employee will experience feelings of stress from working in a location that is not of his/her preference. While other factors should be considered when making decisions to provide alternatives to work

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arrangements, managers should augment this consideration to their decision-making frameworks so to potentially increase the effectiveness of their decisions.

Fourth, we found Virtual Intensity moderates the relationship between work location stress and engagement, especially in remote workers (high virtual intensity). At low levels of stress, high virtual intensity workers (remote) are more engaged than low virtual intensity (on-site) employees that work from an office 1–3 days a week.

Employees when working remotely can be more engaged. As the stress increases, high virtual intensity (remote workers) employees become less engaged. This effect is not the same for employees working in an office setting (1–3 days a week), where engagement has a small uptake when the stress increases. This is also another critical finding of this study. While not the full answer to management concerns about the implications of virtual intensity to engagement, this finding provides evidence that stress experienced by employees (wrong location based on their individual preference) is a critical factor that needs to be considered when making individual decisions of remote workforce arrangements. Managers should carefully evaluate the potential psychological effect that the different levels of virtual intensity have in employees, especially those working remotely. Especially given the most recent attention to the impact of remote work to productivity and engagement, where most organizations, fearing of potentially see their engagement reduced, have opted to retract themselves from having this programs in place. We argue that managers should reconsider this decision. Bringing back workers to the office may not be the solution to increase engagement. Our findings suggest that the decision should consider the individual worker’s location preference, especially in the category of high virtual intensity workers (remote workers). If the employee experiences

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stress in the work location, the employee will be less engaged; this is especially important for remote workers. While stress location is not impactful for on-site workers, it is clear that it is impacting high virtual intensity workers (remote). The right combination for this employee is to be working remotely because he/she enjoys working remotely.

Conversely, if the high worker is not experiencing the stress of the work location, the worker can be more engaged, this is especially true for high virtual intensity workers

(remote). While other factors should be considered when making decisions to provide alternatives to work arrangements, managers should augment this consideration to their decision-making frameworks so to potentially increase the effectiveness of their remote workforce management decisions.

Lastly, while we could not find support for job engagement as a mediator in the whole model, we found the effect between leadership support and job engagement. We also found a strong relationship between leadership support and productivity. This confirms our original finding in our qualitative study, that leadership support, in fact, has a positive relationship to productivity and engagement. And while we predicted a stronger impact on remote workers, our results indicate that the impact of leadership support is independent of whether the employee works on-site or remotely. Employees need the support, whether working from a location with the opportunity to interact in- person with the leader, or from a remote location, employees seemed to equally value the benefits of having leaders that support them, that provide them with the necessary information to their work, and that help them to solve problems. This also strengthens our hypothesis that leaders will have to learn to adapt to the future workplace and will have to develop new skills, behaviors that can enable to maintain and increase the positive

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relationship with employees working remotely, at the same quality level as the relationship with on-site workers. This will produce, in exchange, a similar effect of engagement across the blended workforce, which is the ultimate expected effect that organizations are looking for.

Limitations and Future Research

There are some limitations to our study that should be taken into consideration.

First, on the positive aspects of this study, our model incorporated the application of socio-technical theory, and all variables included, the model explained 60% of what predicts productivity and engagement. Nevertheless, we do recognize that there are other aspects of the personnel, organization/culture, work design as well as technical subsystems that should be considered to provide further answers to drivers of productivity and engagement in the remote and on-site knowledge workers. Therefore, we suggest that further research should be pursued, considering the addition of others factors and that conceptual studies combining existent research should be considered to provide to audiences a further integrated framework of the blended workforce productivity.

Second, our ICT utilization construct is very new, and therefore, further developments to improve the model should be re-evaluated. While the results clearly confirm the impact to productivity, we believe that a better construct can improve the relationship of ICT utilization to productivity. Further studies should be considered to improve this construct.

Third, we surveyed only knowledge workers using perceptual measures rather than conducting an experiment, observation, or measuring productivity on a specific task.

Thus, our productivity measures are subject to the usual perceptual biases. Additionally,

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having a mixed of participants/users surveyed could bring some key differences in results.

Lastly, while we attempted to uncover hidden differences among remote and on- site, and added virtual intensity to further understand key differences, other categorized should be considered to further understand what happens to those employees that are mixing remote and on-site work, while the high and low virtual intensity provides insights, this is not enough to conduct a more accurate differentiation for this particular category. The entrance of this different type of work alternative could create different types of challenges for organizations that at this point, may be hidden because of the lack of further differentiation.

Beyond the limitations and recommendation for future research already mentioned throughout this section, we recommend future research explore what are the implications of ICT utilization, work location enjoyment, and leadership support on productivity and engagement using different worker categories that differentiate those workers that are mixing remote and on-site work. Potentially, workers should be categorized as full-time remote, full-time on-site, and blended workers, this last category to segment those employees that are mixing their working time between traditional office setting and remote location. In addition, because remote work, especially considering now a third category with the blended worker, can trigger important changes in day to day work, and the worker’s ability to cope with this change could be a determinant of how motivated or engage the employee is, the impact on worker’s self-efficacy should be examined.

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Conclusion and Implications for Practice & Academia

This study has opened a new dimension to our understanding of the drivers of productivity and engagement in knowledge workers; and more importantly, has provided insights into the key differences between two categories of workers that exist in the workplace. Our findings have implications for both practitioner and academic.

From the practitioners’ perspective, by assessing the impact of how the worker experiences (enjoyment or stress) working on-site or remotely, leaders and human resources professionals could reduce the potential negative impact to worker’s productivity and engagement. When employees experience stress based on the location

(on-site/remote), this can negatively impact their engagement. When employees enjoy their location (remote or on-site), they can be more productive. Assessing the worker abilities to maximize and use the technology to execute in their work, and aligning their remote worker practices as a requirement for workers to be able to perform work remotely, could lead to higher productivity. Lastly, jointly evaluating the impact of work location preference, the use of technology, the impact of leadership, and levels of virtual intensity, could lead to better remote workforce management policy design, and, therefore, a more productive blended workforce.

From the academic perspective, we have created two new constructs, adapted from Intrinsic Motivation theory (enjoyment or stress constructs) to measure the level of enjoyment or stress experienced by the employee based on the work location, and use it to identified positive or negative effects on productivity and engagement. By doing this, we discovered that the worker’s work location preference is an important factor to productivity in engagement, confirming that enjoyment of location has a positive effect

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on productivity, and is more critical for remote workers. Conversely, Stress/Tension of

Work Location has no significant effect on productivity or engagement. Additionally, we augmented already tested constructs of Virtual Intensity (high or low levels of virtual intensity on the remote work), using this to hypothesized interaction effects to productivity and engagement. We confirm that Virtual Intensity Moderates the effects of

Enjoyment with Work Location and productivity: at higher levels of enjoyment of remote working and high level of intensity, the worker becomes more productive. We also confirm that Virtual Intensity Moderates the effects of stress/tension with Work Location and engagement: at a higher level of stress, the employee becomes less engaged.

While this research advances the knowledge on drivers of productivity and engagement, and some initial insight on key differentiators among the remote and on-site workers, it is imperative that future research explore the implications of ICT utilization, work location enjoyment, and leadership support on productivity and engagement, using different worker categories that differentiate those workers that are mixing remote and on-site work. Potentially, workers should be categorized as full-time remote, full-time on- site, and blended workers; this last category to segment those employees that are mixing their working time between traditional office setting and remote location. In addition, because remote work—especially considering now a third category with the blended worker—can trigger important changes in day to day work, and the worker’s ability to cope with this change could be a determinant of how motivated or engage the employee is, the impact on worker’s self-efficacy should be examined.

Given the speed of change, the acceleration of virtual technologies, and the implications to the workplace, it is critical that further research is conducted to

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understand the drivers of engagement and productivity, and the key differences among the remote, on-site, and more importantly, the newly emerging category of workers that are mixing their time between remote and on-site. This will enable the creation of more accurate and remote management frameworks that can support the needs constantly evolving workplace.

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CHAPTER V: THE IMPACT OF THE BLENDED WORK ALTERNATIVE AND WORKER SELF-EFFICACY

Introduction

In the first study of this research, qualitative data was analyzed which provided some initial answers about the factors currently influencing productivity and engagement in knowledge workers and some of the potential key differences of this factors between remote and on-site knowledge workers. The findings indicated that there are four predominant priorities that workers felt are critical to their personal engagement, and that influences productivity: 1) effective leaders, 2) engagement with work, 3) ICT remote working tools utilization and availability, and 4) the opportunity to work remotely and having work flexibility. In the second study, the factors that influence productivity and engagement were measured and confirmed using a quantitative method approach. The results confirmed that ICT remote working tools, worker’s work location preference

(enjoyment or stress), leadership support, all have an impact on either job engagement or productivity. This second study also provided evidence of the comparisons between the degree of impact for both, remote and on-site workers, and how the levels of virtual intensity (high or low), can lead to different effects on productivity and engagement.

In this section, the focus will be to extend the thesis to understand further differences in three different categories of workers. Previously we had defined two categories of workers, the on-site and the remote workers. The on-site workers were those employees that identified themselves as workers that mainly work on-site premises and are categorized by their employers as on-site workers. The remote workers—those employees identified as remote workers by their company—and that for the most part, work remotely 4–5 days a week. This grouping was a good initial breakdown for the first 179

study and quantitative study. Nevertheless, given the inputs gathered from many employees we interviewed in our qualitative studies, and the data collected during the measurement survey used in both quantitative studies, some of these employees despite having a formal category from their employers, either on-site or remotely, are actually operating more in a new category where work location is happening in both remotely and traditional on-site premises. This third category, the blended worker, are those workers that divide their work location between on-site and remote, and are not abided by a predetermined mandatory schedule other than the worker judgment on when it is appropriate to work from home or when from an office, the variety of remote work can go from 1–3 days per week working remotely, or vice-versa 1–3 days working on-site, or a combination of both. With this background, for the purpose of this extension, three new categories will be a priority, the full-time remote—those employees that work remotely five days a week, the blended—the ones that blend their work location between on-site and remote, and lastly, the full-time on-site—those workers that work full-time from an office environment. In addition, given the changes that workers are experiencing and coping with, working either remotely, on-site or in a blended category, we explored the impact of the worker self-efficacy on productivity and engagement.

The argument of this extension is essentially that, because the blended workers receive the benefits of working remotely and working on-site, the impact of the factors that influence productivity and engagement should be examined. Moreover, because of the emerging entrance of a blended work alternative, employees’ ability to work, manage the tasks of their day to day work may be impacted. Changes of this nature can reflect differently in the worker’s ability to cope with the complexity of this new work

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alternative, either positively or negatively. Therefore, it is important to understand what the impact is on the worker, and how that affects their engagement ultimately with the work. This will be the focus of this study.

The next steps for this section are first, revise the theoretical approach and the foundation of the newly developed hypothesis. Second, a description of the quantitative statistical methods that are used to test the hypothesis is provided. This section concludes with a summary of the findings, limitations and outline of the implications of the results for future research.

Theoretical Foundation, Hypothesis Development, and Conceptual Model

Next, we review research on 1) worker self-efficacy and 2) extended review— virtual intensity. The review is founded on library searches of several reference databases, academic search engines (including searches using EBSCO, IEEE Xplore,

AMJ, First Search and Google Scholar) and many different leading management journals.

We used the following keywords and phrases in our search: “self-efficacy”, “general self- efficacy”, “remote work arrangement” “virtual intensity” “remote intensity” “part-time remote workers” to identify critical theories pertinent to this extended study.

As shown in Figure 18, because this is an extension of our second study, the theories remain the same with two key differences. We add self-efficacy as one of the independent variables and we add a different type of categorization of the worker: full- time remote, full-time on-site and blended.

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Figure 18. Theoretical Framework – Quantitative Research (EXPANSION)

Expansion Theoretical Framework Socio%Technical,Theory (Trist,,Eric;,Bamforth,,Ken;,1951) The#joint#optimization#and#interrelatedness#of social and technical aspects#of#an organization.

Personnel(Sub, Internal(Environment( Organization(&( system Sub,system Work(Design( Sub,system IM#Enjoyment##Scale Worker, Organization Worker& Work##Location#Enjoyment Worker& Leadership# (Deci & Ryan,#1991) Productivity Self* (McLean#and#DeLone,# Exchange Categories:& (Graen#&#UhlGBien,# Efficacy IM#Stress#Scale 1992)# &,Job, 1995)# Full&Time& Work#Location#Stress Engagement (Schwarze (Deci &#Ryan,#1991) Onsite,&Full& (Schaufeli#and#Bakker#,# Work#Design r,+R.,+&+ 2008)# Virtual#Intensity Time& Worker#Job# (Wiesenfeld,# Jerusalem Engagement Raghuram,#and#Garud# Remote,& (Schaufeli#&#Bakker#,#2008)# ,+M. 1995) 1999) and& Blended Technical(Subsystem Utilization#of#Remote#Work#Technology (Raghuram#et#al.,#2001)

Virtual Work Intensity: Extended Review

In the previous study, an overview of virtual intensity highlighted the danger of treating telecommuting as a single, undifferentiated program, which can lead to overlooking potentially important structural distinctions among work arrangements. The main structural distinction made by previous investigators deals with what we refer to as virtual work intensity: the extent or amount of scheduled time that remote workers spend doing tasks away from a central work location. This idea has been referred to as “virtual status” by Wiesenfeld, Raghuram, and Garud (1999: 782), “virtuality” by Scott and

Timmerman (1999: 242), and as “home-centered versus office-centered” telework by

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crosses a psychological threshold—in a sense, creating two classes, of employees in telecommuting arrangements (Meehl, 1992). High-intensity telecommuters spend the majority or all of their workdays away from a central location. Low-intensity telecommuters spend the majority of their workdays at a central (conventional) location, working remotely for only one or two days a week. Konradt et al. (2003) found that telecommuters who spent more than 50% of their time away from the office (home centered) had different motivations for telecommuting than those who spent less than

Borowicz (2003), Scott and Timmerman (1999), and Taveras (1998) also used similar splits of scheduled work time at work and at home as an indicator of behavioral immersion in telecommuting.

While some organizations may try to limit the frequency with which employees telework (e.g., Kompast & Wagner, 1998), some employees state preferences for part- time, not full-time, telework (e.g., Hamblin, 1995; Teo et al., 1999; Yap & Tng, 1990).

The image of employees working remotely on a full-time basis, while true for some individuals, does not accurately depict the teleworking population as a whole (Ramsower,

1985). Some researchers (e.g., McCloskey & Igbaria, 1998) have noted the probable

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impact of teleworking frequency on outcomes, while some other studies have revealed the limited impact of telework in samples where it is infrequently practiced (Belanger,

1999a) finds that employees who telework part-time are not left out of the office network, nor does telework make a difference in determining which individuals communicate with each other. Similarly, Duxbury and Neufeld (1999) find that part-time telework has little impact on intra-organizational communication. Many authors argue that these studies support that part-time telework may yield few significant impacts. Other studies, however, suggest that infrequent telework may have effects. With the same sample as

Duxbury and Neufeld (1999), Duxbury, Higgins, and Neufeld (1998) show that, over time, teleworkers report less work and family conflict.

In the previous study, two categories of workers were used to conduct the research, the on-site and the remote workers. The on-site workers were those employees that identified themselves as workers that mainly work on-site premises and are categorized by their employers as on-site workers. The remote workers, those employees identified as remote workers by their company, and that for the most part, work remotely

4–5 days a week. This grouping was a good initial breakdown for the first study and quantitative study. Nevertheless, given the inputs gathered from many employees we interviewed in our qualitative studies, and the data collected during the measurement survey used in both quantitative studies, some of these employees despite having a formal category from their employers, either on-site or remotely, are actually operating more in a new category where work location is happening in both remotely and traditional on-site premises. This third category, which for the purpose of this research we will call the blended worker, are those workers that divide their work location between on-site and

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remote, and are not abided by a predetermined mandatory schedule other than the worker judgment on when it is appropriate to work from home or when from an office, the variety of remote work can go from 1 to 3 days per week working remotely, or vice-versa

1–3 days working on-site, or a combination of both. While some could argue that these are part-time remote workers, a blended worker category better reflect the real phenomena that are being identified in the workplace. The data collected in the qualitative study shows that employees that are mixing their time between remote and on- site work, are making this decision based on their judgment, taking in considering the type of work and projects that they are involved at that particular period of time, the need to consult in person with either a peer or a supervisor. Sometimes they choose to go to the office just to develop the bond with their co-workers, etc. In some cases, they may go to the office the full week, and sometimes the next week they don’t show up, and sometimes it really is half and half working remotely or on-site. Putting them in the part-time category may implied some type of arrangement where employees are obligated to show to the office at least 50% of their work time, or that they have to be away 50% of their time, and in some cases, this is restricted to preliminary arrangements on specific dates already pre-arranged with their supervisor, which really does not illustrate the current workplace phenomena.

With this background, for the purpose of this extension, three new categories will be a priority, the full-time remote—those employees that work remotely five days a week, the blended—the ones that blend their work location between on-site and remote, and lastly, the full-time on-site—those workers that work full-time from an office environment. In addition, given the changes that workers are experiencing and coping

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with, working either remotely, on-site or in a blended category, we explored the impact of the worker self-efficacy on productivity and engagement. Next, a theoretical overview on self-efficacy is provided, highlighting the implications for our research.

Self-Efficacy

2004; Stajkovic & Luthans, 1998).

Self-efficacy has been studied in the field of remote work from different angles.

Raghuram et al. (2001) dedicated a full study to understand the role of self-efficacy in determining remote worker adjustment and structuring behavior when telecommuting.

From another perspective, Staples et al. (1999) developed a framework of remote work self-efficacy integrating self-efficacy theory with important management issues. Their findings suggest many important ways in which remote workers’ performance can be enhanced through the improvement of remote worker self-efficacy, and highlights the importance of training these employees on how to work remotely, and continuously supporting them, so they can deliver better outcomes.

The theory is well suited to studying virtual work. For example, the remote workers typically work with minimal supervision and rely heavily on their own abilities

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and initiative to perform their job tasks. Information technology is the typical medium used to communicate with management since face-to-face interaction is rare or infrequent. Often the employee works in a location with few or no co-workers, so the potential for isolation can be high and the availability of co-worker advice is often low.

Since remote employees enjoy considerable work autonomy, the potential impact that their own motivation and beliefs in their abilities (i.e., self-efficacy judgments) can have on their outcomes may be considerably more than for employees whose behaviors are under tighter supervision. Therefore, it is critical for managers to understand the impact of workers' self-efficacy with respect to working remotely, so they can reduce the potential negative or positive impact on engagement.

Given the emerging presence of different worker categories in the workplace: the full-time remote, the full-time on-site, and the blended worker, and the potential implications on the worker’s ability to deal with the changes given the new blended category, in this study we will explore the impact of worker’s self-efficacy on job engagement, and how this impact compares in among the three different categories of workers. Changes of this nature can reflect differently in the worker’s ability to cope with the complexity of this new work alternative, either positively or negatively. Therefore, it is important to understand what the impact is on the worker, and how that affects their engagement ultimately with the work.

In recent years, a derivative of self-efficacy called general self-efficacy (GSE) has been developed. To conduct this research, the general self-efficacy (GSE) questionnaire, which is based on Perceived Self-Efficacy theory (Schwarzer, 1992) was selected for this research given the general characteristics of the questionnaire design.

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The construct of Perceived Self-Efficacy reflects an optimistic self-belief

(Schwarzer, 1992). This is the belief that one can perform a novel or difficult tasks, or cope with adversity in various domains of human functioning. Perceived self-efficacy facilitates goal-setting, effort investment, persistence in the face of barriers and recovery from setbacks. It can be regarded as a positive resistance resource factor. Ten items are designed to tap this construct. Each item refers to successful coping and implies an internal–stable attribution of success (Schwarzer & Jerusalem, 1995).

Theoretical Model - Extension

In the previous quantitative study of this research, we theorized about some key differences between remote and on-site workers, which are the most traditional categories of workers that exist today in remote work research. Nevertheless, if we only follow this traditional grouping of work arrangements, we may mistakenly ignore some critical differences affecting the workforce, and that is the appearance of a potential third category, where employees are blending the type of arrangement by working at home and/or in an office setting, and dividing their work time between these two traditional work arrangements. To address this potential gap in the research, this study expands to examine direct effects to productivity or job engagement in two parts. First, a quantitative analysis of direct effects was conducted to further understand whether there are significant differences for those factors that we found more relevant in our previous quantitative study, among fully remote, blended, and fully on-site workers, in terms of the impact of ICT utilization, work location enjoyment, leadership on productivity.

Second, a direct effect analysis was conducted to examine whether there are significant differences among the fully remote, blended, and fully on-site workers, in terms of the

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impact of self-efficacy on job engagement. Given the importance shown in previous studies, gender and experience were also re-examined.

Hypothesis Development, Research Design and Methods

In our previous quantitative studies, we theorized about some key differences between remote and on-site workers, which are the most traditional categories of workers that exist today in remote work research. Nevertheless, if we only follow this traditional grouping of work arrangements, we may mistakenly ignore some critical differences affecting the workforce, and that is the appearance of a potential third category, where employees are blending the type of arrangement by working at home and/or in an office setting, and dividing their work time between these two traditional work arrangements.

Essentially because the blended workers have the benefit of experiencing both, working remotely and working on-site, the impact of the factors that influence productivity and engagement should be examined. To address this potential gap in the research, this study expands to examine whether there are significant differences for those factors that we found more relevant in our previous quantitative study, among fully remote, blended, and fully on-site workers, in terms of the impact of ICT utilization, work location enjoyment, leadership on productivity. We will also examine whether there are significant differences among the fully remote, blended, and fully on-site workers, in terms of the impact of self-efficacy on job engagement. Given the importance that gender and experience have shown in previous studies, this will also be re-examined.

The intent of this extension is to address the following overarching questions: 1)

Are there any significant differences among the fully remote, blended and fully on-site workers as it refers to the impact of ICT utilization, work location enjoyment, and 189

leadership on productivity? 2) Are there any significant differences among the fully remote, blended and fully on-site workers as it refers to the impact worker self-efficacy and leadership on job engagement? Figure 19 presents our conceptual approach to this extension.

To conduct this extension, the model below is proposed in Figure 19, with four hypotheses that are discussed next.

Figure 19. Research Model Extensions & Hypothesis

Hypothesis Development

We know, based on current literature, and at first hand from the results of the previous quantitative studies that are part of this thesis, that ICT utilization plays a significant role in remote work, given its notable impact on productivity, and how by using the technology effectively, organizations are able to overcome the potential degradation of internal processes that may be created when employees work on a remote

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basis. Previous theory has shown that the most contemporary technology had a better effect on remote work. Mokhtarian and Sato (1994) suggested that commonly available

ICT can have a marked effect on organizational processes. Their research shows that general-purpose ICT applications (e.g., e-mail and the Web) have a larger impact than specialized ICT (e.g., video conferencing) in reducing task uncertainties and facilitating better coordination between an organization and its teleworkers. Their research also shows that effective adoption of a general-purpose communication medium (e.g., e-mail) gave teleworkers an information-rich tool that enhanced their work productivity.

However, most recently, technology has evolved dramatically, and the new state- of-the-art information technologies offer more effective mechanisms for addressing this concern in ways that include information management, monitoring capability, and communication and collaboration support. In particular, the digitalization of data and more sophisticated communication technology enables any worker to belong to the virtual network of a company regardless of his/her geographical location by using not only new different remote work tools, but actually increasing its availability in a variety of different digital accessibility like applications that can be easily accessed either from their computers or their mobile devices like cellphones or tablets. The enhanced quality and availability of remote working tools are expected to reduce frequently discussed side effects of telework such as isolation, role conflict and ambiguity, and difficulties in coordination and supervision of teleworkers. A variety of newly available technologies includes digital applications, cell phone conferencing, internal chat available in mobile tools, videoconferencing, intranet, document share-points for teams, and World Wide

Web. These tools are vastly different in their speed and reach as far as information

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carrying ability, provision of accessibility to information and data, portability, transportability of work and collaboration support.

Because of the rapid spread of accessibility work related and remote work technology, impacting the speed of the information and access to data through different devices, and new technology like digital work applications, internal chat, immediate video that keeps employees connected all the time with just one click, potentially reducing the side effects of working remotely, we hypothesized that the role of ICT technology plays a significantly different role between full-time, blended and full-time remote workers. As an example, full-time remote workers resent the lack of interaction and experience isolation given their remoteness, while those employees that work one or two days a week on-site, and two or three days a week remotely, have the advantage of being able to maintain the connectedness with their peers. In addition, because they still work remotely, they still have to find ways to avoid distractions and keep the focus as needed to perform their jobs, this leading to a higher level of productivity, making them take advantage of both technology and the now physical access to the traditional workplace setting. Gajendran and Harrison (2007) found that high-intensity remote working had a negative effect on co-worker relationships. Conversely, because on-site workers still have the challenge to maintain their productivity, and still deal with a) the lack of interaction with their full-time remote co-workers, and b) still have to suffer the consequences of continuous interruptions that characterized the traditional on-site work environment, especially in more contemporary workplace environments, where different organizations are promoting more and more open workplace offices, making employees more prone to interruptions. We believe these two factors combined may negatively

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impact productivity, and make these employees more dependent on technology to compensate for both, workplace interruptions and the impact of having the lack of interaction with their remote colleagues. Therefore, we hypothesized that:

Hypothesis 1. The direct impact of ICT utilization on productivity will be significantly higher for full-time remote and full-time on-site workers compared to the blended worker.

Research has shown that remote work is a driver of work satisfaction although most studies are anecdotal that can point to how impactful it is to consider the worker location preference and its implications to productivity, in our previous quantitative study we provided evidence that in fact this factor impacts the productivity of the workforce.

To measure this impact, we used the “Interest/Enjoyment” subscale (Deci & Ryan, 1985) to measure the degree to which remote workers are intrinsically motivated to work remotely or on-site (interested, enjoyment of virtual environment, experience positive feelings while working remotely). We also define enjoyment of work location as the degree in which the employee experiences enjoyment and positive feelings towards the work location. The findings of this research showed that there is a significant impact on productivity, both in remote workers and on-site workers, and we confirmed our hypothesis that this effect was statistically more significant for remote workers. The first qualitative study part of this research gave us anecdotal examples on the importance that remote work is taking as a factor of retention and motivation in employees, positively influencing engagement and productivity. In this qualitative study, remote work was clearly identified as a new factor of engagement, clearly perceived as becoming the norm in the workplace. We also found evidence that the lack of alternatives to work remotely and on-site was a major driver of lack of productivity and engagement. This indicates that

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the worker preference to remote work may be turning towards enjoying more the flexibility of either working remotely full-time or, at least, working in a blended alternative where the employee may work on-site but also remotely, and as a consequence creating a new source of intrinsic motivation that is positively impacting productivity and engagement. Conversely, this also indicates that the alternative to working purely on-site can be less attractive for the knowledge workforce, hindering productivity and engagement. Therefore, we theorize that:

Hypothesis 2a. The positive impact of work location enjoyment on productivity will be significantly higher in the full-time remote worker category compared to the blended and full-time on-site workers.

Hypothesis 2b. Work location preference will also be a lower predictor of productivity for on-site workers.

Remote working and the inevitable absence of the physical office environment bring unique challenges regarding leadership and monitoring employee effectiveness.

Leaders may not have the same opportunity to get to know the employee as remote access via telephone or e-mail does not always result in building high-quality interpersonal relationships as direct contact does. As part of the previous qualitative study, evidence that leadership support was equally important for remote and on-site workers to be productive and engaged was found. Workers indicated that supportive, trusting leaders that know how to provide the right direction and opportunities for development were essential for both categories of employees, remote and on-site workers. We also tested this in our quantitative studies finding support that leadership support impacts remote and on-site worker, and we found no statistical differences between the two groups as far as the strength of the significance of the impact on job engagement. The previous studies also confirmed that leadership support has a significant 194

impact on productivity for on-site workers. Surprisingly, we could not find support to confirm that leadership support is a significant predictor of productivity for remote workers. This may be due to the fact that leaders and subordinates that work remotely do not have the same opportunities to interact and build trust as do the regular on-site employees. While this may be the case for full-time remote workers, workers that have a blended work arrangement may actually be able to interact more frequently, having a greater opportunity to establish that emotional link with the supervisor, have productive discussions about goals, as well as the opportunity to potentially participate in projects were physical presence may be required—eliminating the challenge of being physically absent from the workplace. Additionally, because the blended workforce has the benefits of having access to both the traditional on-site and the virtual environment, this diminishes the emotional impact of isolation. Previous research has shown that those employees engaged in part-time telecommuting arrangements are also likely to experience increased autonomy because of the flexibility they are afforded over the location of their work (Shamir & Salomon, 1985). Experiencing autonomy, and the freedom to work remotely as well as on-site, as well as the opportunity to have access to leaders at the same time, can then lead to a significantly different impact of leadership support on job engagement for the blended workers. Therefore, we expand our hypothesis and posit that:

Hypothesis 3. Leadership support will be a significant predictor of productivity for the blended workers and those that work full-time on-site. Nevertheless, leadership support will have no significant impact on full-time remote workers, and this will be significantly lower compared to the full-time on-site workers.

Perceived self-efficacy is concerned with people’s beliefs in their capabilities to produce given attainments (Bandura, 1997). One cannot be all things, which would 195

require mastery of every realm of human life. Although efficacy beliefs are multifaceted, social cognitive theory identifies several conditions under which they may co-vary even across distinct domains of functioning (Bandura, 1997). When different spheres of activity are governed by similar sub-skills, there is some inter-domain relation in perceived efficacy. Proficient performance is partly guided by higher-order self- regulatory skills. These include generic skills for diagnosing task demands, constructing and evaluating alternative courses of action, setting proximal goals to guide one’s efforts, and creating self-incentives to sustain engagement in taxing activities and to manage stress and debilitating intrusive thoughts. People differ in the areas in which they cultivate their efficacy and in the levels to which they develop it even within their given pursuits. For example, workers may be really good in project management which may make them very productive, but when exposed to different types of work environment, the level of efficacy that they had previously demonstrated differ, creating a different kind of performance or productivity. This is because remote working can trigger important changes in day to day work, and workers’ ability to cope with this change could determine they can have when working on remote arrangements.

In the previous study, the findings showed that self-efficacy is a significant driver of engagement for remote and on-site workers, and while there was no statistical difference between groups, the estimates for the remote workers was a medium significance, meaning that self-efficacy in remote workers had a lesser impact on productivity. This may relate to the fact that the further away that the worker is from the office, the harder it is to maintain the focus. The closer to the physical work environment, the easier it is for employees to maintain the focus, the only presence of the daily

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interaction with the supervisor and their co-workers signals reminders to workers to keep up with the goals and work priorities, and they can quickly access their supervisors and co-workers to solve particular problems of their work, and as consequence improving their individual outcomes, which ends enhancing their satisfaction and work engagement, motivating them to exert greater effort, and therefore increasing the impact on job engagement (Gist & Mitchell, 1992; Phillips & Gully, 1997; Stevens & Gist, 1997). The question then is, what happens to the workers that are working in the blended category, those that work some days from home and some days from the office, and is it possible that by re-examining the impact of self-efficacy in three categories, the results will be different?. One could argue that for the blended workers, worker’s self-efficacy will be a much more significant predictor for engagement than it is for those that work full-time remotely, where self-efficacy, whilst still can have a significant impact on job engagement, will have a much less significance compared to the other categories, the full-time on-site and the full-time remote. This mainly is as it is being argued, that the workers that enjoy the benefits of blending the work location between on-site and remote, can more easily overcome the impact of being working remotely, and more importantly, because they have the opportunity to flex their time, their level of engagement and motivation to exert in their work may be higher. Therefore, we posit that:

Hypothesis 4. The worker self-efficacy will be a significantly higher predictor on job engagement of the full-time on-site and the blended workers compared to the full-time remote workers.

Measurement Development

To empirically test our hypothesis, the proposed model used the baseline of the survey and already tested model in the previous quantitative study. The extended

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construct of self-efficacy was measured as well as part of this study, as well as the new categorization was build based on data collected in the same survey. Next, we describe the operationalization of self-efficacy and the different group comparisons.

Construct Operationalization

The constructs in this study are the same ones that had been used in the previous quantitative study of this research. To avoid repetition, an explanation is only provided on the newly added constructs and their respective operationalization.

Virtual Intensity

In order to further extend our understanding of the impact of virtual intensity, a new conceptualization of worker category was created. The full-time remote—those employees that work remotely five days a week, the blended—the ones that blend their work location between on-site and remote, and lastly, the full-time on-site—those workers that work full-time from an office environment. Participants were asked how many days a week they work from home or on-site, given option to respond from one to five days a week. The model was run using z-scores from the derived factors. The moderator variable, the number of days remote, was dichotomized as full-time on-site workers—those that indicated working 5 days a week in an on-site location (n=81), the full-time remote—those that indicated working 5 days a week on a remote location (105), and lastly, the blended workers—those that indicated working from home or remotely between 1–4 days a week (n=118).

Self-Efficacy

A four-item, 5-point Likert scale was adapted from the GSE (General Self-

Efficacy Questionnaire). The construct of Perceived Self-Efficacy reflects an optimistic

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self-belief (Schwarzer, 1992). This is the belief that one can perform a novel or difficult tasks, or cope with adversity—in various domains of human functioning. Perceived self- efficacy facilitates goal-setting, effort investment, persistence in the face of barriers and recovery from setbacks. It can be regarded as a positive resistance resource factor. The scale reliability (Cronbach α) was .807 and higher of .8 as recommended to meet scale reliability.

Controls and Demographics

Experience has often been considered as a control in remote work research. Pynes

Data Collection and Sample

Data was collected over a three-month period from early November 2014 to late-

January 2015 using an online survey tool (Qualtrics), a popular survey research platform.

Participants for our research were directed to complete an identical survey. As shown in

Table 16, the total participants in the survey were 304, from which 152 categorized themselves as remote workers (50%) and 152 categorized on-site workers (50%).

Nevertheless, the categorization used for this study was dichotomized as full-time on-site, full-time remote and blended workers. Table 16 also shows this distribution.

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Table 16. Participants' Demographics

Methods

Comparative research, simply put, is the act of comparing two or more things with a view to discovering something about one or all of the things being compared. The goal of comparative social science is to produce explanations of social phenomena that are general but also show an appreciation of complexity. Comparative social scientists recognize that a good social scientific explanation is relevant to a variety of cases but at the same time, they recognize that social phenomena are complex and that a general explanation is a partial explanation at best. Thus, generality and complexity often compete with each other, even in a single study. An appreciation of complexity sacrifices generality; an emphasis on generality encourages a neglect of complexity. According to

Ragin (1989), it is difficult to have both.

There are different strategies that exist today to perform a comparative study: 1) the case-oriented approach, 2) a variable-oriented approach, and 3) a combined (case and variable-oriented) approach (Ragin, 1989). There are certain methods that are far more common than others in comparative studies; however, quantitative analysis is much more 200

frequently pursued than qualitative, and this is seen by the majority of comparative studies which use quantitative data. In variable-oriented strategies, by contrast, generality is given precedence over complexity. This is because investigators who use this approach are more interested in testing propositions derived from general theories than they are in unraveling the historical conditions that produce different historical outcomes. The variable-oriented strategy usually tests hypotheses derived from theory. In line with how a lot of theorizing has gone in the last century, comparative research does not tend to investigate "grand theories". It instead occupies itself with middle-range theories that do not purport to describe our social system in its entirety, but a subset of it. A good example of this is the common research program that looks for differences between two or more social systems, then looks at these differences in relation to some other variable coexisting in those societies to see if it is related.

To conduct this comparative analysis, direct effects analysis was conducted using path analysis in SPSS Amos version 23, as well as multi-group analysis will be applied.

Given the nature of multiple group comparison, this statistical strategy is best suited to our analysis. For the multi-group analysis, a chi-square test was conducted to understand the differences at the model level (Byrne, 2004). Because the χ2 diff test for the null and free model was found to be statistically significant, a pair-wise parameter comparison was used (Arbuckle & Wothke, 1999) to determine which pairs of parameters were significantly different at the individual factor level. For the pair-wise parameter comparison test, critical ratios for differences between two parameters in question were calculated by dividing the difference between the parameter estimates by an estimate of the standard error of the difference (Arbuckle & Wothke, 1999).

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Hypothesis Testing

The results obtained from the direct effects model fit of ICT utilization, worker location enjoyment, and leadership on productivity, as well as the comparative results between the groups overall indicate good fit. CFI=0.947, TLI=0.869, IFI=0.952 and

RMSEA=0.052, CMIN/DF was 1.800, all of which indicate a good model fit. The results obtained of the direct effects of self-efficacy on job engagement also indicate good model fit. CFI=0.947, TLI=0.869, IFI=0.952 and RMSEA=0.052, CMIN/DF was 1.800, all of which indicate a good model fit. We also tested our models for chi-square differences for both models. For the model of productivity, the unconstrained model had a chi-square of

11.507, df = 8., and the constrained model had a chi-square of 27.365, df= 12. The p- value =.015, indicating that the models were not invariant; therefore, we concluded that there were differences at the model level. For the engagement model, the unconstrained model had a chi-square of 32.406, df = 18., and the constrained model had a chi-square of

52.541, df= 28. The p-value =.028, indicating that the models were not invariant, and, therefore, we concluded that there were differences at the model level. Further path analysis was conducted and the results are explained below.

Table 17 shows the estimates from the model, encompassing the direct effects of

ICT utilization, worker location, and leadership, on productivity as well as the comparative results between the groups. We next summarize the results.

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Table 17. Direct Effects on Productivity (Comparative Results)

The impact of IT utilization on productivity is statistically significantly different

(z-stat: 2.495**) among the full-time on-site (.206 p= .002) and blended workers (.017, p

= .777)., indicating that IT utilization has a much higher impact in the full-time on-site workers and has no significant impact on the blended workers, since the estimates are not significant for this category. When comparing the blended workers versus the full-time 203

remote workers, there is also a statistically medium significant difference (z-stat: 1.733*) among the full-time workers (.124, p = .02) and blended (.017, p=.777). When comparing the results between full-time on-site (.206, p=.002) and the full-time remote (.124, p =

.02), we can see that there is no significant difference between the two categories. These results overall indicate that technology as a predictor of productivity, ICT utilization has a bigger impact on the productivity of the full-time workers and the full-time remote workers, and this indicates that IT utilization is less a good predictor of productivity for a blended workforce.

The impact of work location enjoyment on productivity is significantly different

(z-stat = 2.717***) in the blended workforce (.513, p=.000) compared to the full-time on- site workforce (.206, p= .002). Nevertheless, when comparing the impact of work location enjoyment on productivity among blended (.513, p = .000) and full-time remote

(.686, p =.000), there is no statistically significant difference among these two groups.

Nevertheless, when comparing the full-time on-site (.206, p = .012) versus the full-time remote (.686, p = .000) the data shows that there is a statistically significant difference among these two groups (z-stat: 3.705***). This means that, when considering work location enjoyment as a predictor of productivity, the best prediction will be on full-time remote workers, followed by the blended workers. Contrary to current anecdotal information in the field, these findings indicate that solely working on-site can be a detriment to productivity.

The impact of leadership on productivity is not statistically significant different

(z-stat: .904) between full-time on-site (.286, p =.000) and the blended workers (.200, p =

.002). When comparing to the impact of leadership on productivity for the full-time

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remote (.044, p =.567 ns) versus the blended workers (.200, p = .002), we can see that they are at the cusp of statistical differences among them. Lastly, when comparing the full-time on-site workers (.286, p = .000) versus the full-time remote workers (.044, p =

.567 ns), there is significant statistical difference in the effect of leadership to productivity. Altogether, these findings indicate that leadership will be a better predictor of productivity for the full-time on-site worker and the blended workers, but not a predictor of productivity for full-time remote workers. One could infer that in order to make employees that are working remotely, leadership support will have to improve to increase the impact to productivity. Lastly, we did not find any evidence of critical differences given gender or experience.

Table 18 shows the results for the comparison of self-efficacy on job engagement.

The impact of worker self-efficacy on job engagement despite being very significant in both cases, is not significantly different (z-stat: 037) among the full-time on-site (1.306, p

= .000) and blended workers (1.314, p = ,000). Despite both being significant predictors to job engagement, there are statistically significant differences among the full-time remote (.677, p = .000) and the full-time on-site (1.306, p = .000). This means that, considering self-efficacy as a predictor of productivity, the most significant impact will be on the full-time on-site and blended workers while for the full-time workers while significant, is less than half of the significance when compared to the other two groups.

While we did not find any significant differences given worker gender, we did find a medium negative significant effect (-.223**) given experience to job engagement between the blended (-.094, p = 0004) and full-time on-site workers.

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Table 18. Direct Effects on Engagement (Comparative Results)

Findings

Based on the results shown above, most of the predicted hypotheses were supported. First, as it relates to the impact of ICT utilization, evidence was found that

ICT utilization has a higher effect on the productivity of full-time on-site and remote workers compared to the blended workers. Evidence was also found to support that ICT utilization has a lesser impact on the blended workers, but unexpectedly, ICT utilization has no significant impact on the productivity of the blended workers. This hypothesis is categorized with medium support since the significance of the differences is a medium significance. Second, evidence was also found to support Hypothesis 2a. The results show that the impact of work location enjoyment is much higher in the remote workers and the blended workers. The results also confirm our prediction in Hypothesis 2b, work 206

location enjoyment is the lowest predictor of productivity for on-site workers. Third, support was found to confirm our Hypothesis 3, that leadership support is a significant predictor of productivity for the blended workforce, and that leadership support has a significantly lower effect on full-time remote workers. Lastly, support was found for

Hypothesis 4, that self-efficacy is a significant predictor of productivity of the full-time on-site and the blended workers, and this is statistically significantly different compared to the full-time remote. Table 19 below summarizes the results

Table 19. Hypothesis Testing Summary

Hypothesis Result

Hypothesis 1: The direct impact of ICT utilization on Medium support, differences productivity, will be significantly higher for full-time remote and have medium significance, and full-time on-site workers compared to the blended worker surprisingly the impact on the blended worker is not as significant Hypothesis 2a: The positive impact of work location enjoyment Supported on productivity, will be significantly higher in the full-time remote worker category compared to the blended and full-time on-site workers. Hypothesis 2b: Work location preference will also be a lower Supported predictor of productivity for on-site workers. Hypothesis 3: Leadership support will be a significant predictor Supported of productivity for the blended workers and those that work full- time on-site. Nevertheless, leadership support will have no significant impact on full-time remote workers, and this will be significantly lower compared to the full-time on-site workers. Hypothesis 4: The worker self-efficacy will be a significantly Supported higher predictor on productivity of the full-time on-site and the blended workers compared to the full-time remote workers.

Discussion

The purpose of this study was to extend our understanding of the critical differences among three different categories of workers. Therefore, three new categories took priority in this extension, the full-time remote—those employees that work remotely

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five days a week, the blended – the ones that blend their work location between on-site and remote, and lastly, the full-time on-site—those workers that work full-time from an office environment. Because the blended workers receive the benefits of working remotely and working on-site, the impact of the factors that influence productivity and engagement needed to be re-examined, to uncover hidden implications, either positive or negative, to productivity and engagement of the workforce. The overall results provide new meaningful insights to this research.

First, in the previous quantitative study, evidence was found that ICT utilization had an impact to productivity of on-site workers and remote workers, and no statistical significant differences were identified among the two groups. In this extension, using the new three different categories of workers (full-time on-site, full-time remote and blended workers), we were able to uncover the hidden effects on the different categories, and confirmed, contrary to our previous results, that ICT utilization impact on productivity has different significant differences when using these different categorization of workers.

Evidence was found that ICT utilization has a higher effect on the productivity of full- time on-site and remote workers compared to the blended workers. Evidence was also found to support that ICT utilization has a lesser impact in the blended workers.

Nevertheless, while we expected the impact to be lower in the blended workers, ICT utilization show no statistical significant impact on productivity of the blended workers.

The obvious differences extracted given the new categorization of groups of workers, provides new knowledge in the field in the sense that, while ICT utilization is an enabler of productivity for full-time remote and on-site workers, it has a much lower effect in the blended workers. This is to say, that the effect of the blended worker

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experience (the advantage of the remote and the physical resources of the workplace), is dampening the effect that technology may be having in this category of worker (blended worker). A potential explanation may be that, in this case, the value of ICT utilization is being replaced or interchanged by the presence of the physical access that the workers have to the workplace, their ability to establish relationships with peers, supervisors, as well as the opportunity to take advantage of the benefits of working remotely, like avoiding interruptions, and having the flexibility to work from home.

Second, evidence was found that confirms that the impact of work location enjoyment is much higher in the remote workers and the blended workers. The results also confirm our prediction in Hypothesis 2b, work location enjoyment is the lowest predictor of productivity for on-site workers. This finding provides new knowledge in the field and further provides evidence that, as argued in our previous studies, work location preference has an impact on productivity, but more importantly, it expands our knowledge and uncovers that, work location enjoyment is the lowest predictor of productivity for on-site workers. This means that, when considering work location enjoyment as a predictor of productivity, the best prediction will be on full-time remote workers, followed by the blended workers. Contrary to current anecdotal information in the field, these findings indicate that solely working on-site can be a detriment to productivity. This also supports the findings of the qualitative study part of this research, which gave us anecdotal examples on the importance that remote work is taking as a factor of retention and motivation in employees, positively influencing productivity.

Remote work was clearly identified as a new factor of engagement, perceived as becoming the norm in the workplace, and therefore expected by employees. In the

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qualitative study of this research, workers indicated that the lack of alternatives to work remotely and on-site was a major driver of lack of productivity and engagement. The findings in this extension provide evidence, with explanatory data and confirms the initial indications that the worker preference to remote work may be turning towards enjoying more the flexibility of either working remotely full-time or, at least, working in a blended alternative where the employee may work on-site but also remotely, and as a consequence creating a new source of intrinsic motivation that is positively impacting productivity. Conversely, this also indicates that the alternative to working purely on-site can be less attractive for the knowledge workforce, and as confirmed with this results, may be hindering productivity in the workforce.

Third, this study provides with new extended knowledge on regards the impact of leadership support on productivity. While previous results had only provided evidence that this factor had an impact on the productivity of the on-site workers, it provided no evidence that it had an impact on remote workers. Given the new categories, evidence was found additional support and confirmed that besides being a driver of productivity for on-site workers, it also has a significant impact on the productivity of the blended workforce. This is a confirmation of how the workers that have a blended work arrangement are able to interact more frequently in person more with their supervisors and co-workers given the blended status, therefore, having a greater opportunity to establish that emotional links with peers and supervisors, have productive discussions about goals, as well as the opportunity to potentially participate in projects were physical presence may be required, eliminating the challenge of being physically absent from the workplace. Additionally, because the blended workforce has the benefits to having access

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to both the traditional on-site and the virtual environment, this diminishes the emotional impact of isolation. We did confirm the results of the previous quantitative study, as far as the limited impact that leadership support has in the remote worker. While this is not a positive result, it is expected given that research has shown similar results in the past.

This is an indication that organizations continue to have a major opportunity to improve the productivity of the workers by improving the relationship between managers and full- time remote workers.

Lastly, support was found for Hypothesis 4, that self-efficacy is a significant predictor on job engagement in the full-time on-site and the blended workers, and this is statistically significantly different compared to the full-time remote. While this confirms results obtained in the previous qualitative studies, this study extends our knowledge on the comparative impact that self-efficacy has in the different group. By re-examining this impact with three different categories, evidence was uncovered about the key differences on the impact that self-efficacy has among the three different categories. As shown, self- efficacy, while significant at all levels, it is a better predictor on engagement for the full- time on-site and blended workers but has a lower significance in the remote worker. This confirms that the further away that the worker is from the office, the harder it is to maintain the focus as an example. The closer to the physical work environment, the easier it is for employees to maintain the focus, as in some cases, the presence of the daily interaction with the supervisor and their co-workers signals reminders to workers to keep up with the goals and work priorities, and as consequence improving their individual outcomes, which ends enhancing their satisfaction and work engagement, motivating them to exert greater effort, and therefore increasing the impact on job engagement.

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Altogether, this comparative analysis provided with some critical knowledge that is important to consider when implementing remote work practices, as shown in the picture below.

Figure 20. Comparative Analysis (On-site, Blended, and Remote Worker)

Comparative+Analysis+– Critical+Factors+Impacting+Productivity+and/or+Engagement Onsite&Worker Blended&Worker Remote&Worker

ICT$utilization High$predictor$of$productivity$ Lower$predictor$of$productivity$ High predictor$of$productivity$ compared$to$other$categories compared$to$other groups compared$to$other$groups,$but$ similar$to$the$onsite Work While significant,$is$the$lower$ Higher$predictor of$productivity$ High predictor$of$productivity$ Location$ predictor$of$productivity$ compared$to$the$other$two$ compared$to$the$other$two$ Enjoyment compared$to$the$other$categories categories categories Leadership$ High predictor$of$productivity$ High predictor$of$productivity$ Low or$no$predictor$ of$productivity$ Support compared$to$other$categories compared$to$other$categories compared$to$other$categories SelfCefficacy High predictor$of$job$engagement$ High$predictor$of$job$engagement While significant,$is$the$lower$ compared$to$other$categories compared$to$other$categories predictor$of$productivity$ compared$ to$the$other$categories

Limitations and Future Research

Because this study is an extension of the previous quantitative studies, some of the limitations prevail. First, the ICT utilization construct is a new adaptation, and while the results clearly confirm the impact on productivity, we believe that a better construct can improve the relationship of ICT utilization to productivity. Further studies should be considered to improve this construct. Second, we surveyed only knowledge workers using perceptual measures rather than conducting an experiment, observation, or measuring productivity on a specific task. Thus, our productivity measures are subject to

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the usual perceptual biases. Additionally, having a mixed of participants/users surveyed could bring some key differences in results.

Conclusion and Implications for Practice & Academia

This extension has provided a new dimension of knowledge in the field and provided key insights about how the different groups of full-time on-site, blended and full-time remote workers compare. By re-examining the implications to productivity and engagement of the factors that we had identified before, we now know three new things that are critical for academia but also for practitioners. First, contrary to the believe that

ICT had a significant impact across the workforce and even when our initial conclusion based on the previous quantitative studies were that there were no differences between these two groups, we now have expanded our knowledge that ICT utilization has no as significant impact as it does for the full-time remote and the full-time on-site, this provides real evidence that for this category of blended workers, the impact of ICT utilization is being dampened by the combined benefits of being part of the two worlds, the virtual and the traditional on-site workplace environment. Second, while we had confirmed the significant impact of worker location preference on productivity, with this extension, evidence was provided that contrary to anecdotal experience, having employees working full-time on-site may not be the formula, and reconsideration to continue to explore the benefits of a blended workforce should be examined by organization, in order to improve productivity and overall outcomes of remote workforce practices. Third, even though we found no evidence that leadership has an impact on the productivity of the remote worker, we found that it has an impact on the blended workforce. This leads to conclude as well that if the organization could improve the

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management of their workforce with better leaders that are supportive of the remote workers, the impact on productivity could be greater. Lastly, in terms of extending the knowledge of the impact of self-efficacy, with this extended study, evidence is provided that self-efficacy is significant for all the different group. Nevertheless, there are key significant differences between the groups. If considering self-efficacy as a predictor of engagement, the biggest impact will be on the full-time and blended workers, and it will be less significant for the remote workers.

In summary, by expanding in this comparative analysis at the construct level and by breaking it down into different groups, a new perspective of management of the remote workforce has emerged, and that is, the entrance of a blended workforce. While not completely showing the same traditional results as the on-site workers, the blended workforce appears to be not only a potential next alternative, but in some cases, a probing better alternative to increasing productivity and engagement.

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CHAPTER VI: INTEGRATED FINDINGS AND DISCUSSION

Summary of Integrated Findings

The results of the three studies of the research were triangulated to produce four major integrated findings, which are discussed next. One of the main goals of this research was to confirm, given the most current workplace dynamics, the factors that are more relevant to drive productivity and engagement in the knowledge workforce, and the differences between remote and on-site workers. As the research evolved, further analysis was extended to compare the differences amongst the: full-time on-site, full-time remote and blended workers. Another goal was to uncover new factors that had not been previously identified in similar research, as new potential drivers of productivity and engagement. The results of the research indicate that, given the current workplace dynamics, the factors that drive productivity and engagement of the workers are 1) Work location preference, remote work and work location flexibility, 2) ICT utilization, 3)

Leadership support, and 4) Self-efficacy. However, the impact on the workforce may be significantly different among the different kind of workers that are more predominant in the workplace today: full-time remote, full-time on-site, and blended workers.

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Table 20. Integrated Findings

Number Integrated Finding Research Questions addressed Finding 1 2.! Work Location Preference, Remote Work & Work Location Flexibility

•! Workers value remote work and the flexibility of work location. •! Q1, Q2, Q3

•! Work location enjoyment has an effect on productivity for all workers. However, is a higher it has a greater positive impact in full-time remote and blended workers, than it •! Q2, Q3 does in the on-site worker. This may be an indication that working full-time on-site may not be the best alternative to enhance productivity of the knowledge worker. •! Q2, Q3 •! Work location Stress has an effect on engagement but mainly in high virtual intensity (remote workers)

Finding 2 Technology Impact •! Q1, Q2, Q3 ICT utilization has a significant impact on productivity. Nevertheless, ICT utilization is a better predictor of productivity in full-time on-site and full-time remote, than in the blended workers

Finding 3 Leadership Impact •! Q1, Q2, Q3 Leadership Support impacts the productivity of full-time on-site and blended workers, but has no impact on full-time remote workers

Finding 4 Worker Self-Efficacy •! Q1, Q3 Worker self-efficacy impacts engagement in all the categories of workers, however, the impact is significantly higher in full-time on-site and blended workers, and is a lower predictor of productivity in remote workers

Work Location Preference, Remote Work & Work Location Flexibility

In our first study, we identified that new, different remote working arrangements are invading the workplace. Early in the quantitative study, it was identified that this new phenomenon is mainly driven by the entrance of more diverse technology in the workplace. The literature review also shows that additional drivers such as the global nature of the organizations, the continued demand for cost reduction and the need to relocate and distribute workforce capability are other factors that are connected to this phenomenon. These new work arrangements of remote work are becoming the norm, and this is manifested in different ways in the workplace. For example, in some cases, employees work full-time remotely (in some cases, at the company request and in some cases, at their own request). In some other cases, employees provided examples where,

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even when they are not working remotely, they are given the option to choose if they want to work remotely, and at the discretion of their work and with supervisor support, they can choose the best location to work on a given day. We also found that some workers have arrangements to work two days at home, and then three days from the office, and some others are actually quite happy working from the office, and love to have the flexibility to leave a few hours later and replace those hours in the evening at home. We deducted in our study one that workplace preference is, in fact, a significant driver of engagement in the workforce. As a sequence to this finding, using ground theory and inform by current research in the field, we conducted a quantitative study to further investigate the impact of this phenomenon on the productivity and engagement of the workforce.

Throughout our second and third studies, we confirm that in fact, worker location preference matter and that it has a significant impact on the worker productivity levels.

First, we compared the impact between remote and on-site workers, and the impact was significant in both categories, but we could not find differences among these two categories. An additional analysis was conducted to further understand the implications of the high and low virtual intensity, and how that impacts productivity and engagement of the workforce. We confirmed that virtual intensity moderates the relationship between enjoyment of work location and productivity, especially in high virtual intensity workers

(those that work 4–5 days a week remotely). At low levels of work location enjoyment, high virtual intensity workers are less engaged than employees that with low virtual intensity (those that work remotely 2 or a maximum of 3 days a week remotely.

Conversely, at high levels of work location enjoyment, high virtual intensity workers

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have a higher level of productivity compared to low virtual intensity workers). This is one of the most critical findings of this study. While not the full answer to management concerns about the implications of virtual intensity to productivity and engagement, this finding provided initial evidence that employees’ work location preference is a critical factor that needs to be considered when making individual decisions of remote workforce arrangements.

An extended comparative analysis was conducted that provided further insights on the impact of work location preference (work location enjoyment) among three different worker categories: the full-time on-site, full-time remote and blended worker.

Evidence was found that show that work location enjoyment is a higher predictor of productivity for the blended and the remote worker compared to the on-site workers. This is also an interesting finding as this may indicate that working on-site full-time, may not be the best alternative to maximize productivity in the knowledge worker. Given the current confusion that exists in the field as it refers to what the best alternative may be to buster productivity in the workforce, these findings may indicate that the blended remote work may be the best alternative for organizations to buster productivity in the knowledge workers, and contrary to anecdotal data, on-site workers are not necessary more productive than the on-site workers.

In an environment where some organizations, fearing the negative impact of remote work on their organizational productivity, have opted to retract themselves from having this programs in place, this finding provides a strong argument that managers should reconsider this decision. Bringing workers back to the office may not be the solution to increase productivity. Our findings indicate that the decision should consider

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the individual worker’s location preference, especially in the categories of full-time remote and blended workers. If the employee prefers and enjoys working remotely, and can be productive, then this is how the manager will maximize productivity in this employee. The right combination for this employee is to be working remotely because he/she enjoys working remotely. Conversely, if the employee enjoys working remotely but is forced to work at an on-site location, this could be detrimental to their own productivity because the employee will experience feelings of stress from working in a location that is not of his/her preference. While other factors should be considered when making decisions to provide alternatives to work arrangements, managers should augment this consideration to their decision-making frameworks so to potentially increase the effectiveness of their decisions.

In the second study, it was confirmed that virtual intensity moderates the relationship between work location stress and engagement, especially on high virtual intensity workers. At low levels of stress, high virtual intensity workers (those working remotely 4–5 days a week) are more engaged than low virtual intensity workers (those working remotely a maximum of 3 days a week). Employees when working remotely can be more engaged. As the stress increases, high virtual intensity employees become less engaged. This effect is not the same for employees working in an office setting (1–3 days a week), where engagement has a small uptake when the stress increases. This is also another critical finding of this study. While not the full answer to management concerns about the implications of virtual intensity to engagement, this finding provides evidence that stress experienced by employees (wrong location based on their individual preference) is a critical factor that needs to be considered when making individual

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decisions of remote workforce arrangements. Managers should carefully evaluate the potential psychological effect that the different levels of virtual intensity have in employees, especially those working remotely. Especially given the most recent attention to the impact of remote work to productivity and engagement, where most organizations, fearing of potentially see their engagement reduced, have opted to retract themselves from having this programs in place. We argue that managers should reconsider this decision. Bringing workers back to the office may not be the solution to increase engagement. Our findings indicate that the decision should consider the individual worker’s location preference, especially in the category of high virtual intensity workers

(remote workers). If the employee experiences stress in the work location, the employee will be less engaged; this is especially important for remote workers. While stress location is not impactful for on-site workers, it is clear that it is impacting high virtual intensity workers (remote). The right combination for this employee is to be working remotely because he/she enjoys working remotely. Conversely, if the high worker is not experiencing the stress of the work location, the worker can be more engaged, this is especially true for high virtual intensity workers (remote). While other factors should be considered when making decisions to provide alternatives to work arrangements, managers should augment this consideration to their decision-making frameworks so to potentially increase the effectiveness of their remote workforce management decisions.

ICT utilization

In our first study, we identified that utilization of the ICT Technology is enabling the worker to be more productive, and in some cases, employees expressed how much they enjoy having tools that make their day-to-day work easier and faster—as an

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example, in disseminating and communicating information to their peers. We identified

ICT Working Tools as another source of positive energy impacting the productivity and engagement of the worker. As a sequence to this finding, and informed by current research in the field, we conducted a quantitative analysis in our second and third studies to further investigate the impact of this phenomenon on the productivity and engagement of the workforce. We found confirmation in alignment with previous research that ICT utilization, the use of tools is a driver of productivity and engagement for remote and on- site workers. In addition, evidence was found that ICT utilization has an impact on the productivity of on-site workers and remote workers, and no statistically significant differences were identified between the two groups. Using the new three different categories of workers (full-time on-site, full-time remote and blended workers), we were able to uncover the hidden effects on the different categories, and confirmed, contrary to our previous results, that ICT utilization impact on productivity has different significant differences when using this different categorization of workers. Evidence was found that

ICT utilization has a higher effect on the productivity of full-time on-site and remote workers compared to the blended workers. Evidence was also found to support that ICT utilization has a lesser impact on the blended workers. Nevertheless, while we expected the impact to be lower in the blended workers, ICT utilization show no statistically significant impact on the productivity of blended workers.

The obvious differences extracted given the new categorization of groups of workers, provides new knowledge in the field in the sense that, while ICT utilization is an enabler of productivity for full-time remote and on-site workers, it has a much lower effect on the blended workers. This is to say, that the effect of the blended worker

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Leadership Support

In our first qualitative study, employees provided with strong indication that leadership support as the one of the critical important driver of productivity and engagement in the workforce, the majority of the interviewees expressed that having the right leaders that are supportive, provide the right direction, and gave them opportunities to participate in important and innovative work was extremely important to their productivity and engagement. As a sequence to this finding, and informed by current research in the field, in the second study we conducted a quantitative analysis to further investigate the impact of leadership support on the productivity and engagement of the workforce. We found that leadership support had a significant effect on the engagement of both, remote and on-site workers. We also confirmed a strong relationship between leadership support on productivity of the on-site workers, but surprisingly, we did not find a significant effect from leadership support on productivity of remote workers. An

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extension to further scrutinize the impact of leadership on productivity, an additional comparative analysis was conducted with three different worker categories: full-time on- site, full-time remote and blended worker. Given the new categories, evidence was found additional support and confirmed that besides being a driver of productivity for on-site workers, it also has a significant impact on the productivity of the blended workforce.

This is a confirmation of how the workers that have a blended work arrangement are able to interact more frequently in person more with their supervisors and co-workers given the blended status, therefore, having a greater opportunity to establish that emotional links with peers and supervisors, have productive discussions about goals, as well as the opportunity to potentially participate in projects were physical presence may be required, eliminating the challenge of being physically absent from the workplace. Additionally, because the blended workforce has the benefits of having access to both the traditional on-site and the virtual environment, this diminishes the emotional impact of isolation.

We did confirm the results of the previous quantitative study, as far as the limited impact that leadership support has in the remote worker. While this is not a positive result, it is expected given that research has shown similar results in the past. This is an indication that organizations continue to have a major opportunity to improve productivity of the workers by improving the relationship between managers and full-time remote workers, and this also strengthens the fact that leaders need to learn to adapt to the future workplace and need to develop new skills, behaviors that can enable to maintain and increase the positive relationship with employees working remotely, at the same quality level as the relationship with on-site workers

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Worker Self-Efficacy

Self-Efficacy has been widely studied for the last 20 years. It has become one of the most widely studied variables in the educational, psychological, and organizational sciences. We also argued that the theory appears to be particularly well suited to studying virtual organizations, given the impact that the change of the physical work conditions can create in the worker, either with positive or negative effects.

In our third study, we expanded our model and integrated to the previous finding to evaluate whether worker self-efficacy had an impact on engagement. We found that self-efficacy is highly related to job engagement. Furthermore, support was found showing that self-efficacy is a significant predictor on job engagement in the full-time on-site and the blended workers, and this impact is statistically significantly different compared to the full-time remote. By examining this impact with three different categories, evidence was uncovered about the key differences surrounding the impact that self-efficacy has among the three different categories. As shown, self-efficacy, while significant at all levels, it is a better predictor on engagement for the full-time on-site and blended workers, but is has a lower significance in the remote worker. This confirms that the further away that the worker is from the office, the harder it is to maintain the focus as an example. The closer to the physical work environment, the easier it is for employees to maintain the focus, as in some cases, the presence of the daily interaction with the supervisor and their co-workers signals reminders to workers to keep up with the goals and work priorities, and as consequence improving their individual outcomes, which ends enhancing their satisfaction and work engagement, motivating them to exert greater effort, and therefore increasing the impact on job engagement.

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While this finding does not provide new knowledge in the field, it provides the evidence and confirmation that self-efficacy is an important driver of engagement for employees, and highlights the key differences amongst the difference amongst the different types of workers. Therefore, organizations should continue to be concerned with assessing the social behaviors, skills and competencies of the workers, with the intent to understand how to better help employees to be successful in the workplace, and more importantly, maximize their professional and personal capabilities.

Contributions to Theory

The study contributes to remote workforce management theory in multiple ways.

Firsts, we have created two new constructs, adapted from Intrinsic Motivation theory

(enjoyment or stress constructs) to measure the level of enjoyment or stress experienced by the employee based on the work location, and use it to identified positive or negative effects on productivity and engagement. By doing this, we discovered that the workers’ work location preference is an important factor to productivity and engagement, confirming that enjoyment of location has a positive effect on productivity, and is more critical for remote workers. Conversely, Stress/Tension of Work Location has no significant effect on productivity or engagement.

Second, we augmented already tested constructs of Virtual Intensity (high or low levels of virtual intensity on the remote work), using this to hypothesized interaction effects to productivity and engagement. We confirm that Virtual Intensity Moderates the effects of Enjoyment with Work Location and productivity: at higher levels of enjoyment of remote working and high level of intensity, the worker becomes more productive. We also confirm that Virtual Intensity Moderates the effects of stress/tension with Work

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Location and engagement: at a higher level of stress, the employee becomes less engaged.

Third, by expanding our study with a comparative analysis of the full-time on- site, full-time remote and blended workers, we identified key differences that had not been identified before. We uncovered that ICT utilization has a higher impact on full- time on-site and full-time remote, but not in the blended workers, where we saw some indications that other factor may be damping the effect. We also uncovered that working full-time on-site, may not be the predictor of productivity and engagement. As expected, we found that leadership support has a positive impact on the full-time on-site, but clearly differentiated that the effects are similar for the blended workers.

While this research advances the knowledge of drivers of productivity and engagement, it is of the utmost importance that the field expands the research to other horizons that can generate more integrated models of drivers of productivity and engagement in the new blended workforce. Given the speed of change, the acceleration of virtual technologies, and the implications for the workplace, it is critical that more research is provided to solve for the new workplace of the future.

Socio-Technical Framework – Workforce Productivity and Engagement

The ultimate goal of this research was to build an integrated model of how human resources practitioners and management can improve the management of the new blended workforce. To achieve this, using a mixed methods research approach, we integrated our research previously described in three different studies. We anchored the framework of our research on Trist and Bamforth (1951), Katz and Kahn (1966) socio- technical systems theory. This theory incorporates four elements that are critical to

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transforming work systems into outputs: technology, social and people-related factors.

The theory posits that these subsystems continually and jointly interact with each other to produce work systems outcomes (Trist & Bamforth, 1951). For the purposes of our research, we focused on three key dimensions to anchor the workplace dynamics: 1) personnel subsystem: worker location preference (enjoyment or stress), worker self- efficacy, 2) technical systems: ICT remote work technology (WebEx, email, intranet, chat), and 3) organization/work (leadership Support, high and low virtual intensity, remote and on-site worker categorization, full-time on-site-full-time remote-blended categories). By using this framework that incorporates the different factors that can enable the productivity and engagement of the workforce, leaders can avoid problematic pitfalls, implement effective programs to manage not the remote or the on-site, or the blended workforce, but the workforce overall, and benefit completely of the joint optimization of their people resources, ICT investments, organizational policy design, and management. Figure 21 shows the integrated framework that can be used by management when implementing this innovative workforce management practices.

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Figure 21. Socio-Technical Framework – Workforce Productivity & Engagement Socio Technical System Framework Worker Productivity and Engagement (Full Time Onsite, Full Time Remote and Blended)

Personnel( Technical( Organization(&( Internal Subsystem Subsystem Work(Subsystem Outcomes

Job(Work(Location (Onsite,(Blended,(Remote) Effective( Right(IT( Leaders Technology Higher( Work(Tools Productive( Employee(Work(Location( Right( and( Work( Preference( Engaged( (Onsite)/(Remote) Video Flexibility( Intranet Policy Blended( Chat (Onsite,( Workforce Blended( Worker( and(( Characteristics(Assessment Remote) (i.e..(Self(Efficacy)

Implications for Practice

This research has opened a new dimension to our understanding of the drivers of productivity and engagement in knowledge workers, and more importantly, has provided insights into the key differences between two categories of workers that exist in this new blended workplace. From the practitioner’s perspective, by assessing the impact of how the worker experiences (enjoyment or stress) working on-site or remotely, leaders and human resources professionals could reduce the potential negative impact to worker’s productivity and engagement. When employees experience stress based on the location

effectively work remotely, and aligning their remote worker practices as a requirement for workers to be able to perform work remotely, could lead to higher productivity.

Lastly, evaluating jointly the impact of work location preference, the use of technology, the impact of leadership, and levels of virtual intensity, could lead to better remote workforce management policy design, and, therefore, a more productive blended workforce.

Limitations

There are some limitations to our study that should be taken into consideration.

First, while our attempt to organize the framework of our model included the application of socio-technical theory, and the variation explained by our model reaches close to 60%, we do recognize that there are other aspects of the personnel, organization/culture, and work design as well as technical sub-systems that should be considered to provide further answers to drivers of productivity and engagement in the remote and on-site knowledge workers. Therefore, we suggest that further research should be pursued, considering the addition of others factors and that conceptual studies combining existent research should be considered to provide to audiences a further integrated framework of the blended workforce productivity.

Second, our ICT utilization construct is new and therefore, further developments to improve the model should be re-evaluated. While the results clearly confirm the impact to productivity, we believe that a better construct can improve the relationship of ICT utilization to productivity. Further studies should be considered to improve this construct.

Third, we surveyed only knowledge workers using perceptual measures rather than conducting an experiment, observation, or measuring productivity on a specific task.

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Thus, our productivity measures are subject to usual perceptual biases and the specific period of time when we collected the data. Additionally, having a mix of participants/users surveyed, could bring some key differences in results.

Beyond the limitations and recommendation for future research already mentioned throughout this section, we recommend future research explore other potential aspects be evaluated using the socio-technical framework so to increase the explained variance, and perhaps uncover new drivers of productivity and engagement still unknown.

Future Research

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Appendix A: Interview Protocol for Qualitative Study

Part 1 – Initial / Background Questions: 1.! Please tell me about yourself, both personally and professionally? 2.! Please describe your current roles and responsibilities

Part 2 – Core Questions: 1. Think back in the last two or three years of your career. Can you tell me about an event, or events, that you experienced at work, where you felt really excited about work? Additional probing inquiries: (a) Describe how you were feeling during that circumstance. (b) How long did the good time last? Why did it end? (c) Tell me about your co-workers at that time? (d) Tell me about the leadership and culture at that time? (e) Tell me about your performance? What kind of contributions were you making? Did you feel productive or engage? 2. Think back in the last two or three years of your career. Can you tell me about an event, or events, that you experienced at work, where you felt really unenthusiastic about work? Additional probing inquiries: (a) Describe how you were feeling during that circumstance. (b) How long did the good time last? Why did it end? (c) Tell me about your co-workers at that time? e) Tell me about your performance? What kind of contributions were you making? Did you feel unproductive or disengage? 4. Tell me about a time that a change in your company's technologies had a positive effect or a negative effect on your work/life? a) Describe how that change has impacted you, if in any way, your work efforts b) Tell me about other changes in your company or colleagues at work, have there been any changes in who you know and how has impacted you, if in any way, personally and at work.

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Appendix B: Survey Instrument for Quantitative Study One

Which of the following best describes your normal work location?

!! An office or on-site setting (1) !! Remote (2) !! Other (3) If!Remote!Is!Not!Selected,!Then!Skip!To!End!of!Block!

JOB ENGAGEMENT: Please answer the following questions based on your current job experience.

Almost!Never! Rarely!(once!a! Sometimes!(A! Never! Often!(Once!a! ! (A!few!times!a! month!or!less)! few!times!a! (1)! week)!(5)! year)!(2)! (3)! month)!(4)! At my work, I feel bursting with energy !! !! !! !! !! (1)

At my job, I feel strong and vigorous (4) !! !! !! !! !!

I am enthusiastic about my job (5) !! !! !! !! !!

When I get up in the morning, I feel like !! !! !! !! !! going to work (7)

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PRODUCTIVITY: Please answer the following questions based on your experience with your current organization

Neither!Agree! Strongly! Strongly!Agree! ! Disagree!(2)! nor!Disagree! Agree!(4)! Disagree!(1)! (5)! (3)! Working Remotely has resulted in !! !! !! !! !! overall productivity improvement (4)

Working Remotely has resulted in !! !! !! !! !! improved outcomes or outputs (5)

Working Remotely has resulted in an

increased capacity to manage a growing

volume of activity (e.g., transactions, !! !! !! !! !! serve more customers, complete more

projects, etc.) (6)

Working Remotely has resulted in better !! !! !! !! !! positioning for Business (8)

LEADERSHIP EXCHANGE: Describe the relationship with your leader

! 1!(1)! 2!(2)! 3!(3)! 4!(4)! 5!(5)! Regardless of how much formal authority he/she has built into his/her position,

what are the chances that your leader would use his/her power to help you !! !! !! !! !! solve problems in your work?1=None 2=Small 3=Moderate 4=High 5=Very

High (4)

Again regardless of the amount of formal authority your leader has, what are

the chances that he/she would “bail you out” at his/her expense?1=None !! !! !! !! !! 2=Small 3=Moderate 4=High 5=Very High (5)

I have enough confidence in my leader that I would defend and justify his/her

decision if he/she were not present to do so?1=Strongly Disagree 2=Disagree !! !! !! !! !! 3=Neutral 4=Agree 5=Strongly Agree (6)

How would you characterize your working relationship with your leader?

1=Extremely Ineffective 2=Worse than Average 3=Average 4= Better than !! !! !! !! !! Average 5=Extremely Effective (7)

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WORK LOCATION - ENJOYMENT

Q9 Please describe your experience when working remotely

Not!at!all!true! Somewhat! ! 2!(2)! 4!(4)! Very!true!(5)! (1)! true!(3)! When I work remotely, I think about how much I !! !! !! !! !! enjoy it (1)

I feel it is my choice to work remotely (3) !! !! !! !! !!

I think I am pretty good in working remotely (4) !! !! !! !! !!

I feel relax while working remotely (9) !! !! !! !! !!

I enjoyed working remotely very much (10) !! !! !! !! !!

I feel like I am doing what I want to do when !! !! !! !! !! working remotely (14)

I feel pretty skill at working remotely (15) !! !! !! !! !!

I think working remotely is interesting (16) !! !! !! !! !!

I would describe working remotely as very !! !! !! !! !! enjoyable (19)

After working remotely for a while, I feel pretty !! !! !! !! !! competent (21)

WORK LOCATION - STRESS

Q9 Please describe your experience when working remotely

Not!at!all!true! Somewhat! ! 2!(2)! 4!(4)! Very!true!(5)! (1)! true!(3)! I feel tense when working remotely (6) !! !! !! !! !!

I am anxious when I work remotely (12) !! !! !! !! !!

I think working remotely is boring (13) !! !! !! !! !!

I feel pressure while working remotely (17) !! !! !! !! !!

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VIRTUAL WORK INTENSITY – MODERATOR: How many days a week do you work from a remote location

!! One day a week (1) !! Two days a week (2) !! Three days a week (3) !! Four days a week (4) !! All week (5)

YEARS OF EXPERIENCE (CONTROL): How many years of professional work experience do you have?

!! 3 years or less (1) !! 5 years or less (2) !! 10 years or less (3) !! 15 years or less (4) !! 20 years or less (6) !! More than 20 years (7)

ICT UTILIZATION (FREQUENCY) – MODERATOR: How often do you use the following tools to perform your work?

Most!of!the! Sometimes! ! Always!(1)! Rarely!(4)! Never!(5)! Time!(2)! (3)! Video Conference (Skype, WebEx, other) (2) !! !! !! !! !!

Internal Collaboration tools (Google docs, !! !! !! !! !! Dropbox, Doc Sharing) (5)

Internal Company Intranet (6) !! !! !! !! !!

Internal employee chat (Sametime, IM, etc.) (7) !! !! !! !! !!

GENDER (CONTROL): Q19 Please select your gender

!! Male (1) !! Female (2)

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Appendix C: Survey Instrument – Quantitative Study Two

Which of the following best describes your normal work location?

!! An office or on-site setting (1) !! Remote (2) !! Other (3) If!Remote!Is!Not!Selected,!Then!Skip!To!End!of!Block!

JOB ENGAGEMENT: Please answer the following questions based on your current job experience.

I find the work that I do full of meaning !! !! !! !! !! and purpose (2)

At my job, I feel strong and vigorous (4) !! !! !! !! !!

I am enthusiastic about my job (5) !! !! !! !! !!

My job inspires me (6) !! !! !! !! !!

When I get up in the morning, I feel like !! !! !! !! !! going to work (7)

PRODUCTIVITY: Please answer the following questions based on your experience with your current organization

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Neither!Agree! Strongly! Strongly!Agree! ! Disagree!(2)! nor!Disagree! Agree!(4)! Disagree!(1)! (5)! (3)! Working Remotely has resulted in !! !! !! !! !! overall productivity improvement (4)

Working Remotely has resulted in !! !! !! !! !! improved outcomes or outputs (5)

Working Remotely has resulted in an increased capacity to manage a growing volume of activity (e.g., transactions, !! !! !! !! !! serve more customers, complete more projects, etc.) (6)

Working Remotely has resulted in !! !! !! !! !! improved business processes (7)

Working Remotely has resulted in better !! !! !! !! !! positioning for Business (8)

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LEADERSHIP EXCHANGE: Describe the relationship with your leader

Strongly! Neither!Agree! Disagree! Agree! Strongly! ! Disagree! nor!Disagree! (2)! (4)! Agree!(5)! (1)! (3)! Regardless of how much formal authority he/she has built into his/her position, what are the chances that your leader would use his/her power to help you solve !! !! !! !! !! problems in your work?1=None 2=Small 3=Moderate 4=High 5=Very High (4) Again regardless of the amount of formal authority your leader has, what are the chances that he/she would “bail !! !! !! !! !! you out” at his/her expense?1=None 2=Small 3=Moderate 4=High 5=Very High (5) I have enough confidence in my leader that I would defend and justify his/her decision if he/she were not !! !! !! !! !! present to do so?1=Strongly Disagree 2=Disagree 3=Neutral 4=Agree 5=Strongly Agree (6) How would you characterize your working relationship with your leader? 1=Extremely Ineffective 2=Worse !! !! !! !! !! than Average 3=Average 4= Better than Average 5=Extremely Effective (7)

WORK LOCATION - ENJOYMENT

Q9 Please describe your experience when working remotely

Not!at!all!true! Somewhat! ! 2!(2)! 4!(4)! Very!true!(5)! (1)! true!(3)! When I work remotely, I think about how much I !! !! !! !! !! enjoy it (1) Working remotely is fun (8) !! !! !! !! !! I feel relax while working remotely (9) !! !! !! !! !! I enjoyed working remotely very much (10) !! !! !! !! !! I feel pretty skill at working remotely (15) !! !! !! !! !! I think working remotely is interesting (16) !! !! !! !! !! I would describe working remotely as very !! !! !! !! !! enjoyable (19) After working remotely for a while, I feel pretty !! !! !! !! !! competent (21)

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WORK LOCATION - STRESS

Q9 Please describe your experience when working remotely

Not!at!all!true! Somewhat! ! 2!(2)! 4!(4)! Very!true!(5)! (1)! true!(3)! I feel tense when working remotely (6) !! !! !! !! !! I don’t really have a choice but working remotely !! !! !! !! !! (11) I am anxious when I work remotely (12) !! !! !! !! !! I think working remotely is boring (13) !! !! !! !! !! I feel pressure while working remotely (17) !! !! !! !! !! I feel like I have to work remotely (18) !! !! !! !! !! I do work remotely because I have no choice (20) !! !! !! !! !!

WORKER SELF-EFFICACY

Q9 Please describe your experience when working remotely

Not!at!all!true! Somewhat!true! ! !(2)! (4)! Very!true!(5)! (1)! (3)! I can remain calm when facing difficulties !! !! !! !! !! because I can rely on my coping abilities. (7) When I am confronted with a problem, I can !! !! !! !! !! usually find several solutions. (8) If I am in trouble, I can usually think of a !! !! !! !! !! solution. (9) I can usually handle whatever comes my way !! !! !! !! !! (10

VIRTUAL WORK INTENSITY – MODERATOR: How many days a week do you work from a remote location

!! One day a week (1) !! Two days a week (2) !! Three days a week (3) !! Four days a week (4) !! All week (5)

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YEARS OF EXPERIENCE (CONTROL): How many years of professional work experience do you have?

!! 3 years or less (1) !! 5 years or less (2) !! 10 years or less (3) !! 15 years or less (4) !! 20 years or less (6) !! More than 20 years (7)

ICT UTILIZATION (FREQUENCY) – MODERATOR: How often do you use the following tools to perform your work?

Most!of!the! Sometimes! ! Always!(1)! Rarely!(4)! Never!(5)! Time!(2)! (3)! Video Conference (Skype, WebEx, other) (2) !! !! !! !! !!

Internal Collaboration tools (Google docs, !! !! !! !! !! Dropbox, Doc Sharing) (5)

Internal Company Intranet (6) !! !! !! !! !!

Internal employee chat (Sametime, IM, etc.) (7) !! !! !! !! !!

GENDER (CONTROL): Q19 Please select your gender

!! Male (1) !! Female (2)

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