Emotional Responses to Technology Failure

EMOTIONAL RESPONSES TO TECHNOLOGY FAILURE:

LOOKING BEYOND THE APPRAISAL OF SUBJECTIVE IMPORTANCE

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the

Degree of Doctor of Philosophy in the

Graduate School of The Ohio State University

Evan T. Straub, M.A.

*****

The Ohio State University 2008

Dissertation Committee: Approved by

Dr. Heather Davis, Adviser

Dr. Anita Woolfolk Hoy ______

Dr. David Stein Adviser

Dr. Rick Voithofer College of Education

Evan Tennell Straub

2007

ABSTRACT

While media frequently popularizes the idea smashing one’s computer, little

research up to this point has explored whether if there is something specific about

technology that induces the unpleasant emotions. Most current research offers strictly

cognitive perspectives on individuals’ interactions with technology, and therefore fails to

account for the emotions that may occur in these interactions, although emotions are

known to influence attitudes, beliefs and behaviors (Ajzen, 1980; Bandura, 1986).

Emotional appraisal theory suggests that individuals make judgments about situations

which in turn result in an emotional experience (Smith, 1991). The purpose of this study

was to examine the theoretical understanding of two new constructs: how appraisals of

importance may extend beyond personal relevance in technology failure situations and

trust in technology. A model was developed hypothesizing that the primary appraisal process is more multi-dimensional when the situation is dependent on an external factor,

like technology, as well as the general antecedents that may influence self reports of

emotions. A two part survey was developed. The first part measured the general

antecedent of trust in technology, self-efficacy in technology use, affinity to technology

and coping strategies when technology when technology fails. The second part used four

vignettes to manipulate personal importance (high/low) and perceived technology

importance (high/low) in a 2x2 design. ii Participants self-reported emotions in terms of anger, anxiety, challenge and frustration in response to the vignettes. The survey was administered to 544 adults including students at a large, Midwestern university, as well as adults in the surrounding area.

Using structural equation modeling, this study tested a model of a dual primary appraisal process, influenced by the general antecedents of trust, self-efficacy, technology affinity and coping strategies in each of the four vignettes. Findings indicated that the model fit well in each vignette scenarios. Participants’ subjective rating of the importance of the tech was found to be influential in reports of emotions in three out of the four vignettes. In addition, trust was found to predict perceptions of technology importance as well as lessen reports of unpleasant emotions in some of the vignettes.

Implications of the results of this study and recommendations for future research are discussed.

iii “Your work is to discover your work and then with all your heart to give yourself to it.”

Buddha

Dedicated to my daughter, Elise.

May you find your path with joy and

success, whatever it may be.

iv ACKNOWLEDGMENTS

“It is better to travel well than to arrive.”

Buddha

It is absurd to think that that any one person could make this journey without the influence and assistance of others. As I reach the end of this part of my adventure, I realize that truly, I have traveled well!

Words can not express the depth of my gratitude to Dr. Heather Davis, my adviser. You have been a mentor, a friend, a teacher and an amazing role model as what an educator and as an academic should be. Without you, I would not have been able to

‘make it work.’ Thank you for your guidance, advice and assistance along the way.

I had the pleasure of working of a fantastic group of academics on my committee.

I began my first educational psychology studies with Dr. Anita Woolfolk Hoy. Thank you for your contagious excitement and enthusiasm for educational psychology, and your thoughtful challenges. I met Dr. Rick Voithofer in my first few months of coming to

Ohio State, when I was an employee, but not yet a student. He not only advised me on my Master’s degree, but encouraged me to meet Dr. Davis when she first came to Ohio

State. Thank you for all the conversations and advice, clearly I wouldn’t be here without it. Lastly, I have to thank Dr. David Stein for his unending passion for learning and

v exploration. You create an environment that allows students to delve into learning about

research and the process of becoming an academic. It was an invaluable experience.

Outside of my committee, I need to thank the gracious individuals who allowed

me to intrude in their classes and/or life to collect data, including Dr. Robert Hite, Dr.

Sofia Lee, Paul Sturr, Carey Andrzejewski, Ryan Poirier, Jeanine Hetlzer, Kevin

Balough, Dr. Tom Reed, Dr. Bogdon, Sarah Silverman, Susan O’Connor, Anthony Durr,

and Michael Yough. In addition, I need to thank two years of my section of EDU PL 309

that patiently participated in my pre-pilot, pilot and final studies.

I also owe a debt of gratitude to my friends in the Ohio State community who

helped me along the way, both present and past. In the college of Education, Carey,

Ryan, Melissa, Marissa, Elif and Gonul were always there for me. And a very special

thank you to the best writing group ever – Sarah Silverman and Mei-Lin Chang. I will

deeply miss our coffees and debates. In addition, I would like to thank the past and present members of the Distance Education study group for their support and

conversations, particularly Dr. Connie Wanstreet, Dr. Hilda Glazier, Dr. Jennifer Conrad,

Lynn Trinko and Susan Johnston. Chuck Schiebler and Dr. Jackie Goodway Schiebler

offered their generous support and dinner parties to my family during my time working in

PAES. And if it wasn’t for the long hours of support and encouragement of my

colleagues at Technology Enhanced Learning and Research, especially Nick Terrible,

Kelvin Trefz and Bryce Bate, I may have never started this journey to begin with. vi I have a large circle of family and friends who sheltered me from the academic world when I needed it. Wendy and Jason Boggs, Emilie Greenwald and Ian Brown and reminded me about life beyond the dissertation. My sister Susan and her husband Sean were relentless in tracking down possible participants. Dick and Gerry Straub and

Christine Straub offered much assistance in the form of dinners, childcare and dog care during times of need.

My parents, Tom and Sara Ogg, have offered unbounded support in every way, from patiently listening to me talk about my research to driving copies of my dissertation across the city. I would not be who I am today if it was not for the values you have instilled in me. Thank you for teaching me to love learning, to laugh at one’s mistakes, to work through the hard times and the importance of Sunday dinners.

I thank my daughter Elise for understanding all those nights when I had to work late or when my patience was not what it should have been. You can always make me laugh with your innocence and enthusiasm. Thank you for reminding me what is really important out of life. Finally, I need to thank my husband Steve. You were there from the start. You listened as I muddled through my own thoughts, proofread my papers, and gave me time when I panicked over yet another deadline. You celebrated my triumphs and commiserated when I mourned my failures. I always dreamed of completing this degree, and I could not have done it without your help. I am so glad to have you by my side, and I am excited and hopeful for the future ahead of us. vii VITA

May 30, 1974………………………………..Born – Columbus, Ohio

1996 ………………………………………...B.S. Psychology, Bowling Green State

University

2003…………………………………………M.A. Education, The Ohio State University

2005 – 2007…………………………………Graduate Teaching Assistant, The Ohio

State University

FIELDS OF STUDY

Major Field: Education

Areas of focus: Adult learning, technology in education, methods

viii TABLE OF CONTENTS

Page

Abstract...... ii Dedication...... iv Acknowledgements ...... v Vita ...... viii List of Tables ...... xiii List of Figures...... xvi

Chapters 1. Introduction Background of the Study ...... 1 Statement of Problem ...... 6 Theoretical and Conceptual Framework...... 7 Adoption Theory...... 7 Theory of Reasoned Action...... 8 Social Cognitive Theory...... 9 Emotional Appraisal Theory ...... 10 Purpose of Study...... 11 Research Questions...... 12 Contributions to the Field...... 12 Limitations of Study ...... 13 Operational Definitions of Terms...... 15

ix 2. Literature Review ...... 17 Appraisals and Emotions...... 20 Emotional Appraisal: Overview, Overlap and Differentiation.... 20 Primary and Secondary Appraisals...... 23 Who Gets Mad? Antecedents in Emotional Responses to Technology Failure ...... 25 Trust in Technology...... 26 Self-Efficacy...... 29 Beliefs and Feelings about Technology...... 34 Coping ...... 37 Gender Differences...... 42 Age...... 42 Summary...... 44 3. Method...... 46 Purpose ...... 46 Research Design ...... 48 Modeling a Dual Primary Appraisal Process (Context Specific Antecedents) ...... 48 General Antecedents of Appraisals and Emotions ...... 51 Design...... 54 Participants ...... 54 Measures...... 58 General Antecedents ...... 58 Technology Failure Vignettes...... 61 Emotion...... 63

x Data Analysis...... 64 Differences by Age and Gender ...... 65 Structural Equation Modeling ...... 67 Summary...... 73 4. Results ...... 74 Validating the Measurement Model ...... 74 General Trust ...... 75 Self-Efficacy...... 78 Affinity ...... 80 Coping ...... 84 Research Questions...... 94 Question One: Does a dual-appraisal process model of situational and technology importance fit the data on technology failure?....94 Question Two: How do the variables of trust, self-efficacy, affinity, and coping styles influence self-reports of unpleasant emotions in technology-failure situations? ...... 95 Question Three: Does an individual’s perceived importance of the situation influence perceptions of technology importance?...... 99 Question Four: Do unpleasant emotions differ depending upon the appraisals of importance? ...... 100 5. Discussion...... 102 Purpose ...... 102 Bringing Together Substance and Structural Validation...... 102 Summary of Findings ...... 103

xi Theoretical Contributions...... 107 Emotion Appraisal Theory ...... 107 Trust in Technology...... 108 Implications for Practice...... 109 Facilitating Technology Adoption ...... 109 Age and Technology...... 110 Why Is a Positive Experience Important?...... 111 Limitations and further research ...... 112

List of References...... 114

Appendices...... 123 Appendix A: Institutional Review Board Approval ...... 123 Appendix B: Measures...... 125 Appendix C: Participant Descriptives...... 133 Appendix D: Frequency Data ...... 136 Appendix E: Selected LISREL Outputs Vignette A...... 155 Appendix F: Selected LISREL Outputs Vignette B ...... 164 Appendix G: Selected LISREL Outputs Vignette C...... 173 Appendix H: Selected LISREL Outputs Vignette D ...... 182

xii LIST OF TABLES

Table Page

Table 3.1: Crosstabulation of Gender by Education and Age...... 56

Table 3.2: Participants Excluded by Vignette...... 57

Table 3.3: Structure of Vignettes ...... 63

Table 3.4: Differences by Age and Gender...... 66

Table 4.1: General Trust Reliability with Question 1 dropped...... 76

Table 4.2: Measurement model for General Trust ...... 77

Table 4.3: Scale Statistics for Self-Efficacy...... 79

Table 4.4: Measurement Model for Self-Efficacy ...... 80

Table 4.5: Scale Statistics for Technology Affinity...... 82

Table 4.6: Model for Technology Affinity...... 83

Table 4.7: Scale Statistics for Problem Focused Coping ...... 85

Table 4.8: Measurement Model for Problem-Focused Coping...... 86

Table 4.9: Scale Statistics for Emotion Focused Coping...... 87

Table 4.10: Measurement Model for Emotion-Focused Coping...... 88

Table 4.11: Summary of Full Measurement Model ...... 92

Table 4.12: Summary of Model Statistics by Vignette ...... 95

Table 4.13: Paths by Exogenous Variable ...... 97 xiii Table 4.14: Paths by Endogenous Variable ...... 98

Table 4.15: Indirect Effects on Emotions...... 99

Table 4.16: Correlation of Technology Importance and Situation Importance ...... 100

Table 4.17: Repeated Measures ANOVA by Individual Emotion...... 101

Table C1: Participant Age Demographics...... 133

Table C2: Participant Gender Demographics...... 133

Table C3: Participant Education Demographics ...... 134

Table C4: Crosstabulation: gender by education and age ...... 135

Table D1 : Situation Importance by Vignette...... 137

Table D2: Technology Importance by Vignette ...... 138

Table D3: Anger by Vignette...... 139

Table D4: Challenge by Vignette ...... 140

Table D5: Frustration by Vignette...... 141

Table D6: Anxiety by Vignette...... 142

Table D7: Appraisals and Emotions for Vignette A: High Situational Importance/High

Technology Importance...... 143

Table D8: Appraisals and Emotions for Vignette A: High Situational Importance/High

Technology Importance...... 144

Table D9: Appraisals and Emotions for Vignette C: High Situational Importance/Low

Technology Importance...... 145 xiv Table D10: Appraisals and Emotions for Vignette D: Low Situational Importance/Low

Technology Importance...... 145

Table D11: Descriptive Statistics for General Trust...... 147

Table D12: Descriptive Statistics for Self-Efficacy ...... 149

Table D13: Descriptive Statistics for Technology Affinity...... 151

Table D14: Descriptive Statistics for Emotion Focused Coping...... 153

Table D15: Descriptive Statistics for Problem Focused Coping ...... 154

xv LIST OF FIGURES

Figure 1.1: Technology helps complete tasks ...... 2

Figure 1.2: When technology might not help complete tasks...... 4

Figure 2.1: Influence of appraisals on emotion...... 18

Figure 2.2: General antecedents and context antecedents affecting unpleasant emotions

during technology failure...... 19

Figure 3.1: Full model of the factors influencing unpleasant emotions...... 47

Figure 3.2: Current emotional appraisal theory: Situational importance influences

emotions...... 49

Figure 3.3: A dual appraisal process: Situational importance and technology importance

influence unpleasant emotions in technology-based failure situations...... 50

Figure 3.4: Situational importance influences technology importance...... 51

Figure 3.5: Factors affecting appraisal of technology importance...... 52

Figure 3.6: Coping strategies measurement model...... 69

Figure 4.1: Final measurement model...... 93

Figure 4.2: Range of effects across vignettes...... 96

Figure E1: Structural model for vignette A (high personal / high technology)...... 163

xvi Figure F1: Structural model for vignette B (low personal / high technology) ...... 172

Figure G1: Structural model for vignette C (high personal / low technology) ...... 181

Figure H1: Structural model for vignette D (low personal / low technology)...... 190

xvii

CHAPTER 1

INTRODUCTION

Background of the Study

When I graduated with my undergraduate degree in psychology, I was still

uncertain about my academic aspirations. Although I felt that psychology was my noble passion, I took a job working with and teaching computer-based technology to pay the bills. Technology had been a half-hearted hobby of mine. I had always quickly grasped

the ins and outs of programming and design, but had not considered it a career.

However, during the late 1990s, work in the technology field was easily available, and jobs were plentiful. In those first few years of work, I began to notice with curiosity the

emotional reactions of individuals struggling to acquire the necessary skills to use the

new tool. I observed anger, frustration, and one time, borderline violence against

technology, and this fascinated me. How could a machine seem to bring out so many

strong emotions in people?

Ten years later, technology has become even more pervasive in every day life.

Yet, the emotional divide between humans and technology is still present. The use of

technology seems to be a catalyst for emotions. Even now, someone asks about my line

of research, and I explain about my interests in emotion and technology the overwhelming response is, “You should use me as one of your test subjects!” This anecdotal response continues to affirm my passion for this line of research: How can a non-human object bring out that which makes us so uniquely human?

My personal interest in technology revolves around the current gap in the literature surrounding the informal and formal use of technology in everyday life. If we think about why we create technologies, the idea is that these tools make lives easier in some way. Therefore, the basic premise for the use of most technologies is:

Figure 1.1: Technology helps complete tasks

Humans first implemented technology when we began using tools to assist our

tasks. Current research on computers in everyday life most often focuses on occupational

ramifications of technology, or in other words, how does technology facilitate those job-

related tasks. The effect of technology on employees is a financial issue for many

companies. Formal training on new technology takes money and time away from the job

and change in general can be a source of stress. The time it takes for employees to

acquire new technology skills means that employees are less productive for a period of

time as they adapt. Thus, many researchers focus on what characteristics predict satisfaction with technology in an occupational setting.

While few decades ago, technology was relegated primarily to the office atmosphere, today’s individual interacts with more and more technology outside of the office as well. Tasks that were never automated or electronic are now more frequently done through electronic means, like email. In addition, technology acts as a conduit, bringing work into home life and home life into work (Chelsey, 2005). Computers have an increasing presence in the home as well (at least in the US). An individual may have a cell phone, a PDA or a combination of the two. Other non-specifically computer devices like the IPOD, or even microwaves and washing machines are becoming more and more integrated into personal life. A person not only have to learn how to use a spreadsheet for his office, but now need to use the Internet to balance his checkbook, or rely solely on a cell phone as his means of communication. However, with this new reliance on technology, remarkably little is known about the impact of depending on a tool to complete so many daily tasks. How does an individual feel if something he relied on for consistent performance fails?

There are times when task completion fails due to technology malfunction.

However, there are also times when failure occurs due to other issues outside of the technology itself. Individuals may rely so much on technology that they are incapable of seeing past the use of the one specific tool. Or, personal beliefs and attitudes about technology may cause individuals to struggle with the use of technology. An individual may not believe that she is capable of effectively using a tool. Now, the very tool that

was supposed to help complete tasks is now seen as a barrier to that task completion due to the baggage an individual brings with her (Figure 1.2). The technology that was supposed to help individuals complete a task is now hindering them. It is this interruption, or failure of technology to actually make task completion easier that first intrigued me.

Figure 1.2: When technology might not help complete tasks

The term novice is defined as, “someone who is new to a field or activity, a beginner.” Oatley (2004 p. 693) notes in a commentary about computers, “It is long been

known that many users, particularly novices, find computers frustrating, annoying and

humiliating.” While anecdotally this is an easy statement to make, there is no research base to support this statement. The current literature is still searching for an explanation

as to why users find computers so affectively challenging.

Each new technology or modified version of a technology places an individual right back into the “novice” category. Therefore, if we consider the velocity of 4

technology change, it is likely that an individual will be considered a novice frequently.

Is it truly novices who feel the most angry, the most frustrated, the most humiliated? Or are there other characteristics or beliefs that preclude someone from getting frustrated? Is it the emotional experience that hinders the learning process or is there something else that is interfering?

While it may be mildly frustrating to try to learn a new word processing program, occupational demands may require an individual to push through the emotional response.

More complex tasks may result in even more intense emotions, leading to decreased persistence with a task. In my professional experience, it was rarely a totally new technology that caused so much frustration. Rather, it was the upgraded technology, the newest version, or a technology that the individual had been comfortable with, but had been changed, even subtly that seemed to cause the strongest emotions. When learning a new task, the initial learning processes utilize a large amount of the individual’s limited resources (Mayer & Moreno, 2003). As the task becomes integrated into an individual’s arsenal of knowledge, the task becomes automatic – implicit and non-conscious. This shifting of knowledge development from explicit to automatic frees up cognitive resources for other tasks (Karoly, Boekarts, & Maes, 2005). However, take that automaticity away, and the individual may feel an emotional response in a reaction to the perception of having to start over again.

If computers can evoke emotions like anger, frustration and humiliation and emotions can reduce cognitive resources for task completion, what happens then? One implication is that if an emotional response to computers interferes with the learning of a

technology, it may in turn influence the adoption of said technology. The initial emotional response may have a lasting effect on perceptions and usage of future technologies. The questions in the previous section have no clear answers. It is ultimately my goal to try and understand the emotional experience involving computers: why/when do we become emotional at (or with) them, and why/when do we not?

Statement of Problem

Currently, research on the use and impact of technology spans many fields.

Cognitive systems engineering research is studying technology usability – the design and

implementation of software systems so that they are easy to use and useful for all potential clients of the system. The fields of business and information technology focus

on acceptance and adoption of technology, and the facilitation of acceptance and

adoption, but primarily in terms of the workplace and how adoption affects the business.

In education, the use of technology primarily concentrates on how to integrate technology

to assist in the learning of other subjects, but does not generally focus on the learning of

technology for its own sake.

Most current understanding about the use and acceptance of technology offers strictly cognitive perspectives on individual decisions to use or not to use a technology.

Even though so much research is being done on technology, there is still little on the emotional interactions between humans and computers. The research in this study strives to begin developing a understanding for stepping beyond the cognitive aspects of technology usage and adoption.

Theoretical and Conceptual Framework

While the conceptual model for this study is new, it is based upon previous researched theories. To understand how emotion may influence individuals’ usage of technology, one must look to the research that has already been conducted on models of innovation adoption, decision making, and how individuals learn in connection to the world around them.

Adoption Theory

Life is a process of changes and choices. The choice of whether or not to adopt a particular technology and the time frame involved with that decision has been a long source of research across multiple disciplines (Straub, under review). Historically, adoption has been understood in terms of some kind of behavior change. Adoption theory then, examines the individual, and the choices an individual makes to accept or reject a particular innovation.

Adoption theory is a micro-perspective on change, focusing not on the whole, but rather the pieces that make up the whole. Most believe the adoption process is not a single event. While the decision to or not to adopt an innovation can be a one time event, the route that leads to an individual’s decision does not take place in a vacuum. Beliefs and attitudes are formed over time and, in turn, may influence decisions. Roger’s theory of adoption and diffusion (Rogers, 1995) is one of the most influential theories of adoption used in understanding acceptance of innovations. His work describes the process and stages that an individual progresses through when making decisions about accepting or rejecting an innovation.

Adoption theory has influenced much of the understanding of how any innovation becomes diffused through a population. In adoption theory, an innovation is not presumed to be an improvement, and in fact, adoption theory has examined the failures of innovations that should make life “easier” or “better,” but not adopted by a population

(i.e. VHS video format versus Beta). In terms of technology, encouraging the adoption and usage of a new technology is critical to many businesses that now focus on how to get their products both to the business and home markets.

Theory of Reasoned Action

Azjen’s work (1996) in decision-making processes led him to the theory of reasoned action, and its successor, the theory of reasoned behavior as a way to understand the relationship between intention as a mediator between action and attitudes (see also

Ajzen & Fishbein, 1980). This theory postulates that an individual’s behavior is a result of his or her attitudes about the expectation of a behavior and social norms about a particular behavior (Ajzen & Fishbein, 1980).

The theory of reasoned action has been a major influence in many of the studies

that are the foundation in adoption theory. Applying the theory of reasoned action to

technology adoption, if an individual believes that a particular piece of technology is

valuable, he or she will then choose to use that technology. This link between attitudes

and actions underscore the importance of understanding the individual’s belief structure

about technology. Many studies have focused on a pre-post test design about beliefs

about technology or a particular technology, but at this point, no studies have looked at the intersection of beliefs and emotional responses to technology outside of computer anxiety.

Social Cognitive Theory

Social cognitive theory (Bandura, 1986) is a significant theory in psychology and education today for understanding how an individual interacts with the environment around him or her. Individuals are capable of learning not just from their own experiences, but from the experiences of those around them (Bandura, 1986). The ability for a human to learn vicariously, that is by learning by observation of others rather than their own experience, is one of the foundational concepts of social cognitive theory. The observational learning processes are regulated by four sub-functions – attentional processes (Is this behavior important and accessible to me?), retention processes (Is it salient enough to remember?), production processes (Can I reproduce the action?) and motivational processes (Am I encouraged to do this again?) (Bandura, 2001).

In addition, social cognitive theory suggests the concept of self-efficacy. Self- efficacy refers to the “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (Bandura, 1997, p. 3). Self-efficacy is always forward-thinking, and judgments based on beliefs about personal capability.

Bandura also suggested that self-efficacy has several sources: mastery experiences, vicarious experiences, social persuasion, and emotional arousal.

Emotional Appraisal Theory

First suggested by Magda Arnold in the early 1960’s (Arnold, 1960), emotional appraisal theory suggests discrete emotions can be differentiated based on the individual’s appraisal of the situation. Following in her footsteps, researchers like

Richard Lazarus, Craig Smith and Klaus Scherer further defined the field, focusing on the different states that could distinguish different emotions

Appraisal theory suggests that discrete evaluations of events are the foundation of the emotional experience. As such, these experiences are highly personalized - previous experiences, attitudes, beliefs and other individual factors all influence the way an individual appraises a situation (Frijda, 2005). Appraisals are functional. They occur to help an individual make meaning out of a situation (Smith & Kirby, 2001a). While appraisals are based in individual evaluations they are not necessarily strictly cognitive- based, that is the emotional experience in turn influences the future appraisals made by

individuals (Smith, Haynes, Lazarus, & Pope, 1993). .

Smith’s theory of appraisals currently in the literature describes the appraisal process as a process with two main components (Smith, 1991; Smith & Kirby, 2001b).

The primary appraisal process involves the individual making a judgment about the personal importance (congruence) and relevance of the situation to his or her personal

goals. This first appraisal then influences the secondary appraisal process, where the

individual makes judgments about their ability to cope with the situation.

Purpose of Study

This study suggests a structural model for understanding the emotional reactions some individuals have to technology failure. The specific purpose of this study was to form the foundation of a theoretical understanding of two new constructs: how appraisals of importance may extend beyond personal relevance in certain situations and trust in technology. A model was developed hypothesizing that the primary appraisal process is more multi-dimensional when the situation is dependent on an external factor, like technology. Benson (1998) suggests construct validation is a process consisting of three aspects: the substantive component in which the construct is developed and operationalized based on prior research and theory, the structural aspect in which the construct is examined for relationships with variables and lastly, the external aspect which relates the prior two components to other constructs.

Using Benson’s (1998) stages as a guide, this study focuses on the second stage of construct validation. The first stage was completed through the literature review reflected here, and in previous empirical research (Straub, 2007a). Specifically, this study tested a model of how an individual’s appraisals are related to the unpleasant emotions they report. It is hypothesized that it is not just the importance of a situation to an individual that factors into the emotional experience, but also the perceived importance of technology. To further parse out what might influence certain appraisals, this study includes several controlling factors. In this study, a trust scale was examined for reliability for use in the model, as well as several other scales that have already been shown to be reliable through research. In addition, this study begins to examine the idea

of how an individual’s trust in technology affects his/her interactions with technology.

Lastly, this study used structural equation modeling to study the dynamics between these several different constructs.

Research Questions

The following research questions were addressed in this study:

1. Does a dual-appraisal process model of situational and technology importance

fit the data on technology failure?

2. How do the variables of trust, self-efficacy, affinity, and coping styles mediate

self-reports of unpleasant emotions in technology-failure situations?

3. Does an individual’s perceived importance of a situation influence his or her

experience of importance of technology to a situation?

4. Do unpleasant emotions differ depending on the appraisals of importance?

Contributions to the Field

This paper is poised to bring contributions to the fields of psychology, education, and human-computer interaction. Emotional appraisal theory suggests that situational appraisals by individuals cause emotions. In this theory, whether or not technology is involved in the situation should not matter, as the appraisal process is somewhat linear and driven by situational importance. Appraisals and re-appraisals are theorized to be continually made (Smith & Kirby, 2001b) but little is known about how these continuous appraisals affect each other. This study posits that it is not just the primary appraisal

process that influences the emotional reaction in technology-based scenarios, but a dynamic series of primary appraisals that in turn also affect the secondary appraisal processes.

Adoption theory outlines a complex interaction of possible factors that influence the decisions an individual makes about choosing to incorporate an innovation into his or her life. This study suggests the perceived importance individuals place on a piece of technology influences their emotional arousal with that piece of technology. An unpleasant emotional experience may potentially discourage adoption of technology

(Straub, under review). Particularly for educators and administrators, this can be a dangerous proposition. As demand grows for technology-integrated classrooms, the need for teachers to have increased technology knowledge and skills also grows. This study is poised to add another dimension to helping administrators facilitate the transition of a new technology into a district.

Limitations of Study

This study, like all research, has limitations. Because undergraduate students will be the primary subject base, the age, education and experience of the population may be

an influential factor affecting the external validity of the study. This risk was minimized by the inclusion of off-campus recruiting of subjects. However, differences due to age

and gender are still currently being debated in the research (Czaja, Charness, Fisk,

Herzog, Nair, Rogers, & Sharit, 2006). As participation will be on a voluntary basis,

subjects will self-select to participate, and the researcher will have no control over age,

gender, and other demographic data that could influence the results and affect validity.

A second limitation of the study involves the unnatural setting of experimental conditions. The study proposed a series of vignettes, which does not allow for the actual experience, but instead has the subject make an informed decision about how they might feel. This could influence the external validity of the study. This is minimized by selecting situations that could be considered common to many people in today’s society.

A third limitation of the study is that it relies self-reports of emotional experiences. Self-reports can be susceptible to researcher bias due to the way the surveys are developed (Swarz, 1999). In addition, self-reflection may not always be the most accurate representation. One criticism of this design may be that cognitive reflection is a limited mode of understanding (Fenwick, 2003). Memories of previous experiences are influenced by the individual perceptions, emotions that shape and shift the actual events

(Marsick & Volpe, 1999; Walsh, 2005). This re-framing of an experience will then influence future experiences, perpetuating a skewed view. However, there is also some research that suggests that when the situation is familiar or the participant has experienced the situation before, the self-report is more accurate (Dunn & Laham, 2006).

Operational Definitions of Terms

Adoption The process of choosing to accept (or reject) an

innovation.

Affinity An affective feeling of liking or enjoying the use of

technology.

Computer A specific type of electronic machine that performs

different types of tasks.

Computer Attitude A set of affective beliefs about computers and

technology.

Computer Experience A bi-dimensional construct consisting of quantity of

computer use and quality of computer experiences.

Emotion An organized psychobiological response linking

physiological, cognitive and motivational states.

Emotion Appraisal A relative, individualized, evaluative judgment that

results in emotion.

Emotion-Focused Coping A strategy to manage psychological stress through

regulating or expressing one’s emotions.

Pleasant Emotion An emotion derived from a situation that was appraised

to be more positive than the original motivation expected

(i.e. happiness, joy, love).

Problem-Focused Coping A strategy to manage psychological stress through action

on the environment.

Reasoned Action The theory that individuals make choices based off

attitudes that in turn influences actions.

Self-Efficacy Beliefs in one’s future capabilities to organize and

execute the courses of action required.

Situational Importance A subjective judgment of the personal salience of the

situation an individual is in.

Technology Electronic-based tools and machines created by humans.

Examples include computers, cell phones, cars.

Technology Importance A subjective judgment of the criticality of technology to

the success of a situation.

Trust An affective feeling about the reliability of a technology

to perform as expected.

Unpleasant Emotion An emotion that suggests the appraisal of the

motivational expectations were not positively correlated

with the outcome (i.e. fear, anger, sadness).

CHAPTER 2

LITERATURE REVIEW

A German man, who startled his neighbors when he hurled his computer out of the window in the middle of the night, was let off for disturbing the peace by police who sympathized with his technical frustrations. Police in the northern city of Hanover said they would not press charges after responding to calls made by residents in an apartment block who were woken by a loud crash in the early hours of Saturday. Officers found the street and pavement covered in electronic parts and discovered who the culprit was. Asked what had driven him to the night-time outburst, the 51-year-old man said he had simply got annoyed with his computer. "Who hasn't felt like doing that?" said a police spokesman. (Reuters, July 2007)

Technology is becoming ever integrated in day-to-day life. Computers that were once isolated only to research institutions, then to the workplace, have now infiltrated into middle-class family homes. Processing power that once took up large sections of an office building now can reside in a palm pilot, and heavy VCRs have evolved into hand- held DVD players. It is estimated that there are over 190 million cell phone users in the

United States (CTIA, 2005) as of July, 2005. With decreases in price, consumers are continually encouraged to continually upgrade as the technology improves. How an individual learns that technology, for the most part is incidental – learning takes place

experientially, through the interactions of individuals with technology. Rarely do individuals sit for a training session on a cell phone or a washer and dryer; instead, they figure it out for themselves. Even day-to-day computer tasks may involve some level of new knowledge acquisition to pursue a desired task.

Even though technology is integrated into daily life, it does not always function as

the individual planned. The emotional experience that results from technology the

failure of technology may in turn influence the eventual adoption of technology. The

model for this study incorporates several different types of antecedents prior to an

emotional experience (Gross, 1999). First, the model describes how appraisals directly

influence emotions (in this study, unpleasant emotions in particular). These context-

specific antecedents are the appraisals that an individual makes about the situation.

Figure 2.1: Influence of appraisals on emotion

These appraisals do not take place in a vacuum. Based on the literature, several additional generalized antecedents are hypothesized to affect the appraisals individuals

make, as well as the unpleasant emotions they may report. These general antecedents

(coping, self-efficacy, trust, and affinity) are believed to indirectly mediate the intensity of an emotion through the appraisal process as well as directly influence the discreet unpleasant emotions a person may report experiencing unpleasant emotions because of technology failure. In addition, two additional factors that are frequently reported in technology-based studies, gender and age, will be reviewed as to why they were hypothesized not to be significant factors in this model.

Figure 2.2: General antecedents and context antecedents affecting unpleasant emotions during technology failure

Appraisals and Emotions

Emotional Appraisal: Overview, Overlap and Differentiation

Although emotional experiences take place daily, most individuals take for granted the complexity and inventory of these internal states. However, these internal states, emotions, take into account a number of “affective, cognitive, behavioral, expressive and a host of physiological changes” (Panksepp, 2005 p. 30). Why do individuals have emotional reactions? How can the same situation evoke different emotional reactions in different people? How can the same situation evoke different emotions in the same person at a different time?

Emotion appraisal theory suggests that is the judgments an individual makes about a situation are the cause of the subsequent emotional feelings that an individual experiences. Other theories of emotion suggest that responses to environmental stimuli or that biological bases of emotion are predecessors to the experience of emotion

(Roseman & Smith, 2001). Although neurobiological research has demonstrated that there are physiological changes that take place in proximity to a reported emotional experience, there is no causation in terms of appraisals, physiological reaction and emotions at this time (Panksepp, 2005; Schachter & Singer, 1997). Appraisal theory suggests that the cognitive assessment is the “organizer” of all the response systems (van

Reekum, Johnstone, Banse, Etter, Wehlre, & Scherer, 2004).

When individuals encounter an event, they make judgments about the situation.

These judgments may be conscious or unconscious, meaning that the individual may or may not be cognitively aware of the judgment.. These appraisals are the subjective

judgments and interpretations about the situation. Appraisal theorists suggest

“evaluations and interpretations of events, rather than the events per se, determine whether an emotion will be felt and which emotion it will be” (Roseman, Spindel, &

Jose, 1990 p. 899). These personalized judgments then explain why one person may feel anger because of a situation while another may feel sadness. It is the appraisals of the situation that differentiate the subsequent emotional experience.

Appraisal theory has several primary assumptions. First, appraisals are relational to the individual. No two individuals will appraise the situation in the same way. At a funeral, one person may laugh, the other may cry. Previous experiences, attitudes, beliefs and other individual factors all influence the way an individual appraises a situation

(Frijda, 2005). Next, appraisals occur to help an individual make meaning out of a situation and are evaluative (Smith & Kirby, 2001a). Appraisals are individual and personal evaluations, that may not be strictly cognitive-based, but also may include emotion (called “hot cognition”) (Gregoire, 2003).

Another assumption is that appraisals take place continuously (Smith & Kirby,

2001a). Roseman and Smith note “appraisals may be causes of emotions, components of emotions, and consequences of emotions” (Roseman & Smith, 2001 p. 15). Although much debate has gone on over the years about which came first (Roseman & Evdokas,

2004), the emotion or the appraisal, most researchers believe that narrowing the process to a strict linear antecedent-consequence perspective may be artificial (Scherer, 2001).

However to this point, few researchers have demonstrated this process.

If the appraisal process leads to an emotion, how do different emotions arise from similar situations? A final assumption is that emotions occur due to the evaluations being highly differentiated. This differentiation in terms of motivations and drives, explains different but similar emotions (like anger vs. frustration). Appraisal theorists suggest that the overall appraisal of a situation is a more macro event: an individual makes subjective judgments. However, within that appraisal, there are smaller components. These micro appraisal processes form the differentiation of discreet emotions.

It is during micro, or secondary, processes that many appraisal theorists differ in the way they conceptualize the differentiation of the appraisals. Roseman suggested that there are five types of differential appraisals that cause emotions, and that any combination of them would lead to a different type of emotion (Roseman, 1979;

Roseman et al., 1990). Scherer’s appraisal patterns includes a slightly different appraisal pattern, including goal significance (similar to motivational state), coping potential

(similar to power and agency) and novelty (Scherer, 1988). Scherer also suggested that emotions should not be viewed as strictly categorical, but rather continuous in nature.

Smith and Lazarus (1990a) suggest a primary and a secondary appraisal process, where the individual first evaluates importance of an event, and secondly evaluates the impact of the event and his or her ability to cope with the event.

Although appraisal theory is still developing, these similarities indicate that the basic components seem to be consistent across domains. In recent research, there has been some attempt to identify and collapse the various appraisals to develop a more unified theory of appraisal. Models are being refined and differentiated to find the most

significant subcomponents of the appraisal process. Appraisals previously hypothesized to be salient to discreet emotions were later shown to not have the anticipated influence

(Roseman, Anoniou, & Jose, 1996). For example, Smith and Lazarus’ 1990 model of appraisal collapse the idea of accountability into one category, while later models (Smith

& Kirby, 2001b) break out accountability into two separate categories: one for self and one for external accountability.

These shifts in theory (sometimes subtle, sometimes not so subtle) suggest that appraisal theory is still evolving. Additional components of the appraisal process are still being refined and conceptualized. The dynamic nature of this model may be a sign that additional mechanisms of the appraisal process are still being exposed.

Primary and Secondary Appraisals

One consistent concept across appraisal theorists is the idea that certain appraisals are more influential than others. If not, then every activity could and would arouse the most extreme emotions. Imagine the same intensity of sadness at the death of a broken fingernail as at the death of a loved one! Instead, most theorists suggest that there is a primary appraisal process in which the individual judges relevance or importance

(Lazarus, 2001). The result of this appraisal shapes the intensity of the emotional experience.

If the primary appraisal provides the boundaries for emotional intensity, it is then the secondary appraisal process that shapes the flavor of the emotional experience.

These consequent judgments determine the discrete emotional experience, be it sadness, anger, or frustration. The individual must make some sort of conclusion about how he or

she is going to handle the situation at hand, whether or not they can change it, who was at the cause of the situation, etc.

Lazarus argues that the concept of primary and secondary appraisals has nothing to do with the importance of the appraisal, but rather, it demonstrates the inseparable nature of the two appraisals (Lazarus, 2001). Primary appraisals do not appear independent of secondary appraisals. However, for the purposes of this study, primary appraisal process is the most important appraisal because it shapes whether or not an emotional experience will even take place. If the situation is deemed to be unimportant, it does not warrant an emotional investment.

Smith, Lazarus and colleagues proposed a seven component appraisal theory

(Smith & Kirby, 2001b; Smith & Lazarus, 1990b). Their model focuses on two primary appraisal processes: motivational relevance and motivational congruence, in which an individual identifies if a situation is important to him or her, and in line with other goals.

In addition, there are five secondary appraisal components that then further shape the ultimate emotional experience: problem-focused coping potential, emotion focused coping potential, self accountability, other accountability and future expectancy. The result of the primary appraisal directly influences the secondary appraisals. For example, the individual’s coping needs to change when the situation is not as personally salient to him or her. A woman who finds out that her favorite TV show is no longer aired will probably act differently than when diagnosed with a life threatening disease.

The dilemma with appraisal theory is how it plays out in real life situations. How do appraisals take place constantly? How can one appraisal influence another appraisal?

Can appraisal patterns be predicted based on previous belief patterns? The literature has few answers for these questions. The literature currently suggests two important points.

First, primary and secondary appraisals are contiguous, and second, while primary appraisals shape the magnitude of the emotional experience, secondary appraisals form the name individuals put on that emotion.

This study was different because it challenged the idea that primary and secondary appraisals are always contiguous and linear. The model hypothesizes that there are contexts when a single primary appraisal process does not account for the judgments made in a situation. This study hypothesizes that contexts in which an individual has to appraise not only the personal importance of the situation, but also the personal beliefs about the importance of an external factor to the situation (in this case, technology), more than one primary appraisals take place.

Who Gets Mad? Antecedents in Emotional Responses to Technology Failure

Emotional appraisals are not always simple cognitive evaluations. What is important to one individual may not be important to another, and those subjective judgments are shaped by an individual’s experiences with his or her world around them.

Social cognitive theory (Bandura, 1986) suggests that cognition is influenced by the environment and previous behaviors. Therefore, individuals will appraise similar situations differently, informed by personal characteristics, experiences and attitudes. In their study on perceived core hassles, Gruen and colleagues (1988) suggest appraisals of important tasks are individualized. The basis for these discrepancies resides in the differences in personality, culture, beliefs and experience of the participants.

In interactions with technology, attitudes and beliefs about technology are not product of behavioral interactions with technology, but also antecedent factors that may or may not influence the use of that technology. No one characteristic, belief or attitude is sufficient to explain behavioral interactions with technology. In a recent survey of faculty and their beliefs and attitudes toward technology, more than two-thirds held positive attitudes toward the use of the Internet in the classroom, yet did not reflect the actual use of technology (Vodanovich & Piotrowski, 2004-2005). Even though faculty felt positively about the technology, they did not choose to use the technology. It may be assumed then that the use and adoption of technology is influenced by not one factor, but a complex web of factors. One of these factors may be the affective experience of interacting with a piece of technology. To better understand how an affective experience takes place during technology failure, the possible factors that mitigate or shape the emotional reaction must be examined.

Because of the multitude of possible factors that could influence an individual’s appraisal of the situation, it would be beyond the scope of this study to examine all the possible factors. Instead, several possible controlling factors were identified based on the literature. These factors were identified as trust, self-efficacy, affinity to technology and coping.

Trust in Technology

As technology becomes more pervasive in American society, it has also become more invisible and implicit. From supermarkets suggesting possible future purchases, to global positioning systems working with the internet to give directions, computing

technology is present in everyday life. With that increased presence, come additional opportunities for individuals to place trust in computing systems. With trust comes a logical correlate of reliance. Conceptually, the more an individual trusts a system, the more they will come to rely on it.

Research on trust and technology has primarily focused on trust in online systems like e-commerce and interface design (Wang & Emurian, 2005) and trust in online communications (Riegelsberger, Sasse, & McCarthy, 2005). Few studies have been done investigating trust and the use of technology, particularly when it fails. De Vries and colleagues (2003) composed one of the few studies examining the role of trust on behavioral uses of technology. In their study, participants became experienced using a route planning software (a software program designed to assist individuals with planning directions from one point to another) using both a manual and an automatic setting.

During the first 20 trials, errors randomly occurred in both automatic and manual mode.

Participants were asked to stake bets on the outcome of whether the route would be faster or slower than a specified criterion. The participants were deceived to believe that the total number of credits they had at the end of the experiment would determine the amount of money they received at the end of the study. Finally, there were 6 trials where participants were free to choose manual or automatic mode. DeVries and colleagues hypothesize that when the automatic mode worked reliably, participants would be more likely to trust the system (de Vries, Midden, & Bouwhuis, 2003). If a high number of errors occurred during manual mode, participants would show evidence of a decrease in trust in their own ability to complete the task (note – this is the original researchers’ term,

although this author would argue that trust in one’s own ability would actually be self- efficacy). The results indicate low errors in automatic mode led to participants choosing automatic mode more frequently. Interestingly, although high errors in manual mode did lead participants to refrain from manual mode, the effect was much smaller. In addition, the researchers hypothesized that participants would be more likely to choose manual mode at any time, and the results confirmed this hypothesis. This suggests that individuals implicitly trust their skills over a technological intervention.

Many of the studies of trust believe that some level of risk is required for trust (for a review, see Riegelsberger et al., 2005), however there are still many debates about the nature and operational definition of trust (Clegg, Unsworth, Epitropaki, & Parker, 2002).

The concept of trust implies possible risk of negative outcomes, but possible positive outcomes as well. The interdisciplinary perspectives and usage of the term trust suggests that a single definition of trust is not possible. Therefore, this paper will suggest a new conception of trust in technology; one that measures the perceived reliability of an technology, the perceived reliance on a technology, and the perceived pervasiveness of a technology.

The pervasiveness of technology means that individuals may not consciously weigh the ultimate risks and benefits each time they use a piece of technology.

Technology trust then may be argued to be implicit (when one steps into an elevator, one trusts it to work) or explicit (when you receive directions from a web site, you make a choice whether to believe the information). Trust in technology may be broken up into the subcomponents of perceived reliability of technology and pervasiveness of the

technology. If an individual perceives that a technology is not reliable or stable, trust will suffer. Similarly, the perceived pervasiveness of a technology may also influence trust.

Elevators are a common technology and rarely do individuals question whether the elevator will work properly, unless previous experiences showed them to be unreliable.

Self-Efficacy

Self-efficacy refers to “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (Bandura, 1997 p. 3). Perceived self-efficacy is always forward-thinking, judgments based on beliefs about personal capability. It is not to be confused with self-esteem or self-confidence, two related, but different concepts. While self-esteem and self-confidence deal with a more holistic view of one’s capabilities, perceived self-efficacy is an individual’s belief that he/she can complete a specific task given a set of circumstances. Self-efficacy is also different than trust although both have the outcome of a successful accomplishment of a task. Self- efficacy measures individuals belief that they can accomplish a task while trust would measure individuals belief that the technology should behave as they anticipate. Self- efficacy would influence reliance on a technology however – individuals who believe in their own abilities to complete a task do not need to rely on computer technology (de

Vries et al., 2003).

Bandura (1997) contends that the development of self-efficacy is informed by the following factors: mastery experiences, vicarious experiences, verbal persuasion and psychological and affective states. Examining the literature on computer self-efficacy, these factors are measured in various ways. Mastery experiences are generally equated

with quantity of technology experiences, as most of the research assumes that quantity of experience will equate with quality of experience (Smith, Caputi, & Rawstorne, 2000), although the quality of such experiences is also entering the research. Vicarious experience and verbal persuasion are sometimes measured through experiences with technology training (Compeau & Higgins, 1995). The presumption is that the trainer acts as change agent by encouraging participants to have mastery experiences within a training environment, as well as participants have the opportunity to observe others using the same technology (Simon, Grover, Teng, & Whitcomb, 1996). Modeling also takes place during training environments, with the instructor modeling how the particular technology should be used. Psychological and affective states are measured in terms of computer attitudes and computer anxiety among others (Compeau, Higgins, & Huff,

1999).

Computer self-efficacy is a frequent inclusion in end-user computing research in some form. The judgments individuals make about their capability for completing technology tasks has been linked to computer attitudes which are in turn linked to future technology usage (Compeau et al., 1999).

Measuring computer self-efficacy. The way self-efficacy is conceptualized is not consistent across the literature. Computer efficacy has been measured in terms of perceived ease of use (Davis, 1989), computing autonomy and control (Charlton, 2005), as well as specific task related judgments (Likert scale skill self-assessments).

Frequently, self-efficacy is measured with Likert scale self-ratings of individual confidence of how to complete specific tasks, or general comfort using a specific

technology. There are several potential weaknesses with this method of understanding self-efficacy. First, individuals have inaccurate representations of their skills. Secondly, this researcher has noticed breakdowns in terms of terminology between expert and novices with computers. Although this has not been documented in the research yet, in the studies proposed in this paper, there was considerable concern for vocabulary discrepancies.

General or domain specific? Computer self-efficacy has been measured both as a general construct (i.e. self-efficacy with all computing interactions) as well as a more domain or application specific efficacy (e.g. word processing, programming etc.)

(Agarwal, Sambamurthy, & Stair, 2000; Compeau & Higgins, 1995). General computer efficacy is conceptualized as an individual’s overall efficacy in the computer domain.

Compueau and colleagues (1995) suggest that general computer self-efficacy is related to how likely an individual is to seek out new technology experiences (personal innovativeness) and prior experiences. Their study compared a general computer self- efficacy assessment with software specific self-efficacy measure, and perceived ease of use. Students were given a measure of personal innovativeness and general computer self-efficacy, then received training on Windows 95, and took a measure of software- specific self-efficacy and perceived ease-of-use. Finally, the subjects received training in

Lotus 123, and were given a measure of self-efficacy and perceived ease-of-use. Their results suggest that there is a relationship between domain-specific self-efficacy and general computer self-efficacy. General computer self-efficacy predicted self-efficacy in a computer operating system which in turn predicted self efficacy with a spreadsheet

program. While it might be assumed that generalized computer self-efficacy would generally also predict software-specific efficacy, this study found that computer self- efficacy has what can almost be considered a limited lifespan. General computer self- efficacy was only predictive of software specific self-efficacy early in the study.

Software-specific self efficacy was generally more predictive of other software specific self-efficacy. This almost suggests a generalization effect – as an individual becomes more efficacious with a piece of software, their over-all efficacy increases as well. One limitation of this study was that general self-efficacy was not measured in between trainings for the two software packages, so training on one type of software could have increased general self-efficacy.

The difficulties in delineating experience, attitude and self-efficacy. It is important to note the difficulty in delineation between some of the constructs discussed under the context of individual differences. Much of the literature overlaps. Early research in technology measured experience strictly in terms of amount of time (Smith et al., 2000). However, later research started measuring technology not just in quantity of time, but also in quality. This definition of technology experience is described in terms more associated with attitudes and beliefs, and thus, attitudes are inseparable from experience (Smith et al., 2000). While this does make it difficult to tease out the direct effects of experience, examining both quantity and quality provides a greater depth of understanding that experience alone does not indicate better attitudes.

In addition, the concept of self-efficacy has also been at times misconstrued or used in inappropriate ways. Some studies designed to measure attitudes, affect or

computer experience also include questions more suited to a self-efficacy measure

(Edison & Geissler, 2003). For example, measurements of participants’ perceived ease- of-use about a technology was also used as an indicator of self-efficacy (Davis, 1989).

Perceived ease-of-use is a judgment about the qualities of a technology, while self- efficacy is a judgment about the abilities of an individual. This is not to say that there is not a link between ease-of-use and self-efficacy. Some researchers suggest that perceived self-efficacy in a particular computer-based task may in turn influence the perceived ease-of-use (Agarwal et al ., 2000). The previous research mentioned above

suggests that the delineation and influence of experiences (both first-hand and vicarious),

attitudes, and the overarching concept of self-efficacy is still not clear.

A secondary issue involves the generality or specificity of some individual

differences. For example, self-efficacy is generally considered in specific situations –

how an individual feels about that particular situation and his or her possible success.

However, researchers also propose that there may be a more general concept of self-

efficacy which may help understand self-efficacy in less specific situations (Agarwal et

al., 2000).

Technology self-efficacy: general or specific? A second over-arching theme

across many of the individual differences presented here involved the debate about global

versus domain-specific evaluations. Is technology self-efficacy a global construct

encompassing all areas of technology or domain-specific? In short, the answer is “yes.”

So far, the literature reviewed in this section points to both a global perception about

technology, as well as a more specific perception of technology. Individuals may have a

general global self-efficacy on the use of technology in their lives, as well as a more accurate representation of their self efficacy about specific technologies. Suggested further research may be to further investigate and compare these differences, as well as the interaction between global perspectives on technology versus more specific domains of technology.

Beliefs and Feelings about Technology

Social learning theory also suggests individuals’ attitudes and beliefs will influence an individual’s behavior. Attitudes and beliefs about technology have long been a source of research, as they are expected to influence self-efficacy and future use of

technology. This section will discuss how attitudes about technology are conceptualized

and measured, and review some salient attitudes frequently examined in the research.

Attitudes about technology first came into focus because of the Ajzen’s theory of

how attitudes influence future behavior. The theory of reasoned behavior (and its predecessor, the theory of reasoned action) is a popular theory to understand intention as

a mediator between action and attitudes (Ajzen, 1996). This theory postulates that an

individual’s behavior is a result of his or her attitudes about the expectation of a behavior

and social norms about a particular behavior (Ajzen & Fishbein, 1980). Social cognitive

theory also proposes that beliefs and attitudes will influence actions, which will in turn

influence future beliefs (Bandura, 1986).

The term “computer attitudes” in the research is used as a blanket description of

various affective and cognitive components. Different researchers describe computer

attitudes to include computer confidence (Smith et al., 2000), computer anxiety (Brosnan,

1999), preferences for using computers (computer liking) (Garland & Noyes, 2003), and positive or negative feelings toward technology (Torkzadeh, Pflughoeft, & Hall, 1999) among others. Computer confidence is sometimes included as an attitude, but also may be considered as a separate construct (Compeau et al., 1999). In Garland and Noyes’

(2003) review of the attitude and experience literature, they noted the difficulty the field has had with the construct of attitude. Adding to the difficulty, computer confidence is frequently operationalized in such a way that educational researchers would typically call it self-efficacy. Because the literature considers both attitudes and affect in the same scales, this section will briefly review both aspects in the literature.

Affective responses. As mentioned earlier in this paper, affective beliefs about technology are closely tied to experiences with technology (Smith, Caputi, Crittenden,

Jayasuriya, & Rawstorne, 1999), so it is difficult to discuss quantity of experience without quality of experiences. However, the opposite may be true – individuals can have affective responses to technology without having the experience. For example, an individual can have an attitude about a technology before any experiences with technology take place.

Much of the research on affective responses has focused on one specific type of affective response, computer anxiety. This anxiety has been loosely defined in the literature not as the literal fear of computers but, rather, a situation-specific emotion where the individual is afraid of possible negative outcomes from the use of a computer

(e.g. breaking the computer or the program, losing valuable data etc.) (Barbeite & Weiss,

2004). It has been linked to computer aptitude and performance (Szajna, 1994) and use of computers.

There is a body of research arguing that there are particular traits that predispose certain people to adopt innovations in general, and/or adopt them more quickly than others. Much of this research comes from studies of consumer purchases – how to identify people who are likely to purchase newer products (Venkatraman, 1991; Wood &

Swait, 2002). In addition, computer science research has produced a model for understanding technology-specific (rather than global) preference for technologies, for example the gadget guy or girl who purchases a new technology as quickly as it comes on the market. One such measure is the Personal Innovativeness in Information Technology

(PIIT), which measures affective reactions to computer technology through a construct they call “playfulness” (Agarwal & Prasad, 1998). These studies suggest that there may be an affective response to the novelty of technology, a rush or excitement on the other end of the spectrum from computer anxiety.

Cognitive beliefs: negative vs. positive. Although computer anxiety refers to a particular affective reaction, it is not to be confused with negative versus positive attitudes toward technology. In Smith and colleagues’ review of the computer attitude literature (2000), they explored the many ways (at least 14) that attitude has been defined and measured in various studies. In their definition, computer attitude is, “a person’s general evaluation or feeling of favorableness or unfavorableness toward computer technologies … and specific computer-related activities” (p. 61). Attitudes toward

computers are frequently noted as a predictor for computer satisfaction and usage, and can be persistent and difficult to change (Torkzadeh et al., 1999).

Social cognitive theory suggests that attitudes and beliefs affect behavior, which in turn influences beliefs and attitudes. As mentioned previously, the research is still developing that delineates the effects between experience, attitude and technology.

Experience and attitudes have been found to be correlated in the research (Venkatesh,

Morris, Davis, & Davis, 2003). However, Garland and Noyes (2005) found that experience can be a poor predictor of computer attitude. Experience can be both positive and negative, and situation specific. Positive attitudes and experiences toward a word processor may not equal positive attitudes and experiences with a spreadsheet program,

yet in terms of measurement, many studies search for a global understanding of attitudes

about computers.

Coping

Individuals can experience a feeling of stress, which can be generally defined as

“any event to which environmental demands, internal demands or both tax or exceed the

adaptive resources of an individual, social system or tissue system” (Monat & Lazarus,

1991 p. 3). The ability to handle these stressors is generally referred to as the process of

coping. Coping refers to “an individual’s efforts to master demands (conditions of harm,

threat or challenge) that are appraised (or perceived as exceeding or taxing his or her

resources” (Monat & Lazarus, 1991 p. 5). Lazarus later defines coping as “the effort to

manage psychological stress” (Lazarus, 2001 p. 45). If psychological stress is the result

of excess demands on the system, it is a fair conclusion that coping and emotional

regulation are linked in some ways. That is, some coping styles and strategies will also affect emotions.

Lazarus (2001) defines two major types of coping – problem-focused, which is centered on action for changing the stressful environment, and emotion-focused, which is some strategy to regulate the emotions arising from the stressful state. Whereas problem- focused is primarily a cognitive reflection about how to change the reality (if possible) of the presumably undesirable situation, emotion-focused involves the way an individual consciously (and sometimes unconsciously) deals with the emotional events. In general, it is believed that problem-focused coping strategies are used when individuals believe that a change can be made, that they have the ability to change the state, and emotion focused coping strategies are utilized when individuals feels that they have no control

(Carver, Scheier, & Weintraub, 1989).

Although coping is generally viewed as a somewhat unpredictable response to a stressful situation, there is a building body of studies suggesting that there are coping dispositions. Coping strategies and styles, like the appraisals that inform them, are subjective and individualized and tailored by experiences, attitudes and emotions. This fluidity makes it difficult to use as a predictor of future coping strategies (Carver &

Scheier, 1994).

However, some research suggests there are strategies that individuals tend to rely on in similar situations (Carver et al., 1989). Later research conducted by Carver and

Scheier (1994) further explored the idea that individuals may have a tendency for certain coping strategies. In their study, some coping strategies measured prior to a stressful

event correlated with future coping methods used. In their study on coping dispositions, the researchers measured coping dispositions prior to an exam. The participants then completed additional self-reports of coping after the exam, and again after grades were posted. The results suggest that certain coping strategies may have dispositional

tendencies, including active coping, mental disengagement and positive reframing

(Carver & Scheier, 1994).

Coping strategies are influenced by prior affective states, and in turn influence

future affective states. In their research regarding the regulation of anxiety during test-

taking, Schutz, Distefano, Benson and Davis (2004) and Davis and Reiss (2006)

suggested individuals with high test anxiety use more active coping strategies than

individuals with lower anxiety. Theoretically, coping strategies require attentional

resources even though they are evoked to help a person adapt to a given situation. Based

on this theory, the more attentional resources involved in employing coping strategies,

the less attentional resources may be available for other cognitive tasks.

Future reported affective states seem to be influenced by the coping strategies

used by individuals. This is measured by the affective states reported after a particular

coping strategy is used. Emotion-focused coping strategies are correlated with future

depression (Rafnsson, Jonsson, & Windle, 2006) and certain cognitive emotion-focused

strategies are correlated with higher self-reports of unpleasant emotions like anger

(Martin & Dahlen, 2005). While researchers hesitate to label coping strategies positive

or negative, it is suggested that some specific coping strategies are more productive at

helping an individual become better adapted to the environment (Carver et al., 1989;

Lazarus, 2001; Lazarus & Folkman, 1991; Roseman & Smith, 2001). In other words, both problem-focused coping and emotion-focused coping may be adaptive (indicating a productive form of coping) or maladaptive. For example, spending six hours troubleshooting a computer problem instead of calling the help desk could be considered a problem-focused coping strategy, but it may not be adaptive. On the other hand, in certain situations, venting one’s emotions may also be conducive to the healing process.

However, most theorists suggest that problem-focused coping strategies are generally more adaptive (Moos & Holahan, 2003).

It should be noted that alternative theories on problem-focused/emotion-focused coping strategies also exist. One critique of the problem focused/emotion-focused coping strategy theory is that it mixes cognitive elements of coping (thoughts) with behavioral aspects of coping (actions). Garneski, Kraaij and Spinhoven (2001) instead suggest looking at various cognitive coping aspects, focusing on cognitive emotional regulation.

Endler, Parker and Butcher (1994) suggest that instead of two coping dimensions, there are three: task-oriented, emotion-oriented and avoidance-oriented coping dispositions.

Coping with Computers. Very little research has been done regarding the way individuals cope with emotions associated with the daily dealings with technology.

Research by Hudiberg and Necessary (1996) suggest that similar to other models of anxiety and coping, individuals with perceptions of high computer stress and anxiety employ greater coping strategies than those with perceptions of lower computer stress.

However, there is a significant difference, those individuals with high computer stress employed greater emotion-focused coping strategies, while low computer stress

employed greater task-focused coping strategies (Hudiberg & Necessary, 1996). Ropp’s

(1999) study of pre-service teachers suggested that those with greater self-efficacy and computer attitudes were more likely to use coping strategies. However, one limitation or bias of Ropp’s study was the measure of coping strategies (Computer Coping Scale).

The study only included coping strategies that were considered to be adaptive or beneficial to the learning process.

The little research on computer coping suggests that there may be something

fundamentally different about trying to deal with interactions with technology. Humans

are emotional creatures. Computers, on the other hand, (currently) have no emotions.

This discrepancy between emotional and non-emotional may lead to overcompensation

on the part of a human-computer interaction. For example, one study found that when

stressed, individuals sometimes anthropomorphize computers, attributing them with

human emotions and personality (Luczak, Roetting, & Schmidt, 2003). This method of

coping is completely different than something that would be used, for example, during

test taking or coping with a serious illness.

What is particularly interesting about humans coping with computers and

technology is apparent contradiction between rational and irrational reactions. Recent

research suggests that the individuals working with computers are more likely to

acknowledge themselves as being in control of the computer (rather than the computer being in control of the situation, or luck) (Charlton, 2005). The appraisal of “should be in

control” and yet struggling with a technology-based task may be a possible reason for the

emotional reaction that comes with working with technology.

Gender Differences

Studies on gender differences and technology have been mixed. Recent studies on computer anxiety in college students show no significant difference based on gender.

(Korukonda, 2005). Computer self-efficacy studies also report no significant differences

(Torkzadeh et al., 1999). Research comparing gender differences over a ten-year period suggest that some gender difference declines, if not the absence of any gender difference, depending on the application (Colley & Comb, 2003). Although previous studies have shown gender differences in computing, recent research suggests that the pervasiveness of technology in the workforce and in universities has begun to lessen the stigma of computers being a “masculine” tool and instead it is being seen as a more general tool.

Age

Age in the Research . The use of computer technology in adults is a critical matter in the workplace. Researchers have searched for answers to predict which individuals are more likely to adopt and accept a technology (Venkatesh et al., 2003). Much of the research on technology adoption and self-efficacy revolve around adults, particularly in the workforce (Smith et al., 2000; Venkatesh et al., 2003). As the younger generation enters the workforce, there is an anecdotal perception that age plays a significant factor in the use, attitudes and aptitude of computers. The youngest generation in the workforce right now has had much more exposure to computers than that of the eldest generation.

What will happen when this younger generation ages? Will they continue to keep up

their technology skills, or will they lag behind? At this point, only additional long-term research will provide insight. Since the first generations of early-exposed computer users are in their early work-force years, only time will tell.

Age has shown to be a significant factor in some studies regarding technology.

An individual may perceive a technology as being useful, although it does not always reflect their actions (Vodanovich & Piotrowski, 2004-2005). An older individual may have less motivation to use computers (Jay & Willis, 1992), and age was shown to be a moderating factor in intention to use technology (Venkatesh et al., 2003). In a 1988/1989 study, Morris suggested a relationship between age, computer attitude and educational level. It was speculated that although age was a factor, it was the interaction of age and education level that was most important. Morris suggested that older individuals with more education would be more apt to adopt technologies (Morris, 1988-1989). When the study was re-created ten years later (although with a larger sample of older individuals),

DeOllos and Morris (2003-2004) found similar results. Interestingly, in the study there is an anecdotal presumption that as the younger generation ages, they will retain the same interest and skills in computers, even in the face of changing technology. While reports of the current “Generation X” in the workplace suggest that this generation is currently comfortable with the technology they use, it does not speak to how their attitudes, perceptions and behavior will change over time (Bova & Kroth, 2001). The DeOllos and

Morris (2003-2004) study may suggest otherwise. Other than the previous mentioned study, no longitudinal studies on the effect of age and aging on attitudes, aptitude, and usage are in the current literature.

Is age a factor? Although age is frequently considered a factor in technology acceptance and usage models, there is still much research that needs to answer why, and what about the aging process that leads to this discrepancy. Agarwal and Prasad (1999) measured length of tenure in the workforce as a negative correlation with perceived ease- of-use and perceived usefulness. Although it was not a significant factor, it may be an aspect for future research, and was considered in later versions of the technology acceptance model (Venkatesh et al., 2003). The DeOllos and Morris (2003-2004) study suggests there may be something that takes place developmentally that makes adults less apt, less quick, or less motivated to integrate technology into their lives. Cognitive flexibility, changes in goal orientation and changes in self-efficacy may all be possible influences. The current literature base does not answer this question, and it is hopefully a topic for future research.

Summary

While adoption of technologies is, by no means, a new topic; the cycle of technology innovation and adoption has become more salient in recent years. Most theories understanding the decision focus on a cognitive basis for making decisions, excluding or minimizing the influence of emotion on the process.

Based on the literature described in the previous section, I suggest that the appraisal process is critical to understanding the emotions that result from the experience of technology failure. In terms of technology, I believe that how an individual appraises the importance and relevance of technology to the situation will influence the types of emotions an individual experiences. Individual differences, like attitudes and beliefs,

also influence the way individuals interpret and judge the experience of the world. How individuals feel about their own capacity for working with a piece of technology may influence their perceived relevance of the technology to their situation. Age and gender differences, while frequently used for anecdotal explanations for why an individual does not work well with technology, are suggested to be much less important to day-to-day technology than previously.

Understanding how these emotions develop and, in turn, influence the usage and adoption of technology is a complicated issue. Emotional appraisal theory suggests that emotions are the result of an individual’s subjective evaluative assessment of a situation.

The subjective way an individual appraises a situation is influenced by a number of individual differences, such as attitudes, self-efficacy, and coping strategies. It is the hope that future research on technology includes the emotion component – when does technology make individuals angry or frustrated, and what is the influence of this frustration on future usage?

CHAPTER 3

METHOD

Benson (1998) outlines a three-stage process model for developing a strong program of construct validation. . In the substantive stage, the theoretical foundations of the new constructs are investigated based on the current literature. Items for measures are operationalized based on the literature. In the structural stage, relationships hypothesized between items and among the constructs are identified in order to validate items’ connection to the constructs. The external stage connects findings with theoretical evidence and compared with related constructs. In this chapter the substantive validation provided in this chapter bridges the theoretical background summarized in chapter two with the hypotheses developed. The structural validation process continues through the statistical analysis summarized in chapter four, and is ongoing with further research.

Purpose

The purpose of this study was to examine the fit of a dual-appraisal process model of technology failure, and the antecedents that influenced the appraisals and the subsequent emotional experience. Thus, this study was guided by several questions.

First, does the hypothesized model (Figure 3.1) describe the context-specific and general

antecedent factors that influence unpleasant emotions? Second, how do generalized antecedents influence the self-reports of emotions in technology-failure situations?

Finally, how does individuals’ appraisals of the importance of technology to the situation influence expressed unpleasant emotions, both in terms of the discrete emotions reported as well as the relative magnitude reported in a technology-failure situation? The model for this study, as previously mentioned, used the contextual appraisal process as a base

(paths A in Figure 3.1), but also included several factors hypothesized to influence both the appraisal process (paths B in Figure 3.1) and the subsequent emotional experience

(paths C in Figure 3.1).

Figure 3.1: Full model of the factors influencing unpleasant emotions.

Research Design

To understand how a model may explain the emotional reaction to technology failure, the two different types of antecedents described in the model can be examined individually. The first research question involved creating of a model to understand the factors that influence self-reports of unpleasant emotions during technology failure.

Based upon the literature, the model shown in Figure 3.1 was suggested as a possible way to look at how individuals react to technology failure.

Modeling a Dual Primary Appraisal Process (Context Specific Antecedents)

The model can be examined in terms of antecedents to emotions. The first type of antecedents concern the primary appraisal process (see paths A in Figure 3.1), or context- specific antecedents. Current appraisal theory (Smith & Lazarus, 1990b) suggests the primary appraisal process considers only the motivational relevance and motivational congruence of a situation for an individual. As the perceived importance of the situation increases, so does potential for perceived unpleasant emotions. In this study, the motivational congruence and relevance is manipulated by the vignettes, with two of the vignettes having high motivational relevance and congruence, and two having low motivational relevance and congruence. Ultimately, the vignettes describe a technology failure situation, which would be in conflict with the motivational congruence.

Therefore, based upon previous literature, it was hypothesized that the way an individual appraises the importance of a situation will have a direct effect on self-reports of emotion

(Figure 3.2).

Figure 3.2: Current emotional appraisal theory: Situational importance influences emotions.

While most research on appraisals has focused on the relevance of the situation to an individual, little has focused on the role of appraisal of an external tool in the scenario.

Studies of secondary appraisal processes take into consideration accountability (e.g. the computer is at fault, not the individual), but research has not examined the individual’s perceptions of the relevance of the tool prior to the emotional experience. In contrast to traditional views of the appraisal process, it was hypothesized that an additional appraisal process may take place whereby individuals evaluate both the personal relevance of the situation, and the perceived relevance of technology to the situation. Emotion appraisal theory suggests the importance of the tool to the situation would not play a factor in self- reports of emotions. However, this study hypothesized that both the perceived importance of the technology to the situation and the perceived importance of the situation will increase the self-report of unpleasant emotions in technology-based failure situations (Figure 3.3).

Figure 3.3: A dual appraisal process: Situational importance and technology importance influence unpleasant emotions in technology-based failure situations.

Although prior research has not examined the role of the integration of technology into situational importance, appraisal theory suggests that the primary appraisal of importance of the situation would still be the most salient. If something is more personally important, the technology may be perceived to be more critical to achieving the individual goals than when the situation is not as important (Figure 3.4). For example, imagine a situation when a document fails to print. If the document is not due for another week, that particular printer is not as important because there are many alternatives; one could print to another printer or perhaps just wait until the printer is fixed. But when one is on a deadline, that particular printer may become critical; one may not have time to print to a different location, or in frustration, may not be able to see other alternatives. Therefore, it was hypothesized that judgments of personal importance will influence perceived importance of technology to the situation.

Figure 3.4: Situational importance influences technology importance.

General Antecedents of Appraisals and Emotions

Little is known about how individuals come to those judgments that in turn influence emotion. While it is beyond the scope of this study to fully document every aspect of the decision-making process, it was hypothesized there are more salient beliefs, or generalized antecedents, that may influence not only the appraisal of technology importance, but also self reports of emotions (Figure 3.5). These generalized antecedents represent habitual patterns of actions and thoughts that will in turn influence the way an individual judges and reacts to a situation. For example, it was believed that individuals who are highly efficacious and have an affinity to technology will appraise technology as less important to the situation because of their ability to understand and troubleshoot the technology and, perhaps, consider alternatives. On the other hand, individuals with high trust of technology may rely so heavily on the technology, that they may not consider alternatives to that technology, and are hypothesized to consider technology to be more important to the situation. Indirectly, those individuals with higher trust in technology

would also be more likely to report greater intensity of unpleasant emotions in response to the situation.

Figure 3.5: Factors affecting appraisal of technology importance.

In addition, this model investigated and supported additional paths that have been

explored in prior research. These final factors complete the full model (Figure 3.1).

While previous research on computers has examined the effect of affective responses and

future usage of computers (Brosnan, 1999; Czaja et al., 2006) ,few studies have

examined the foundations and/or predictors of these emotions. Although the perceived

importance of a technology is an important factor in explaining emotional response to

technology failure, it is not believed to be the only factor accounting for variability in emotional response. The emotional response an individual feels can also be understood

through individual factors, such as trust, affinity to technology, coping strategies and self- efficacy making up a latent factor of technology attitudes in addition to the appraisal process.

The way individuals cope and the influence of coping strategies on affect have also been previously studied. Studies have shown individuals utilizing more emotion- focused coping tasks are more likely to report experienced depressed affect (Rafnsson et al., 2006), and anger (Martin & Dahlen, 2005). In addition, individuals utilizing more problem-focused coping are less likely to report stress and distress (Struthers, Perry, &

Menec, 2000; Wang & Yeh, 2005). In this study, individuals who coped using more problem-focused means were predicted to report lower levels of unpleasant emotions

than individuals who reported using more emotion-focused coping strategies.

Self-efficacy theory suggests individuals with higher self-efficacy will feel more

confident that they can complete a task. In turn, those with greater self-efficacy for

technology self-report lesser magnitude of unpleasant emotions because they perceive the

technology barrier as more resolvable than those with lower self-efficacy. Research on

computer anxiety showed that reports of lower computer self-efficacy were correlated

with higher reports of unpleasant emotions (Wilfong, 2006). In addition, self-efficacy

may also be related to coping strategies. In a study on student resourcefulness, highly

resourceful Turkish students were found to have higher self-efficacy and higher usage of problem-focused coping strategies (Akgun, 2004). Based upon this research, it was

hypothesized that reports of unpleasant emotions will be influenced by self-efficacy beliefs, and self-efficacy beliefs will correlate with coping strategies.

Several studies suggest if an individual “likes” something a challenge may result in less unpleasant emotions. For example, if a individual likes using or playing with technology, she may not feel as frustrated or angry if it fails to work as anticipated. In a study of college students, a liked but high-effort activity correlated with more feelings of happiness than those activities that were low-effort (Waterman, 2005). In this study, it was hypothesized that individuals reporting higher technology affinity or “liking” of technology will report fewer unpleasant emotions.

Finally, a caveat about two variables that are specifically absent from this model: age and gender. There are many anecdotal perceptions about generation differences in the use of technology. However, it was the hypothesis of this study that age and gender will not play a significant role in the model, that appraisals of technology will not differ by age and gender, and that emotions will also not differ by age or gender.

Design

This study implemented a survey-based, quasi-experimental design. Random

selection of participants did not occur, but participants randomly received one of two

different versions of the survey, which manipulated the order of appraisals in the

vignettes. Data were submitted for structural analysis using LISREL and SPSS.

Institutional review board approval information is available in Appendix A.

Participants

Participants were primarily recruited from undergraduate and graduate classes at a

large, Midwestern university. Data collection took place from November 2006 through

January 2007. Participants were recruited from several academic classes, including EDU

PL 306, 309, 607, 609, 659, 846, as well as EDU PAES 303 and the Nursing Masters program. In addition, a sample of convenience was recruited from the local information technology help desk, several local organizations and places of business. Surveys were distributed in person, with completion of the survey taking place at the time of distribution. In total, 544 surveys were completed and validated for use in the study.

Out of the 544 participants with valid surveys, 30.3% self-identified as male (165),

65.1% self-identified as female (354) and 4.6% chose not to answer (25). Age was self- identified by range. Approximately 56% of the sample was best represented as under the age of 25, the two lowest age ranges. Because of the small number of participants in the later age categories, the original five age categories were condensed to three. In terms of education, 66% of the sample indicated that they had some college experience or a college degree. Another 30.9% indicated education beyond a bachelor’s degree, with

1.5% of those surveyed having a doctorate. Table 3.1 shows the full breakdown of age, gender and education experience.

Gender Age Total

18-25 26-40 41-65+

Male High school 1 3 0 4

Some college 56 19 5 80

College graduate 13 15 8 36

Some graduate /professional 5 8 6 19 school

Masters degree /PhD 0 16 10 26

Total 75 63 29 165

Female High school 2 0 1 3

Some college 173 7 3 183

College graduate 18 21 13 52

Some graduate /professional 23 24 8 55 school

Masters degree / PhD 3 38 25 56

Total 219 90 50 349

Table 3.1: Crosstabulation of Gender by Education and Age

Because this study depends on the success of manipulating participants perceived importance of the situation, some participants were excluded from some or all of the analysis due to the failure of the individual to rate the situation according to the manipulation. Those individuals who did not rate the situational importance within the appropriate low or high range were excluded from analysis of the individual vignette. In addition, individuals who did not answer the initial appraisal question were also dropped from the vignette.

Criteria for removal due Participants Total in Vignette to manipulation failure excluded due to vignette (situational importance) manipulation failure Vignette A: High Score of 2 or less 36 439 Situation Importance

Vignette B: Low Score of 5 or greater 82 425 Situation Importance

Vignette C: High Score of 2 or less 54 409 Situation Importance

Vignette D: Low Score 5 or greater 31 425 Situation Importance

Table 3.2: Participants Excluded by Vignette

Measures

In this study three different types of measures were utilized. First, individuals reported on a series of beliefs and attitudes toward technology, and responded to optional demographic information. Next, a series of four vignettes described a technology-based situation designed to manipulate individuals self-reported their beliefs about importance, after which individuals rated perceived importance of both the technology and the situation. Thirdly, after the vignette describes the technology failure, participants self- reported unpleasant emotions.

General Antecedents

The following measures were collected prior to participants reading the vignettes.

Several previously validated measures were selected for use in this study, although some were altered as noted in the following sections. Where at all possible, individual items were taken from, or based on, existing instruments. Those instruments and measures for this study were developed using several steps, including conferring with experts and other graduate students in the field.

Trust. Trust was operationalized to be a feeling of confidence in the behavior of a specific piece of technology. In other words; do individuals believe that a particular piece of technology will behave (or act) in the anticipated way? Since this study was not concerned with any one particular piece of technology, trust was measure in a generic way to apply to all technologies. General trust questions included, “I believe that most times, my cell phone works the way I think it should,” and, “When I use technology, I

frequently think of alternatives in case it stops working.” Initial reliability for the Trust scale indicated a Cronbach’s alpha of .61.

Self-efficacy . Self-efficacy is defined as the specific beliefs that an individual holds about his or her ability to complete a course of action (Bandura, 1997). In terms of technology, this meant self-efficacy for specific technology tasks. However, some researchers suggest that there is also a more general sense of self-efficacy related to computing (Agarwal et al., 2000). Because the focus of this study was not on any one technology in particular, ten questions assessing the individual’s general technology self- efficacy were included in the survey. A general non-technology self-efficacy scale developed by Schwarzer and Jerusalem (1995) was modified by adding technology phrases to make the scale more specific to technology. The original general self-efficacy scale has been previously shown to have high cross-cultural reliability (Scholz, Dona,

Sud, & Schwarzer, 2002). Items on the original scale including, “I am confident that I could deal efficiently with unexpected events” were modified to incorporate technology in the statement (i.e. “I am confident that I could deal efficiently with unexpected technology events”). The scale was modified slightly to put items more in line with

Bandura’s suggestions for efficacy scales (Bandura, 1997), and questions were put on a six point scale from 0 to 5.

Technology affinity. While many attitude scales deal with specific technologies, the affinity for technology scale was developed by Edison and Geissler (2003) to measure a general attitude towards technology. The original scale includes ten items asking individuals to rate on a 1 to 5 scale their attitude toward technology in general. Items

include: “I find most technology easy to learn,” and, “If I am given an assignment that requires I learn to use a new program or how to use a machine, I usually succeed.”

Reliability of the measure was reported as Cronbach’s alpha of r =.88 for the original measure (Edison & Geissler, 2003). The scale was modified to remove questions that overlap with self-efficacy and to focus wording on emotional feelings about technology.

Cronbach’s alpha for the scale in this study was 0.90.

Coping strategies . In their work on test-taking and anxiety, Schutz and Davis

(2000) discussed the role of task-focused strategies and emotion-focused strategies as

regulation techniques that can also predict achievement in test-taking scenarios (Schutz,

Davis, & Schwanenflugel, 2002). Carver’s Methods of Coping scale (1989) includes

several subscales that describe different types of coping strategies. Based upon Carver’s

original scale, two sub-scales were constructed of task-focused coping strategies and

emotion-focused coping strategies consisting of strategies hypothesized to be most salient

to coping with computers. Several subscales were excluded from Carver’s scale after being evaluated as not relevant to this study. For example, the subscales for turning to

religion and the use of alcohol were excluded.

Participants were asked to consider the coping strategies they used when

interacting with technology. The task-focused coping strategies used consist of active

coping, seeking instrumental social support and positive reinforcement. Emotion-focused

coping strategies included disengagement from the task and venting. Some of the

original questions from the COPE scale were retained, for example, “I get upset and let

my emotions out” is an example emotion-focused coping. Some items were modified to

reflect the use of technology, particularly in terms of problem-focused coping. Carver’s original question “I take action to try to get rid of the problem” was modified to “I try to find answers online or from the manual” as an example of problem-focused technology coping question. Carver (1997; 1989) reports inexact reliability for the original scale, but that all versions of the scale have reliabilities greater than .60.

Coping strategies were hypothesized to be composed of two factors: problem- focused coping and emotion-focused coping. This second-order model, in which the statistical analysis will impose a correlation structure to the two coping factors (Byrne,

1998), suggested that individuals can have both problem-focused coping strategies

(which will be operationalized as “approach” and “avoid”) and emotion-focused coping strategies (“high” and “low”). For example, it was theorized that an individual could potentially rate high on problem-focused coping strategies (approach) and high on emotion-focused coping strategies as well.

Technology Failure Vignettes

The technology failure vignettes involved short stories describing technology situations (Straub, 2007). The goal of the vignettes was to manipulate the appraisals of personal importance and technology importance. Vignettes were written in the first person to support participants putting themselves in the position of the scenario. The

situations themselves were written to simulate common situations that would occur to

adults learners regardless of age, gender or experience with technology. Several drafts

were written and received input from experts in the field of educational psychology, as

well as graduate students in the area.

The vignettes were modeled to anticipate the following four scenarios manipulating technology importance and situational importance in terms of an everyday technology with which the subjects would be familiar. The four vignettes encompass the possible scenarios as listed in Table 3.3. The vignette begins introducing an everyday scenario that includes the use of technology. An example of the beginning of a vignette is:

“I am driving on the freeway on my way to work. I am running a little late today. Shortly after I get on the freeway, I realize there has been an accident. Traffic is completely stopped. I am stuck between exits and unable to get off the freeway. I have no idea how long I am going to be stuck here, but I do know that I am going to be late for work. My boss would not have a problem with me being late, but, I must call him. If I do not, I will lose a percentage of my day’s salary. I have my cell phone with me, so I reach to make the call.”

After the description of the scenario, the participants were asked to rate their perception of personal importance of the situation, and secondly, their perception of

importance of technology to the situation. Next, a failure of the technology was written

into the vignette. For example in the previous vignette, it continues: “As I start to dial

my cell phone, it completely goes dead. I check the battery, and try turning it on and off

again. It is not working. I am unable to call my office.” After reading an entire vignette, participants were then asked to rate their emotions.

Vignettes A and C were designed to both be rated high on situational importance,

and Vignettes B and C were designed be rated significantly lower in situational

importance. In terms of technology importance, Vignette A (High Personal/High Tech)

and Vignette B were both written in such a way that the success of the situation was

highly dependent on technology, and thus would be rated high technology importance;

Vignettes C and D were written with alternatives to the technology, and therefore would be considered low technology importance (see Table 3.3 for each vignette manipulation).

A pilot study was conducted to test the reliability and proposed manipulation of the vignettes with acceptable results (Straub, 2007a). The pilot study also found no significant differences in appraisals due to gender or age.

Vignette A: Vignette C:

High Technology Importance / Low Technology Importance /

High Situation Importance High Situation Importance

Vignette B: Vignette D:

High Technology Importance / Low Technology Importance/

Low Situation Importance Low Situation Importance

Table 3.3: Structure of Vignettes

Emotion

There is an entire body of research that emotions into categories (Frijda, 2005).

Happiness feels much different than sadness or anger. Current appraisal theorists

(somewhat hesitantly) categorize emotions into positive or negative (Lazarus, 2001;

Roseman et al., 1990) or, pleasant and unpleasant (Schutz & Davis, 2000; Smith &

Kirby, 2001b). Pleasant emotions like happiness, love, pride, and compassion suggest a

the situation appraisal was more positive than anticipated. In contrast, emotions like fear, frustration, sorrow, anger and regret imply that the motivational expectations were not positively correlated with the outcome.

Because of the highly individualized nature of emotions, discrete differentiations can be difficult to operationalize. Most people know the emotion when they feel the emotion. Yet, researchers have tried to make sense of what an emotion implies. Survey- based emotion self-reports have been used in several studies of emotion, and are considered valuable to collect the subjective experience of emotion (Wallbott & Scherer,

1989). This study used measure similar to the one described by Wallbott and Scherer

(1989) that was used in studies by Roseman and Evodokas (2004) where participants were asked to rate their personal opinion of specific emotions on a scale. Three unpleasant emotions (anger, anxiety, frustration) and one less unpleasant (challenge) was measured on a 0 to 5 scale (“When I am in this situation, I feel…[emotion]”). Zero meant that the individual does not feel this emotion at all, and 5 meant he or she felt the greatest magnitude of this emotion.

Data Analysis

The statistical program SPSS version 15 was used for input and initial statistical analysis, including descriptive statistics, and LISREL version 8.30 was used for computation in structural equation modeling.

Differences by Age and Gender

First, the data was screened to verify for the desired manipulations, and to check for gender and age differences in the initial appraisals. To test for differences by age and gender, composites were made for the Technology Affinity, Trust and Emotions scale.

Because the item for appraisal of Technology Importance was a single item, there was no composite needed.

A repeated measures ANOVA was run on within factors of the emotion composite scores and Technology Importance scales separately, using gender and the collapsed age as the between-subjects factors. In addition, a univariate ANOVA was calculated on the composites of Technology Affinity and Trust, using gender and collapsed age as the between-subjects factors. Each composite was tested for homogeneity of variance, and in those where the variance was heterogeneous, a

Greenhouse-Geisser adjustment was used. For emotions, significant differences were found by vignette (F (2.86,484) =548.08, p<.001) with a moderate effect size. Although

significant differences were also found in emotions by age (F (2.86,484) =6.17, p<.001) and

gender (F (2.86,484) =3.57, p<.001), both interactions have small effect sizes, suggesting that difference may in part be due to the large sample size.

Situation Importance df F Significance Partial Eta Squared

Vignette (main effect) 2.86 725.11 .000 0.70

Vignette*Gender 3 5.84 .001 0.02

Vignette*Age 5.72 6.60 .000 0.04

Vignette*Gender*Age 5.72 0.88 .51 0.01

Technology Importance df F Significance Partial Eta Squared

Vignette (main effect) 2.51 502.63 .00 0.51

Vignette*Gender 2.51 8.64 .00 0.02

Vignette*Age 5.02 6.45 .00 0.03

Vignette*Gender*Age 5.02 1.60 .16 0.01

Trust in Technology df F Significance Partial Eta Squared

Gender 1 0.05 .83 0.00

Age 2 0.10 .91 0.00

Gender*Age 2 0.46 .64 0.00

Continued

Table 3.4: Differences by Age and Gender

Table 3.4 ( continued )

Technology Affinity df F Significance Partial Eta Squared

Gender 1 43.25 .00 0.08

Age 2 3.72 .03 0.02

Gender*Age 2 0.90 .41 0.00

Emotions* df F Significance Partial Eta Squared

Vignette (main effect) 2.86 548.08 .00 0.53

Vignette*Gender 2.86 3.57 .02 0.01

Vignette*Age 5.72 6.17 .00 0.03

Vignette*Gender*Age 5.72 .27 .94 0.00

Structural Equation Modeling

Structural equation modeling (SEM) is a collection of statistical techniques, based on the general linear model, which allows a researcher to test a set of relationships between multiple independent and dependent variables (Ullman, 2006). SEM is different from path analysis and ANOVA because of its capacity to estimate relationships between latent constructs (Weston & Gore, 2006).

Two assumptions influenced the structure and fit of the model. First, the overall hypothesis of the model was that reported unpleasant emotions were influenced by six 67

factors: situational importance, technology importance, coping strategies, self-efficacy, trust, and affinity to technology. There were eight latent variables, and each observed variable was hypothesized load on one and only one latent factor. Second, errors of measurement were hypothesized to be uncorrelated with each other.

Fitting a model. There were several steps to examining model fit. First, structural equation modeling requires the degrees of freedom be greater than the number of the unknown parameters to be estimated, which indicated overidentification. Each individual measurement model in the study, the full measurement model, and the structural model all require an overidentified status. Based upon of the hypothesized parameters, and the number of data points in this study, the model was determined to be overidentified.

Second, a two-step approach to model fit required examining the fit of the measurement model (and the underlying models of the latent variables) first, followed by the fit of the structural model. The measurement model, “provides the link between scores on a measuring instrument… and the underlying constructs they are designed to measure” (Byrne, 1998). The full measurement model involves each observed and latent variable. In addition, each latent variable in the study (e.g.. “coping strategies”) constituted its own measurement model, and was evaluated individually for reliability and validity. Examination of the latent measurement models indicated the observed variables (items on the individual scales) were reliable measurements. Once each individual measurement model was verified, the full measurement model was verified.

Upon verification of the fit of the full measurement model, the hypothesized structural model was tested for fit of the model to the data.

Figure 3.6 Coping Strategies Measurement Model.

The third step was to verify the structure of the overall measurement model. In other words, the model was run with all the latent factors together (Figure 3.6). The full measurement model was translated into a series of equations that included the measurement model, followed by the structural model. The covariance matrixes were inputted in LISREL to test the fit of the model evaluated given the observed data. Once the individual measurement model was determined to be a good fit, the goodness of fit

will be compared against a series of indices to indicate how well the model explains the variance in the sample.

Selecting fit indices. Because structural equation modeling does not have one comprehensive fit index, it is recommended to test the ability of a variety of indices to judge how the model fits the data (Byrne, 1998; Diamantopoulos & Siguaw, 2000). In addition, each index has a different point of criteria in judging goodness of fit for a model. Bollen (1989) notes that these criteria for judging goodness of fit are arbitrary, and thus a researcher may want to compare several indices for improvement of fit rather than a single cut-off point for a model. Therefore, several indexes were selected for comparison.

First, structural equation modeling uses the χ2 to initially judge the overall fit of

the model. As opposed to other kinds of null hypothesis testing, in SEM the null

hypothesis is that the model is a perfect fit. Therefore, a good-fitting model would result

in the failure to reject the χ2 statistic. However, the χ2 has several limitations that make it

difficult to use as a sole judgment in SEM (Loehlin, 2004). First, χ2 is sensitive to sample size: a larger sample size makes it more likely to reject the null hypothesis. In addition, χ2 is sensitive to model complexity; a simpler model with fewer variables will be more likely to fail to reject the null hypothesis, thus suggesting an erroneous good fit.

It is suggested that using the normal theory weighted least squares χ2 statistic is a better statistic with a large sample size (Schumacker & Lomax, 2004).

To provide a more accurate view of model, in addition to the χ2 statistic, several other statistics was used to evaluate the model’s fit. Each index provides a greater 70

understanding of the goodness-of-fit of the model. The comparative fit index (CFI) compares the fit of a baseline model with that of a better the hypothesized model

(Jorkeskog & Sorbom, 1993). An acceptable CFI score is above 0.90. An additional fit index is the root mean square error of approximation (RMSEA), which is a goodness-of- fit test that compares observed covariances to predicted covariances. A good fit for

RMSEA is 0.05, but an acceptable range is between 0.09 and 0.05. Both CFI and

RMSEA are less sensitive to sample size than some other indexes, although RMSEA is generally considered to penalize simpler models (Loehlin, 2004). Finally, the residual errors of the model were examined for small (less than 0.05) residual errors

(Diamantopoulos & Siguaw, 2000). External validity is suggested through several indices indicating a good fit. If the overall measurement model does not fit, the individual items were evaluated as individual indicators of a construct.

Verification of the full model. The evaluation of the full model used the same above described procedure to evaluate the identification and fit of the measurement model. Once the measurement model was verified, a correlation matrix using the trust, coping, affinity and self-efficacy measures and the matrix for each of the four vignettes was submitted to LISREL. In this study, a correlation matrix fo each vignette was constructed (see Appendices E through H for the correlation matrix for each vignette).

By first specifying a hypothesis based on the literature, the analysis was first run on the measurement model, which was identical across each of the four vignettes. Next, the identical hypothesized model was submitted to LISREL for analysis using each of the four vignettes’ vignettes correlation matrices, each time comparing the fit indices of the

specified models. While these individual fit indices cannot be compared directly, they can inform the overall picture of how the model fits each of the four different situations.

Although one area of interest in this study was how participants appraised and reacted in each of the different vignettes, a limitation of SEM is its inability to directly compare repeated measures data. One way to examine whether the pattern of relationships in separate correlation matrices are similar or distinct is to hypothesize a model (or a set of models) and submit them to path analysis in a method comparable to

“seemingly unrelated regression” (SUR) analyses (Bollen, 1989). Byrne (1998) examined whether the relationship between academic self-concept and academic achievement were equivalent when looking at general beliefs versus domain-specific beliefs in English and mathematics. Other methods that have been used to compare patterns of relationships in different subject domains include multi-group analyses

(Byrne, Shavelson, & Marsh, 1992). Although each vignette was completed by each participant, each appraisal of the vignette is seemingly unrelated to the previous vignette.

In a previous study, the order of vignettes had no influence on the self-reports of emotions (Straub, 2007a), therefore indicating the independence of each appraisal.

Using SUR, four sets of LISREL (Joreskog & Sorbom, 1996) analyses were run, one for each vignette. Each time, the identical hypothesized model was submitted using the same data of the controlling factors with different appraisals and unpleasant emotions. Fit indices were then compared for the specified models. Finally, the individual paths within the model will also be tested for significance, and the latent variables will be tested for

the direct effects of technology importance and emotion, as well as direct and indirect effects on emotion.

Summary

The goal of this study was two explore some of the factors that influence emotional experience when difficulties with technology are experienced. Using an experimental design, the study established the role of technology in the appraisal process as influential to self-report of emotions. It was hypothesized individuals would report stronger unpleasant emotions when technology is perceived to be important to a situation.

This study contributes to the understanding of not only emotion, but also technology adoption. The framing of technology adoption in an emotional context rather than a strict cognitive context allows researchers to better understand the tension between cognitive needs for adoption and emotional aversion to technology.

CHAPTER 4

RESULTS

Validating the Measurement Model

Each of the scales employed represented a latent construct in the study. First, items on each scale were evaluated for how accurately they represent the concept being measured. If an item was deemed not to best represent the latent concept, it was removed from the instrument. This screening was done at several points in the process, starting at the descriptive stage where an item was flagged due to excessive skewness and/or kurtosis (see Appendix C). Next, scales were examined for reliability. A Chronbach’s alpha was calculated for each scale to judge reliability. Items were flagged and/or excluded at this point.

The following sections describe how the model was validated using structural equation modeling (SEM). The individual scales were validated through SEM using several different fit indices to judge the fit of the individual items on the latent variable.

Next, all of the latent variables were submitted together to be validated as a measurement model. Finally, the full structural model was submitted to LISREL for each of the four vignettes, where the fit for the model was compared in different situations.

General Trust

The latent variable of General Trust was measured with 10 questions. Full descriptive information for the individual items in the Trust scale is available in Table

D11 in Appendix D. Two items, T4 and T5 were noted to have high skewness and kurtosis (over 1.0), however both items reflected an individual’s potential high trust for technology, so they were noted for future reference.

Reliability for the full General Trust scale was estimated to have an alpha of .61 with 40 excluded cases due to missing data. As shown in Table 4.1, Question 1 in the scale seemed to decrease the reliability of the scale from the desired range. The first question was dropped from the scale due to poor wording (the question was reversed and was confusing), and reliability data was run again with 9 items rather than 10. The alpha estimate increased to .74, which is considered an acceptable reliability. Items 3 and 5 had the highest factor loadings onto the construct of Trust, .57 and .56 respectively.

Cronbach’s alphas when each item is deleted are reported in Table 4.1.

Item-total Alpha if

correlation Item Deleted

T2. I see technology working properly frequently. 0.52 .71

T3. I think that the technology I use is generally 0.53 .71

reliable

T4. Many of my friends, family or colleagues 0.37 .73

successfully use technology in their day-to-day lives

T5. In general, I trust technology to work as I 0.57 .70

anticipate it to.

T6. I believe that most times, my cell phone works the 0.34 .73

way I think it should.

T7. When I use technology, I frequently think of 0.33 .74

alternatives in case it stops working (R)

T8. I think it’s risky to depend too much on 0.44 .72

technology in my daily life (R)

T9. I’m frequently aware of all the things that could 0.32 .74

go wrong when I use technology (R)

T10. I feel like technology never works the way I 0.56 .70

think it should. (R)

Table 4.1: General Trust Reliability with Question 1 dropped

Items were submitted to LISREL for validation of the model. The full model with all items resulted in a rejection of the null hypothesis ( χ2). The original scale contained a

large number of items so to allow for adjustments for the measurement model. Five

items were flagged for further investigation based on a post-hoc review of the scale. Item

4 had been previously flagged due to high skewness and kurtosis issues. Items 7, 8 and 9

were all reverse-coded items and, on further review, may have been difficult for participants to understand. Item 6 was about a specific piece of technology, and a participant’s general trust in technology may not be reflected by his or hers trust in a

specific technology. Items 4, 6, 7, 8 and 9 were dropped one at a time from the scale, and

evaluated for overall fit. The model for Trust was initially validated including items 2, 3,

5, and 10. By dropping item 10, the model was a perfect fit. Table 4.2 summarizes the

measurement model for General Trust.

χ2 df χ2/df RMSEA CFI SRMR

Model with Items 2-10 340.99* 27 12.63 0.16 0.81 0.10

Adjusted model with Items 2, 3, 5, 5.71 2 2.86 0.06 1.00 0.01

*p<.01

Table 4.2: Measurement model for General Trust

Self-Efficacy

Self-efficacy was measured using 9 questions designed to measure an individual’s belief in his or her ability to successfully perform a technology based task in the future.

Descriptive information about the self-efficacy scale is available in Table D 12 in

Appendix D. A review of the items suggested none of the items needed to be flagged at the descriptive level due to skewness or kurtosis.

The alpha for the scale was estimated at r =.93, which is considered an acceptable reliability for a scale. Full reliability information is available in Table 4.3. No items were flagged for reliability problems at this time.

Item Item-total Alpha if correlation item deleted S1: I can always manage to solve a difficult technology 0.76 0.92 problem if I try hard enough

S2: If I am having difficulties solving a problem, I can use 0.64 0.93 technology to find the means and ways to get what I want.

S3: I can deal efficiently with unexpected technology events. 0.83 0.92

S4: Thanks to my resourcefulness, I know how to handle 0.83 0.92 unforeseen technology failures.

S5: I can solve most of my technology problems if I invest the 0.82 0.92 necessary effort.

S6: I can remain calm when facing technology difficulties 0.68 0.93 because I can rely on my coping abilities

S7: When I am confronted with a problem involving my 0.80 0.92 computer, I can usually find several solutions

S8: If my technology crashes, I can usually think of a 0.77 0.92 solution.

S9: I can usually handle whatever comes my way 0.53 0.93

Alpha = .93 N=517

Table 4.3: Scale Statistics for Self-Efficacy

The measurement model for Self-Efficacy was submitted to LISREL for validation of the measurement model. Summary statistics for the Self-Efficacy model are available in Table The initial model, with all nine items, was rejected with a χ2 of

292.59. Because the RMSEA was also unacceptably high, the questions were examined

for variability. Item S9 was flagged for accounting for low amounts of the construct, and because the wording of the question was not technology specific, it was dropped from the

model. At this point, the RMSEA did not improve, but the CFI indicated a good enough

fit to go forward.

χ2 df χ2/df RMSEA CFI SRMR

Model with all items 292.59 27 10.83 0.14 0.94 0.04

Model with S9 and S1 158.03 14 11.28 0.14 0.96 0.03

dropped

Table 4.4: Measurement Model for Self-Efficacy

Affinity

Technology Affinity was measured with nine questions designed to indicate an individual’s attraction and affective responses to the use of technology. Full descriptive information about the scale is available in Table D14 in Appendix D. None of the items were flagged due to abnormal skewness or kurtosis.

The alpha for the scale was estimated at r =.90, which is considered an acceptable reliability for a scale. Item A4, which was reverse coded was flagged because of low inter-item correlation, but did not lower the reliability of the scale enough to be eliminated at this time. Items 6, 2 and 5 had the highest loadings onto the construct of

Technology Affinity. Item-total correlations for technology affinity are reported in Table

4.5.

Item Item-total correlation Alpha if item

deleted

A1. Technology is my friend… 0.56 0.89

A2. I enjoy learning new computer programs 0.78 0.87 and hearing about new technologies

A3. I take pleasure in assignments that 0.75 0.88 require me to learn a new program or learn how to use a new technology

A4. I feel ashamed when I struggle with a 0.10 0.92 new piece of technology (R)

A5. I relate well to technology and machines 0.77 0.88

A6. I am comfortable learning new 0.85 0.87 technology

A7. Solving a technological problem seems 0.73 0.88 like a fun challenge

A8. I find most technology easy to learn 0.78 0.88

A9. I feel as up-to-date on technology as my 0.66 0.88 peers

Alpha = 0.90 N=527

Table 4.5: Scale Statistics for Technology Affinity

The measurement model for Technology Affinity was submitted to LISREL for validation of the measurement model. The initial model, with all nine items, was rejected with a significant χ2 of 381.92. Items A4 and A9 had the lowest loadings onto the construct of Technology Affinity, most likely due to the reverse wording of item 4 as noted in the previous section. Item A4 was dropped, and the model was run again, with a decreased χ2 of 361.97. By dropping item A4, the CFI index remained within an acceptable range, but the RMSEA decreased, suggesting that the model was destabilizing due to the loss of items. Dropping item A9 in addition to A4 resulted in additional instability. Because both χ2 and RMSEA are sensitive to model complexity and degrees

of freedom, the final model to be submitted included 8 items, with item 4 excluded.

Although it was not excluded from the scale, item A9 was flagged for future possible

exclusion because the wording of the question may suggest that individuals may like

technology but still not necessarily be up-to-date on technology. Summary information

for the model is reported in Table 4.6.

χ2 df χ2/df RMSEA CFI SRMR

Model with all 9 items 381.92* 27 14.16 0.16 0.90 0.04

Model with Item 4 dropped 361.97* 20 18.10 0.18 0.90 0.45

Model with Items 4 and 9 295.28* 14 21.09 0.21 0.90 0.45 dropped

*p<.01

Table 4.6: Model for Technology Affinity

Coping

Coping was hypothesized to have a secondary structural model, built upon two separate latent coping structures – problem-focused and emotion-focused coping. These two latent concepts should describe the concept of coping. To test this, both latent structures were validated and the model for coping as structured by the two latent variables was verified.

Problem-focused coping. Problem-focused coping was measured using 8 questions designed to ascertain an individual’s likelihood to cope by focusing on a possible solution to a technology problem. Full descriptive information for the problem- focused coping scale is available in Table D 15 in Appendix D. Two items were flagged at the descriptive level because of high skewness and kurtosis: item PF7 and item PF8.

Initial reliability testing for the problem-focused scale estimated an alpha of .78, which can be interpreted as a low level of acceptable reliability. The wording for item

PF8 overlapped with item PF7. By dropping item PF8, reliability for problem-focused coping increased to a Cronbach’s alpha of .80. Problem-focused coping reliabilities are provided in Table 4.7.

Item Item-total Alpha if item

correlation deleted

PF1. I concentrate my efforts on trying to find a 0.61 0.74 solution.

PF2. I take it one step at a time. 0.56 0.75

PF3. I try to come up with a strategy about what to do. 0.67 0.73

PF4. I focus on dealing with the problem. 0.70 0.73

PF5. I put aside other activities to try to concentrate of 0.49 0.76 resolving the problem.

PF6. I try to find answers online or from the manual. 0.41 0.78

PF7. I talk to my friends to see if they have had a 0.36 0.78 similar problem

PF8. I try to get advice from someone about what to do 0.22 0.80

Alpha = 0.78 N=530

Table 4.7: Scale Statistics for Problem Focused Coping

The initial model, with items 1 – 7, was submitted to LISREL for validation. The model showed a lower than acceptable RMSEA, but an acceptable CFI. Item PF7, which may theoretically differ because talking to one’s friends is not the same as actively

finding a solution, had the next lowest loading on the construct, was then dropped to check for an improved fit. Full summary information is shown in Table 4.8.

χ2 df χ2/df RMSEA CFI SRMR

Model with items 1 - 7 160.26* 14 11.45 0.14 0.92 0.06

Model with items 1 - 6 112.66* 9 12.51 0.15 0.93 0.05

Model with items 1 - 5 82.49* 5 16.50 0.17 0.94 0.05

*p<.01

Table 4.8: Measurement Model for Problem-Focused Coping

Emotion-Focused Coping . Emotion-focused coping was measured using 8 questions designed to ascertain an individual’s likelihood to cope by focusing on regulating and expression of the emotions that might be aroused during a situation.

Descriptive information for the individual items in the scale are available in Table D 13 in Appendix D. No items were flagged at the descriptive level due to skewness or kurtosis.

Initial reliability testing for the emotion-focused scale estimated an alpha of .57, which can be interpreted as an unacceptable level of reliability. Full reliability information is in Table 4.9. Because of the low alpha, the scale was examined to increase reliability. A post-hoc examination of the scale suggested that item EF2 may not be indicative of emotional regulation, and was dropped from the scale, this increased the alpha to .60. Item EF8 was also determined to be an ill-fit for the scale due to the

wording of the instructions to participants which included how they coped during technology failure. Dropping item EF8 increased the reliability estimate to .64. Finally, item EF7 was dropped because the wording of the question may not reflect emotional regulation, which increased the alpha estimate to .73.

Item Item-total Alpha if item

correlation deleted

EF1. I get upset and let my 0.37 0.51 emotions out

EF2. I learn to live with it. 0.05 0.60

EF3. I feel a lot of emotional 0.39 0.51 distress and I find myself expressing those feelings a lot

EF4. I talk to someone about 0.38 0.50 how I feel

EF5. I let my feelings out 0.46 0.48

EF6. I try to do something else 0.31 0.53 to take my mind off the problem.

EF7. I make fun of the situation. 0.18 0.57

EF8. I look for something 0.13 0.59 positive in what is happening

Alpha = 0.57 N=535

Table 4.9: Scale Statistics for Emotion Focused Coping

The initial model for Emotion-focused coping with items 1, 3, 4, 5, and 6 was submitted to LISREL for validation, with a significant χ2 of 65.44. At this time, item 4 displayed high residual errors with items EF1 and EF5, possibly because all three questions directly deal with the expression of emotion. Dropping item EF4 resulted in a model that was significant at the p>.05 level, but all other indices indicated a good fit, as summarized in table 4.10.

χ2 df χ2/df RMSEA CFI SRMR

Model with items 1, 3, 4, 5, 6 65.44* 5 13.01 0.15 0.93 0.05

Model with items 1, 3, 5, 6 12.74** 2 6.37 0.10 0.98 0.03

*p<.01, **p<.05

Table 4.10: Measurement Model for Emotion-Focused Coping

Full coping model . After the validation of the two individual latent models of problem-focused coping and emotion-focused coping, a measurement model using both variables was submitted for validation to LISREL. However, the model with two latent variables for emotion-focused coping and problem-focused coping did not converge into a single latent variable for coping.

A re-examination of the two scales suggested that some of the questions on the two scale overlapped in a way that might result in residual error. Both problem-focused and emotion-focused coping included items that assessed participant’s reliance on other for possible support. For example, the problem-focused scale had a question dealing with

seeking external support for a problem (“I try to get advice from someone about what to do”) as did emotion-focused coping (“I talk to someone about how I feel”). Although in

Carver’s (1989) scales delineated the types of support an individual received based on emotional support versus active resolution of a problem, on both scales, the items that dealt with social coping mechanisms were dropped from the scale.

To account for these variances, a third latent variable was constructed using questions from the emotion-focused coping scale and problem-focused coping scale.

This variable, was made up of three questions, “I talk to someone about how I feel,”

(from emotion-focused coping scale), “I talk to my friends to see if they have had a similar problem” and “I try to get advice from someone about what to do” (from the problem-focused coping scale). These three questions had been previously eliminated

from the latent coping variables.

The model for coping using all three latent variables of coping was submitted to

LISREL for validation. However, the model with three latent variables for emotion-

focused coping, problem-focused coping and social-coping also did not converge into a

single latent variable for coping. The three latent variables were then used in the model

as latent variables rather than in a hierarchal structure in the measurement model.

Verification of the Measurement Model

The full measurement model was submitted to LISREL for analysis with the

latent variables Trust, Self-Efficacy, Technology Affinity, Expression-focused Coping,

Problem-focused coping and Social Coping. The full model did not achieve a good fit by

any measurement, so the model was re-evaluated. First, the scales were reexamined for

items with low loadings on the factors, in this case defined as loadings less than 0.60.

The scales used in the study contained multiple items to allow for dropping of poorly- loading items. Each item was dropped one at a time, with the model resubmitted to

LISREL for model fit. After dropping four low-loading observed variables, the model fit improved by some indexes (RMSEA and CFI), but not by all indices (See Table 4.11).

Therefore, the model was again re-evaluated for items with high cross loading and items with high residual error. Several items were flagged for cross-loading on other constructs or high error. These items were examined to see if theory could explain the reasons for the cross-loading or residual error. For example, two items from the self- efficacy scale S7 (“When I am confronted with a problem involving my computer, I can usually find several solutions”) and S8 (“If my technology crashes, I can usually think of a solution”) were determined to have a high residual rate. Because the questions duplicated each other by asking about finding solutions to a technology problem, S7 was included in the scale due to higher loading. Several items on the self-efficacy scale also cross loaded with other scales. For example, item S6 (I can remain calm when facing technology difficulties because I can rely on my coping abilities) cross-loaded onto the problem-focused coping scale. Ultimately, it was dropped from the scale due to a low

loading on to self-efficacy in addition to the cross loading. Items were also evaluated for

cross loading based on theory. For example, technology affinity item A1 (Technology is

my friend…) was allowed to cross load with social coping and trust because the idea of

“friend” may make participants consider issues of trust, and social networking with

technology. If a person is considered a “friend,” generally he or she has some level of

trust and also imply a social connection. Therefore, if technology is a friend, there is also a level of trust. However, item A7 (Solving a technological problem seems like a fun challenge) was not allowed to cross-load with social coping because there was no theoretical basis for the item loading on both scales.

The measurement model was again submitted to LISREL again (see Table 4.11), with unacceptable results. The construct of social coping was flagged for high error and cross-loading with other constructs. It was determined then that social coping as a latent construct should be removed from the measurement model. Because it was an artifact from the construct of coping and not part of the original model, it did affect the model as originally hypothesized. Removing social coping also eliminated a large source of error.

This final model was submitted to LISREL and overall, the indices indicated an acceptable fit. The structural model is shown in Figure 4.1.

χ2 df χ2/df RMSEA CFI SRMR

Model with original 6 latent 1666.86 362 4.60 0.08 0.88 0.07 structure (29 observed)

Model with 6 latents, low-loading 1175.82 260 4.52 0.08 0.91 0.06 items dropped (T4, T10, A1, P5)

Model with 6 latents, dropped cross-loading and high error items 636.52 174 3.66 0.07 0.94 0.06

(S2, S6, S8, A7)

Model with 5 latents, dropped 398.48 125 3.19 0.07 0.96 0.04 Social Coping

Table 4.11: Summary of Full Measurement Model

Trust

Self- Efficacy

Technology Affinity

Emotion Focused Coping

Problem Focused Coping

Figure 4.1: Final measurement model

The measurement model for the variables was determined to fit the data. Several challenges in fitting the model provide guidance for future researchers. The structure of coping did not fit as hypothesized, but was still able to be used in the final model, therefore future studies may want to re-examine how coping is measured. In addition, self-efficacy is a difficult construct to measure on its own, as ideas about confidence may bleed over into other constructs like coping. However, the model was restructured to achieve an acceptable goodness of fit.

Research Questions

Question One: Does a dual-appraisal process model of situational and technology

importance fit the data on technology failure?

The structural model was submitted to LISREL for each of the four vignettes.

Model fit indices for each of the individual vignettes are reported in Table 4.12. The fit indices indicate a good fit for the model overall for each of the four vignettes.

Vignette A Vignette B Vignette C Vignette D

(High Personal / (Low Personal (High Personal (Low Personal

High Tech) / High Tech) / Low Tech) / Low Tech)

(439) (425) (405) (425)

χ2 567.00 700.36 710.61 645.56

χ2/df (230) 2.71 3.25 3.12 2.96

RMSEA 0.06 0.07 0.07 0.07

CFI 0.95 0.93 0.93 0.95

SRMR 0.04 0.04 0.04 0.04

Table 4.12: Summary of Model Statistics by Vignette

Question Two: How do the variables of trust, self-efficacy, affinity, and coping styles influence self-reports of unpleasant emotions in technology-failure situations?

Each of the direct and indirect paths in the model was tested for significance on the constructs of Technology Importance and Emotions, as well as indirect effect on

Emotions. Tables 4.13 and 4.14 provide summary information for the paths and significance of paths for each of the four vignettes. In addition, Table 4.15 shows the indirect of the latent variables on emotion through appraisals of technology importance.

The path analysis of the model indicated that technology importance influenced self-reports of emotions in three out of the four vignettes. Situational importance

influenced perceptions of technology importance in all four of the vignettes. Trust in technology, a new construct in the model, had a significant effect on appraisals of technology importance in situations of high technology importance (Vignettes A and B) and a significant effect on decreased emotions in situations of low situation importance

(Vignettes B and D). Figure 4.2 shows the model with the range of effects across the four vignettes.

Figure 4.2: Range of effects across vignettes

Vignette A Vignette B Vignette C Vignette D

(sample size) (High Personal / (Low Personal (High Personal (Low Personal

High Tech) / High Tech) / Low Tech) / Low Tech)

(439) (425) (405) (425)

Trust Tech Imp 0.15* 0.16* 0.00 0.05

(0.004)

Trust Emotions -0.07 -0.11* -0.03 -0.13*

Self Efficacy Tech Imp 0.05 -0.03 0.06 -0.27*

Self Efficacy Emotions 0.00 -0.22* -0.02 -0.02

(-0.002)

Affinity Tech Imp -0.18* 0.43* -0.09 0.20*

Affinity Emotions 0.01 0.08 0.10 0.02

Express Focus Coping 0.41* 0.12* 0.16* 0.07

Emotions

Problem Focus Coping -0.16* -0.06 -0.19* -0.02

Emotions

Situational Imp Tech 0.52* 0.30* 0.52* 0.64*

Imp

Situational Imp 0.12* 0.32* 0.04 0.32*

Emotions

Tech Imp Emotions 0.28* 0.07 0.33* 0.27*

* p>.05

Table 4.13: Paths by Exogenous Variable

Vignette A Vignette B Vignette C Vignette D

(High Personal / (Low Personal / (High Personal (Low Personal

High Tech) High Tech) / Low Tech) / Low Tech)

(439) (425) (405) (425)

Tech Importance

Trust Tech Imp 0.15* 0.16* 0.00 0.05

Self Efficacy Tech Imp 0.05 -0.03 0.06 -0.27*

Affinity Tech Imp -0.18* 0.43* -0.09 0.20*

Situational Imp Tech 0.52* 0.30* 0.52* 0.64*

Imp

Emotions

Trust Emotions -0.07 -0.11* -0.03 -0.13*

Self Efficacy Emotions 0.00 -0.22* -0.02 -0.02

Affinity Emotions 0.01 0.08 0.10 0.02

Express Focus Coping 0.41* 0.12* 0.16* 0.07

Emotions

Problem Focus Coping -0.16* -0.06 -0.19* -0.02

Emotions

Situational Imp 0.12* 0.32* 0.04 0.32*

Emotions

Tech Imp Emotions 0.28* 0.07 0.33* 0.27*

* p>.05

Table 4.14: Paths by Endogenous Variable

Paths Vignette A Vignette B Vignette C Vignette D

(High Personal (Low Personal (High Personal (Low Personal

/ High Tech) / High Tech) / Low Tech) / Low Tech)

(439) (425) (405) (425)

Emotions

Trust Emotions 0.05 0.01 0.00 0.01

Self Efficacy Emotions 0.02 0.00 0.02 -0.07

Affinity Emotions -0.05 0.00 -0.03 0.05

Situational Imp Emotions 0.14* 0.02 0.17* 0.17*

* p>.05

Table 4.15: Indirect Effects on Emotions

Question Three: Does an individual’s perceived importance of the situation influence perceptions of technology importance?

A two-tailed, bi-variate correlation was run on the appraisals of Technology

Importance and Situation Importance for each vignette. All were significant at the p<.001 level. Summary information for the correlations are shown in Table 4.16.

Vignette r R2

Vignette A: High Technology Importance / .35 .12 High Situation Importance

Vignette B: High Technology Importance / .34 .12 Low Situation Importance

Vignette C: Low Technology Importance / .42 .18 High Situation Importance

Vignette D: Low Technology Importance/ .51 .26 Low Situation Importance all are significant at the p<.000 level

Table 4.16: Correlation of Technology Importance and Situation Importance

Question Four: Do unpleasant emotions differ depending upon the appraisals of importance?

As noted in Table 3.4, differences in a composite of emotions were found between vignettes. A repeated measures ANOVA was run on each individual emotion for each vignette. Because of concerns of variability, a Greenhouse-Geissler adjustment was used. Significant differences and moderate effect sizes were found in each of the four emotions across the vignettes, as summarized in Table 4.17. A post-hoc LSD comparison revealed significant differences on every emotion between every vignette with the exception of Challenge, which did not have significant differences for Vignettes

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B (low personal / high technology) and C (high personal / low technology).

Emotion df F Significance Partial Eta

Squared

Anger 2.84 392.20 .000 0.54

Challenge 2.76 345.94 .000 0.51

Frustration 2.90 499.24 .000 0.60

Anxiety 2.79 593.88 .000 0.64

Table 4.17: Repeated Measures ANOVA by Individual Emotion

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CHAPTER 5

DISCUSSION

Purpose

The purpose of this study was to examine the fit of a dual-appraisal process model for technology failure, including the antecedent factors that influenced the appraisal process and the subsequent emotional experience. Thus, this study was guided by several questions. First, did the hypothesized model describe the context-specific and general antecedent factors that influence unpleasant emotions? Second, how do generalized antecedents influence self-reports of emotions in technology-failure situations? Finally, how do individuals’ appraisals of the importance of technology to the situation influence unpleasant emotions, both in terms of the discrete emotions reported as well as magnitude reported in a technology-failure situation?

Bringing Together Substance and Structural Validation

Benson (1998) notes that the only way that the scores of a measure take on meaning is through the validation of the construct it represents. However, she also suggests that this is not a one-time occurrence; validation takes place through considerable time and replication of studies. A strong validation of a construct will be grounded in theory and previous research (substantive validation), use statistical methods

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to determine how that construct might relate to other similar constructs (structural validation and external stage with SEM), and then seek to find meaning based off of the tested conclusions. This study adds to a body of evidence that supports the consideration of two new constructs: a primary appraisal of importance when an external tool like technology is involved, and the construct of trust in technology.

Summary of Findings

The model as tested in this study contributes several major points to the current understanding of how individuals make appraisals regarding the technology involved in task-based situations. A better understanding of the dynamics that influence appraisals and emotions may be achieved by applying the model to four different vignettes. While the paths and the loadings of the factors in the vignettes cannot be directly compared statistically due to the limitations of SEM, looking across the four vignettes paints a broader picture of how different factors influence judgments and emotions at different times.

Although the model fit all four vignettes, it did not appear to fit in the same way across the four different correlation matrices. This is illustrative of the complexity of the appraisal process, influenced both contextual antecedents, but also by more generalized antecedents. In each of the four vignettes, the perceived situational importance influenced the perceived importance of technology to the situation. When an individual perceived the situation to be important, it influenced his or her perception of the importance of the technology to the situation. The situational importance affected reports of emotions both directly in three of the vignettes (Vignette C being the exception), but also indirectly through the perceptions of technology importance. Technology

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importance also played a significant role in participants’ self-reports of emotions, significantly influencing emotions in three of the vignettes (Vignette B being the exception).

Based on current understanding of appraisal theory (Lazarus, 2001), it should not be surprising that that personal importance was such an important antecedent particularly in Vignettes A (high personal / high technology) and D (low personal / low technology) where technology importance and personal importance were aligned as high/high or low/low. It is in Vignettes B (high technology importance / low personal importance) and Vignette C (low technology importance / high personal importance) that the more salient aspects of importance differentiate. In Vignette C, although it was rated high in situational importance, it was the technology failure that showed a stronger influence on the emotional response. In terms of personal importance, this suggests that although personal relevance is important, when an individual relies on an external tool and it fails, it is the failure of the tool that upsets the individual the most.

Depending on the situation, different generalized antecedents may take on a more important role in shaping the emotional experience. Trust was found to have a significant effect on reports of unpleasant emotions when the technology was viewed as important to the situation. This suggests that when technology is important and it fails, the disconnect between trust and action may be a factor in unpleasant emotions.

While this study did not have participants actively involved in the task as described, higher self-efficacy was hypothesized to mediate the magnitude of unpleasant emotions reported. Self-efficacy, as described by Bandura (1986), is the present assessment of one’s ability to accomplish a task in the future. It was theorized that the

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more confident an individual felt that he or she could accomplish a task, the less she or he would experience the unpleasant emotions they might have felt if technology failed. One interesting item of note is that self-efficacy was only a significant path two out of the eight times in the four models.

Why was this not the case? There are several possible explanations. According to Bandura (1999), self-efficacy has four sources: social persuasion, mastery experiences, vicarious experiences and emotional arousal. In this study, the vignettes described the situation only up to the point of technology failure. An individual, no matter how confident she or he may feel about his or her ability to complete the task, may still experience an emotional arousal. However, the time in which an individual resolves this emotional arousal may differ by self-efficacy. It may be that the initial reaction to technology failure may be an emotional arousal, but more efficacious people are able to re-focus their energies on resolving the technology difficulties. An alternative explanation may be that those who have high technology self-efficacy may be better judges as to whether the technology failure is resolvable or terminal. For example, those with high technology self-efficacy may be better at recognizing when to troubleshoot a technology problem versus when to seek alternatives.

Another explanation may be the way self-efficacy was measured in this study.

Self-efficacy was measured using a global, non-domain specific efficacy scale in this study. However, the technologies used in the vignettes were common, every-day technologies, such as the cell phone. This lack of influence of self-efficacy in the overall model may be supportive of Compeau and Higgins suggestion that a general self-efficacy scale is more accurate when participants are looking at unknown, but similar technologies

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(Compeau & Higgins, 1995). Because the technologies in the vignettes were common, a individual’s specific self-efficacy with the technologies may have created a more representational model of how individuals felt about his or her own capability to troubleshoot the failure in question.

A third possibility is the particularly technologies described in the vignettes may not have equal task quality. Vignette B (Low Personal/High Tech) was the only vignette that may have been perceived as an actively resolvable task. Most individuals do not consider how to fix an elevator, but those with the skills to do so may be able to resolve a computer problem, such as the one described in the vignette. Self-efficacy would only be influential on those tasks that an individual believed he or she could resolve. Because the purpose of the vignettes in this study was to manipulate the appraisals of importance, future research should look at more specific technologies and self-efficacy.

A second contribution of this model is the use of computer affinity rather than computer anxiety to understand how individuals interact with technology. While computer anxiety, a situation-specific emotion where the individual is afraid of possible negative outcomes from the use of a computer (Barbeite & Weiss, 2004), has been linked to computer aptitude and performance (Szajna, 1994), the downfall of computer anxiety is that it is conceptualized as situation-specific. In addition, because it has been suggested computer anxiety can be lessened with mastery experiences (Wilfong, 2006), and may have little influence on the performance of tasks (Beckers, Rikers, & Schmidt,

2006), this study suggests that technology affinity rather than anxiety may be a generally better predictor of certain appraisals about technology. Although fear may be a discourager of the use of certain technologies, it can be at least somewhat eased.

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However, there is no such opposing effect for affinity; at this point there has been no research or suggestion of ways to make people “like” technology.

In this study, technology affinity did not have a direct effect on the emotions reported by participants in any of the vignettes. However, technology affinity did have an indirect effect on emotions by having a significant influence on how participants appraised the importance of technology. Those individuals with higher affinity to technology rated the technology as less important in those vignettes where the situation was important. Conversely, technology affinity significantly influenced the importance of technology in all but one vignette (Vignette C). The appraisal of technology importance was a significant path in the model in 3 out of the 4 vignettes (A, C, D).

Theoretical Contributions

Emotion Appraisal Theory

Theoretically, this study makes several significant contributions to the understanding of emotional appraisal process. First, that the primary appraisal process is not limited to the appraisal of personal importance. In this study, the appraisal of how important the technology was to the situation also had a significant effect of self-reports of unpleasant emotions, even when controlling for appraisals of situational importance.

This suggests that the appraisal of personal relevance is not the only primary appraisal that shapes an emotional experience. Although the appraisal of personal importance is still key, there are other appraisals about the situation that interact when an individual is making judgments. Subjective relevance is important, but this study suggests that the path from primary to secondary appraisals may not be continuous. The difference in self-

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reports of emotions in this study can be accounted for primarily by the difference in appraisals of the importance of technology to the situation.

A second contribution of this study is that it demonstrates the multi-faceted process of appraisals. While theorists have suggested that the appraisal process is not linear (Scherer, 2001), this study provides evidence of how this non-linear process might be operationalized in a real-world situation. In addition, the results of the study provides an example of how multiple primary appraisals may interact to shape an emotional experience. Individuals do not just appraise their interactions with the situation, they also appraise the environment’s influence on the situation as well.

Trust in Technology

The idea that individuals have a level of expectations about the possible interactions they have with a specific technology has only been touched on in previous research. Trust in technology, defined in this study as a belief that the technology involved in a situation will be reliable and perform as expected, played a significant role in several of the vignettes. For vignettes A (high personal / high technology) and B (low personal / high technology), trust in technology had a significant effect on how participants appraised the importance of technology to the situation. Both vignettes A and B were manipulated to be rated high in technology importance. This suggests that, at times, a higher expectation of the reliability of technology influences how important an individual feels the technology is to the situation. In addition, trust in technology also was a significant factor on emotion in vignettes B and D (low personal importance).

Ultimately, it is important for researchers to recognize that interactions with technology are not as simple as human with an inanimate machine. As technology

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increased in complexity, so does its ability to create more dynamic interactions. When it does, the technology forms a part of one’s social web. Computers are personalized, and may even be seen as an extension of one’s self (Turkle, 1995). While this is not to say that a laptop would replace a human companion, the idea that humans can and do forge trust relationships with technology points to the increasing blurring of lines between who and what humans form bonds with.

Implications for Practice

Facilitating Technology Adoption

For administrators who are considering how to encourage the adoption of a particular technology, there are several lessons to be learned from this study. Many studies focus on the cognitive ramifications of technology adoption, but this study suggests that cognitive factors alone should not be the only factors considered.

One practical implication is to find ways to take the pressure off of adoption. The model suggests that as personal importance and perceived technology importance increase, so does the likelihood of unpleasant emotions if technology failure occurs.

These unpleasant emotions may then in turn influence the learning process by taking away valuable cognitive resources from the task at hand, or the unpleasant experience with technology may impact an individual’s choice to use the technology in the future.

Ironically, this study suggests that one way to facilitate a successful technology adoption is to make the technology seem less important to the situation. By downplaying the importance of a specific technology, individuals may feel less paralyzed when or if the technology fails. Instead of focusing on the technology and the specifics of how to do a

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task using a technology, educators and/or adoption facilitators may wish to focus on alternative ways of accomplishing the task.

Along with the suggestion to downplay the importance of the technology, individuals facilitating technology adoption may want to focus on technology-alternative awareness. As technology becomes further integrated into society, individuals may forget that there are low-tech solutions to tasks that take no more time than the high-tech alternatives. Previous research suggests that emotions can take away from cognitive resources needed to complete a task (Wegner, Erber, & Zanakos, 1993).

Age and Technology

There are anecdotal beliefs, particularly in the popular media, that the younger generation finds more comfort in the use of technology (Friedman, 2007; Streit, 2007).

Books like Educating the Net Generation (Oblinger & Oblinger, 2005) speak broadly about the comfort differences between the younger generations and how they use technology. But this study suggests that future research may need to closer examine the idea that the younger generation is all that more at home with technology than others.

Although there were slight gender differences in technology affinity (with males identifying themselves as being more attracted to technology), age differences were small

(although significant, but the effect size was very small), which might suggest that in this study, even the younger generation may not be any more attracted to the use of technology than other generations. In addition in this study, differences in emotion due to age, although significant, had a very low effect size, more indicative of the large sample size used in this study. However, because of the sample of this study was still

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heavily based in college-age students, these suggestions are more cautions and suggestions for further research than conclusions.

Although those under the age of 21 may use technology in more unconsciously streamlined ways, it does not mean that they “like” technology, nor do they get any less frustrated when technology fails them. In a recent study of undergraduates’ views about technology, Lohnes and Kinzer (2007) found that not all students, even of this younger net-savvy generation are interested in utilizing technology specifically in the classroom.

It may be that the technology skills of the younger generation are limited to what they perceive to be “fun.” When technology is difficult, or fails to work as designed, this study suggests that individuals get just as frustrated.

This study reinforces previous research suggesting that problem-focused coping strategies may be more adaptive than emotion or expression-based coping strategies.

When technology fails, venting or retaliation is not as effective in preventing an unpleasant experience. Problem-focused coping had a significant effect on reducing unpleasant emotions when the situation was rated as important to individuals, and emotion-focused coping had a significant effect on increasing unpleasant emotions.

Why Is a Positive Experience Important?

Ultimately, it is the hope that an experience with technology may be as positive

as possible, even when failure is involved. Although experience is frequently considered

a factor in studies examining the impact of technology (Agarwal & Prasad, 1999;

Bonzionelos, 2001; Compeau et al., 1999), it is generally measured in terms of quantity

of time, rather than quality of time. For example, in the Unified Theory of Acceptance

and Usage of Technology (UTAUT), experience was broadly classified into 3 categories

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– high, medium and low experience (Venkatesh et al., 2003), but these categories were based on self-reports of how experienced an individual felt with the technology.

While this definition of experience seems straightforward, the time an individual spends using technology may not be positive time, and a positive experience (no matter how much time is involved) can affect many different aspects of the acceptance of technology. Mastery experiences can build self-efficacy. Conversely, unsuccessful experiences may degrade one’s confidence in one’s abilities. In addition, because emotional arousal is a source of self-efficacy an unpleasant emotional reaction may influence an individual’s beliefs about his or her ability to work with technology in the future.

Limitations and further research

As Benson suggests (1998), this study adds just another piece to the literature and understanding of individual’s interactions with technology. With all studies, there are limitations to the research and opportunities to expand on the ideas developed in this study. Limitations include the predominant college-age sample base, as well as the artificial nature of survey research rather than a study that had participants actively involved in the use of technology. Furthermore, because of the changes noted in the measures in this study, a subsequent study may further refine the scales used to evaluate the constructs. Because of the limitations of SEM, another alternative would be to administer only one vignette to each participant, which would be able to generate a more global model that takes into account all different situations. In addition, alternative methods for analyzing the data like hierarchical linear modeling might show a different pattern of the interaction between the two appraisals.

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Because this study is survey-based, a logical extension would be to create a more interactive design in which individuals are using the technology as it fails. This could take place in a manipulated scenario where participants interact with a technology and it is manipulated to fail. Another possibility is to examine a more longitudinal perspective on technology adoption by following a large-scale technology implementation. Many universities utilize technology tools such as course management systems. A future study may want to follow faculty as they learn and convert knowledge to a new system.

Finally, although the model suggested in this study did fit the data, there are always additional possible models. This model was just one of many possible models for understanding emotional responses to technology failure. There may be additional factors that can be included for future investigations.

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APPENDIX A

INSTITUTIONAL REVIEW BOARD APPROVAL

123

124

APPENDIX B

MEASURES

125

126

127

128

129

130

131

APPENDIX C

PARTICIPANT DESCRIPTIVES

132

Age Frequency Percent 18-21 141 25.9 22-25 164 30.1 26-30 80 14.7 31-35 47 8.6 36-40 25 4.6 41-45 26 4.8 46-55 33 6.1 56-65 23 4.2 65+ 4 .7 Missing 1 .2

Table C1: Participant Age Demographics

Gender Frequency Percent Male 165 30.3 Female 354 65.1 Missing 25 4.6

Table C2: Participant Gender Demographics

133

Level of education Frequency Percent High school 8 1.5 Some college 272 50.0 College graduate 92 16.0 Some graduate /professional school 80 14.7 Masters degree 80 14.7 PhD 8 1.5 Missing 4 .7

Table C3: Participant Education Demographics

134

Gender Age Total 18- 22- 26- 31- 36- 41- 46- 56- 21 25 30 35 40 45 55 65 65+ Male High school 1 0 2 1 0 0 0 0 0 4 Some college 27 29 11 5 3 1 0 4 0 80 College graduate 0 13 6 6 3 3 4 1 0 36 Some graduate /professional 1 4 5 2 1 3 2 1 0 19 school Masters degree 0 0 4 9 3 3 3 2 1 25 PhD 0 0 0 0 0 1 0 0 0 1

29 46 28 23 10 11 9 8 1 165 Total Female High school 2 0 0 0 0 0 0 1 0 3 Some college 102 71 4 2 1 0 2 1 0 183 College graduate 2 16 12 6 3 2 8 2 1 52 Some graduate /professional 0 23 15 6 3 4 1 2 1 55 school Masters degree 0 3 13 9 5 8 8 3 1 50 PhD 0 0 1 0 0 1 0 4 0 6 Total 11 106 45 23 12 15 19 13 3 349 3

Table C4: Crosstabulation: gender by education and age

135

APPENDIX D

FREQUENCY DATA

136

Frequency Item 1 2 3 4 5 6 Mean SD Skew Kurtosis Vignette A: (High Situational 15 23 30 59 162 250 5.00 1.28 -1.47 1.60 Importance/High Technology Importance) Vignette B: (Low Situational 115 121 103 117 65 20 2.92 1.45 0.25 -0.97 Importance/High Technology Importance) Vignette C: (High Situational 33 24 52 123 185 122 4.43 1.38 -0.95 0.32 Importance/Low Technology Importance) Vignette D: (Low Situational 235 141 68 58 23 13 2.13 1.32 1.11 0.42 Importance/Low Technology Importance)

Table D1 : Situation Importance by Vignette

137

Frequency Item 1 2 3 4 5 6 Mean SD Skew Kurtosis Vignette A: (High Situational Importance/High 1 2 3 14 77 444 5.77 0.59 -3.50 16.57 Technology Importance) Vignette B: (Low Situational Importance/High 37 23 33 58 144 246 5.77 0.59 0.35 16.57 Technology Importance) Vignette C: (High Situational Importance/Low 47 53 66 114 116 143 4.17 1.60 -0.54 -0.80 Technology Importance) Vignette D: (Low Situational Importance/Low 260 124 66 44 25 21 2.10 1.40 1.26 0.70 Technology Importance)

Table D2: Technology Importance by Vignette

138

Frequency Item 1 2 3 4 5 6 Mean SD Skew Kurtosis Vignette A: (High Situational Importance/High 31 21 52 109 171 156 4.55 1.40 -0.10 0.36 Technology Importance) Vignette B: (Low Situational Importance/High 189 136 78 77 41 18 2.44 1.45 0.75 -0.48 Technology Importance) Vignette C: (High Situational Importance/Low 148 90 94 98 73 34 2.93 1.60 0.29 -1.12 Technology Importance) Vignette D: (Low Situational Importance/Low 313 110 55 34 16 8 1.79 1.19 1.60 1.97 Technology Importance)

Table D3: Anger by Vignette

139

Frequency Item 1 2 3 4 5 6 Mean SD Skew Kurtosis Vignette A: (High Situational Importance/High 38 22 58 146 153 122 4.34 1.41 -0.82 0.09 Technology Importance) Vignette B: (Low Situational Importance/High 194 110 104 88 32 11 2.42 1.38 0.62 -0.63 Technology Importance) Vignette C: (High Situational Importance/Low 209 113 85 68 45 18 2.41 1.48 0.77 -0.52 Technology Importance) Vignette D: (Low Situational Importance/Low 341 104 49 24 11 7 1.66 1.09 1.91 3.44 Technology Importance)

Table D4: Challenge by Vignette

140

Frequency Item 1 2 3 4 5 6 Mean SD Skew Kurtosis Vignette A: (High Situational Importance/High 9 5 22 55 167 284 5.25 1.04 -1.82 3.86 Technology Importance) Vignette B: (Low Situational Importance/High 114 120 107 107 64 29 2.95 1.49 0.31 -0.92 Technology Importance) Vignette C: (High Situational Importance/Low 79 77 72 124 114 72 3.62 1.62 -0.21 -1.12 Technology Importance) Vignette D: (Low Situational Importance/Low 253 121 69 59 25 12 2.11 1.34 1.10 0.28 Technology Importance)

Table D5: Frustration by Vignette

141

Frequency Item 1 2 3 4 5 6 Mean SD Skew Kurtosis Vignette A: (High Situational Importance/High 28 15 50 100 144 202 4.71 1.39 -1.10 0.57 Technology Importance) Vignette B: (Low Situational Importance/High 297 121 66 33 14 9 1.84 1.18 1.53 1.90 Technology Importance) Vignette C: (High Situational Importance/Low 240 91 67 75 35 29 2.37 1.57 0.85 -0.47 Technology Importance) Vignette D: (Low Situational Importance/Low 372 89 39 19 7 11 1.57 1.08 2.31 5.33 Technology Importance)

Table D6: Anxiety by Vignette

142

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis Situational Importance 15 23 30 59 162 250 5.00 1.28 -1.47 1.60 Technology Importance 1 2 3 14 77 444 5.77 0.59 -3.50 16.57 Anger 31 21 52 109 171 156 4.55 1.40 -0.10 0.36 Challenge 38 22 58 146 153 122 4.34 1.41 -0.82 0.09 Frustration 9 5 22 55 167 284 5.25 1.04 -1.82 3.86 Anxiety 28 15 50 100 144 202 4.71 1.39 -1.10 0.57

Table D7: Appraisals and Emotions for Vignette A: High Situational Importance/High

Technology Importance

143

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis Situational 115 121 103 117 65 20 2.92 1.45 0.25 -0.97 Importance Technology 37 23 33 58 144 246 5.77 0.59 0.35 16.57 Importance Anger 189 136 78 77 41 18 2.44 1.45 0.75 -0.48 Challenge 194 110 104 88 32 11 2.42 1.38 0.62 -0.63 Frustration 114 120 107 107 64 29 2.95 1.49 0.31 -0.92 Anxiety 297 121 66 33 14 9 1.84 1.18 1.53 1.90

Table D8: Appraisals and Emotions for Vignette A: High Situational Importance/High

Technology Importance

144

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis Situational 33 24 52 123 185 122 4.43 1.38 -0.95 0.32 Importance Technology 47 53 66 114 116 143 4.17 1.60 -0.54 -0.80 Importance Anger 148 90 94 98 73 34 2.93 1.60 0.29 -1.12 Challenge 209 113 85 68 45 18 2.41 1.48 0.77 -0.52 Frustration 79 77 72 124 114 72 3.62 1.62 -0.21 -1.12 Anxiety 240 91 67 75 35 29 2.37 1.57 0.85 -0.47

Table D9: Appraisals and Emotions for Vignette C: High Situational Importance/Low

Technology Importance

145

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis Situational Importance 235 141 68 58 23 13 2.13 1.32 1.11 0.42 Technology Importance 260 124 66 44 25 21 2.10 1.40 1.26 0.70 Anger 313 110 55 34 16 8 1.79 1.19 1.60 1.97 Challenge 341 104 49 24 11 7 1.66 1.09 1.91 3.44 Frustration 253 121 69 59 25 12 2.11 1.34 1.10 0.28 Anxiety 372 89 39 19 7 11 1.57 1.08 2.31 5.33

Table D10: Appraisals and Emotions for Vignette D : Low Situational Importance/Low

Technology Importance

146

Frequency General Trust 1 2 3 4 5 6 Mean SD Skew Kurtosis T1. I rarely wonder if my computer is 122 152 100 86 62 19 2.76 1.44 0.48 -0.79 going to fail while I’m using it (R) T2. I see technology 2 22 43 144 243 84 4.59 1.00 -0.79 0.62 working properly frequently. T3. I think that the technology I use is 0 6 37 107 281 101 4.82 0.86 -0.73 0.57 generally reliable T4. Many of my friends, family or colleagues 1 3 13 73 237 215 5.19 0.82 -1.07 1.78 successfully use technology in their day-to-day lives. T5. In general, I trust technology to 3 5 31 102 295 104 4.84 0.87 -1.05 2.10 work as I anticipate it to.

Continued

Table D11: Descriptive Statistics for General Trust

147

Table D11 continued

T6. I believe that most times, my cell phone works the 6 20 49 88 226 145 4.77 1.13 -1.03 0.79 way I think it should. T7. When I use technology, I frequently think of 41 74 97 114 161 56 3.83 1.44 -0.36 -0.86 alternatives in case it stops working (R) T8. I think it’s 41 85 132 122 124 39 3.59 1.38 -0.13 -0.84 risky to depend too much on technology in my daily life (R) T9. I’m frequently 35 102 143 119 118 27 3.49 1.32 -0.03 -0.85 aware of all the things that could go wrong when I use technology (R) T10. I feel like 10 31 76 138 205 81 4.37 1.18 -0.68 0.06 technology never works the way I think it should. (R)

148

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis SE1. I can always manage to solve a difficult technology 43 103 93 132 123 46 3.61 1.44 -0.15 -0.97 problem if I try hard enough SE2. If I am having difficulties solving a problem, I can use 9 30 76 181 160 85 4.31 1.16 -0.49 -0.02 technology to find the means and ways to get what I want. SE3. I can deal efficiently with 21 63 127 166 122 40 3.79 1.24 -0.22 -0.48 unexpected technology events. SE4. Thanks to my resourcefulness, I know how to handle 34 88 119 159 104 39 3.60 1.34 -0.13 -0.69 unforeseen technology failures.

Continued

Table D12: Descriptive Statistics for Self-Efficacy

149

Table D12 continued

SE5. I can solve most of my technology 25 54 105 152 155 53 3.95 1.30 -0.42 -0.45 problems if I invest the necessary effort. SE6. I can remain calm when facing technology difficulties 30 76 102 145 145 43 3.79 1.34 -0.32 -0.71 because I can rely on my coping abilities SE7. When I am confronted with a problem involving my 23 90 125 154 114 36 3.65 1.28 -0.12 -0.71 computer, I can usually find several solutions SE8. If my technology crashes, I can usually 35 87 99 153 127 37 3.67 1.35 -0.25 -0.77 think of a solution. SE9. I can usually handle whatever 8 24 46 157 216 82 4.49 1.09 -0.84 0.77 comes my way

150

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis A1. Technology is 9 18 50 148 205 110 4.58 1.11 -0.83 0.71 my friend… A2. I enjoy learning new computer programs and 26 56 93 121 149 96 4.11 1.41 -0.44 -0.67 hearing about new technologies A3. I take pleasure in assignments that require me to learn a 45 78 110 134 107 68 3.71 1.46 -0.16 -0.87 new program or learn how to use a new technology A4. I feel ashamed when I struggle with 10 67 128 153 127 55 3.90 1.24 -0.11 -0.69 a new piece of technology (R) A5. I relate well to technology and 13 41 107 179 146 57 4.06 1.18 -0.36 -0.23 machines

Continued

Table D13: Descriptive Statistics for Technology Affinity

151

Table D13 continued

A6. I am comfortable learning new 9 28 79 159 177 87 4.35 1.17 -0.55 -0.03 technology A7. Solving a technological 62 101 116 123 86 54 3.43 1.49 0.05 -0.94 problem seems like a fun challenge A8. I find most technology easy to 12 38 94 179 162 56 4.13 1.16 -0.46 -0.10 learn A9. I feel as up-to- date on technology 21 43 97 134 166 81 4.15 1.32 -0.51 -0.40 as my peers

152

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis EF1. I get upset and let 25 66 98 149 150 55 3.92 1.33 -0.38 -0.60 my emotions out EF2. I learn to live 14 33 106 182 159 47 4.07 1.15 -0.45 -0.01 with it. EF3. I feel a lot of emotional distress and I find myself 38 85 140 163 86 29 3.48 1.28 -0.08 -0.55 expressing those feelings a lot EF4. I talk to someone 52 65 84 119 171 51 3.82 1.47 -0.47 -0.81 about how I feel. EF5. I let my feelings 28 62 98 158 141 55 3.90 1.33 -0.38 -0.53 out EF6. I try to do something else to take 33 94 153 165 74 21 3.40 1.21 -0.02 -0.46 my mind off the problem. EF7. I make fun of the 61 83 120 129 100 48 3.50 1.46 -0.08 -0.89 situation. EF8. I look for something positive in 62 86 112 148 102 32 3.44 1.41 -0.14 -0.85 what is happening

Table D14: Descriptive Statistics for Emotion Focused Coping

153

Frequency 1 2 3 4 5 6 Mean SD Skew Kurtosis PF1. I concentrate my efforts on trying to find 4 18 61 158 226 74 4.49 1.02 -0.67 0.44 a solution. PF2. I take it one step 6 15 65 174 211 66 4.43 1.02 -0.65 0.59 at a time. PF3. I try to come up with a strategy about 6 10 45 163 230 85 4.59 0.99 -0.80 1.15 what to do. PF4. I focus on dealing 4 12 50 140 251 85 4.62 0.98 -0.82 0.91 with the problem. PF5. I put aside other activities to try to 13 40 95 162 167 63 4.15 1.20 -0.47 -0.22 concentrate of resolving the problem. PF6. I try to find answers online or from 33 40 57 110 212 88 4.28 1.39 -0.87 -0.02 the manual. PF7. I talk to my friends to see if they 8 22 43 103 266 100 4.65 1.09 -1.12 1.30 have had a similar problem PF8. I try to get advice from someone about 8 9 25 73 252 174 4.99 1.02 -1.48 2.95 what to do

Table D15: Descriptive Statistics for Problem Focused Coping

154

APPENDIX E

SELECTED LISREL OUTPUTS VIGNETTE A

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DATE: 7/20/2007 TIME: 9:42

L I S R E L 8.30

Karl G. Jöreskog & Dag Sörbom

This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2000 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com

The following lines were read from file C:\LISREL83\MODA1.LPJ:

TI: Full Measurement Model (T6drpd S9drpd A3drpd P5drpd) Observed variables: VA1-VA6 T2 T3 T5 S1 S3 S4 S5 S7 ST1-ST4 A2 A5 A6 A7 A8 A9 E1 E3 E5 P1 P3 P4 Correlation matrix from file moda.cor Means from file moda.mea Standard deviations from file moda.sd Sample size 439 Latent Variables: TST Self AFF EXCOPING PING PFCOPE TECHIMP SITIMP EMOA Relationships: T2 = CONST TST T3 = CONST TST T5 = CONST 1*TST

S3= CONST Self S4= CONST 1* Self S5 = CONST Self S7 = CONST Self

A2= CONST AFF A5 = CONST AFF A6 = CONST 1* AFF

A8 = CONST AFF A9 = CONST AFF

156

E1=CONST 1*EXCOPING E3=CONST EXCOPING E5=CONST EXCOPING

P1=CONST PING

P3=CONST PING P4=CONST 1*PING

VA3=CONST EMOA VA4=CONST EMOA VA5=CONST 1*EMOA VA6=CONST EMOA

VA1 = 1*TECHIMP VA2 = 1*SITIMP

Set the error variance of VA1 to 0 Set the error variance of VA2 to 0

EMOA = TECHIMP SITIMP EXCOPING PING Self TST AFF TECHIMP = AFF SITIMP TST Self

print residuals path diagram end of problem

Sample Size = 439

157

TI: Full Measurement Model (T6drpd S9drpd A3drpd P5drpd)

Number of Iterations = 7

LISREL Estimates (Maximum Likelihood)

Measurement Equations

VA1 = 1.00*TECHIMP,, R² = 1.00

VA3 = 0.85*EMOA, Errorvar.= 0.45 , R² = 0.54 (0.051) (0.037) 16.79 12.12

VA4 = 0.70*EMOA, Errorvar.= 0.63 , R² = 0.37 (0.053) (0.047) 13.16 13.54

VA5 = 1.00*EMOA, Errorvar.= 0.24 , R² = 0.76 (0.030) 7.97

VA6 = 0.87*EMOA, Errorvar.= 0.43 , R² = 0.57 (0.050) (0.036) 17.22 11.84

VA2 = 1.00*SITIMP,, R² = 1.00

T2 = 1.06*TST, Errorvar.= 0.28 , R² = 0.72 (0.053) (0.027) 19.96 10.22

T3 = 1.15*TST, Errorvar.= 0.15 , R² = 0.85 (0.054) (0.025) 21.22 6.08

T5 = 1.00*TST, Errorvar.= 0.35 , R² = 0.65 (0.030) 11.78

S3 = 0.96*Self, Errorvar.= 0.21 , R² = 0.79 (0.032) (0.018) 29.98 11.27

S4 = 1.00*Self, Errorvar.= 0.14 , R² = 0.86 (0.015) 9.36

S5 = 0.93*Self, Errorvar.= 0.25 , R² = 0.75 (0.034) (0.021) 27.76 12.13

S7 = 0.87*Self, Errorvar.= 0.35 , R² = 0.65 (0.037) (0.027)

158

23.85 13.12

A2 = 0.84*AFF, Errorvar.= 0.38 , R² = 0.62 (0.036) (0.028) 23.37 13.58

A5 = 0.94*AFF, Errorvar.= 0.22 , R² = 0.78 (0.031) (0.018) 30.71 12.14

A6 = 1.00*AFF, Errorvar.= 0.12 , R² = 0.88 (0.013) 9.19

A8 = 0.95*AFF, Errorvar.= 0.21 , R² = 0.79 (0.030) (0.018) 31.34 11.95

A9 = 0.82*AFF, Errorvar.= 0.41 , R² = 0.59 (0.037) (0.030) 22.12 13.74

E1 = 1.00*EXCOPING, Errorvar.= 0.20 , R² = 0.80 (0.031) 6.40

E3 = 0.88*EXCOPING, Errorvar.= 0.38 , R² = 0.62 (0.048) (0.034) 18.52 11.02

E5 = 0.86*EXCOPING, Errorvar.= 0.41 , R² = 0.59 (0.048) (0.035) 18.04 11.52

P1 = 0.85*PING, Errorvar.= 0.39 , R² = 0.61 (0.040) (0.031) 21.15 12.80

P3 = 0.98*PING, Errorvar.= 0.19 , R² = 0.81 (0.036) (0.022) 27.07 8.49

P4 = 1.00*PING, Errorvar.= 0.15 , R² = 0.85 (0.022) 7.02

159

Structural Equations

TECHIMP = 0.18*TST + 0.059*Self - 0.18*AFF + 0.52*SITIMP, Errorvar.= 0.70 , R² = 0.30 (0.061) (0.095) (0.095) (0.041) (0.047) 2.98 0.62 -1.87 12.62 14.72

EMOA = 0.28*TECHIMP - 0.071*TST - 0.0022*Self + 0.012*AFF + 0.41*EXCOPING - 0.16*PING + 0.12*SITIMP, Errorvar.= 0.39 , (0.042) (0.054) (0.084) (0.086) (0.049) (0.054) (0.042) (0.042) , 6.60 -1.31 -0.026 0.14 8.31 -2.94 2.75 9.29

R² = 0.48

Reduced Form Equations

TECHIMP = 0.18*TST + 0.059*Self - 0.18*AFF + 0.0*EXCOPING + 0.0*PING + 0.52*SITIMP, Errorvar.= 0.70, R² = 0.30 (0.061) (0.095) (0.095) (0.041) 2.98 0.62 -1.87 12.62

EMOA = - 0.020*TST + 0.014*Self - 0.038*AFF + 0.41*EXCOPING - 0.16*PING + 0.26*SITIMP, Errorvar.= 0.44, R² = 0.41 (0.056) (0.088) (0.090) (0.049) (0.054) (0.038) -0.36 0.16 -0.42 8.31 - 2.94 6.80

160

Covariance Matrix of Latent Variables

TECHIMP EMOA TST Self AFF EXCOPING ------TECHIMP 1.00 EMOA 0.37 0.75 TST 0.08 -0.13 0.65 Self -0.10 -0.27 0.32 0.86 AFF -0.11 -0.27 0.36 0.73 0.88 EXCOPING 0.08 0.42 -0.17 -0.32 -0.31 0.80 PING -0.04 -0.28 0.25 0.50 0.53 - 0.28 SITIMP 0.53 0.32 0.01 -0.15 -0.12 0.14

Covariance Matrix of Latent Variables

PING SITIMP ------PING 0.85 SITIMP -0.04 1.00

161

Vignette A Goodness of Fit Statistics

Degrees of Freedom = 230 Minimum Fit Function Chi-Square = 622.67 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 567.00 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 337.00 90 Percent Confidence Interval for NCP = (270.87 ; 410.83)

Minimum Fit Function Value = 1.42 Population Discrepancy Function Value (F0) = 0.77 90 Percent Confidence Interval for F0 = (0.62 ; 0.94) Root Mean Square Error of Approximation (RMSEA) = 0.058 90 Percent Confidence Interval for RMSEA = (0.052 ; 0.064) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.016

Expected Cross-Validation Index (ECVI) = 1.72 90 Percent Confidence Interval for ECVI = (1.52 ; 1.84) ECVI for Saturated Model = 1.37 ECVI for Independence Model = 18.25

Chi-Square for Independence Model with 276 Degrees of Freedom = 7944.09 Independence AIC = 7992.09 Model AIC = 755.00 Saturated AIC = 600.00 Independence CAIC = 8114.11 Model CAIC = 1232.95 Saturated CAIC = 2125.35

Normed Fit Index (NFI) = 0.92 Non-Normed Fit Index (NNFI) = 0.94 Parsimony Normed Fit Index (PNFI) = 0.77 Comparative Fit Index (CFI) = 0.95 Incremental Fit Index (IFI) = 0.95 Relative Fit Index (RFI) = 0.91

Critical N (CN) = 199.94

Root Mean Square Residual (RMR) = 0.042 Standardized RMR = 0.042 Goodness of Fit Index (GFI) = 0.90 Adjusted Goodness of Fit Index (AGFI) = 0.87 Parsimony Goodness of Fit Index (PGFI) = 0.69

TI: Full Measurement Model (T6drpd S9drpd A3drpd P5drpd)

162

163

APPENDIX F

SELECTED LISREL OUTPUTS VIGNETTE B

164

DATE: 10/17/2007 TIME: 17:08

L I S R E L 8.30

Karl G. Jöreskog & Dag Sörbom

The following lines were read from file C:\LISREL83\MODB1.LPJ:

Vignette B: Structural Model

Observed variables: VB1-VB6 T2 T3 T5 S3 S4 S5 S7 ST1-ST4 A2 A5 A6 A8 A9 E1 E3 E5 P1 P3 P4 Correlation matrix from file modb.cor Means from file modb.mea Standard deviations from file modb.sd Sample size 425 Latent Variables: TST Self ST11 ST22 ST33 ST44 AFF EXCOPING PING SOCOPE PFCOPE TECHIMP SITIMP EMOB COPE Relationships: T2 = CONST TST T3 = CONST TST T5 = CONST 1*TST

S3= CONST Self S4= CONST 1* Self S5 = CONST Self S7 = CONST Self

A2= CONST AFF A5 = CONST AFF A6 = CONST 1* AFF

A8 = CONST AFF A9 = CONST AFF

E1=CONST 1*EXCOPING E3=CONST EXCOPING E5=CONST EXCOPING

165

P1=CONST PING

P3=CONST PING P4=CONST 1*PING

VB3=CONST EMOB VB4=CONST EMOB VB5=CONST 1*EMOB VB6=CONST EMOB

VB1 = 1*TECHIMP VB2 = 1*SITIMP

Set the error variance of VB1 to 0 Set the error variance of VB2 to 0

EMOB = TECHIMP SITIMP EXCOPING PING Self TST AFF TECHIMP = AFF SITIMP TST Self

print residuals LISREL Output: EF path diagram end of problem

TI: Full Measurement Model (T6drpd S9drpd A3drpd P5drpd)

166

LISREL Estimates (Maximum Likelihood)

Measurement Equations

VB1 = 1.00*TECHIMP,, R² = 1.00

VB3 = 1.01*EMOB, Errorvar.= 0.23 , R² = 0.77 (0.045) (0.026) 22.31 9.17

VB4 = 0.89*EMOB, Errorvar.= 0.41 , R² = 0.59 (0.048) (0.033) 18.59 12.21

VB5 = 1.00*EMOB, Errorvar.= 0.26 , R² = 0.74 (0.026) 9.72

VB6 = 0.88*EMOB, Errorvar.= 0.42 , R² = 0.58 (0.048) (0.034) 18.17 12.39

VB2 = 1.00*SITIMP,, R² = 1.00

T2 = 1.02*TST, Errorvar.= 0.32 , R² = 0.68 (0.053) (0.030) 19.26 10.70

T3 = 1.13*TST, Errorvar.= 0.15 , R² = 0.85 (0.054) (0.026) 20.97 5.82

T5 = 1.00*TST, Errorvar.= 0.34 , R² = 0.66 (0.030) 11.13

S3 = 0.98*Self, Errorvar.= 0.18 , R² = 0.82 (0.032) (0.017) 30.75 10.77

S4 = 1.00*Self, Errorvar.= 0.15 , R² = 0.85 (0.015) 9.62

S5 = 0.95*Self, Errorvar.= 0.23 , R² = 0.77 (0.033) (0.020) 28.48 11.73

S7 = 0.89*Self, Errorvar.= 0.32 , R² = 0.68 (0.036) (0.025) 24.50 12.81

A2 = 0.84*AFF, Errorvar.= 0.38 , R² = 0.62 (0.037) (0.028) 22.96 13.35

167

A5 = 0.96*AFF, Errorvar.= 0.19 , R² = 0.81 (0.030) (0.017) 32.13 11.27

A6 = 1.00*AFF, Errorvar.= 0.12 , R² = 0.88 (0.013) 9.05

A8 = 0.93*AFF, Errorvar.= 0.23 , R² = 0.77 (0.032) (0.019) 29.64 12.07

A9 = 0.79*AFF, Errorvar.= 0.45 , R² = 0.55 (0.039) (0.033) 20.37 13.66

E1 = 1.00*EXCOPING, Errorvar.= 0.19 , R² = 0.81 (0.032) 5.99

E3 = 0.90*EXCOPING, Errorvar.= 0.34 , R² = 0.66 (0.048) (0.034) 18.75 10.10

E5 = 0.85*EXCOPING, Errorvar.= 0.42 , R² = 0.58 (0.048) (0.036) 17.49 11.60

P1 = 0.86*PING, Errorvar.= 0.38 , R² = 0.62 (0.041) (0.031) 20.83 12.40

P3 = 0.99*PING, Errorvar.= 0.19 , R² = 0.81 (0.038) (0.023) 26.07 8.05

P4 = 1.00*PING, Errorvar.= 0.17 , R² = 0.83 (0.023) 7.53

168

Structural Equations

TECHIMP = 0.16*TST - 0.029*Self + 0.043*AFF + 0.30*SITIMP, Errorvar.= 0.89 , R² = 0.11 (0.069) (0.10) (0.10) (0.046) (0.061) 2.34 -0.28 0.42 6.58 14.53

EMOB = 0.073*TECHIMP - 0.11*TST - 0.22*Self + 0.078*AFF + 0.12*EXCOPING - 0.064*PING + 0.32*SITIMP, Errorvar.= 0.53 , (0.040) (0.058) (0.088) (0.089) (0.052) (0.059) (0.042) (0.051) , 1.80 -1.93 -2.50 0.88 2.36 -1.09 7.76 10.37

R² = 0.29

Reduced Form Equations

TECHIMP = 0.16*TST - 0.029*Self + 0.043*AFF + 0.0*EXCOPING + 0.0*PING + 0.30*SITIMP, Errorvar.= 0.89, R² = 0.11 (0.069) (0.10) (0.10) (0.046) 2.34 -0.28 0.42 6.58

EMOB = - 0.100*TST - 0.22*Self + 0.081*AFF + 0.12*EXCOPING - 0.064*PING + 0.35*SITIMP, Errorvar.= 0.53, R² = 0.29 (0.058) (0.088) (0.089) (0.052) (0.059) (0.040) -1.73 -2.52 0.91 2.36 -1.09 8.62

169

Covariance Matrix of Latent Variables

TECHIMP EMOB TST Self AFF EXCOPING ------TECHIMP 1.00 EMOB 0.15 0.74 TST 0.10 -0.17 0.66 Self 0.07 -0.22 0.32 0.85 AFF 0.09 -0.18 0.35 0.72 0.88 EXCOPING -0.01 0.22 -0.20 -0.34 -0.32 0.81 PING 0.03 -0.20 0.25 0.49 0.52 - 0.30 SITIMP 0.30 0.36 -0.05 0.05 0.05 0.09

Covariance Matrix of Latent Variables

PING SITIMP ------PING 0.83 SITIMP -0.05 1.00

170

Vignette B Goodness of Fit Statistics

Degrees of Freedom = 230 Minimum Fit Function Chi-Square = 746.70 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 700.36 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 470.36 90 Percent Confidence Interval for NCP = (394.54 ; 553.79)

Minimum Fit Function Value = 1.76 Population Discrepancy Function Value (F0) = 1.11 90 Percent Confidence Interval for F0 = (0.93 ; 1.31) Root Mean Square Error of Approximation (RMSEA) = 0.069 90 Percent Confidence Interval for RMSEA = (0.064 ; 0.075) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00

Expected Cross-Validation Index (ECVI) = 2.10 90 Percent Confidence Interval for ECVI = (1.86 ; 2.24) ECVI for Saturated Model = 1.42 ECVI for Independence Model = 18.98

Chi-Square for Independence Model with 276 Degrees of Freedom = 7999.45 Independence AIC = 8047.45 Model AIC = 888.36 Saturated AIC = 600.00 Independence CAIC = 8168.70 Model CAIC = 1363.25 Saturated CAIC = 2115.63

Normed Fit Index (NFI) = 0.91 Non-Normed Fit Index (NNFI) = 0.92 Parsimony Normed Fit Index (PNFI) = 0.76 Comparative Fit Index (CFI) = 0.93 Incremental Fit Index (IFI) = 0.93 Relative Fit Index (RFI) = 0.89

Critical N (CN) = 161.59

Root Mean Square Residual (RMR) = 0.043 Standardized RMR = 0.043 Goodness of Fit Index (GFI) = 0.88 Adjusted Goodness of Fit Index (AGFI) = 0.84 Parsimony Goodness of Fit Index (PGFI) = 0.67

TI: Full Measurement Model (T6drpd S9drpd A3drpd P5drpd)

171

172

APPENDIX G

SELECTED LISREL OUTPUTS VIGNETTE C

173

DATE: 11/ 1/2007 TIME: 19:25

L I S R E L 8.30

Karl G. Jöreskog & Dag Sörbom

The following lines were read from file C:\LISREL83\MODC1.LPJ:

Vignette C

Observed variables: VC1-VC6 T2 T3 T5 S3 S4 S5 S7 ST1-ST4 A2 A5 A6 A8 A9 E1 E3 E5 P1 P3 P4 Correlation matrix from file modc.cor Means from file modc.mea Standard deviations from file modc.sd Sample size 405 Latent Variables: TST Self ST11 ST22 ST33 ST44 AFF EXCOPING PING SOCOPE PFCOPE TECHIMP SITIMP EMOC COPE Relationships: T2 = CONST TST T3 = CONST TST T5 = CONST 1*TST

S3= CONST Self S4= CONST 1* Self S5 = CONST Self S7 = CONST Self

A2= CONST AFF A5 = CONST AFF A6 = CONST 1* AFF

A8 = CONST AFF A9 = CONST AFF

E1=CONST 1*EXCOPING E3=CONST EXCOPING

174

E5=CONST EXCOPING

P1=CONST PING

P3=CONST PING P4=CONST 1*PING

VC3=CONST EMOC VC4=CONST EMOC VC5=CONST 1*EMOC VC6=CONST EMOC

VC1 = 1*TECHIMP VC2 = 1*SITIMP

Set the error variance of VC1 to 0 Set the error variance of VC2 to 0

EMOC = TECHIMP SITIMP EXCOPING PING Self TST AFF TECHIMP = AFF SITIMP TST Self

print residuals path diagram LISREL Output: EF end of problem

175

Measurement Equations

VC1 = 1.00*TECHIMP,, R² = 1.00

VC3 = 1.02*EMOC, Errorvar.= 0.30 , R² = 0.70 (0.055) (0.030) 18.76 10.12

VC4 = 0.97*EMOC, Errorvar.= 0.38 , R² = 0.62 (0.055) (0.033) 17.43 11.34

VC5 = 1.00*EMOC, Errorvar.= 0.33 , R² = 0.67 (0.031) 10.69

VC6 = 1.01*EMOC, Errorvar.= 0.33 , R² = 0.68 (0.055) (0.031) 18.36 10.57

VC2 = 1.00*SITIMP,, R² = 1.00

T2 = 1.03*TST, Errorvar.= 0.29 , R² = 0.71 (0.054) (0.029) 19.16 9.95

T3 = 1.12*TST, Errorvar.= 0.17 , R² = 0.83 (0.055) (0.027) 20.43 6.17

T5 = 1.00*TST, Errorvar.= 0.34 , R² = 0.66 (0.031) 10.93

S3 = 0.98*Self, Errorvar.= 0.17 , R² = 0.83 (0.032) (0.017) 30.57 10.34

S4 = 1.00*Self, Errorvar.= 0.15 , R² = 0.85 (0.015) 9.42

S5 = 0.94*Self, Errorvar.= 0.25 , R² = 0.75 (0.035) (0.021) 26.87 11.82

S7 = 0.90*Self, Errorvar.= 0.31 , R² = 0.69 (0.037) (0.025) 24.43 12.43

A2 = 0.84*AFF, Errorvar.= 0.38 , R² = 0.62 (0.037) (0.029) 22.81 13.09

A5 = 0.94*AFF, Errorvar.= 0.22 , R² = 0.78

176

(0.031) (0.019) 30.34 11.72

A6 = 1.00*AFF, Errorvar.= 0.11 , R² = 0.89 (0.013) 8.56

A8 = 0.95*AFF, Errorvar.= 0.20 , R² = 0.80 (0.030) (0.017) 31.43 11.41

A9 = 0.79*AFF, Errorvar.= 0.44 , R² = 0.56 (0.039) (0.033) 20.29 13.37

E1 = 1.00*EXCOPING, Errorvar.= 0.16 , R² = 0.84 (0.033) 4.82

E3 = 0.86*EXCOPING, Errorvar.= 0.38 , R² = 0.62 (0.048) (0.036) 17.96 10.78

E5 = 0.84*EXCOPING, Errorvar.= 0.40 , R² = 0.60 (0.048) (0.036) 17.64 11.11

P1 = 0.84*PING, Errorvar.= 0.40 , R² = 0.60 (0.043) (0.033) 19.74 12.31

P3 = 0.99*PING, Errorvar.= 0.19 , R² = 0.81 (0.039) (0.024) 25.41 7.86

P4 = 1.00*PING, Errorvar.= 0.16 , R² = 0.84 (0.023) 7.01

Structural Equations

TECHIMP = 0.0043*TST + 0.061*Self - 0.093*AFF + 0.52*SITIMP, Errorvar.= 0.73 , R² = 0.27 (0.064) (0.10) (0.10) (0.043) (0.051) 0.068 0.60 -0.91 12.12 14.20

EMOC = 0.33*TECHIMP - 0.028*TST - 0.016*Self + 0.10*AFF + 0.16*EXCOPING - 0.19*PING + 0.043*SITIMP, Errorvar.= 0.48 , (0.045) (0.057) (0.092) (0.094) (0.050) (0.058) (0.044) (0.052) , 7.37 -0.49 -0.17 1.09 3.21 -3.36 0.98 9.32

177

R² = 0.28

Reduced Form Equations

TECHIMP = 0.0043*TST + 0.061*Self - 0.093*AFF + 0.0*EXCOPING + 0.0*PING + 0.52*SITIMP, Errorvar.= 0.73, R² = 0.27 (0.064) (0.10) (0.10) (0.043) 0.068 0.60 -0.91 12.12

EMOC = - 0.027*TST + 0.0047*Self + 0.072*AFF + 0.16*EXCOPING - 0.19*PING + 0.22*SITIMP, Errorvar.= 0.56, R² = 0.16 (0.061) (0.098) (0.10) (0.050) (0.058) (0.041) -0.44 0.048 0.72 3.21 - 3.36 5.25

178

Covariance Matrix of Latent Variables

TECHIMP EMOC TST Self AFF EXCOPING ------TECHIMP 1.00 EMOC 0.37 0.67 TST -0.01 -0.06 0.66 Self -0.05 -0.12 0.33 0.85 AFF -0.07 -0.11 0.37 0.74 0.89 EXCOPING 0.06 0.19 -0.18 -0.33 -0.34 0.84 PING -0.02 -0.18 0.24 0.49 0.52 - 0.30 SITIMP 0.52 0.23 0.01 -0.07 -0.07 0.10

Covariance Matrix of Latent Variables

PING SITIMP ------PING 0.84 SITIMP -0.01 1.00

179

Vignette C: Goodness of Fit Statistics

Degrees of Freedom = 230 Minimum Fit Function Chi-Square = 717.78 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 710.61 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 480.61 90 Percent Confidence Interval for NCP = (404.09 ; 564.73)

Minimum Fit Function Value = 1.78 Population Discrepancy Function Value (F0) = 1.19 90 Percent Confidence Interval for F0 = (1.00 ; 1.40) Root Mean Square Error of Approximation (RMSEA) = 0.072 90 Percent Confidence Interval for RMSEA = (0.066 ; 0.078) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00

Expected Cross-Validation Index (ECVI) = 2.22 90 Percent Confidence Interval for ECVI = (1.98 ; 2.37) ECVI for Saturated Model = 1.49 ECVI for Independence Model = 19.28

Chi-Square for Independence Model with 276 Degrees of Freedom = 7740.14 Independence AIC = 7788.14 Model AIC = 898.61 Saturated AIC = 600.00 Independence CAIC = 7908.23 Model CAIC = 1368.97 Saturated CAIC = 2101.17

Normed Fit Index (NFI) = 0.91 Non-Normed Fit Index (NNFI) = 0.92 Parsimony Normed Fit Index (PNFI) = 0.76 Comparative Fit Index (CFI) = 0.93 Incremental Fit Index (IFI) = 0.94 Relative Fit Index (RFI) = 0.89

Critical N (CN) = 160.18

Root Mean Square Residual (RMR) = 0.044 Standardized RMR = 0.044 Goodness of Fit Index (GFI) = 0.87 Adjusted Goodness of Fit Index (AGFI) = 0.83 Parsimony Goodness of Fit Index (PGFI) = 0.67

TI: Full Measurement Model (T6drpd S9drpd A3drpd P5drpd)

180

181

APPENDIX H

SELECTED LISREL OUTPUTS VIGNETTE D

182

DATE: 11/ 1/2007 TIME: 19:36

L I S R E L 8.30

Karl G. Jöreskog & Dag Sörbom

The following lines were read from file C:\LISREL83\MODD1.LPJ:

Vignette D

Observed variables: VD1-VD6 T2 T3 T5 S3 S4 S5 S7 ST1-ST4 A2 A5 A6 A8 A9 E1 E3 E5 P1 P3 P4 Correlation matrix from file modd.cor Means from file modd.mea Standard deviations from file modd.sd Sample size 425 Latent Variables: TST Self ST11 ST22 ST33 ST44 AFF EXCOPING PING SOCOPE PFCOPE TECHIMP SITIMP EMOD COPE Relationships: T2 = CONST TST T3 = CONST TST T5 = CONST 1*TST

S3= CONST Self S4= CONST 1* Self S5 = CONST Self S7 = CONST Self

A2= CONST AFF A5 = CONST AFF A6 = CONST 1* AFF

A8 = CONST AFF A9 = CONST AFF

E1=CONST 1*EXCOPING E3=CONST EXCOPING E5=CONST EXCOPING

183

P1=CONST PING

P3=CONST PING P4=CONST 1*PING

VD3=CONST EMOD VD4=CONST EMOD VD5=CONST 1*EMOD VD6=CONST EMOD

VD1 = 1*TECHIMP VD2 = 1*SITIMP

Set the error variance of VD1 to 0 Set the error variance of VD2 to 0

EMOD = TECHIMP SITIMP EXCOPING PING Self TST AFF TECHIMP = AFF SITIMP TST Self

print residuals path diagram end of problem

Sample Size = 425

184

Measurement Equations

VD1 = 1.00*TECHIMP,, R² = 1.00

VD3 = 1.06*EMOD, Errorvar.= 0.17 , R² = 0.83 (0.041) (0.017) 26.08 9.71

VD4 = 1.02*EMOD, Errorvar.= 0.23 , R² = 0.77 (0.042) (0.020) 24.39 11.21

VD5 = 1.00*EMOD, Errorvar.= 0.26 , R² = 0.74 (0.022) 11.80

VD6 = 1.03*EMOD, Errorvar.= 0.22 , R² = 0.78 (0.042) (0.020) 24.55 11.10

VD2 = 1.00*SITIMP,, R² = 1.00

T2 = 1.06*TST, Errorvar.= 0.31 , R² = 0.69 (0.058) (0.030) 18.27 10.25

T3 = 1.17*TST, Errorvar.= 0.15 , R² = 0.85 (0.060) (0.028) 19.44 5.53

T5 = 1.00*TST, Errorvar.= 0.39 , R² = 0.61 (0.033) 11.71

S3 = 0.98*Self, Errorvar.= 0.20 , R² = 0.80 (0.033) (0.018) 29.16 10.92

S4 = 1.00*Self, Errorvar.= 0.16 , R² = 0.84 (0.016) 9.74

S5 = 0.96*Self, Errorvar.= 0.23 , R² = 0.77 (0.034) (0.020) 27.92 11.49

S7 = 0.88*Self, Errorvar.= 0.35 , R² = 0.65 (0.038) (0.027) 22.99 12.92

A2 = 0.84*AFF, Errorvar.= 0.37 , R² = 0.63 (0.036) (0.028) 23.30 13.32

A5 = 0.94*AFF, Errorvar.= 0.22 , R² = 0.78

185

(0.030) (0.018) 30.79 11.81

A6 = 1.00*AFF, Errorvar.= 0.11 , R² = 0.89 (0.013) 8.56

A8 = 0.93*AFF, Errorvar.= 0.23 , R² = 0.77 (0.031) (0.019) 30.13 12.00

A9 = 0.79*AFF, Errorvar.= 0.45 , R² = 0.55 (0.038) (0.033) 20.60 13.65

E1 = 1.00*EXCOPING, Errorvar.= 0.19 , R² = 0.81 (0.034) 5.51

E3 = 0.88*EXCOPING, Errorvar.= 0.37 , R² = 0.63 (0.049) (0.036) 17.82 10.53

E5 = 0.85*EXCOPING, Errorvar.= 0.42 , R² = 0.58 (0.049) (0.037) 17.18 11.34

P1 = 0.82*PING, Errorvar.= 0.42 , R² = 0.58 (0.042) (0.033) 19.77 12.75

P3 = 0.97*PING, Errorvar.= 0.20 , R² = 0.80 (0.038) (0.024) 25.74 8.34

P4 = 1.00*PING, Errorvar.= 0.15 , R² = 0.85 (0.023) 6.43

Structural Equations

TECHIMP = 0.045*TST - 0.27*Self + 0.20*AFF + 0.64*SITIMP, Errorvar.= 0.58 , R² = 0.42 (0.058) (0.084) (0.082) (0.037) (0.040) 0.78 -3.18 2.41 17.18 14.40

EMOD = 0.27*TECHIMP - 0.13*TST - 0.018*Self + 0.016*AFF + 0.072*EXCOPING - 0.018*PING + 0.32*SITIMP, Errorvar.= 0.42 , 8 (0.045) (0.052) (0.078) (0.077) (0.044) (0.050) (0.045) (0.039) , 6.06 -2.57 -0.23 0.21 1.66 -0.36 7.17 10.58

186

R² = 0.44

Reduced Form Equations

TECHIMP = 0.045*TST - 0.27*Self + 0.20*AFF + 0.0*EXCOPING + 0.0*PING + 0.64*SITIMP, Errorvar.= 0.58, R² = 0.42 (0.058) (0.084) (0.082) (0.037) 0.78 -3.18 2.41 17.18

EMOD = - 0.12*TST - 0.091*Self + 0.069*AFF + 0.072*EXCOPING - 0.018*PING + 0.49*SITIMP, Errorvar.= 0.46, R² = 0.38 (0.054) (0.080) (0.079) (0.044) (0.050) (0.038) -2.24 -1.13 0.88 1.66 - 0.36 13.12

187

Covariance Matrix of Latent Variables

TECHIMP EMOD TST Self AFF EXCOPING ------TECHIMP 1.00 EMOD 0.49 0.74 TST -0.03 -0.13 0.61 Self -0.10 -0.12 0.32 0.84 AFF -0.04 -0.11 0.34 0.72 0.89 EXCOPING 0.06 0.12 -0.18 -0.31 -0.30 0.81 PING -0.10 -0.14 0.24 0.48 0.52 - 0.27 SITIMP 0.64 0.51 -0.06 -0.05 -0.07 0.06

Covariance Matrix of Latent Variables

PING SITIMP ------PING 0.85 SITIMP -0.14 1.00

188

Vignette D: Goodness of Fit Statistics

Degrees of Freedom = 230 Minimum Fit Function Chi-Square = 679.94 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 645.56 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 415.56 90 Percent Confidence Interval for NCP = (343.57 ; 495.19) Minimum Fit Function Value = 1.60 Population Discrepancy Function Value (F0) = 0.98 90 Percent Confidence Interval for F0 = (0.81 ; 1.17) Root Mean Square Error of Approximation (RMSEA) = 0.065 90 Percent Confidence Interval for RMSEA = (0.059 ; 0.071) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00 Expected Cross-Validation Index (ECVI) = 1.97 90 Percent Confidence Interval for ECVI = (1.74 ; 2.10) ECVI for Saturated Model = 1.42 ECVI for Independence Model = 20.11 Chi-Square for Independence Model with 276 Degrees of Freedom = 8478.15 Independence AIC = 8526.15 Model AIC = 833.56 Saturated AIC = 600.00 Independence CAIC = 8647.40 Model CAIC = 1308.46 Saturated CAIC = 2115.63 Normed Fit Index (NFI) = 0.92 Non-Normed Fit Index (NNFI) = 0.93 Parsimony Normed Fit Index (PNFI) = 0.77 Comparative Fit Index (CFI) = 0.95 Incremental Fit Index (IFI) = 0.95 Relative Fit Index (RFI) = 0.90 Critical N (CN) = 177.36 Root Mean Square Residual (RMR) = 0.042 Standardized RMR = 0.042 Goodness of Fit Index (GFI) = 0.89 Adjusted Goodness of Fit Index (AGFI) = 0.85 Parsimony Goodness of Fit Index (PGFI) = 0.68

189

190