Optimizing Training Effectiveness: the Role of Regulatory Fit

OPTIMIZING TRAINING EFFECTIVENESS: THE ROLE OF REGULATORY FIT

A Dissertation

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Zhivka Petkova

December, 2011

OPTIMIZING TRAINING EFFECTIVENESS: THE ROLE OF REGULATORY FIT

Zhivka Petkova

Dissertation

Approved: Accepted:

______Advisor Department Chair Dr. Rosalie Hall Dr. Paul E. Levy

______Committee Member Dean of the College Dr. Robert Lord Dr. Chand Midha

______Committee Member Dean of the Graduate School Dr. Joelle Elicker Dr. George R. Newkome

______Committee Member Date Dr. Philip Allen

______Committee Member Dr. Susan Olson

ABSTRACT

Designing effective training programs has been a key challenge for HR and

education professionals for years. Today, a deep understanding of learning processes is

even more crucial as rapid technological advancements necessitate continuous learning

and development of the workforce. The current study aimed to address this need by

integrating research on regulatory fit theory (Higgins, 2000) and training and testing a

theoretical model of training effectiveness recently developed by Gully and Chen (2010).

To that end, a total of 172 university students completed a three-stage project. First, participants underwent an online individual assessment. Next, they watched and responded to one of two versions of a Money Management training program framed and presented in either a promotion focused or a prevention focused manner. Finally, their reactions to and application of the trained material were assessed again several weeks after the completion of the training. Results demonstrated that when the framing and presentation style of the training video matched the learner’s dominant chronic regulatory focus, affective, cognitive, and behavioral indicators of training effectiveness were enhanced. These positive effects were observed both immediately after the training program and a few weeks later. Thus, the current project attested that customizing a training program to match or fit learners’ basic motivational orientation (i.e., regulatory focus) can result in a series of favorable training outcomes. Theoretical and practical implications, as well as directions for further research are discussed.

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ACKNOWLEDGEMENTS

I would not have been able to complete this project (and most probably survive this many years of schooling) without having such wonderful, supporting, and loving friends and family around me. The list of people I am thankful to is endless so I am mentioning here those, who have been by my side while writing this dissertation.

First of all, I would like to thank my advisor, Dr. Rosalie Hall, whose invaluable thoughts, suggestions, and guidance helped me to complete this work successfully. Thank you for believing in me the whole way through and for always supporting my ideas and decisions. Thank you, to my wonderful committee members—Dr. Robert Lord, Dr. Joelle

Elicker, Dr. Philip Allen, and Dr. Susan Olson, for your thoughtful comments and willingness to work with my rather tight schedule. I appreciate your flexibility and understanding tremendously! I would also like to thank my three amazing research assistants—Whitney Cale, Noelle Frantz, and Ali Benedetti, who conducted and overlooked most of the experimental portion of this research. Thank you for all the hard work and for treating my project as your own. A special thanks goes to Mike Plybon and

Karen Todaro as well, who helped me run this process smoothly, even though I was across an ocean.

On a more personal note, thank you to my mentor and friend Ali O’Malley for being there for me from the first day I visited Akron. You showed me what a wonderful and friendly place Akron was and made me convinced I wanted to spend the next five

years of my life there. Thank you for always being a shoulder I could lean on. Thank you

Megan Chandler, Grace Leung, and Katey Foster, for the great times we’ve had together—the great conversations over building puzzles in the corner office, the fun trips we’ve taken together, and the long discussions about nail polish. I am so grateful I got to know and be friends with each one of you and I hope you get to come visit me in

Bulgaria some day. And of course, thank you Karen Aiken Marando! Thank you for being the star in my dissertation video; thank you for introducing me to the wonders of

American food; thank you for living with me and still willing to let me sleep in your guest bedroom for months; thank you for drinking champagne with me whenever I needed it; thank you for giving me a home and being my family, far away from my own.

I miss you every day!

Finally, I want to thank a few non-gradschool individuals, who have reminded me that there is life outside of school. Thank you to my friend Vladimira for going on so many unforgettable trips with me and for bringing fun and excitement into my life. Thank you to my loving parents who have always supported my decisions and believed in what I have been doing, even though to this day they are still unsure what “regulatory fit” is.

And finally, thank you to my fiancé Krasio for patiently listening to me complain about statistical software, for putting up with my nervous breakdowns when deadlines approached, and for loving me even though I have spent the last year staring at the computer screen instead of at him.

TABLE OF CONTENTS

Page

LIST OF TABLES ...... ix LIST OF FIGURES ...... xi CHAPTER I. STATEMENT OF THE PROBLEM ...... 1 Regulatory Fit Theory ...... 3 Regulatory Fit and Training: Proposed Model ...... 6 Summary and Contributions ...... 9 II. LITERATURE REVIEW ...... 12 Training ...... 12 Historical Background ...... 12 Theoretical Framework for Trainees’ and Treatments’ Effects on Training Outcomes ...... 14 Attribute-Treatment Interactions ...... 17 A Special Case of ATI: Regulatory Fit...... 19 Regulatory Focus Theory ...... 19 Assessing Chronic Regulatory Focus ...... 25 Regulatory Fit Theory ...... 30 Regulatory Fit in Training: Development of Hypotheses ...... 43 III. METHODOLOGY ...... 55 Overview and Sample ...... 55 Procedure ...... 56 Personality Assessment ...... 56 In-lab Experimental Session ...... 57 Follow-up Survey...... 59

Experimental Budget Management Training Program ...... 59 Measures ...... 62 Trait Regulatory Focus ...... 62 Manipulation Checks ...... 65 Intervening Mechanisms ...... 66 Outcome Variables...... 69 Control and Exploratory Variables ...... 71 Statistical Analysis Strategy ...... 76 IV. RESULTS ...... 78 Initial Data Screening ...... 78 Participant Descriptive Statistics ...... 79 Preliminary Assessment of Factor Structure and Development of Scale Scores ...... 81 Intervening Mechanisms ...... 82 Outcome Variables...... 90 Video Framing Manipulation Checks ...... 96 Descriptive Statistics and Correlations ...... 101 Hypothesis Testing Procedure ...... 101 Hypothesis Testing ...... 105 In-lab Variables Models ...... 105 Follow-up Variables Model ...... 115 Additional Analyses ...... 122 Creating a Budget ...... 122 Using Expense-Reduction Strategies ...... 125 V. DISCUSSION ...... 129 Summary of Results ...... 129 Contributions and Implications...... 131 Training ...... 133 Regulatory Fit Theory ...... 137 Learning and Development Practice ...... 140 Limitations and Future Research ...... 141 vii

Study Design ...... 141 Issues Related to Choice of Statistical Analysis ...... 145 Theoretical Considerations ...... 148 VI. SUMMARY ...... 152 REFERENCES ...... 154 APPENDICES ...... 169 APPENDIX A. MONEY MANAGEMENT TRAINING PROGRAM ...... 170 APPENDIX B. REGULATORY FOCUS QUESTIONNAIRE ...... 187 APPENDIX C. GENERAL REGULATORY FOCUS MEASURE ...... 188 APPENDIX D. BIS/BAS SCALE ...... 189 APPENDIX E. MANIPULATION CHECKS ...... 191 APPENDIX F. INTERVENING MECHANISMS...... 193 APPENDIX G. OUTCOME VARIABLES ...... 196 APPENDIX H. DECLARATIVE KNOWLEDGE CODING RUBRIC ...... 199 APPENDIX I. CONTROL AND EXPLORATORY VARIABLES ...... 202 APPENDIX J. LATENT VARIABLES CFA MODELS ...... 210 APPENDIX K. IMPLICIT PROCESSING FLUENCY ...... 217 APPENDIX L. REGULATORY FOCUS MEASURES AND HYPOTHESIS TESTING WITH GRFM ...... 220 APPENDIX M. RFQ ADDITIONAL HYPOTHESIS TESTING ...... 229 APPENDIX N. IN-LAB VARIABLES MEASUREMENT MODEL ...... 235 APPENDIX O. HYPOTHESES 2 AND 3 SEPARATELY ...... 236 APPENDIX P. ALTERNATIVE MEDIATED MODELS ...... 238 APPENDIX Q. FOLLOW-UP VARIABLES MEASUREMENT MODEL ...... 240 APPENDIX R. REGULATORY FIT AND MOOD ...... 242 APPENDIX S. IRB APPROVAL ...... 249

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LIST OF TABLES

Table Page

2.1 Restatement of Hypotheses ...... 54

3.1 Sequence of Procedures ...... 60

4.1 Participant Demographic Descriptors by Experimental Condition ...... 79

4.2 Finance-related Participant Descriptors by Experimental Condition ...... 80

4.3 Results from the CFA of Emotional Fit Items ...... 84

4.4 Results from the CFA of Motivation Items ...... 86

4.5 Results from the CFA of Cognitive Fit Items ...... 88

4.6 Results from the CFA of Intervening Mechanisms Indicators ...... 90

4.7 Results from the CFA of In-lab Affective Reactions Items ...... 92

4.8 Second-order In-lab Affective Reactions Factor Loadings ...... 93

4.9 Results from the CFA of Follow-up Survey Affective Reactions Items ...... 94

4.10 Second-order Follow-up Affective Reactions Factor Loadings ...... 96

4.11 Results from the CFA of Promotion/Prevention Consistency Items ...... 97

4.12 Correlations and Descriptive Statistics of In-lab Study Variables ...... 102

4.13 Correlations and Descriptive Statistics of In-lab Study and Follow-up Survey Variables ...... 103

4.14 Summary of Hypothesis Testing Results ...... 121

4.15 Regression Coefficients for Binary Logistic Regression Predicting Budget Creation ...... 123

4.16 Means and (Standard Deviations) for Focal Dependent Variables by “Created a Budget” Group ...... 125

4.17 Regression Coefficients for Binary Logistic Regression Predicting Strategies Use ...... 126

4.18 Means and (Standard Deviations) for Focal Dependent Variables by “Strategies Used” Groups ...... 127

LIST OF FIGURES

Figure Page

1.1. Hypothesized Model ...... 9

2.1. Theoretical Model Developed by Gully and Chen (2010) ...... 13

2.2. Detailed Hypothesized Model ...... 44

4.1. In-lab Variables Interaction Model ...... 107

4.2. Regulatory Focus X Video Frame Interaction Effect on Emotional Fit ...... 108

4.3. Regulatory Focus X Video Frame Interaction Effect on Cognitive Fit ...... 109

4.4. Regulatory Focus X Video Frame Interaction Effect on Motivation ...... 110

4.5. Regulatory Focus X Video Frame Interaction Effect on Affective Reactions .....110

4.6. In-lab Variables Mediated Model ...... 112

4.7. Follow-up Variables Interaction Model ...... 117

4.8. Regulatory Focus X Video Frame Interaction Effect on Follow-up Affective Reactions ...... 118

4.9. Follow-up Variables Mediated Model ...... 119

4.10. Binary Logistic Regression for Regulatory Focus X Video Frame Interaction Effect on Budget Creation ...... 124

4.11. Interaction of Regulatory Focus and Video Frame and Number of Strategies Used ...... 128

CHAPTER I

STATEMENT OF THE PROBLEM

Optimizing training effectiveness is of crucial importance for companies as well as for education professionals in general. The ever-changing work environment, rapid advancements in technologies, and the globalization of the workplace necessitate continuous learning and development so companies can remain competitive. At the same time, the faltering economic environment has taken a toll on organizations, forcing them to lower their learning and development (L & D) budgets by 11% for the past year, with the median spending falling to $714 per learner (O’Leonard, 2010). Consequently, companies need to be much more efficient in their training and development spending to ensure a competent workforce at a lower price. Understanding how training programs can be structured to maximize training effectiveness is vital.

Although a lot of the research on training effectiveness has focused on design characteristics, the role of the trainee has been well recognized. Even back in 1986,

Pintrich, Cross, Kozna, and McKeachie (1986) noted that people are active participants in training programs as “they transform what they receive from instruction and create and construct knowledge in their own minds” (p. 613). Following such a trainee-centered logic, researchers started paying more attention to individual differences as important determinants of training outcomes (Baldwin & Ford, 1988; Colquitt, Lepine & Noe,

2000; Noe, 1986). What is more, Cronbach and colleagues pioneered the idea that 1

individual differences interact with training design features to affect training outcomes, arguing that unless it is absolutely clear that a training program leads to equivalent outcomes for everyone, training programs should be tailored to fit trainees’ specific characteristics and abilities (Cronbach, 1957; Cronbach & Snow, 1969). Research based on such an attribute-treatment interaction (ATI) perspective has greatly influenced the training literature and practice, yet empirical research using this paradigm is surprisingly limited and much more remains to be discovered (Campbell & Kuncel, 2002; Gully &

Chen, 2010). In their recent review of the training effectiveness literature, Gully and

Chen (2010) also note that a sound theoretical framework that discusses how, why, and when certain individual differences and design features (as well as their interaction) enhance training effectiveness is much needed (also noted by Bell & Kozlowski , 2008;

Debowski, Wood, & Bandura, 2001; Kozlowski & Bell, 2006). To address this need,

Gully and Chen (2010) develop a model where trainee characteristics and training design independently and/or together impact a set of intervening mechanisms which in turn affect a set of training outcomes.

The purpose of the current study is to explore a novel case of an attribute- treatment interaction and empirically test a modified version of Gully and Chen’s (2010) proposed theoretical model. Specifically, the current study will integrate research on regulatory fit theory (Higgins, 2000) within a training context and attempt to demonstrate that training effectiveness is enhanced when a trainee’s chronic regulatory focus matches the framing and presentation style of a training program. In what follows, the basic tenets of regulatory fit theory will be briefly summarized and the mechanism through which the

proposed attribute-treatment fit optimizes training effectiveness indicators will be presented.

Regulatory Fit Theory

The basic principle of Regulatory Fit Theory (Higgins, 2000, 2005, 2006) is that when one’s motivational orientation matches the goal pursuit strategy necessitated by the task at hand, task value, motivation, and engagement are enhanced. Although individuals’ motivational orientation can be operationalized in numerous ways, most of the research on regulatory fit has utilized Regulatory Focus Theory (RFT) (Higgins, 1997) and because of its dense theoretical and empirical support, RFT will be employed here as well.

Regulatory focus theory distinguishes between prevention and promotion motivational orientation each of which is associated with a set of different cognitive, affective, and behavioral experiences. Promotion focused individuals are generally motivated by nurturance related goals of advancement, growth, and development, while prevention focused individuals are motivated to pursue security related goals of shelter, safety, and protection. Differences in such basic motivational needs lead promotion and prevention focused people to approach goals in fundamentally different ways. Promotion focused people represent their goals as hopes and ideals, care about the presence and absence of positive outcomes, and are more motivated by gain-framed incentives (Crowe

& Higgins, 1997; Higgins, 1997; Idson, Liberman, & Higgins, 2000; Shah & Higgins,

1997; Shah, Higgins, & Friedman, 1998). On the other hand, prevention focused people

represent their goals as duties and obligations, are concerned with the presence and

absence of negative outcomes, and are more motivated by loss-frame incentives.

When pursuing goals and making decisions, promotion focused individuals have a natural inclination to approach matches to desired end-states and they prefer to do so

using eager strategies to ensure gains, resulting in a risky bias (Crowe & Higgins, 1997),

faster but less accurate performance (Förster, Higgins, & Bianco, 2003), enhanced creativity and optimism (Friedman & Förster, 2001; Grant & Higgins, 2001; Liberman,

Molden, Idson, & Higgins, 2001), as well as greater flexibility and adaptability

(Liberman, Idson, Camacho, & Higgins, 1999; Shah & Higgins, 1997). In contrast, prevention focused individuals are naturally inclined to avoid mismatches to desired end- states and prefer to use vigilant strategies to ensure safety and non-losses. This results in a conservative bias (Crowe & Higgins, 1997), more accurate but slower task performance

(Förster et al., 2003), as well as more cautious processing style (Crowe & Higgins, 1997;

Friedman & Förster, 2001; Liberman et al., 2001).

Recently, interest in nonverbal cues associated with promotion and prevention focus has reemerged (Cesario, 2006; Cesario & Higgins, 2008; Ritchie, 2009).

Specifically, drawing from a few earlier studies which had demonstrated that certain arm movements (i.e., flexion and extension) are intensified differently for promotion and prevention oriented people, Cesario and Higgins (2009) showed that different presentation styles are perceived differently depending on regulatory focus. On the one hand, an animated delivery style with hand gestures openly projecting outward, approaching forward-leaning body positions, raised eyebrows, and generally fast speech

rate was shown to appeal more to and “fit” better promotion focused viewers. On the

other, a delivery style implying vigilance and involving gestures that show precision,

“pushing” motions, slightly backward-leaning body position, and generally slower,

cautious speech rate was shown to “fit” better prevention focused individuals (Cesario &

Higgins, 2008; Ritchie, 2009).

Based on the fundamental motivational differences between promotion and

prevention focused individuals discussed thus far, Higgins (2000) developed Regulatory

Fit Theory. He argued that when situational demands sustain one’s regulatory focus, people experience regulatory fit and this match between individuals’ natural preference and the situational characteristics results in experiencing a feeling of “rightness” (Appelt,

Zou, Arora, & Higgins, 2008; Cesario & Higgins, 2008). In the past decade, research on regulatory fit has flourished and Higgins and colleagues have demonstrated that regulatory fit theory can be applied to a variety of contexts like advertisement, decision making, health promotion, etc. This body of research has demonstrated that when the manner in which people pursue a task fits their regulatory concern, they experience an

increased sense of value towards the given task, feel “right” about what they are doing,

their strength of engagement in the goal-pursuit activity is enhanced, and their overall performance is increased (Cesario, Higgins, & Scholer, 2008; Higgins, 2000, 2005,

2006). Additionally, regulatory fit has been associated with enhanced memory (e.g.,

Higgins, Roney, Crowe, & Hymes, 1994; Higgins & Tykocinski, 1992), enhanced task engagement and performance (e.g., Cesario, Grant, & Higgins, 2004; Shah et al., 1998), enhanced task enjoyment (e.g., Freitas & Higgins, 2002), more processing fluency (e.g.,

Lee & Aaker, 2004), as well as increased value assigned to objects, decisions, and behaviors (e.g., Higgins, Idson, Freitas, Spiegel, & Molden, 2003).

Regulatory Fit and Training: Proposed Model

The current study aims to apply regulatory fit theory to the context of training, an area not yet fully explored within the regulatory fit literature. Regulatory focus has been recognized as a relatively stable individual difference which classifies people as generally motivated by hopes and ideals or by duties and obligations (Higgins, 1997). A training program, on the other hand, can be designed to emphasize either advancement and prosperity or safety and security. Additionally, it can require trainees to engage in either risky, explorative behaviors or in step-by-step diligent procedures. Thus, a training program can have characteristics that better fit promotion vs. prevention focused individuals. Drawing from Gully and Chen’s (2010) theoretical framework, the current dissertation proposes a model where chronic regulatory focus (attribute) interacts with the frame and presentation style of a training program (treatment) to impact subjective feelings, processing fluency, attentional focus, and motivation which in turn impact cognitive, affective, and behavioral indicators of training effectiveness. The overall rational for the proposed model is discussed next.

Numerous studies have demonstrated that when one’s motivational orientation matches the framing of a message or is sustained by the goal pursuit strategies of a task, people experience a subjective feeling of “rightness” (Camacho, Higgins, & Luger, 2003;

Cesario & Higgins, 2008; Ritchie, 2009) and enjoyment (Freitas & Higgins, 2002;

Higgins, Pittman, & Spiegel, 2006). Additionally, Higgins (2000, 2008) has argued that

such a match also creates a sense of value derived from the strategic manner of goal

directed behaviors (i.e., value from fit) and that this created value enhances people’s

evaluative judgments or “liking” of tasks, messages, objects, decisions, etc. (Avnet &

Higgins, 2003, 2006; Brodscholl, Kober, & Higgins, 2008; Förster & Higgins, 2005;

Higgins, et al., 2003; Ritchie, 2009; Wang & Lee, 2006).

A fit between one’s regulatory focus and situational characteristics affects

cognitive processes like processing fluency as well (e.g., Cesario & Higgins, 2008; Lee &

Aaker, 2004). Research in cognitive and social-cognitive psychology provides an explanation for such effects, as mood-congruent, value-consistent, and self-relevant information (information consistent with one’s motivational orientation in this case) is more efficiently processed, stored, and accessed in memory (Bargh, 1982; Bodenhausen

& Lichtenstein, 1987; Fiske & Neuberg, 1990; Macrae, Milne, & Bodenhausen, 1994,

Ross & Sicoly, 1979; Taylor & Fiske, 2007). A connectionist view of information processing also supports the idea that information that matches our motivational concerns should be processed more fluently (Lord & Brown, 2004; Smith, 1996; Thagard &

Kunda, 1998).

In addition to processing self-relevant information more easily, people also pay more attention to information that is personally important. Even back in 1948, Postman,

Bruner, and McGinnies have noted that we “select from a near infinitude of potential percepts” for further processing “a servant of [our] interests, needs, and values” (p. 142).

Support for this notion also exists in the regulatory fit literature which has shown that when people experience regulatory fit, they tend to be more engaged in and absorbed by

the task at hand (e.g., Förster et al., 1998; Vaughn, Hesse, Petkova, & Trudeau, 2009). In

line with findings that regulatory fit enhances processing fluency, task focus, and task

engagement, empirical studies have repeatedly demonstrated its impact on people’s

motivation in different contexts as well (Hong & Lee, 2008; Idson, Liberman, & Higgins,

2004; Koenig, Cesario, Molden, Kosloff, & Higgins, 2009; Latimer et al., 2008; Spiegel,

Grant-Pillow, & Higgins, 2004).

Subjective feelings, information processing, attentional focus, and motivation, as

just discussed, are the intervening mechanisms proposed to explain the attribute-

treatment interaction (i.e., regulatory fit) effects on training outcomes. Consistent with

Kraiger, Ford, and Salas’ (1993) multidimensional framework of training effectiveness as

well as with Gully and Chen’s (2010) theoretical model, a wide range of training

outcomes are considered here. Grouped in three broad categories, these outcomes include

affective indicators of training effectiveness (satisfaction with training, self-efficacy, perceived utility of the training program, intentions to transfer), cognitive evidence

(declarative knowledge), as well as behavioral change (training transfer or utilizing learned material in everyday life). Figure 1.1 (next page) depicts the proposed model.

In order to test hypothesized relationships, participants watched one of two versions of a videotaped Budget Management training session. The two versions of the same training content were designed to either match promotion focused individuals’ concerns for advancement or prevention focused individuals’ concerns for security. In addition to verbally framing certain statements in promotion/prevention terms, the delivery style of the presentation was varied such that the presenter in the promotion

framed lecture engaged in more eager and approach-oriented gestures, while the presenter

in the prevention framed lecture engaged in more cautious and avoidance-oriented

gestures. Affective and cognitive training effectiveness indicators were collected upon

completion of the training. Information about behavioral transfer, or the extent to which

participants used the learned material in their daily lives, was collected two to three

weeks after the in-lab experiment. This was done in order to give participants some time

to start utilizing some budget management strategies and also to demonstrate that

regulatory fit effects extend beyond the boundaries of the lab.

INTERVENING LEARNING LEARNING MECHANISMS OUTCOMES TRANSFER

Subjective feelings

Follow-up Processing fluency Affective outcomes reactions

Trainee RF Attentional Focus Cognitive outcomes Application

Motivation

Video frame & presentation style

Figure 1.1. Hypothesized Model Note. Trainee RF = Trainee dominant regulatory focus.

Summary and Contributions

The main goal of the current study was to demonstrate that a training program can

be optimally designed to match one’s motivational orientation and that this “fit” in turn

can affect a broad set of training effectiveness indicators, through enhanced subjective

feelings, processing fluency, attentional focus, and motivation. By doing so, the current study has a great potential to enrich both the training literature as well as the literature on regulatory fit theory.

As noted by Gully and Chen (2010), research explaining how and why training features and individual differences independently and interactively affect training effectiveness is limited. The current study addresses this gap by investigating a set of

intervening mechanisms which might provide the how and why a special case of an

attribute treatment interaction (i.e., regulatory fit) affects training outcomes. Getting

insight about these explanatory mechanisms can be very fruitful for developing

hypotheses regarding other cases of ATIs, a training area in need of more empirical

findings.

The current study also contributes to the regulatory fit literature which has been

largely developing in lab settings and has to date been applied mainly to topics of

persuasion and advertisement. Although the current study was also a controlled lab

experiment, it attempted to provide a realistic representation of a real-life training

situation. By having participants listen to a 15-minute lecture, asking them to think of ways they can apply the information in their daily lives, and testing their knowledge at the end of the training, participants went through a scenario very similar to a real-life training session. What is more, by following up with them two to three weeks after they had completed the training and exploring their reactions to and utilization of the training material, the current study aimed to demonstrate that regulatory fit is not just a transient in-lab experience. Instead, regulatory fit can have a prolonged effect on both our

reactions and behaviors long after the regulatory fit manipulation has taken place. Thus,

the current study takes regulatory fit theory outside the lab to verify its real-world application.

CHAPTER II

LITERATURE REVIEW

Training

Historical Background

Originally, training researchers were interested in specific design features that

would enhance training outcomes. However, more trainee-centered views on training began to emerge as several influential reviews of the training literature (e.g., Campbell,

1988) noted that design characteristic effects can vary across individuals, thus suggesting that different designs may be optimal for different types of learners. Authors started arguing that trainees are active participants in the training as they interpret and construct knowledge in their minds and therefore trainee characteristics must be explicitly considered as they have the potential to interact with environmental and/or training features to impact performance (Pintrich et al., 1986).

Based on the view that trainees play a central role in the learning process, much training research in the past two decades has included individual differences as one important determinant of learning and behavioral changes (Baldwin & Ford, 1988;

Colquitt et al., 2000; Noe, 1986). However, as Gully and Chen (2010) note, despite this awareness of the important role that trainees play in the training process, numerous gaps in the literature exist. For example, although person analysis is included in training

textbooks as an important step in the training process (Goldstein & Ford, 2002), assessing

individual characteristics prior training is still generally neglected (Colquitt et al., 2000;

Salas & Cannon-Bowers, 2001).

Gully and Chen (2010) point out that a sound theoretical framework that discusses

how, why, and when certain individual differences enhance training effectiveness is

missing and that this critical deficiency in the literature might be one of the reasons trainees’ characteristics and their impact on training are still not fully understood. The authors draw attention to the need for a framework that explains the processes through which individual attributes and training characteristics affect, independently as well as

interactively, a series of different training outcomes. In an effort to initiate research that

can fill this gap, Gully and Chen offer a broad theoretical model which is depicted in

Figure 2.1. In this model, individual differences and treatment features interact to affect a set of intervening mechanisms, which in turn explain variances in training outcomes. In what follows, Gully and Chen’s (2010) framework is further discussed as it serves as a

foundation for the model that was developed and tested in the current study.

Treatments Training Design Features Situational Characteristics

Trainee Learning Outcomes Characteristics Intervening Cognitive Outcomes Capabilities Mechanisms Behavioral Outcomes Demographics Learning Outcomes Personality Traits

Figure 2.1. Theoretical Model Developed by Gully and Chen (2010)

Theoretical Framework for Trainees’ and Treatments’ Effects on Training Outcomes

Gully and Chen’s (2010) framework is designed to explain both trainee and

training main effects and attribute-treatment interactions. The authors include in their

model four broad categories of individual characteristics—capabilities, demographics,

personality traits, and values and interests, a list consistent with individual differences

frequently examined in the training literature (Colquitt et al., 2000). Treatments in this

model are defined to include both training program design characteristics as well as

contextual and situational characteristics of the training system as a whole. Consistent with a learner-centered view on training, individuals’ characteristics and training program characteristics can interact to affect a series of motivational, cognitive, and affective processes, which in turn influence a series of training outcomes.

Gully and Chen (2010) discuss four intervening mechanisms that potentially mediate the effects of individual differences on training outcomes: (a) information-

processing capacity, (b) attentional focus and meta-cognitive processing, (c) motivation

and effort allocation, and (d) emotional regulation and control. A need for explanatory

mechanisms that drive individual and training characteristics effects on outcomes has

been noted by others as well (e.g., Bell & Kozlowski , 2008; Debowski et al., 2001;

Kozlowski & Bell, 2006) and this set of explanatory processes provides a good starting

point. The intervening processes included in Gully and Chen’s (2010) model are

consistent with other models of self-regulation that point out the importance of self-

monitoring, evaluation, and affective experiences during goal pursuit (Ackerman &

Kanfer, 2004; Beier & Kanfer, 2010; Bell & Kozlowski, 2010). Each explanatory

mechanism is briefly described next.

Information-processing capacity refers to how we process and organize information. General intelligence, age, and task-related experience have been considered as some of the main determinants of information-processing capacity (Kanfer &

Ackerman, 2004; Ree, Carretta, & Teachout, 1995). It is also reasonable to expect that certain information can be more or less easily processed by certain individuals. For example, we tend to be more sensitive to, and better remember, information that is consistent with our attitudes, beliefs, self-concepts, motivational orientations, etc. (Taylor

& Fiske, 2007).

Attentional focus and meta-cognitive processing are associated with the amount of cognitive resources engaged in learning task-relevant information versus engaging in task-irrelevant activities (e.g., ruminating about inability to understand the material) and with the extent to which we tend to engage in planning, monitoring, and revisions of goal-directed behaviors. Such processes emphasize trainees’ active role in the training experience as different individuals might attend to different aspects of the training program and engage in different levels of meta-cognition.

Motivation and effort allocation have to do with the direction, effort, intensity, and persistence trainees utilize during goal-pursuit. The importance of motivation during learning has been well established (e.g., Baldwin & Ford, 1988; Colquitt et al., 2000).

Enhanced trainee self-efficacy has been consistently shown to lead to higher training

motivation (Salas & Cannon-Bowers, 2001) and training motivation has been linked to

important training outcomes (Colquitt et al., 2000).

Finally, emotional regulation and control refer to emotional regulatory processes

that control negative affective experiences during training (e.g., anxiety in response to

inadequate performance; Kanfer & Heggestad, 1997). The importance of emotional control has been noted by several authors because anxiety and other negative emotions tend to lead to distractive thoughts and rumination which hinder training performance

(Kanfer, Ackerman, & Heggestad, 1996). Additionally, Keith and Frese (2005) demonstrated that emotional control and meta-cognition mediate the effects of error management training programs on training effectiveness.

Consistent with Kraiger et al.’s (1993) multidimensional framework of training effectiveness, Gully and Chen’s (2010) model incorporates a wide range of training outcomes grouped into three broad categories—affective, cognitive, and behavioral (also see Colquitt et al., 2000; Ford, Kraiger, & Merritt, 2010). Affective outcomes encompass motivational factors and thus include satisfaction with the training program and the trainer, self-efficacy, expectancy, and perceived utility of the training. Cognitive outcomes include declarative, procedural, and strategic knowledge as well as the ability

to apply learned knowledge to new situations (cognitive transfer). Finally, behavioral

outcomes include transfer of training to real-world situations in terms of skill

generalization and adaptability (Baldwin & Ford, 1988). Sometimes attitudinal outcomes

can also be investigated in terms of changes in attitudes towards the task, the job, or the

training topic in general. In a recent discussion of trends in training research, Ford et al.

(2010) note the importance of measuring affective outcomes other than reactions to the

training program. For example, they note that changes in training motivation or changes

in motivational orientations (e.g., from a performance to a mastery goal orientation)

might be one affective indicator of training effectiveness. The current study utilizes and

further develops Gully and Chen’s (2010) general framework to examine the interactive

effects of a broad, motivational individual difference (i.e., regulatory focus) and the

design characteristics of a training program on affective, cognitive, and behavioral training effectiveness outcomes.

Attribute-Treatment Interactions

The Attribute-Treatments Interaction (ATI) paradigm suggests that certain individual characteristics (demographics, personality, abilities, etc.) interact with aspects of the training program (content, delivery, context, etc.) to impact training effectiveness.

Indeed, theory and research have demonstrated that both the trainee and the training program have an impact on training outcomes and often function in an interactive fashion

(see Gully & Chen, 2010 for a recent review). Such logic is also consistent with theories and research on person-job and person-environment fit which have demonstrated that when a person “fits” well within his job, unit, or organization, personal as well as organizational outcomes are optimized through supplementary and complementary means

(Cable & Edwards, 2004; Edwards, Cable, Williamson, Lambert, & Shipp, 2006; Kristof-

Brown, Zimmerman, & Johnson, 2005). Thus a good “fit” between individuals’ characteristics and the training program should result in optimal outcomes.

A rationale for considering aptitude/attribute-treatment interactions was introduced by Lee Cronbach in the late 1950’s, in a presidential address to the American

Psychological Association. He contrasted experimental and correlational psychology, and saw a consideration of ATIs as a means to reconcile the strong points of both. He argued that unless one treatment (e.g., training program) is clearly best for everyone, professionals need to design different programs that best fit or are modified to maximize outcomes for trainees with certain patterns of abilities and characteristics.

Although early work in this area encountered challenges in terms of demonstrating strong and clear ATIs, Cronbach and colleagues strongly influenced training-related research. For example, they were first to demonstrate that students’ cognitive ability should be taken into consideration when designing training programs because students with higher cognitive ability benefited more when they were given more responsibility and control over their learning, while lower ability students tended to benefit more from highly structured training programs (Cronbach & Snow, 1969, 1977).

Research since Cronbach’s initial studies have further developed these ideas by showing that student’s anxiety also interacts with imposed structure to affect performance (Snow,

1991). Overall, research on ATIs has investigated how demographics, personality traits, self-concept traits, values, cognitive styles, and ability interact with training content, design, or context to affect training outcomes and thus many important effects have been uncovered. However, as Gully and Chen (2010) note in their review of this literature, the empirical research on ATIs is surprisingly limited and much more remains to be discovered about them (also see Campbell & Kuncel, 2002).

A Special Case of ATI: Regulatory Fit

The current study investigated whether the experience of regulatory fit can enhance people’s learning, performance, and overall reactions during a training program.

As is discussed in more detail shortly, regulatory fit results from a match between one’s regulatory focus and his/her goal pursuit strategy (Higgins, 2000). Regulatory focus is a relatively stable individual difference which classifies people as generally more motivated by hopes and aspirations (promotion focused) or by duties and obligations

(prevention focused; Higgins, 1997). A training program, on the other hand, can be designed to emphasize either advancement and prosperity or safety and security.

Additionally, it can require trainees to engage in either risky, explorative behaviors or a step-by-step diligent assignment. Thus, a training program can have characteristics that better fit promotion vs. prevention focused individuals. In what follows, an argument is developed that a match between the training program’s characteristics and the trainee’s regulatory focus results in regulatory fit, which in turn enhances the trainee’s experiences during the training, his/her overall performance, and his/her reactions towards the training program.

Regulatory Focus Theory

Regulatory Focus Theory, developed by E. T. Higgins (e.g., Higgins, 1997) suggests that people’s behavior is generally motivated (self-regulated) by the fulfillment of two types of needs—nurturance-related and security-related needs. People who are generally motivated to pursue nurturance related goals of advancement, growth, and development are said to be promotion focused. On the other hand, people who are

generally motivated to pursue security related goals of shelter, safety, and protection are said to be prevention focused. Higgins (1997) proposes that because of these differences in basic psychological needs, promotion and prevention focused people have very different motivational, cognitive, and emotional experiences. Next, the basic characteristics of promotion and prevention focus are summarized and several measures of chronic regulatory focus are discussed.

Promotion focus. Promotion focused individuals are generally concerned with growth and advancement, and they represent their goals as hopes, aspirations, and ideals.

They strive towards the presence of positive outcomes (gains) and try to avoid their absence (non-gains). Consequently, when asked to engage in tasks with gain-focused incentives as outcomes (i.e., success results in rewards or gains while failure results in absence of rewards or non-gains) they tend to perform better because such incentives are perceived as more goal-relevant (Crowe & Higgins, 1997; Higgins, 1997; Idson et al.,

2000; Shah & Higgins, 1997; Shah et al., 1998). When successful in accomplishing their goals (presence of a gain), promotion focused people experience high-intensity positive emotions like elation and cheerfulness (Higgins, 1987, 1997). Förster, Grant, Idson, and

Higgins (2001) have shown that the success feedback which enhances such high activation emotions also intensifies participants’ subsequent motivation and commitment towards approaching further successes. In contrast, when promotion focused people fail

(absence of a positive outcome) they experience low-intensity negative emotions like sadness and dejection (Higgins, 1987, 1997). Because such low-intensity emotions are

associated with lower arousal, failure feedback tends to result in smaller increases in

commitment toward avoiding subsequent failures (Förster et al., 2001).

In addition to differential reactions to positive and negative outcomes, promotion

and prevention focused individuals differ in their preferred strategies during goal

attainment and decision making. Because a promotion focus creates a concern for gains,

promotion focused individuals have a natural inclination to approach matches to desired

end-states and they prefer to do so using eager strategies to ensure gains and

accomplishment. In signal detection terms, they strive for hits and want to avoid errors of

omission (overlooking positive outcomes). As a result, they are more likely to engage in a

risky bias in memory classifications tasks (Crowe & Higgins, 1997) as well as in faster

but less accurate task performance (Förster et al., 2003). In decision making tasks, promotion focused people are inclined to consider a wider range of alternative hypotheses. Thus, they tend to endorse more explanations for others’ as well as their own behaviors and consequently tend to make less certain predictions about the future

(Liberman et al., 2001; Molden & Higgins, 2004). Additionally, promotion focus is associated with an exploratory processing style, enhanced creativity, and optimism

(Crowe & Higgins, 1997; Friedman & Förster, 2001; Grant & Higgins, 2001; Liberman et al., 2001), as well as with greater flexibility and adaptability during goal pursuit

(Liberman et al., 1999; Shah & Higgins, 1997). Finally, promotion focused individuals tend to engage in more abstract processing and use and are affected by more abstract language (Semin, Higgins, de Montes, Estourget, & Valencia, 2005). Relatedly, they

prefer to think about their goals in more abstract, global terms and to project them into

the distant future (Förster & Higgins, 2005; Pennington & Roese, 2003).

Prevention focus. Prevention focused individuals, on the other hand, are generally

concerned with safety and security, and represent their goals as duties, obligations, and

oughts. When pursuing these prevention concerns, people tend to be more focused on

losses—they strive towards the absence of negative outcomes (non-losses) and avoid

their presence (losses). Thus, prevention focused individuals tend to prefer and perform

better in tasks with loss-focused incentives (i.e., where success results in elimination of

punishment or non-loss while failure results in the presence of punishment/loss; Crowe &

Higgins, 1997; Idson et al., 2000; Shah & Higgins, 1997). When successful (absence of a

punishment/loss), prevention focused people experience low-intensity positive emotions like relaxation and quiescence. When they fail (presence of a punishment/loss), they experience high-activation, negative emotions like agitation and nervousness (Higgins,

1987, 1997). Consequently, for prevention focused individuals, success feedback tends to lead to little increase in subsequent motivation and task commitment while failure feedback leads to enhanced motivation to avoid losses in the future (Förster et al., 2001).

In order to fulfill their needs for security and safety, prevention focused people are naturally inclined to avoid mismatches to desired end-states and prefer to use vigilant strategies to ensure safety and non-losses. In signal detection terms, they strive to engage in correct rejections (eliminating negative outcomes) and to ensure against errors of commission (incorrect hits). Such a vigilant task performance strategy has been shown to result in a conservative bias in memory classification tasks (Crowe & Higgins, 1997), as

well as in more accurate but slower task performance (Förster et al., 2003). Overall, prevention focus is associated with a more cautious processing style and inhibited creativity (Crowe & Higgins, 1997; Friedman & Förster, 2001; Liberman et al., 2001), as well as with more careful and limited consideration of alternatives during decision- making (Liberman et al., 2001). Semin et al. (2005) showed that prevention focused people tend to engage in more focused and concrete processing and use and are affected by more concrete language. They also prefer to think about their goals in more specific/local terms and usually set them for the near future (Förster & Higgins, 2005;

Pennington & Roese, 2003).

Although the summary of Regulatory Focus Theory research presented thus far seems to suggest that prevention focused individuals are generally “worse-off,” it is important to keep in mind that in certain situations more vigilant and cautious processing and goal-pursuit strategies might be beneficial (Molden, Lee, & Higgins, 2008). For example, Freitas, Liberman, and Higgins (2001) showed that prevention focused participants performed better than promotion focused ones on tasks that required vigilance against tempting distracters. Additionally, Fuglestad, Rothman, and Jeffrey

(2008) showed that even though promotion focused participants were more likely to engage in behavioral change (starting a weight loss program, quitting smoking), prevention focused participants were better at maintaining their smoking cessation and weight loss intervention behaviors because they tended to be more vigilant in avoiding failures to preserve their desired end states. Thus, the optimistic approach of promotion oriented people helps them initiate change as they believe more strongly that they can

succeed in the change effort; but, the vigilant strategies of ensuring against failure associated with a stronger prevention concern are more important in sustaining that changed behavior.

Regulatory focus and nonverbal cues. Recently, interest in specific nonverbal behaviors associated with promotion and prevention foci has been spurred (Cesario,

2006; Cesario & Higgins, 2008; Ritchie, 2009). Initial work on Regulatory Focus Theory by Higgins and colleagues (e.g., Förster, Higgins, & Idson, 1998; Förster et al., 2001) suggested that certain arm movements (flexion vs. extension; pressing up on a surface vs. pressing down on a surface) are intensified for people with certain regulatory foci. For example, promotion-focused participants pressed up on a surface (pulling closer) harder than prevention-focused participants, while prevention-focused participants pressed down on a surface (pushing away) harder than promotion-focused participants (Förster et al.,

1998). These initial results suggested that promotion focus is associated with intensified approach movements (pulling up, arm flexion) while prevention focus is associated with intensified avoidance movements (pushing down, arm extension).

Related to this idea, it is reasonable to expect that certain body movements, body positions, gestures, and other nonverbal cues might be more or less appealing to promotion/prevention focused individuals. To test this hypothesis, Cesario and Higgins

(2008) designed a video-taped presentation that employed either an eager, approach- oriented or vigilant, avoidance oriented delivery style. Specifically, the authors reasoned that because promotion focused individuals are concerned with advancement, a promotion focused delivery style should involve eager movements forward. Some

presentation characteristics that imply eagerness include animated, broad opening

movements, hand gestures openly projecting outward, approaching forward-leaning body

positions, raised eyebrows, and generally fast body movements and speech rate. In

contrast, because prevention focused individuals are concerned with safety and caution, a

prevention focused delivery style should involve vigilant carefulness. Delivery

techniques implying vigilance include gestures that show precision, “pushing” motions

representing slowing down, slightly backward-leaning body position implying avoidance,

furrowed brow, and generally slower, cautious body movement as well as speech rate

(Cesario & Higgins, 2008). These researchers showed that when the presenter’s delivery

style was consistent with a viewer’s chronic focus (promotion or prevention) the

presentation was processed more easily, felt more “right”, and was more persuasive.

Assessing Chronic Regulatory Focus

Regulatory focus can be situationally primed through different environmental cues (e.g., thinking about hopes or obligations, focusing on positive or negative outcomes, eliciting cheerful/dejected or relaxed/agitated emotions, etc., see Molden et al,

2008 for a comprehensive review). However, Regulatory Focus Theory (Higgins, 1997) recognizes that regulatory focus is also a stable individual difference that has developed through positive and negative experiences with different goals, successful or unsuccessful goal pursuit strategies (Higgins, Friedman, Harlow, Idson, Ayduk, & Taylor, 2001), enhanced concern with actual/ideal or actual/ought self-discrepancies (Higgins 1987,

1997), and through interactions with different role models or specific parenting styles

(Higgins, 1997; Lockwood, Jordan, & Kunda, 2002). Several measures of dispositional

regulatory focus have been developed and used in the literature. They are briefly summarized and compared next.

Selves Questionnaire. Based on Higgins’ self-discrepancy theory, the Selves

Questionnaire (Higgins, Bond, Klein, & Staruman, 1986) compares ones’ actual, ideal, and ought selves. Respondents are asked to list a number of attributes that describe each of the three selves and are then asked to rate the extent to which they actually possess, would ideally like to possess or believe they ought to possess each attribute. The actual/ideal discrepancy and the actual/ought discrepancy are calculated by looking at the matches/mismatches of the actual attributes listed and the ideal/ought self attributes.

Higgins and colleagues argued that these discrepancies indicate the extent to which a certain self-regulatory concern chronically dominates within the person, with larger discrepancies signifying larger self-regulatory concern. A concern with ideal self- standards has been associated with an activation of a promotion motivation while a concern with ought self-standards has been associated with an activation of a prevention motivation (Molden et al., 2008).

Based on attitude accessibility research showing that stronger attitudes are more readily accessible in memory, Higgins and colleagues adapted the Selves Questionnaire to assess one’s “Self-Guide Strength.” Self-guide strength is indicated by the time needed to list ideal/ought self attributes and to rate the extent to which one possess each of them

(Higgins, 1996). The more accessible a concern is in memory, the faster the responses are. Strong ideal/ought self-guides serve as strong reference points. Consequently, relevant attributes are retrieved faster and perceived standing on each of the attributes is

more salient and thus estimated faster. The computer adapted version of this updated

Selves Questionnaire calculates both a self-discrepancy and a self-guide strength indicator (Higgins, Shah, & Friedman, 1997).

Regulatory Focus Questionnaire (RFQ). Because of the relatively complex manner of administering and scoring the Selves Questionnaire, researchers have started developing alternative measures of chronic regulatory focus. One of the most widely used today is Higgins et al.’s (2001) RFQ. This measure is based on the assumption that past success with promotion-related eagerness results in subsequent preference for eager strategies during goal pursuit while past success with prevention-related vigilance results in subsequent preference for vigilant strategies. Thus, the general aim of the RFQ is to assess perceived history of effective and ineffective promotion and prevention self- regulation. Overall, the scale contains two psychometrically different subscales. The prevention subscale focuses on past experiences with successfully or unsuccessfully avoiding negative outcomes while the promotion subscale focuses on past successful or unsuccessful accomplishment of positive outcomes. In a series of studies, Higgins et al.

(2001) confirmed convergent and discriminant validity of the measure and demonstrated good internal consistency and test-retest reliability. In a more recent study utilizing the

RFQ, Latimer et al. (2008) showed that participants’ scores on the RFQ were not susceptible to common regulatory focus manipulations. Such a finding should not be surprising as the RFQ assesses one’s past successes/failures with promotion/prevention concerns and situational cues related to prevention/promotion focus should not affect responses regarding such past experiences.

General Regulatory Focus Measure (GRFM). Another frequently used measure of

chronic regulatory focus is the one developed by Lockwood et al. (2002). For this

measure, respondents are asked to indicate the extent to which they endorse 18 items

indicative of promotion or prevention focus. Half of these items tap promotion related goals of success and advancement (e.g., “In general, I am focused on achieving positive outcomes in my life”) and the other half tap on prevention related goals of failure and

security (e.g., “I often think about the person I am afraid I might become in the future”).

Each individual ends up having two scores, one for each subscale, indicating the strength

of the person’s promotion and prevention goals. In their study, Lockwood et al. (2002)

showed that on average, their participants were more likely to have stronger promotion

(vs. prevention) goals and that stronger promotion goals were associated with recalling

influential positive role models, while stronger prevention goals were associated with

recalling influential negative role models.

Relationships between chronic regulatory focus measures. Recently, several

authors have questioned the construct validity of the different types of chronic regulatory

focus measures and have expressed concern about the non-significant relationships

among them. For example, Haaga, Friedman-Wheeler, McIntosh, and Ahrens (2008)

compared the RFQ and three regulatory focus indices derived from the Selves

Questionnaire (two indicating self-guide strength and one indicating actual/ideal and

actual/ought discrepancies). The researchers demonstrated good test-retest reliability for the RFQ and the reaction time-based measure of self-guide strength. However, convergent and discriminant validity evidence was generally disappointing as

relationships among promotion and prevention subscales were weak and only the RFQ subscales related to other personality measures in the expected directions. Although this study provided limited evidence for the utility of the Self-Guide Strength measure, it is important to note that such reaction-time based measures assess implicit motives and attitudes which are often times only weakly related to explicit reports of the same attitudes (McClelland, Koestner, & Weinberger, 1989; Taylor & Fiske, 2007).

In a different paper, Summervile and Roese (2008) compared the RFQ, the

General Regulatory Focus Measure (GRFM), and Carver and White’s (1994) BIS/BAS scale. These authors showed that the GRFM and BIS/BAS sensitivity seemed to overlap in terms of the constructs they assessed while the RFQ was largely unrelated to either of these scales. This lack of a relationship suggests that these measures are assessing different underlying constructs, probably stemming from different regulatory focus definitions. On the one hand, the RFQ is based on Higgins’ (1986) original self- discrepancy and self-guide theory. In this definition, promotion focus is defined in terms of a concern with achieving personally valuable ideals, hopes, and aspirations, while prevention focus is defined in terms of a concern with meeting duties, obligations, and responsibilities. In contrast, the GRFM is based on the “reference-point” definition of regulatory focus, which distinguishes between different end states that promotion vs. prevention focused people strive to achieve (presence/absence of positive outcomes vs. presence/absence of negative outcomes, respectively).

Summervile and Roese (2008) also showed that the RFQ subscales were unrelated to positive and negative affectivity while the GRFM promotion subscale was positively

related to PA and the GRFM prevention focus subscale was positively related to NA as measured by the PANAS (Watson, Clark, & Tellegen, 1988). In his work, Higgins (1997) has argued that chronic promotion/prevention focus should not be related to heightened general positive/negative affectivity and this notion has been demonstrated by numerous studies (Förster et al., 1998; Förster et al., 2003; Seibt & Förster, 2004; Shah et al., 1998).

Instead, promotion/prevention self-regulation is associated with specific and predictable positive and negative emotions in response to feedback associated with one’s progress towards the end state/goal/standard (i.e. success/failure feedback; Brockner & Higgins,

2001; Carver & Scheier, 2000). Thus, the lack of association between the RFQ subscales and PA and NA in Summervile and Roese’s (2008) research is consistent with Higgins’

(1997) view of promotion and prevention focus. In contrast, the GRFM heightens the salience of gain/loss experiences (and consequently emotional reactions related to those) associated with promotion/prevention goals and thus a relationship with positive/negative affectivity is expected. Overall, while it is true that these two measures assess seemingly different aspects of regulatory focus (general ideal/ought concerns vs. more specific concerns with gains/non-gains and losses/non-losses), both are consistent with general definitions and findings of Regulatory Focus Theory research.

Regulatory Fit Theory

The basic premise of regulatory fit theory (Higgins, 2000, 2005, 2006) is that specific goal-pursuit strategies are naturally preferred by individuals with certain motivational concerns and orientations. For example, as previously described, promotion focused individuals prefer to use eager strategies during goal attainment as such strategies

sustain their concern with the presence or absence of positive outcomes and ensure

matches to desired end states. In contrast, prevention focused individuals prefer to use vigilant strategies during goal attainment as such strategies sustain their concern with the presence or absence of negative outcomes and ensure against mismatches to desired end states (Cesario et al., 2004; Crowe & Higgins, 1997; Liberman et al., 2001; Shah et al.,

1998). Regulatory fit results from a match between one’s motivational concern and the

goal pursuit strategy he/she uses. Although most research on regulatory fit theory has

used regulatory focus theory to examine fit effects, regulatory fit (non-fit) can result from

a match (mismatch) between other types of motivational orientations (e.g., regulatory

mode of assessment vs. regulatory mode of locomotion, Kruglanski et al., 2000) and

fitting/non-fitting strategies (e.g., comparison of alternatives vs. moving a task ahead).

Because it has a denser theoretical and empirical basis, the regulatory focus paradigm

was utilized in the current study to explore regulatory fit effects in a training context.

However, the reader should keep in mind that regulatory fit effects are not limited to the

promotion/prevention distinction and can be derived from other sources as well.

In his initial proposal of regulatory fit theory, Higgins (2000) argued that when

the manner in which people pursue a goal sustains their regulatory orientation they feel

“right” (see Cesario & Higgins, 2008 for evidence for the mediating role of “rightness”

feelings; also Appelt et al., 2008) about what they are doing and experience value from

the fit of their motivational concern and strategic means employed. In contrast with other

theories which focus on the value derived from goal-related outcomes (and their effects

on motivation, i.e., valence, goal expectancy, instrumentality) or value from socially

proper or justifiable means (e.g., distributed justice, socially acceptable behaviors, norms,

etc.), regulatory fit theory focuses on the value people derive from the specific strategic

manner in which they are accomplishing their goal. Because the means people use are

consistent with their internal motivational orientation, these means seem to feel “right”

and create a sense of value and importance of the task at hand. Thus, when the manner in

which people pursue a task fits their regulatory concern, they experience an increased

sense of value towards the given task, feel “right” about what they are doing, their

strength of engagement in the goal-pursuit activity is enhanced, and their overall

performance is increased (Cesario et al., 2008; Higgins, 2000, 2005, 2006). Based on this

logic, the literature on regulatory fit theory to date has demonstrated a variety of effects

of regulatory fit on performance, decision making, and persuasion. More specifically,

studies have shown that regulatory fit (vs. non-fit) increases people’s memory (e.g.,

Higgins et al., 1994; Higgins & Tykocinski, 1992), task engagement and performance

(e.g., Cesario et al., 2004; Shah et al., 1998), task enjoyment (e.g., Freitas & Higgins,

2002), processing fluency (e.g., Lee & Aaker, 2004), as well as the value assigned to

objects, decisions, and behaviors (e.g., Higgins et al., 2003). Because all of these

outcomes are important in a training context, literature pertaining to these effects is

summarized next.

Regulatory fit effects on evaluative judgments and task enjoyment. When the goal pursuit strategy we use matches our motivational orientation, we experience regulatory fit. This fitting experience makes us feel “right” about what we are doing and enhances our evaluation of the task (Higgins, 2000). Freitas and Higgins (2002) provided evidence

for this effect by demonstrating that when participants were asked to “find helpful

elements” (eager strategy), promotion (vs. prevention) focused participants enjoyed the

task more. In contrast, when they were asked to “eliminate harmful elements” (vigilant

strategy), prevention (vs. promotion) focused participants enjoyed the task more. In

another study, Higgins et al. (2003) asked participants to come up with suggestions about

improving children’s transition from elementary to middle school. In addition to coming

up with more strategies, fit participants (promotion focused coming up with eager

strategies and prevention focused coming up with vigilant strategies) also rated middle

school experiences as being more important than non-fit participants. Furthermore,

Higgins et al., (2006) showed that when participants were given a choice of whether to

continue working on the focal experimental task or engage in a different activity (while

the experimenter was gone for five minutes), those experiencing fit chose to keep

working on the target task five times as often as the non-fit participants did! All of these

studies suggest that when we are engaging in a task in a manner that sustains our

motivational orientation, we value and enjoy that task more.

In a different set of studies—the well-known and often cited coffee mug studies—

Higgins et al. (2003) demonstrated that we experience value from the strategic means during goal pursuit and that this experienced value can later transfer to irrelevant judgment tasks through the process of source confusion (e.g., Schwarz & Clore, 1983). In three different studies with similar designs, the researchers showed that when the strategic means used to make a choice between a coffee mug and a pen fit with participants’ regulatory focus, participants assigned greater monetary value and were

willing to pay more money for the object they had chosen. In fact, in the first study, fit

participants assigned a 50% higher value to the mug as compared to non-fit participants

and in the second study, fit individuals were willing to pay up to 70% more than the non-

fitting ones. Importantly, no main effects for regulatory focus or strategy used to make

the choice were observed which demonstrates that it was the experience of fit that

affected the value assigned, and not the strategy or participants’ regulatory focus.

Additionally, fit effects were independent of how good/bad participants felt after their

decisions. The same pattern of result has been replicated by others, using the same

general paradigm but different fit/non-fit manipulations (Avnet & Higgins, 2003, 2006;

Brodscholl et al., 2008; Förster & Higgins, 2005).

Regulatory fit effects on task engagement. In addition to creating value, regulatory

fit also enhances engagement in the task at hand (Higgins, 2000, 2006, 2008). Some of

the initial studies on regulatory fit effects have directly demonstrated this increase in

strength of engagement. In three different studies Förster et al. (1998) assessed chronic

regulatory focus or manipulated it. Then, they asked participants to solve a series of

anagrams while either pressing down (an avoidance-related movement of pushing away) or pressing up (an approach-related movement of pulling toward oneself) on a surface designed to measure the exerted pressure. The results of all three studies showed that when participants’ arm movement was consistent with their regulatory focus, they pressed the surface harder. Specifically, promotion focused participants pressed up the

surface harder, while prevention focused individuals pressed down harder. Furthermore,

participants persisted longer on the anagram task when they experienced fit (i.e., engaged

in arm movement consistent with their chronic regulatory focus). Several other studies

using similar research designs have replicated these results. For example, Shah et al.

(1998) found that as participants’ promotion focus was increasing (i.e. stronger ideal self-

guide), their performance on an anagram task with a gain framed incentive (earning an

extra dollar for good performance) was better than when the incentive was framed as a

non-loss (not losing a dollar for poor performance). In contrast, as participants’

prevention focus was increasing, their performance was better in the non-loss-framed condition than in the gain-framed condition (also see Förster et al., 2001 and Roney,

Higgins, & Shah, 1995). Importantly, all of these effects on task engagement were

independent of participants’ positive or negative feelings during the experiment.

In an attempt to explain regulatory fit effects on engagement, Idson et al. (2004) looked at participants’ experience of emotions in response to different promotion/prevention successes and failures. The researchers proposed that when a future outcome sustains one’s regulatory state, people’s motivation to approach the outcome if it is desirable or avoid it if it is undesirable, is intensified. They argued that potential desirable outcomes better fit promotion-focused individuals (who are generally concerned with the presence or absence of positive outcomes and experience intense positive emotions in response to a successful gain) and consequently they should be more engaged in approaching those. In contrast, potential negative outcomes better fit prevention-focused individuals (who are generally concerned with the presence or absence of negative outcomes and experience intense negative emotions in response to a loss) and consequently they should be more engaged in avoiding such undesirable

outcomes. Indeed, the researchers showed that when they had to imagine making a

desirable choice, promotion (fitting) participants experienced more intense positive

emotions than prevention (non-fitting) participants. When participants had to imagine

making an undesirable choice, prevention focused people reported more intense negative

feelings than promotion people did.

Regulatory fit effects on memory. Research on memory effects has repeatedly

demonstrated that we are more sensitive and have better memory for information that is

consistent with our beliefs, attitudes, self-concepts, etc. (Taylor & Fiske, 2007).

Consistent with this logic, numerous studies on regulatory fit have shown that when

information is framed in a manner consistent with one’s regulatory focus, we tend to

remember it better. Specifically, studies have found that promotion focused individuals

remember better information consistent with advancement as well as the presence or

absence of positive outcomes, while prevention focused people remember more

information associated with security and safety, as well as with the presence or absence

of negative outcomes (Higgins & Tykocinski, 1992). Higgins et al. (1994) reported that

promotion focused participants better remembered episodes that had to do with approaching a match to a desired end state, while prevention focused participants

remembered more episodes exemplifying avoidance for mismatches to desired end states.

Lockwood et al. (2002) showed that individuals with stronger promotion goals were more

likely to recall memories about influential positive role models, while people with

stronger prevention goals were more likely to remember influential negative role models.

Using a different manipulation of fit, Bianco, Higgins, and Klem (2003) first manipulated

participants’ intrinsic beliefs about whether a task is fun or whether it is important. Then,

in order to create fit (non-fit), they matched (mismatched) the instructions about the

“funness” or importance of the task. Again, participants who experienced fit performed

better on three different memory tasks (predictive learning, paired associate learning, free

recall of movie scenes) than non-fit participants. Others have also provided evidence that information consistent with our regulatory orientation is more readily remembered and recalled (Evans & Perry, 2003; Jain, Agrawal, & Maheswaran, 2006).

Regulatory fit effects on processing fluency. In addition to having better memory for self-relevant information, we also tend to process such information more easily

(Taylor & Fiske, 2007). At least two studies have provided evidence that when information is framed and/or presented in a way that fits our regulatory orientation, this experience of fit enhances the ease with which we process that information. In a set of studies on message persuasiveness, Lee and Aaker (2004) argued that when a message frame is consistent with how we generally think about the world (in terms of promotion or prevention terms), we process that message more easily. These authors showed that promotion focused people had a stronger preference for gain-framed messages (fitting

frame) while prevention focused people preferred the loss-framed appeals. Additionally,

when the message frame fit participants’ regulatory orientation, they experienced

enhanced ease of information processing (measured both through a self-report and a perceptual identification task). Subsequently, the researchers showed that this heightened processing fluency enhanced perceived message effectiveness which in turn affected brand preferences (i.e., message persuasiveness). In a different study, Cesario and

Higgins (2008) designed two versions of a presentation where the exact same message

content was used, but the non-verbal cues expressed by the presenter were varied. The researchers found that when the delivery style involved eager and approach-oriented movements and gestures, promotion focused individuals processed the message more easily. In contrast, when the delivery style involved vigilant, avoidance-oriented movements and gestures, prevention focused individuals processed it more easily.

Regulatory fit effects on persuasion. As evident from the research summarized above, regulatory fit affects basic processes including task engagement, memory, processing fluency, and task evaluations. Such effects in turn have been shown to lead to overall increases in message persuasiveness and task performance. This section and the next summarize relevant findings.

Regulatory fit theory has been applied to the consumer preferences literature quite a bit. Specifically, numerous studies have shown that fit impacts our choices of different products, as well as our health behaviors such as getting tested for certain diseases, engaging in more exercise, or having a healthier diet. Cesario et al. (2004) and Lee and

Aaker (2004) provide the first evidence that regulatory fit enhances persuasion. As mentioned in the previous sections, Lee and Aaker (2004) showed that regulatory fit enhanced processing fluency which in turn enhanced perceptions of message effectiveness and persuasion as a whole. Furthermore, Cesario et al. (2004) argued and presented evidence that regulatory fit creates feelings of “rightness” which are used as information when evaluating the presented message. In one of their studies, Cesario et al.

(2004) manipulated participants’ regulatory focus and showed that when the message

frame (a health message about eating more fruits and vegetables) was consistent with one’s regulatory focus, participants tended to report higher intentions to comply with the message (eat more fruits and vegetables) and rated the message as more believable. In a second study, the researchers measured participants’ chronic regulatory focus and again had them read either a promotion or prevention-framed message about the benefits of a new student after-school program. The results revealed that promotion focused individuals were more persuaded by a message that consisted of eager means of advocating the new program, while prevention focused participants were persuaded more by a message consisting of vigilant means. What is more, the authors demonstrated that these fit effects were independent of participants’ positive or negative mood. In an effort to understand the underlying mechanism that drives regulatory fit effects on persuasion, in two other studies the authors demonstrated that the experience of “rightness” from regulatory fit can transfer to a later unrelated evaluative decision to increase persuasiveness.

Numerous other studies have demonstrated that a match between one’s regulatory focus and the framing of the persuasive message enhances persuasiveness. For example,

Florack and Scarabis (2006) found that promotion focused participants showed a stronger brand association for ads with a promotion frame than for ads with a prevention frame.

Wang and Lee (2006) also looked at consumer preferences and how regulatory fit affects those. They manipulated participants’ regulatory focus and then asked them to rate two different toothpastes—one had strong promotion characteristics and weak prevention characteristics and the other had weak promotion characteristics and strong prevention

characteristics. Consistent with the hypotheses, participants evaluated the toothpaste more favorably when its strong characteristics fit with their regulatory orientation.

Additionally, “fitting” participants were more likely to choose to use that toothpaste.

Regulatory fit effects on product choice, however, were moderated by participants’ involvement. Specifically, fit effects were observed only when participants were not very involved in the decision process. In contrast, no regulatory fit effects were present when participants were told that they represented an exclusive focus group whose feedback was very important for the upcoming launch of the toothpaste (high involvement condition).

Similarly, Evans and Petty (2003) showed that need for cognition moderates fit effects, with such effects disappearing for participants high in need for cognitions. Finally, although not directly related to persuasion, Vaughn et al. (2009) showed that when people experienced regulatory fit in an earlier unrelated task, they were more

“transported” by a story they read later. As defined in the narrative literature, being

“transported” by a narrative is a flow-like experience, where one is highly absorbed by the story and almost feels as if part of it (Green & Brock, 2000, 2002). The authors provided evidence that the incidental experience of fit intensified participants’ engagement in the narrative and enhanced their story-consistent beliefs.

Regulatory fit effects on performance. The impact of regulatory fit on overall task performance has been well supported in the literature. Starting with initial basic lab studies, Higgins and colleagues showed that participants persisted longer and solved more anagrams when there was compatibility between their regulatory orientation

(promotion/prevention) and the incentive frame of the task (approaching matches to a

desired end state or avoiding mismatches to a desired end state) or the strategic means

required by the task (eager or vigilant; Förster et al., 1998, 2001; Shah et al., 1998).

Positive effects of fit on performance have been demonstrated in applied studies as well.

For example, Spiegel et al. (2004) applied the idea that regulatory fit increases

motivational strength to two real-life situations—turning in a report and having a

healthier diet. For the first study, participants were asked to come to a lab session in

which their chronic regulatory focus was assessed. Then they were told they would have

to write a report about how they would spend the upcoming Saturday and mail their

report back to the researchers as soon as they were done. To induce fit/non-fit, Spiegel et

al. (2004) asked participants to imagine either eagerness- or vigilance-related aspects of

the when, where, and how of the report-writing process. As expected, those participants who experienced regulatory fit were 48% more likely to turn in the report than those who experienced non-fit. In their second study, the authors looked at whether regulatory fit would also enhance students’ health behaviors, specifically eating more fruits and vegetables. This time the researchers situationally induced regulatory focus by having participants read a health message, emphasizing a concern with either accomplishment

(promotion focus) or safety (prevention focus). The health messages were also framed in terms of benefits one could get from compliance (fitting a promotion focus as it represents a match to a desired end state) or costs one could suffer from not complying with the message (fitting a prevention focus as it represents a mismatch to a desired end state). Participants were then asked to keep a daily nutrition log recording the amount of fruits and vegetables they ate each day for the following seven days. Again, as expected,

when promotion focus was primed, framing the message as benefits from compliance

resulted in higher average fruits and vegetables intake than when the message was framed

in terms of costs. In contrast, when prevention focus was primed, framing the message in

terms of costs resulted in a healthier diet than framing it in terms of benefits. Overall, fit

participants reported having 21% more servings of fruits and vegetables than non-fit

participants. Thus this set of studies demonstrates that regulatory fit effects go beyond the

lab and performance on anagrams to impact behaviors we engage in on a daily basis.

Furthermore, a set of studies by Hong and Lee (2008) provide evidence that regulatory fit impacts self-regulatory performance. These researchers argued that fit results in enhanced motivation to self-regulate towards desirable outcomes and away from undesirable outcomes (Idson et al., 2004). In contrast, when non-fit is experienced, the task at hand seems uneasy, is less engaging, and consequently people are less

motivated to self-regulate towards positive end states and away from negative ones. In

four different studies, the researchers tested and provided support for their hypotheses.

They showed that participants’ handgrip performance was enhanced following a

regulatory fit manipulation and diminished after regulatory non-fit manipulation.

Additionally, participants who experienced regulatory fit were more likely to resist a

tempting food (chocolate) and chose the healthier option (apple) in comparison to a

control group that did not receive a fit/non-fit manipulation. In contrast, participants who

experienced regulatory non-fit were less likely to resist the tempting option than the

control group. In terms of percentages, 83.3% of fit participants chose the apple as

compared to 52.6% of participants in the control condition and 20.0% of participants in

the non-fit condition. Others have shown that regulatory fit enhances physical activity among inactive individuals (Latimer et al., 2008), participants’ willingness to get tested for hepatitis (Hong & Lee, 2008), as well as volunteerism (Koenig et al., 2009).

Regulatory Fit in Training: Development of Hypotheses

The goal of the current study is to integrate regulatory fit theory (Higgins, 2000) within a training context. It is proposed that a match between one’s chronic regulatory focus and the design of the training program (content frame and presentation style) will result in enhanced training effectiveness, as indicated by affective, cognitive, and behavioral outcomes. To develop and test this broad hypothesis, Gully and Chen’s (2010) training effectiveness framework served as a foundation and was amended to include the basic regulatory fit processes of subjective feelings of “rightness,” enjoyment, and liking.

Specifically, it is proposed that the individual difference of regulatory focus and the training characteristics of lecture content framing and presentation style will interact to influence subjective feelings, information-processing, attentional focus, and motivation.

Subjective feelings, information-processing, attentional focus and motivation in turn are expected to affect a variety of outcomes including cognitive (information recall), affective (satisfaction with training/trainer, self-efficacy, perceived utility of the training program, intentions to transfer), and behavioral (utilizing learned material in everyday life, or transfer) outcomes. Figure 2.2 (next page) depicts a detailed version of the proposed model. Specific hypotheses are developed next.

INTERVENING LEARNING LEARNING MECHANISMS OUTCOMES TRANSFER Subjective Affective feelings outcomes Feelings of Satisfaction “rightness” Money management Enjoyment self-efficacy Trainee RF Liking Perceived utility Behavioral Intentions to transfer Outcomes Info application Processing Follow-up reactions fluency Video frame & presentation style Cognitive Attentional focus outcomes Info recall

Motivation

Figure 2.2. Detailed Hypothesized Model Note. Trainee RF = Trainee dominant regulatory focus.

As evident from research findings summarized earlier, regulatory fit has been

shown to have a wide array of effects on various training-related outcomes (e.g.,

processing fluency, memory, evaluative judgments, task performance, etc.). Despite its

clear potential benefits in a training context though, regulatory fit theory has rarely been applied to learning and training situations. One exception that applies regulatory focus theory and implies regulatory fit effects is Zhao’s (2006) dissertation on regulatory focus, safety climate, and training effectiveness. In his research, Zhao first situationally induced trainees’ regulatory focus and then exposed all participants to the same videotaped training program on how to make hiring decisions. His presentation included both

promotion framed (e.g., “Look for general traits of success”) and prevention framed (e.g.,

“Don’t stereotype the candidate”) rules for hiring, with an underlying logic that

promotion-primed participants would be better at recalling the promotion-framed rules while the prevention-primed participants would be better at recalling the prevention- primed rules.

Indeed, the results supported these hypothesized relationships. The findings from this research are consistent with findings from the regulatory fit theory literature that people are more sensitive to, recall, and use information that is consistent with their regulatory focus (Higgins, 2000). However, Zhao’s study fails to consider the full range of possible outcomes that such a regulatory fit experience might produce (e.g., affective outcomes, training transfer). Additionally, it speaks to the types of information people attend to during a training program, but it does not investigate the possibility of specifically tailoring a training program to promotion/prevention individuals so that trainees retain and use the entire set of guidelines, not just the ones that match their motivational orientation.

As noted, the current study seeks to demonstrate that training programs can be optimally designed to fit one’s regulatory orientation and consequently enhance training effectiveness. Based on the regulatory focus and regulatory fit literatures, two versions of the same Money Management training program were designed, videotaped, and presented to participants. The promotion-framed presentation involves eager statements with an emphasis on accomplishment, advancement, and opportunities. Additionally, based on recent research on nonverbal cues (Cesario, 2006; Cesario & Higgins, 2008; Ritchie,

2009), the presenter engages in eager and approach-oriented nonverbal behaviors. The prevention-framed presentation involves more vigilant statements with an emphasis on

safety, security, and stability. Additionally, nonverbal cues of cautiousness and avoidance are employed.

As summarized above, numerous studies have demonstrated that a match between one’s motivational orientation and the message framing and presentation style enhances subjective feelings of “rightness” (Camacho et al., 2003; Cesario & Higgins, 2008;

Ritchie, 2009) and enjoyment (Freitas & Higgins, 2002; Higgins et al., 2006).

Additionally, Higgins (2000, 2008) has argued that the match between one’s motivational orientation and the strategic framing of a goal or a message creates “value from fit” which is independent of task outcomes and instrumentality. Several studies have demonstrated that regulatory fit enhances people’s evaluative judgments of tasks, messages, objects, decisions, etc. (Avnet & Higgins, 2003, 2006; Brodscholl et al., 2008;

Förster & Higgins, 2005; Higgins, et al., 2003; Wang & Lee, 2006). In line with Ritchie’s

(2009) operationalization of “value from fit,” participants’ liking of the training program and the instructor are used as indicators of value from fit. Thus, it is expected that:

Hypothesis 1: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance subjective feelings of “rightness,” enjoyment, and liking.

In addition to affecting subjective feelings, a match between one’s regulatory focus and message frame has been noted to have an impact on cognitive processes as well. One study in particular has demonstrated that regulatory fit enhances both subjective and objective processing fluency (Lee & Aaker, 2004). Cesario and Higgins

(2008) also report that when a message is presented with gestures that fit one’s regulatory focus, observers claim to process the presented message more easily. Lee and Aaker

(2004) explain these results based on the stereotype literature which has demonstrated

that stereotype-consistent (vs. inconsistent) information is expected and consequently

more conceptually fluent (e.g., Bodenhausen & Lichtenstein, 1987; Fiske & Neuberg,

1990; Macrae et al., 1994). Others have also shown that self-relevant information is more efficiently processed and more easily accessed in memory (Bargh, 1982; Markus, 1977;

Ross & Sicoly, 1979).

Enhanced processing fluency for information which has been framed and presented in a way consistent with our motivational orientation is also consistent with a connectionist view of information processing. A detailed description of connectionist networks is beyond the scope of this paper and thus only the relevant main points are briefly summarized here (see Smith, 1996 and Thagard & Kunda, 1998 for details). In

general, connectionist networks are neuron-like processing units that integrate a variety

of incoming information which consequently activates or inhibits adjacent processing

units (Hogue & Lord, 2007; Lord & Brown, 2004; Lord, Brown, Harvey, & Hall, 2001).

When people process the information in their external environment, they take in

numerous pieces of information at the same time. Thus, patterns of activations, not

individual characteristics or behaviors, influence their interpretation and sense-making

processes (Lord & Brown, 2004). Additionally, the more often certain patterns are

activated together, the stronger the links between the units within these patterns are. Even

when a single unit of this network is present in the external environment, the whole

pattern is activated and serves as a top-down context within which the stimuli in the

environment are interpreted. As Epitropaki and Martin (2004) and Lord and Brown

(2004) note, our environment, culture, and experiences shape the types of networks that we develop. In this same sense, our experiences with different goal pursuit strategies shape our motivational orientations (i.e., regulatory foci) which are broad networks of interconnected units that serve as a context within which we interpret the world around us. Thus, if a message is framed and presented in a way consistent with one’s chronic regulatory focus, a whole pattern of connected units is activated and this in turn makes the processing and interpretation of similar stimuli easier.

Hypothesis 2: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance processing fluency.

A major premise of regulatory fit theory is that it enhances people’s engagement in, and motivation during, goal pursuit. Numerous studies have demonstrated that when participants are in fitting conditions, they are more engaged in and more focused on the task at hand (e.g., Förster et al., 1998). Additionally, it is proposed that the congruence of the message with one’s motivational orientation deems the information more self-relevant and important, which in turn enhances attentional focus. For example, Postman et al.,

(1948) demonstrated that participants recognized personally important values at a briefer presentation than other words. The researchers continued to argue that because of their self-relevance, such words have a lower threshold for perceptual selection. In further investigations utilizing the Stroop-color-naming paradigm, Bargh and Pratto (1986) as well as Geller and Shaver (1976) demonstrated that self-relevant words are processed both more efficiently and are more likely to distract attention from a primary task. In a completely different line of research on narrative “transportation,” Vaughn et al. (2009)

showed that participants who had experienced regulatory fit were more absorbed by a

story they read later. Thus, because a message framed and presented in a manner consistent with our chronic regulatory focus is perceived as more self-relevant (due to its similarity with our personal frame of information processing and preferred goal pursuit strategies), it is expected that we will be more attentive to it and engage in it more strongly.

Hypothesis 3: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance attentional focus.

In line with findings that regulatory fit enhances processing fluency, task focus,

and task engagement, empirical studies have repeatedly demonstrated its impact on

people’s motivation as well. For example, Spiegel et al. (2004) showed that participants

experiencing fit were more likely to turn in a report assignment and stick to a healthier

diet than participants experiencing non-fit. In an effort to explain the motivational effects

of regulatory fit on behavior, Idson et al. (2004) argued that when future outcomes

sustain people’s regulatory states, their motivation to approach the outcome if it is

desirable, or avoid it if it is undesirable, is strengthened as a result of the intensified

positive (negative) emotions associated with a successful (unsuccessful) approach

(avoidance) of the desired (undesired) end state. As already noted earlier, regulatory fit

has been shown to enhance participants’ handgrip performance (Hong & Lee, 2008),

physical activity (Latimer et al., 2008), and volunteerism (Koenig et al., 2009).

Consequently, it can be expected that the experience of regulatory fit will enhance

motivation within a training context as well.

Hypothesis 4: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance participants’ motivation to learn about money management and budget in general.

Gully and Chen (2010) suggested that a set of intervening mechanisms

(processing capacity, attentional focus, motivation, and emotional control) mediate the

relationship between the attribute/treatment interaction and the outcome variables

(affective, cognitive, and behavioral indicators). Similarly, the current model proposes that the set of explanatory mechanisms, including subjective feelings, processing fluency, attentional focus, and motivation, mediate the relationship between the regulatory focus

X training design interaction and the training effectiveness indicators (affective,

cognitive, and behavioral). In his papers on regulatory fit theory, Higgins (2000, 2006,

2008) argues that the feelings of “rightness” associated with regulatory fit are the driving

force of many of the observed fit effects. Specifically, when the strategic means within a

message or a task sustains the person’s regulatory orientation, the person feels “right”

about the message/task and it is experienced as more valuable and important. In support

of this logic, Cesario and Higgins (2008) demonstrate the mediating role of feelings of

“rightness” in a persuasive context (also see Appelt et al., 2008). Additionally, Freitas

and Higgins (2002) showed that participants valued and enjoyed tasks more when those

tasks fit their regulatory orientation, and the participants also experienced an intensified

sense of task success. Thus, based on the major premise of regulatory fit theory, it is

expected that the subjective feelings associated with regulatory fit will mediate the

relationship between a trainee regulatory focus/training design match and the set of

affective outcomes.

Hypothesis 5a: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance affective reactions to the training program.

Hypothesis 5b: Subjective feelings of “rightness,” enjoyment, and liking will partially mediate the relationship between fit and affective outcomes.

As previously discussed, when a message is framed so it matches our self-focus,

the message is processed more easily. The literature on ease of processing paradigm has

demonstrated that such processing fluency in turn impacts affective judgments and such

effects have been demonstrated with a variety of fluency-inducing manipulations across a variety of settings. For example, the advertising industry has taken a great advantage of the finding that perceptual fluency and consequently favorable attitudes are enhanced by prior exposure (e.g., Seamon et al., 1995) and enhanced visual clarity (Reber,

Winkielman, & Schwarz, 1998). In an effort to test a different type of fluency effects, i.e., conceptual fluency, on brand evaluation, Lee and Labroo (2004) demonstrated that when a target is presented within a conceptually similar context (e.g., a bottle of beer featured in an advertisement that shows a man entering a bar), the target is processed more fluently. This in turn enhances positive judgments of the target (also see Whittlesea,

1993). In the context of regulatory fit theory, Lee and Aaker (2004) provide evidence that processing fluency as a result of regulatory fit enhances message effectiveness and persuasion as a whole. Cesario et al., (2004) and Wang and Lee (2006) also demonstrated that regulatory fit enhances affective judgments of health messages, after-school programs, and toothpaste. Thus, it is expected that regulatory fit will enhance subjective evaluative judgments of the training program, and this effect will be partially explained by enhanced processing fluency.

Hypothesis 5c: Processing fluency will partially mediate the relationship between regulatory fit and affective outcomes.

In addition to affecting a wide range of affective outcomes like enjoyment,

satisfaction, and a variety of evaluative judgments and decisions, regulatory fit has been

shown to affect cognitive performance as well. As noted earlier, promotion focused

individuals are more likely to remember information that is consistent with advancement

and the presence or absence of positive outcomes while prevention focused individuals

are more likely to remember information consistent with security and safety as well as

with the presence or absence of negative outcomes (Higgins & Tykocinski, 1992;

Higgins et al., 1994; Lockwood et al., 2002). In his dissertation, Zhao (2006) clearly

demonstrates that promotion-primed participants paid attention to and remembered more approach-framed guidelines, while prevention-primed participants paid attention to and remembered more avoidance-framed outcomes. Bianco et al., (2003) also showed that participants experiencing fit performed better on a variety of memory tasks. Regulatory fit has been shown to affect overall cognitive task performance as well (e.g., solving anagrams; Förster et al., 1998, 2001; Shah et al., 1998). Here, it is proposed that regulatory fit enhances cognitive performance (information recall) and that this effect is

explained by enhanced processing fluency, attentional focus, and motivation.

Hypothesis 6a: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance training-related information recall.

Hypothesis 6b: Processing fluency will partially mediate the relationship between regulatory fit and information recall.

Hypothesis 6c: Attentional focus will partially mediate the relationship between regulatory fit and information recall.

Hypothesis 6d: Motivation will partially mediate the relationship between regulatory fit and information recall.

Finally, it is expected that all of these positive effects of regulatory fit on affective and cognitive outcomes will lead to a more probable transfer of the training program.

Spiegel et al.’s (2004) study is one of very few that shows that regulatory fit effects can impact behaviors outside the experimental setting. These authors showed that “fitting” participants were more likely to turn in a report and were also more likely to change their diet habits. Thus, it is expected here that regulatory fit will enhance the set of intervening mechanisms, which will enhance in-lab affective reactions and information recall, which in turn will promote training transfer as indicated by participants reactions towards the training and the extent to which they are applying the learned information two to three weeks after the lab session.

Hypothesis 7a: A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance affective reactions towards the training program and utilization of learned material two to three weeks after the in-lab session.

Hypothesis 7b: There will be a significant indirect effect from fit to follow-up affective outcomes, through the intervening mechanisms and in-lab affective reactions.

Table 2.1

Restatement of Hypotheses

Hypothesis 1 A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance subjective feelings of “rightness,” enjoyment, and liking.

Hypothesis 2 A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance processing fluency.

Hypothesis 3 A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance attentional focus.

Hypothesis 4 A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance participants’ motivation to budget and learn about money management.

Hypothesis 5a A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance affective reactions towards the training program.

Hypothesis 5b Subjective feelings of “rightness,” enjoyment, and liking will partially mediate the relationship between fit and affective outcomes.

Hypothesis 5c Processing fluency will partially mediate the relationship between regulatory fit and affective outcomes.

Hypothesis 6a A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance training-related information recall.

Hypothesis 6b Processing fluency will partially mediate the relationship between regulatory fit and information recall.

Hypothesis 6c Attentional focus will partially mediate the relationship between regulatory fit and information recall.

Hypothesis 6d Motivation will partially mediate the relationship between regulatory fit and information recall.

Hypothesis 7a A match between one’s chronic regulatory orientation and the frame and presentation style of the training program will enhance affective reactions towards the training program two to three weeks after the in-lab session.

Hypothesis 7b There will be a significant indirect effect from fit to follow-up affective outcomes, through the intervening mechanisms and in-lab affective reactions.

CHAPTER III

METHODOLOGY

Overview and Sample

The main goal of this study was to evaluate the effectiveness of a training program designed to either fit or misfit one’s dominant chronic regulatory focus. In order to do that, participants were asked to complete a three-stage assessment, consisting of: (a) a one-hour personality assessment questionnaire administered via the Internet, (b) a one- hour lab-based experimental training session, and (c) a half hour follow-up online questionnaire e-mailed to participants two to three weeks after the training session.

Approximately 1000 University of Akron students recruited from a variety of

Psychology courses completed the personality assessment stage of the study in exchange for two research points towards a course of their choice. Participants’ chronic regulatory focus scores from this initial survey were examined and an effort was made to invite for further participation only individuals who scored differently on the promotion vs. prevention subscales of Lockwood et al.’s (2002) regulatory focus measure (specifically, they had to score in different quartiles on the two subscales). This resulted in 198 individuals who completed the one-hour Budget Management training program, administered in a computer lab. Participants completed the experiment one to six at a time with each participant viewing the video presentation on his/her own computer

screen. Participants received five research points for participating in the experimental training. Two to three weeks after the in-lab portion of the study, all individuals who had completed the training were asked several follow-up questions via an online survey, earning them two additional research points. To encourage commitment to the entire project, participants who had completed all three stages of the study were entered into a raffle to win one of 11 money prizes (one $50 and ten $10 prizes).

Procedure

Personality Assessment

During the first stage of this study, participants were asked to complete a one-hour online survey via Survey Monkey (a web-based survey administration service) consisting of a series of individual difference measures. These included two trait regulatory focus measures (Higgins’ RFQ and Lockwood et al.’s GRFM), Carver and White’s (1994)

BIS/BAS measure, as well as a series of scales assessing previous knowledge and experience with budget management, motivation to learn about how to manage money more effectively, general self-efficacy, goal orientation, trait affect, need for cognition, and the Big Five personality traits. Demographic information was also collected at this time.

One of the main purposes for completing this set of measures prior to the experimental portion of the study was to not overwhelm participants with a huge amount of questions at one point of time and to thus avoid fatigue effects. Additionally, because dominant regulatory focus effects were to be examined (dominant regulatory focus was calculated by subtracting one’s prevention score from his/her promotion score), it was

important that participants scored differently on the prevention vs. promotions subscales

of the regulatory focus measures. To achieve this goal, participants’ scores were split into

quartiles and only participants whose promotion and prevention scores fell into different quartiles were invited for further participation. Finally, because this initial questionnaire

was completed via the Internet, a large number of responses could be collected and

relationships among personality constructs could be examined in an exploratory fashion.

In-lab Experimental Session

For the second stage of the study, 198 participants were randomly assigned to one

of two training programs. One version of the training program was framed and presented

in promotion terms and the other version was framed and presented in prevention terms.

The experiment was conducted in a small computer lab, with a six-person capacity. Each

computer was situated in a separate cubicle and equipped with a headset. Up to six

students could participate at a time, with exact number of participants varying by session.

Upon arrival, each participant was seated at a separate computer and asked to put on

his/her headphones. Participants were informed that they would be witnessing a training

program on budget management and that their input about the program would be very

helpful in further developing and making it even more effective.

After completing a short demographic questionnaire and repeating the two previously used trait regulatory focus measures (to ensure the test-retest reliability of the scales), participants were asked to watch a 15-minute budget management lecture. Each participant viewed the video on his/her own monitor. Right after the video, participants

completed one explicit and two implicit manipulation checks. The explicit manipulation

check was created for the current study and was based on the information provided

during the budget management training. One of the implicit measures was embedded in

the explicit one, as reaction times for the responses were collected. The second implicit

manipulation check was an extended version of Ritchie’s (2009) lexical decision task

where participants had to distinguish between non-words and words (some of which were

promotion-related and the other prevention-related). Following the manipulation checks, participants were asked to complete a 60-second task where they had to come up with

ways they planned to use what they had learned in their daily lives (i.e., implementation

intentions). After also completing a short current mood measures, all participants

watched the same video on how to be a smarter grocery shopper. Participants were then

urged to continue with a set of measures designed to assess their experience of regulatory

fit as well as the effectiveness of the training program. All measures were completed on

the computer. The constructs that were assessed include subjective feelings (“rightness,” enjoyment, and liking), ease of processing, attentional focus, motivation, satisfaction with the training, perceived utility of the learnt material, participants’ budget management self-efficacy, their intentions to use what they had learned in their daily lives, and a short-

answer recall questionnaire. Upon completion of this final step, participants were either

reminded not to forget, or encouraged (in correspondence with the prevention/promotion

frame of the training), to use what they had learned in the training program, and then

allowed to leave.

Follow-up Survey

Two to three weeks after completing the training, participants were contacted via

e-mail and asked to complete the final part of the study. This final part consisted of a follow-up online questionnaire designed to assess training transfer, or to what extent participants were using what they had learned during the training in their everyday lives.

Participants were asked whether they had created their own budget, whether they had used some of the tips presented in the training and if ‘yes’ which ones. Additionally, they were given a similar set of outcome measures as at the end of the in-lab session, asking them about their satisfaction with the training program, the perceived utility of the training content, their money-management self-efficacy, the extent to which they are current using the information presented during the training, as well as their intentions to use it later in their lives. Table 3.1 (next page) details the sequence of procedures.

Experimental Budget Management Training Program

Two versions of a Budget Management training program were specifically created for the current study. Most of the budget-related information was pulled from several websites designed to help people create their personal budgets and give them advice on how to lower their expenses (http://www.sayplanning.com/; http://www.dacomp.com/whatis.html; http://www.saylowerbills.com/).

There are two main features that differentiate the promotion and prevention framed training sessions—lexical framing and presentation delivery style. The lexical framing of the two versions was based on the basic definitions and assumptions of regulatory focus theory (Higgins, 1997), as well as on previous empirical research

Table 3.1

Sequence of Procedures

Personality assessment 1. Introduction & consent (Online Survey) 2. Trait regulatory focus (Regulatory Focus Questionnaire) 3. Trait regulatory focus (General Regulatory Focus Measure) 4. BIS/BAS 5. Previous knowledge of and experience with budget management 6. General money management self-efficacy 7. Pre-training motivation to learn 8. Trait affect 9. Big Five personality traits 10. Need for cognition 11. Goal orientation 12. Demographics 13. Debriefing

Training session 1. Introduction & consent (In-lab Experiment) 2. Trait regulatory focus (RFQ, GRFM) 3. Money Management training program (Experimental manipulation) 4. Manipulation checks 5. 60-second task 6. Current mood 7. Grocery shopping video 8. Intervening mechanisms a. Feelings of “rightness” b. Enjoyment c. Liking d. Processing fluency e. Attentional focus (Attentional processes, Cognitive interference) f. Motivation (Motivation to budget, Motivation to learn) 9. Outcome variables a. Satisfaction with training and instructor b. Perceived utility of the training program c. Money management self-efficacy d. Intentions to transfer e. Declarative knowledge (short-answer recall) 10. Additional demographics 11. Debriefing

Follow-up survey 1. Introduction & consent (Online Survey) 2. Effort 3. Satisfaction with training program 4. Money management self-efficacy 5. Utility of the training program 6. Use of learned material (yes/no questions) a. Created a budget b. Used strategies (which ones) 7. Information recall 8. Use of material now (scale) 9. Intentions to use material in the future 10. Debriefing

employing prevention/promotion framing. Specifically, the promotion-framed lecture

included words and phrases that emphasized accomplishment, advancement, and success

and focused on the presence and absence of positive outcomes (e.g. “Be sure to include

everything!”). On the other hand, the prevention framed lecture included words and

phrases emphasizing security, cautiousness, and responsibility and focused on the presence and absence of negative outcomes (e.g., “Don’t forget anything!”). To make the lexical manipulation even stronger, Microsoft Power Point slides were presented in a

second adjacent window on the computer screen with the important promotion/prevention points emphasized (see Appendix A for slides).

Additionally, when presenting the promotion framed lecture, the presenter engaged in animated, broad opening movements, hand gestures openly projecting outward, approaching forward-leaning body positions, raised eyebrows, and generally fast body movements and speech rate. In contrast, when presenting the prevention framed lecture, the presenter engaged in gestures that showed precision, “pushing” motions representing slowing down, slightly backward-leaning body position implying avoidance, furrowed brow, and generally slower, cautions body movement as well as speech rate.

Everything other than the promotion/prevention framed words and phrases (as well as the pictures on the Power Point slides) and the delivery was kept constant between the two versions. Specifically, both lectures were approximately the same length and the same instructor was used in both. See Appendix A for exact wording of both versions.

To ensure the flow of the training programs, four graduate students from the

Psychology department took the in-lab portion of the study. Several typos and issues with

instructions were identified and fixed. Additionally, four undergraduate students watched

the promotion-framed training program and rated a series of consistency items. As

expected, the promotion (video-consistent) items were rated as more consistent with the

training program (M = 2.17) than were the prevention items (M = 3.46; higher values

indicate less consistency), providing initial support for the merit of the experimental

manipulation.

Measures

Trait Regulatory Focus

Two trait regulatory focus measures have been mostly utilized in the regulatory focus and regulatory fit literatures—the RFQ (Higgins et al., 2001) and the General

Regulatory Focus measure developed by Lockwood et al. (2002). Recently, authors have suggested that these two scales do not necessarily tap the same underlying constructs, as evident by the relatively weak correlations of their subscales (Summervile & Roese,

2008). Therefore, both the RFQ and the GRFM were used in the current study to assess participants’ chronic regulatory foci.

Regulatory Focus Questionnaire (RFQ). The RFQ (Higgins et al., 2001) is an 11- item scale based on the assumption that past success with promotion-related eagerness results in subsequent preference for eager strategies during goal pursuit while past success with prevention-related vigilance results in subsequent preference for vigilant strategies. Thus, the RFQ generally aims to assess perceived history of effective and

ineffective promotion and prevention self-regulation. Overall, the scale contains two

psychometrically different subscales. The prevention subscale focuses on past

experiences with successfully or unsuccessfully avoiding negative outcomes (e.g., “Not

being careful enough has gotten me into trouble at times.”) while the promotion subscale

focuses on past successful or unsuccessful accomplishment of positive outcomes (e.g., “I

feel like I have made progress toward being successful in my life.”). Participants

responded on a 5-point Likert scale (1 = Never or seldom/Certainly false, 5 = Very often/Certainly true). Reliabilities for the two subscales were Promotion α = .675 and

Prevention α = .803. See Appendix B for scale items.

In a series of studies, Higgins et al. (2001) confirmed convergent and discriminant validity of this measure and demonstrated good internal consistency and test-retest

reliability. In a more recent study utilizing the RFQ, Latimer et al. (2008) showed that

participants’ scores on the RFQ were not susceptible to common regulatory focus

manipulations. Such a finding should not be surprising given the RFQ assesses one’s past

successes/failures with promotion/prevention concerns and situational cues related to

prevention/promotion focus should not affect responses regarding such past experiences.

General Regulatory Focus Measure (GRFM). The GRFM is an 18-item scale

developed by Lockwood et al. (2002). Like the RFQ, the GRFM consists of two

subscales and has been validated and established in the literature. The promotion focus

subscale (α = .867) assesses individuals’ endorsement of promotion-related goals (e.g.,

“In general, I am focused on achieving positive outcomes in my life.”) while the

prevention focus subscale (α = .823) assesses individuals’ endorsement of prevention-

related goals (e.g., “I often think about the person I am afraid I might become in the future.”). Participants responded on a 7-point Likert scale (1 = Not at all true of me, 7 =

Very true of me). See Appendix C for scale items.

BIS/BAS. The BIS/BAS measure (Carver & White, 1994) was designed to assess the sensitivity of one’s behavior activation and behavior inhibition systems (Gray, 1990) or overall approach and avoidance motivation. Because theoretical as well as empirical data support the notion that some overlap exists between BIS/BAS sensitivity and prevention/promotion focus (see Summervile & Roese, 2008), this scale was also utilized here. Additionally, approach/avoidance motivation has been used occasionally in studies examining regulatory focus and regulatory fit (e.g., Yi & Baumgartner, 2008).

The BIS scale consists of seven items (e.g., “If I think something unpleasant is going to happen, I usually get pretty ‘worked up’.”) and assesses people’s responsiveness to negative situations and punishments. The BAS scale consists of 13 items and assesses individuals’ sensitivities to impending rewards and stimulation. This scale is further broken down into three subscales—Reward Responsiveness (five items, e.g., “When I get something I want, I feel excited and energized.”), Fun Seeking (four items, e.g., “I will often do things for no other reason than that they might be fun.”), and Drive (four items, e.g., “When I go after something I use a ‘no holds barred’ approach.”). The Reward

Responsiveness (RR) subscale has been shown to be most strongly related to promotion focus (Summervile & Roese, 2008; Yi & Baumgartner, 2008). For all statements in the

BIS/BAS measure, participants responded on a 7-point Likert scale (1 = Very untrue of me, 5 = Very true of me). Reliabilities for all subscales were acceptable, BIS α = .797,

BAS Reward Responsiveness α = .862, BAS Fun Seeking α = .884, BAS Drive α = .822.

See Appendix D for specific items.

Manipulation Checks

Consistency measure. A video content-consistency measure was developed for the current study. A total of 12 items were created, six of which were more consistent with the promotion-framed video (e.g., “Having a budget will allow me to go to my dream holiday.”) and the other six were more consistent with the prevention-framed video (e.g.,

“A budget will prevent me from failing my long-term duties and responsibilities.”).

Participants were asked to indicate how consistent each of the 12 statements was with the video they had just watched on a 7-point Likert scale (1 = Very consistent, 7 = Not consistent at all; Appendix E lists all items). Additionally, the time it took participants to rate each statement was recorded and used as an implicit manipulation check. Based on the ease of processing literature, it was expected that participants would rate the video- consistent items more quickly than they would the video-inconsistent items (Taylor &

Fiske, 2007).

Lexical Decision Task (LDT). A Lexical Decision Task was used as a second implicit manipulation check. Specifically, participants had to identify whether a given letter string represented a word in the English language or not (by either pressing the right arrow key on the keyboard for “yes” or not pressing anything for “no”). To that, a list of 10 promotion words (e.g., opportunity) and 10 prevention words (e.g., responsibility) was developed based on a review of the literature as well as on previous work which has used a similar LDT in a regulatory focus theory context (e.g., Ritchie,

2009; unpublished work by Lord and Dihn, 2010). An effort was made to have words with similar lengths in both the prevention and promotion lists. Additionally, word frequencies in the English language were explored (based on information found on the

English Lexicon Project website, http://elexicon.wustl.edu/) to ensure that both lists consisted of generally similarly frequent words. The English Lexicon Project was also used to generate 20 non-word strings. The non-words list was developed so that the number of letters in the non-word strings matched the number of letters of the real words

(see Appendix E for details).

Intervening Mechanisms

Feelings of “rightness.” Participants’ sense of feeling “right” during the training program was assessed with four items (e.g., “It felt ‘right’ while listening to the information presented in the money management training program.”). Two items were adapted from Ritchie (2009) and Cesario and Higgins (2008) and two were created for the current study. Participants responded on a 7-point Likert scale (1 = Strongly disagree,

7 = Strongly agree) and the alpha coefficient was α = .818. This measure was completed during the laboratory portion of the study. Appendix F lists all subscales assessing the proposed intervening mechanisms.

Enjoyment. In line with others who have operationalized the fit experience with experienced enjoyment (Freitas & Higgins, 2002), participants’ enjoyment during the training was assessed. A five-item measure (e.g., “It was enjoyable to sit on this training program.”) adapted from Freitas and Higgins (2002) was utilized and participants indicated their agreement with each statement on a 7-point Likert scale (1 = Strongly

disagree, 7 = Strongly agree). This scale was completed during the lab portion of the

study and had very good reliability, α = .926.

Liking. Enhanced liking has been considered as one indicator of “value from fit”

(Ritchie, 2009). Participants’ overall perceptions of the extent to which they liked the

instructor of the money management training program were assessed with a two-item scale adapted from Ritchie (2009). Participants responded to items on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree) during the experimental session of the study, α = .926.

Information-processing capacity/Processing fluency. Participants’ processing fluency was assessed with two measures—a self-report subjective scale assessing perceived ease of processing and an implicit processing fluency measure. The perceived ease of processing scale was developed for the current study based on existing measures

(Kettanurak, Ramamurthy, & Haseman, 2001; Lee & Aaker, 2004). Participants indicated

their agreement with four items (e.g., “Overall, the information presented during the

training program was easy to process.”) on a 7-point Liker scale (1 = Strongly disagree, 7

= Strongly agree). The reliability of this measure was good, α = .887. The implicit processing fluency measure was a reaction time-based measure, developed for the current study. Specifically, as discussed in an earlier section, as part of the content-consistency manipulation check participants’ rating response times were collected and used as indicators of implicit processing fluency.

Attentional focus. Participants’ attentional focus was assessed with two

preexisting scales, adapted for the current study. First, they completed a four-item

Attention Processes scale adapted from Yi and Davis (2003). A sample item is “I paid

close attention to the video demonstration.” Again, participants indicated their responses

on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). This scale

demonstrated good reliability, α = .863. Additionally, participants completed a 15-item scale assessing off-task thoughts, adapted from Sarason, Sarason, Keefe, Hayes, and

Shearin’s (1986) Cognitive Inference Questionnaire (e.g., “I thought about something that made me feel tense.”). They indicated the frequency with which the different thoughts appeared in their minds during the training on a 5-point Likert scale (1 = Never,

5 = Very often). Reliability of this measure was α = .841. Finally, participants were asked to indicate the extent to which their minds wandered during the training session on a 7- point Likert scale (1 = Not at all, 7 = Very much). All attentional focus measures were completed during the laboratory portion of the research project.

Motivation. A battery of preexisting scales was utilized to measure participants’ motivation during the training program. Their motivation to learn was assessed with a six-item scale adjusted for the current study (based on preexisting scales by Hicks &

Klimoski, 1987; Noe & Schmitt, 1986; Yi & Davis, 2003) (e.g., “I was very excited to attend this training program.”) and it was given to participants both in the initial personality assessment stage of the study as well as during the experimental session.

Coefficient alpha for this measure was strong (α = .910). Participants’ motivation to budget in general was assessed with a four-item measure, adapted from Yi and Davis’

(2003) Motivation Processes scale (e.g.,” The training provided information that motivated me to create my own budget.”). It was completed only during the in-lab

experiment and demonstrated good reliability, α = .820. For both scales, participants indicated their agreement with each statement on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). Appendix F lists all subscales assessing the intervening mechanisms.

Outcome Variables

Satisfaction with training and instructor. Overall satisfaction with the training program was assessed with a seven-item measure adapted from Kettanurak et al. (2001) and Morgan and Casper (2000). Respondents indicated their agreement which each statement (e.g., “Overall, I was very satisfied with the presentation of the content of the training program.”) on a 5-point Likert scale (1 = Strongly disagree, 5 = Strongly agree).

This scale was completed both during the lab session and during the follow-up survey. Its reliability was good, α = .889. Appendix G lists all outcome variables measures.

Perceived utility/helpfulness of the Money Management training program.

Participants’ perceptions about the extent to which the training program would be beneficial in their daily lives was assessed with a four-item scale adapted from Morgan and Casper (2000) both right after the training video and during the follow-up survey

(adapted to reflect perceived helpfulness of the training program). Participants indicated their agreement with each statement (e.g., “This course will help me improve my money management habits.”) on a 5-point Likert scale (1 = Strongly disagree, 5 = Strongly agree). The reliability of the scale was moderate, α = .708.

Money management-related self-efficacy. Participants’ confidence in their ability to manage their money as a result of the training program was assessed with a six-item

measure, developed for the present study (based on Ford, Smith, Weissbein, Gully, &

Salas, 1998; Kozlowski, Gully, Brown, Salas, Smith, & Nason, 2001). Participants indicated their confidence in successfully completing different aspects of money management, presented in the training program (e.g., “I am confident I can create my personal budget as a result of this training program.”). Participants indicated their responses on a 5-point Likert scale (1 = Strongly disagree, 5 = Strongly agree). They completed this measure at all stages of the experiment. The reliability of the scale for the in-lab portion of the study was α = .793.

Intentions to use information. Participants’ intent to use the learned information was assessed utilizing a four-item scale adapted from Holton, Bates, and Ruona (2000).

They were asked to indicate their responses to each item (e.g., “I anticipate making every effort in the coming weeks to put into practice what I learned in this training”) on a 7- point Likert scale (1 = Definitely no, 7 = Definitely yes) both during the experiment and later on. The same set of statements was adapted to assess the extent to which participants were currently using the information at the time of the follow-up survey. Overall, the reliability of the scale was very good, as indicated by α = .918 based on responses collected during the in-lab portion of the study.

Declarative knowledge. Participants’ declarative knowledge was assessed with several short-answer questions tapping the most important concepts presented during the training lecture (e.g., “What is a budget?”). Answers were coded by two independent raters who followed a grading rubric developed by the experimenter (see Appendix H).

To ensure that the coding rubric was interpreted similarly by the raters, raters’ scores for

each declarative item were correlated. All correlations were strong and positive (ritem1 =

.91, ritem3 = .86, ritem4 = .91). Participants received scores for a total of three recall questions which were averaged to obtain a single recall test grade used in analyses.

Perceived effort. The effort participants had exerted during the training program was assessed during the final stage of the research. They completed a five-item perceived

effort subscale of the Intrinsic Motivation Inventory (Deci & Ryan, 2003). Participants

were asked to indicate their agreement with each statement (e.g., “I put a lot of effort into

this training program.”) on a 5-point Liker scale (1 = Strongly disagree, 5 = Strongly

agree). The scale demonstrated good reliability, α = .877.

Training transfer. In order to assess participant’s transfer of training, they were asked several additional questions during the follow up survey. Specifically, they were

asked to indicate whether they had had a budget before the training program, whether

they had created one as a result of the training, and if not, whether they were planning to

do so in the future. Additionally, to test their memory of the presented material, they were

asked to list several things they remembered from the video as well as list specific

strategies learned during the training that they had been using in their daily lives.

Appendix G lists all measures and items.

Control and Exploratory Variables

Because of the quasi-experimental design of the study, information on a series of demographic and experience variables was collected in order to assess the initial

comparability of the ‘learners’ in the two conditions and to control for extraneous effects

on the dependent variables (Appendix I lists items for all measures described below).

Current mood. Higgins has noted numerous times that the experience of regulatory fit is different from general pleasure and pain (see Higgins, 2008 for a review

of relevant findings). Indeed, several studies have demonstrated that pleasant/unpleasant

mood does not account for regulatory fit effects on performance and judgments (Higgins,

2008). Yet, it is well recognized that moods tend to color much of our information

processing, behaviors, and even response tendencies (Beal, Weiss, Barros, &

MacDermin, 2005; Forgas & George, 2001) and thus questions remain as to how exactly

the fit effects relate to specific affective experiences.

In the current study participants’ mood was assessed right after the 60-second tasks, so mood effects on intervening processes and outcome variables could be controlled for. To do that, a 12-item measure adapted by Ritchie (2009) from Shah and

Higgins (2001) and Naidoo (2005) which taps both mood arousal (high vs. low) and valence (positive vs. negative) was utilized. Participants were asked to indicate on a 5- point Likert scale how well each of 12 discrete emotions described the way they felt at that moment (1 = Very slightly or not at all; 5 = Extremely). Six of these emotions tapped the Cheerfulness—Dejection continuum of emotions (happy, elated, satisfied, sad, disappointed, and discouraged) associated with successful/unsuccessful promotion focused goal pursuit while the other six tapped the Acquiescence—Agitation continuum

(relaxed, calm, carefree, tense, nervous, and agitated) characteristic of successful/unsuccessful prevention focused goal pursuit (Higgins, 2008; Idson et al.,

2004). The reliabilities for each subscale were as follows: Pos/High α = .750, Pos/Low α

= .896, Neg/High α = .759, Neg/Low α = .829.

Trait affect. In addition to current mood, participants’ trait affect was measured during the personality assessment stage of the study, as individual differences exist in the overall positive and negative emotions people generally experience and such differences affect the ways in which we generally behave (Forgas & George, 2001). To control for such differences, trait affect was assessed and controlled for, using the Positive Affect

(PA) and Negative Affect (NA) Scale Expanded Form (PANAS-X) developed by Watson and Clark (1994). Participants were asked to indicate on a 5-point Likert scale (1 = Very slightly or not at all; 5 = Extremely) the extent to which each of 20 adjectives (e.g.,

‘Alert’ for PA, ‘Ashamed’ for NA) described the way they generally felt. Both subscales demonstrated good reliabilities, Positive Affect α = .893, Negative Affect α = .858.

Previous knowledge and experience in budget management. To control for previous knowledge of and experience with budgeting, a short measure asking participants about their general knowledge about money management was developed for the current study. The scale consists of four yes/no questions, where a positive answer demands more information (e.g., “Do you currently keep track of your spending and earning in any way? If yes, how?”).

Pre-training motivation to learn. Participants’ motivation to learn about money management was assessed during the personality assessment phase of the study in order to examined preexisting differences in participants’ interest in the topic. This scale is almost identical to the motivation to learn scale described earlier. Participants indicated their agreement with each of seven items on a 7-point Likert scale (1 = Strongly disagree,

7 = Strongly agree); the scale reliability was good, α = .830.

Goal orientation. Goal orientation has been a prevalent individual difference

examined in the training literature as a moderator of the training design/training

effectiveness relationship (e.g., O’Keefe, 2009; Schmidt & Ford, 2003). It was measured

in the current study and used in exploratory analyses. Elliot and McGregor’s (2001) 2 X 2

achievement motivation framework was utilized as it covers approach/avoidance motives

for both performance and learning goals, unlike other commonly used measures of goal

orientation (e.g., VandeWalle, 1997). The 12-item scale consists of four three-item

subscales: performance approach (e.g., “It is important for me to do better than others in

learning contexts.”), performance avoidance (e.g., “I just want to avoid doing poorly in

training contexts.”), mastery approach (e.g., “I desire to completely master the material

presented in training programs.”), “mastery avoidance (e.g., “I am often concerned that I

may not learn all there is to learn in training programs.”). The scale was slightly adapted

from its original version to tap more general attitudes towards training (as opposed to

attitudes towards a specific course). Participants indicated the extent to which each item

was true for them on a 7-point Likert scale (1 = No at all true of me, 7 = Very true of

me). Reliabilities of the different subscales were as follows: Performance Approach α =

.912, Performance Avoidance α = .888, Mastery Approach α = .884, Mastery Avoidance

α = .822.

Big Five personality traits. The Big Five personality traits were assessed and

served as potential covariates. Recently, openness to experience has been closely related

to promotion and prevention foci and the goal pursuit strategies associated with them

(Vaughn, Baumann, & Klemann, 2008). Additionally, extraversion and neuroticism have

been associated with general approach/avoidance temperaments (Elliot & Thrash, 2002).

In terms of the training context, conscientiousness has been associated with motivation to learn and self-efficacy (Colquitt et al., 2000). Extraversion, neuroticism, conscientiousness, openness to experience, and agreeableness were assessed with the

International Personality Item Pool (IPIP) Big Five Factor Markers measure (Goldberg,

1999; Goldberg et al., 2006) which has been validated and widely applied in the literature

(see Lim & Ployhart, 2006 for a review). Participants indicated the extent to which each statement described them on a 5-point Likert scale (1 = Very inaccurate, 5 = Very accurate). Reliabilities for the different subscales were all good, Neuroticism α = .873,

Extraversion α = .900, Openness to Experience α = .789, Agreeableness α = .818,

Conscientiousness α = .869.

Need for cognition. Need for cognition was also assessed with a scale from the

International Personality Item Pool (IPIP; Goldberg, 1999; Goldberg et al., 2006). The scale is based on Cacioppo and Petty’s (1982) original measure, consists of ten items

(e.g., “I like to solve complex problems.”), and demonstrated good reliability, α = .847.

Participants were asked to indicate the extent to which each statement described them on a 5-point Likert scale (1 = Very inaccurate, 5 = Very accurate).

Demographics. Finally, participants completed a series of demographic questions.

In the preliminary personality assessment they indicated their age, gender, ethnicity, college major, year in college, and employment status. During the experimental portion of the study participants answered a few more demographic questions regarding their socioeconomic status, concern with money, experience with the English language, as well

as experience with the instructor who presented the Money Management training

program. Exact wording of these questions can be found in Appendix I. Additionally,

cognitive ability was assessed based on ACT or SAT test scores obtained from official

university records, with participants’ permission. ACT/SAT scores have been found to be

a valid and reliable indicator of general cognitive ability (Gully, Payne, Kiechel, &

Whiteman, 1999).

Statistical Analysis Strategy

The proposed model was tested with structural equation modeling (SEM), using

LISREL v. 8.80 (Jöreskog & Söbom, 2003), employing maximum likelihood estimation.

A latent variables approach was taken to examine the interaction effects in the structural

equation model and supplemental Sobel tests were performed to examine the

hypothesized mediated relationships. In contrast with other approaches to hypothesis

testing which usually test one set of relationships at a time (e.g., hierarchical multiple

regression, ANOVA), SEM allows for a more comprehensive test of an entire model by

calculating multiple variable relationships simultaneously (Kline, 2005). Following

Kline’s (2005) recommendations, the following fit indexes were reported and interpreted:

the model chi-square, the Steiger-Lind root mean square error of approximation

(RMSEA) with its 90% confidence interval, the Bentler comparative fit index (CFI) and

the standardized root mean square residual (SRMR). Non-significant chi-square value,

RMSEA smaller than .05 with a lower bound of the 90% confidence interval smaller than

.05 and an upper bound smaller than .10, CFI larger than .90, and SRMR values not bigger than .10 are all indicators of favorable model fit (Kline, 2005). Specific

hypotheses were tested by examining the relevant path coefficients. Additionally, most

constructs measured in the study underwent confirmatory factor analyses to attest their factor structure. Hierarchical multiple regression and logistic regression were utilized to conduct additional and exploratory analyses (e.g., selecting appropriate control variables; examining relationships between regulatory focus measures; relationships with discrete dependent variables).

CHAPTER IV

RESULTS

Initial Data Screening

Prior to hypothesis testing, the dataset was screened for potential problems, following Tabachnick and Fidell’s (2007) guidelines for screening grouped data. To

identify potential outliers and influential points, Cook’s D, Mahalanobis distance, and

studentized residual values were examined. One case was marked as problematic, as

indicated by a large Mahalanobis distance (Mahalanobis distance = 7.96, p < .05).

Exclusion of this case from analyses had an impact on the results and thus this case was

deleted from the dataset. Additionally, five participants reported that English was not

their first language and were flagged as potentially problematic because of the nature of

the experimental task. Specifically, because understanding and recall of the presented

information during the video training task were focal indicators of key dependent

variables, fluency in the English language was required. Omission of these five cases

influenced statistical results and a decision was made not to include them in hypothesis

testing. This resulted in N = 192 participants who at least completed the in-lab

experimental portion of the study and N = 172 participants who completed all parts of the

study, including the follow-up survey.

Participant Descriptive Statistics

The mean age of the participants who completed the experimental portion of the

study was 21.54 years (SD = 6.51). Of those completing this portion of the study, 34.4% were male and 65.4% were female. Most participants identified themselves as Caucasian

(81.3%), followed by African American (10.9%), Asian/Pacific Islander (2.6%),

Hispanic/Latino (2.6%), and two or more races (1.6%). Fifty four percent were freshmen,

20.3% were sophomore, 14.1% junior, and 12% were seniors (in their 4th year or more).

Table 4.1 lists percentages for the demographic variables, broken down by experimental

condition. As can be seen, the random assignment procedure resulted in two experimental

groups which were quite similar in gender, age, and ethnicity.

Table 4.1

Participant Demographic Descriptors by Experimental Condition

Experimental Condition Promotion Video Prevention Video

Gender 33.0% Male 35.8% Male 67.0% Female 64.2% Female

Mean Age (SD) 20.96 (5.68) 22.12 (7.12)

Ethnicity 82.1% Caucasian 82.1% Caucasian 11.6% African Am. 10.5% African Am. 2.1% Asian/Pacific 3.2% Asian/Pacific 2.1% Hispanic/Latino 3.2% Hispanic/Latino 2.1% Two or More 1.1% Two or More N 97 95

As the topic of the training video was money management and budgeting, several

indicators of socioeconomic status and overall budgeting awareness were recorded.

Approximately 58% of the participants reported they worked at least part-time. The most common major was Psychology (24.5%); only 6.8% of participants indicated a finance- 79

related major (e.g., Business Administration, Finance). Regarding socioeconomic status, most participants identified themselves as “middle class” (58.9%), followed by “upper middle” (23.4%) and “lower class” (17.7%). Consistent with the overall worldwide economic environment, relatively few participants expressed a below average concern about their financial situation (20.3%), 20.8% chose the midpoint of the “concern” scale, and 58.8% expressed above average concern. Overall, 48.4% of participants indicated they had a budget at the time of the initial survey and 78.6% reported they track their earnings and expenses in some way. Table 4.2 illustrates the dispersion of values of these additional participant descriptive variables across the two experimental conditions.

Again, the distributions of these variables appeared similar across the conditions.

Table 4.2

Finance-related Participant Descriptors by Experimental Condition

Experimental Condition Promotion Video Prevention Video

Worked 55.7% Yes 60.0% Yes 44.3% No 40.0% No

Major 20.6% Psychology 28.4% Psychology 7.2% Finance 6.3% Finance 73.7% Other/Undecided 65.3% Other/Undecided

SES 16.5% Lower class 18.9% Lower class 61.9% Middle class 55.8% Middle class 21.6% Upper middle class 25.3% Upper middle class 0.0% Upper class 0.0% Upper class

Concerned 17.5% Below average 23.2% Below average 33.7% Average 20.8% Average 59.8% Above average 57.9% Above average

Own Budget 47.4% Yes 49.5% Yes 52.6% No 50.5% No

Track Money 80.4% Yes 76.8% Yes 19.6% No 23.2% No N 97 95 80

In sum, as can be seen in Tables 4.1 and 4.2, there did not appear to be any

troubling demographic differences between the participants who watched the promotion-

vs. prevention-framed videos. Additionally, all of the above-mentioned variables were

initially included in hypothesis testing analyses as potential covariates. None of the paths

between these controls and the latent endogenous variables were statistically significant,

except for ‘concern with money’ and ‘ethnicity,’ and thus these variables were the only

demographic or money-related covariates included in the final models reported in this

chapter.

Preliminary Assessment of Factor Structure and Development of Scale Scores

Confirmatory factor analysis (CFA) was used to assess the quality of the measures

used for three different sets of variables, namely, (a) the four processes hypothesized to

mediate between regulatory focus match and the proposed outcomes; (b) four affective

reactions variables measured in the laboratory phase of the study; and (c) six affective

reactions measures collected more distally in the follow-up phase of the study. These preliminary analyses thus allowed an assessment of the construct validity of the scales, and identified any measurement issues in a process that was independent of the hypotheses tests.

The same general procedure was followed in all of these CFAs. First, a measurement model consisting of the same number of latent constructs as measurement scales was tested. This multi-dimensional model had item-level indicators, each with a

single loading on the intended relevant latent construct. The fit and values of the factor

loadings for this model were then evaluated, and modification indices were inspected. If

this initial model had an adequate fit, a competing single-factor model was also estimated

and compared to the first model using a chi-square difference test. This was done to determine whether the single-factor model fit approximately as well as the initial, multi- dimensional model, and thus provided a more parsimonious explanation of the relationships among the observed variables. The expectation was that a single model would instead fit significantly more poorly than the initial model, arguing in favor of retaining the multi-dimensional structure. If the initial multi-dimensional model did not fit adequately, further exploration was done to determine whether good fit could be achieved by accommodating minor sources of misfit such as a need to free correlations between item disturbances. Once an adequately fitting revised multi-dimensional model was found, its fit was compared to that of a single-factor model, following the same logic described above. In some cases, a final additional analysis followed, this time using scale scores as the indicators in the CFA model, in order to determine whether a second-order factor structure was plausible. Path diagrams depicting the final multi-dimensional models are included in a series of figures located in Appendix J; fit information and tables summarizing factor loadings are reported in the following sections.

Intervening Mechanisms

Four intervening mechanisms were hypothesized to mediate the relationship of the match between participants’ chronic regulatory focus and the training video frame with the outcome variables: (a) subjective feelings associated with fit, (b) motivation, (c) processing fluency, and (d) attentional focus. Because several of the indicator scales for

each of these proposed mediators were either created for the current study or modified for

its purposes, the factor structure for each mechanism was examined separately.

Emotional fit. Three subscales assessed feelings associated with regulatory fit—

feelings of “rightness” (four items), enjoyment of the training program (five items), and

liking of the instructor (two items). The items of these three scales were submitted to a

three-factor CFA performed with LISREL v. 8.80 (Jöreskog & Söbom, 2003). This

model provided acceptable, but not perfect fit to the data, χ² (41, N = 192) = 118.06, p <

.001, RMSEA = .099 (.079; .12), CFI = .97, SRMR = .051. Inspection of the modification

indices suggested allowing the disturbances of two pairs of enjoyment items to covary.

Because it also made theoretical sense, the disturbance correlations of items 1 and 2, as

well as items 2 and 3 were freed. The revised model fit the data adequately, χ² (39, N =

192) = 93.55, p < .001, RMSEA = .086 (.063; .11), CFI = .98, SRMR = .047, and its fit

was significantly better than the initial model, Δχ² = 24.51, Δdf = 2, χ²crit = 13.82, p <

.001). Table 4.3 (next page) lists standardized factor loadings and Figure J.1 (see

Appendix J) depicts the factor structure for the revised model. As can be noted, all factor

loadings are strong (all >.69) and statistically significant. The intercorrelations among the

rightness, enjoyment, and liking latent constructs ranged in value from .60 to .77, and

were all statistically significant.

To rule out the possibility that the set of items from these three measures reflects only one lower-order emotional fit factor, a one-factor model was estimated, allowing the same disturbances to covary. This model demonstrated a much poorer fit to the data, χ²

(42, N = 192) = 320.96, p < .001, RMSEA = .186 (.17; .21), CFI = .90, SRMR = .089,

and importantly, clearly had a significantly poorer fit than did the revised three-factor

model, Δχ² = 227.41, Δdf = 3, χ²crit = 16.27, p < .001. Additionally, an alternative plausible two-factor model (a “rightness” factor and an enjoyment/liking factor, again with the same disturbances allowed to covary) also fit poorly, χ² (41, N = 192) = 227.11, p < .001, RMSEA = .154 (.13; .17), CFI = .92, SRMR = .074, and demonstrated significantly poorer fit than the three-factor model, Δχ² = 133.56, Δdf = 2, χ²crit = 13.82, p < .001.

Table 4.3

Results from the CFA of Emotional Fit Items

Factor Subscales and Items Loadings R² p Rightness (α = .818 ) 1. It felt ‘right’ while listening to the information presented in the money .78 .61 <.001 management training program. 2. I felt uneasy while watching the money management training program. .69 .48 <.001 3. I felt comfortable during the training program. .75 .56 <.001 4. Sitting on the money management training program felt ‘wrong.’ .69 .48 <.001

Enjoyment of Program (α = .926 ) 1. It was interesting to sit on this training program. .78 .60 <.001 2. It was enjoyable to sit on this training program. .85 .73 <.001 3. It was exciting to sit on this training program. .79 .62 <.001 4. I liked this training program very much. .91 .83 <.001 5. Sitting on this training program was a pleasure. .84 .70 <.001

Liking the Instructor (α = .926) 1. I think this instructor would make a good friend. .90 .82 <.001 2. I think I would get along well with this instructor. .95 .91 <.001 Note. N = 192. Factor loadings are standardized.

Motivation. Motivation is the second proposed intervening mechanism. Two

subscales tapped this construct—a four-item subscale assessing general motivation to use

a budget and a six-item subscale assessing motivation to learn about money management.

All items of the two subscales were submitted to a two-factor CFA. This model did not fit 84

the data well, χ² (34, N = 192) = 154.61, p < .001, RMSEA = .136 (.11; .16), CFI = .96,

SRMR = .057. Subscale items and modification indices were carefully examined and

three pairs of item disturbances were allowed to covary, specifically item pair 2, 4 in the

general motivation subscale, and item pairs 1, 2 and 5, 6 in the motivation to learn

subscale. The revised model fit the data adequately, χ² (31, N = 192) = 68.50, p < .001,

RMSEA = .080 (.054; .11), CFI = .98, SRMR = .042, and significantly better than the

initial model, Δχ² = 86.11, Δdf = 3, χ²crit = 16.27, p < .001. To ensure that two factors

were indeed present, an alternative one-factor model was tested, allowing the same disturbances to covary. This model fit the data significantly worse, χ² (32, N = 192) =

135.47, p < .001, RMSEA = .130 (.11; .15), CFI = .97, SRMR = .053; Δχ² = 66.97, Δdf =

1, χ²crit = 10.83, p < .001. Therefore, the two-factor model was preferred. Table 4.4

(next page) lists standardized factor loadings and Figure J.2 depicts the model. All factor loadings were strong (all >.53) and statistically significant; the two motivational factors were significantly correlated, r = .83.

Processing fluency and attentional focus. Because processing fluency and attentional focus both reflect cognitive processes, their factor structure was examined jointly. Processing fluency was assessed using a self-report processing fluency measure

(see Appendix K for a discussion of the response-time-based implicit processing fluency measure). Attentional focus was assessed through a self-report overall attention processes subscale and a cognitive interference (CI) subscale assessing frequency of off-task thoughts. CI item responses were reverse coded to match the other indicators, so that higher CI scores mean fewer interfering thoughts. A three-factor CFA model fit the data

Table 4.4

Results from the CFA of Motivation Items

Factor Subscales and Items Loadings R² p Motivation to budget (α = .820) 1. The training provided information that motivated me to create my own .86 .74 <.001 budget. 2. The training helped me see the usefulness of a budget. .53 .28 <.001 3. The training increased my intention to master my budget management .84 .70 <.001 skills. 4. The training showed me the value of having a budget. .63 .39 <.001

Motivation to Learn (α = .910) 1. I was very excited to attend the MMT program. .75 .57 <.001 2. I was interested in learning the material that was covered in this training .77 .60 <.001 program. 3. I tried to learn as much as I can from the MMT program. .79 .63 <.001 4. I was motivated to learn the training material that was emphasized in this .94 .88 <.001 program. 5. I was willing to exert considerable effort to learn the content of the MMT .78 .61 <.001 program. 6. I gave 100% effort to learn as much possible during the training. .69 .48 <.001 Note. N = 192. Factor loadings are standardized. reasonably well, χ² (227, N = 192) = 634.09, p < .001, RMSEA = .097 (.088; .11), CFI =

.91, SRMR = .081, but an inspection of the modification indices and subscale items indicated that the CI scale was somewhat problematic in terms of model fit (perhaps because of the variability of interfering thoughts one might experience). Consequently, where theoretically appropriate, disturbances of CI items were allowed to covary, as well as one pair of disturbances in the attention subscale. These changes resulted in a model with a significantly better fit, χ² (222, N = 192) = 412.57, p < .001, RMSEA = .067 (.057;

.077), CFI = .95, SRMR = .071; Δχ² = 221.52, Δdf = 5, χ²crit = 20.52, p < .001.

An alternative one-factor model (with the same disturbances allowed to covary) provided a significantly worse fit to the data, χ² (225, N = 192) = 1503.27, p < .001,

RMSEA = .172 (.16; .18), CFI = .80, SRMR = .14; Δχ² = 1090.70, Δdf = 3, χ²crit = 86

16.27, p < .001. Additionally, because CI and attentional focus are both indicators of the

attentional focus construct, an alternative two-factor model with attentional focus as one factor and processing fluency as the other was tested (with the same disturbances allowed to covary). Again, this model fit the data significantly worse than the three-factor solution, (χ² (224, N = 192) = 699.12, p < .001, RMSEA = .105 (.097; .11), CFI = .89,

SRMR = .11; Δχ² =286.55, Δdf = 1, χ²crit = 10.83, p < .001. Table 4.5 (next page) and

Figure J.3 represent the final three-factor model with standardized factor loadings. Factor loadings for the processing fluency and attentional focus subscales were all strong (all >

.69) and statistically significant. The CI subscale had some loadings which were weaker, although all except one of these were still statistically significant. However, it was not revised and all items were kept because this scale has been established in the literature

(Sarason et al., 1986). The CI latent construct had moderate correlations with processing fluency and attentional focus latent constructs (.30 and .40, respectively), while the correlations between processing fluency and attentional focus latent constructs was .61.

All correlations were statistically significant.

Overall factor structure of intervening mechanisms. As mentioned earlier, the hypothesized model suggests that the attribute-treatment interaction (i.e., trainee dominant regulatory focus X video frame) affects outcomes through four mediating processes. To confirm that four distinct intervening mechanisms have indeed been assessed, the overall factor structure of the measured emotional, cognitive, and motivational processes was examined next. The CFAs just described all supported the creation of separate subscale scores for each of the measured intervening variables, based

on the factor loadings associated with their respective items. Now it was desired to use the corresponding subscale scores as indicators of yet more global factors, thus, mean scores for all mediating variable subscales were calculated.

Table 4.5

Results from the CFA of Cognitive Fit Items

Factor Subscales and Items Loadings R² p Processing Fluency (α = .887) 1. The Money Management training program was clear. .80 .65 <.001 2. I could easily understand the content presented during the Money .87 .75 <.001 Management training program. 3. The material presented in the training program was comprehensive. .76 .58 <.001 4. Overall, the information presented during the training video was easy to .91 .82 <.001 process.

Attentional Focus (α = .863) 1. I paid close attention to the video demonstration. .88 .77 <.001 2. I was able to concentrate on the video demonstration. .74 .55 <.001 3. The video demonstration held my attention. .77 .60 <.001 4. During the video demonstration, I was absorbed by the demonstrated .69 .48 <.001 information.

Cognitive Interference (α = .841) 1. I thought about how I should listen more carefully. .46 .21 <.001 2. I thought about the purpose of the experiment. .14 .02 .070 3. I thought about how much time there was left of the training. .58 .34 <.001 4. I thought about how often I got confused. .45 .21 <.001 5. I thought about other activities (for example, assignments, work). .64 .41 <.001 6. I thought about members of my family. .53 .28 <.001 7. I thought about friends. .54 .29 <.001 8. I thought about something that made me feel guilty. .51 .26 <.001 9. I thought about personal worries. .71 .50 <.001 10. I thought about something that made me feel tense. .63 .40 <.001 11. I thought about something that made me feel angry. .43 .18 <.001 12. I thought about something that happened earlier today. .54 .29 <.001 13. I thought about something that happened in the recent past (last few days, .61 .37 <.001 but not today). 14. I thought about something that happened in the distant past. .43 .19 <.001 15. I thought about something that might happen in the future. .55 .30 <.001 Note. N = 192. Factor loadings are standardized.

The initial four-factor model included three indicators of emotional fit, two

indicators of motivation, two indicators of attentional focus, and four indicators of

processing fluency (subscale items were used because processing fluency was assessed

through one subscale only). The four-factor model resulted in an acceptable fit, χ² (38, N

= 192) = 94.28, p < .001, RMSEA = .088 (.066; .11), CFI = .97, SRMR = .074. However, a negative error variance for the attention disturbance term raised some concerns about the accuracy of the model specification and subscale items and theory were revisited

(Kolenikov & Bollen, 2008).

Based on the fact that the attentional focus, CI, and processing fluency subscales all refer to cognitive processes, and that they were hypothesized to be influenced by the attribute/treatment interaction in the same manner, and to consequently influence the same outcomes, a model which specified them all as indicators of a single cognitive fit

factor was considered. Additionally, because the “rightness” feelings associated with regulatory fit have been described as somewhat different from other affective feelings

(e.g., Cesario et al., 2004) and have an ease of cognitive processing component to them, the disturbance term for the “rightness” indicator was allowed to covary with the disturbances of both attention and processing fluency. This three-factor model was well- fitting overall, χ² (15, N = 192) = 33.40, p = .004, RMSEA = .080 (.043; .12), CFI = .98,

SRMR = .049, and fit the data significantly better than the initial four-factor model, Δχ² =

60.88, Δdf = 23, χ²crit = 49.73, p < .001.

To investigate the possibility that all indicators represent one overall fit factor, an alternative one-factor solution was also tested. However, it fit the data significantly

worse, χ² (18, N = 192) = 102.89, p < .001, RMSEA = .157 (.13; .19), CFI = .93, SRMR

= .065; Δχ² = 69.49, Δdf = 3, χ²crit = 16.27, p < .001. Therefore, the three-factor model was deemed to best fit the data. Standardized factor loadings are presented in Table 4.6 and the model is depicted in Figure J.4. The correlations among the three factors ranged in value from .68 to .85, and were all statistically significant.

Table 4.6

Results from the CFA of Intervening Mechanisms Indicators

Factor Construct & Scale Indicators Loadings R² p Emotional Fit Rightness .65 .43 <.001 Enjoyment .95 .91 <.001 Liking .61 .37 <.001

Motivation Motivation to budget .76 .58 <.001 Motivation to learn .94 .88 <.001

Cognitive Fit Attentional focus .94 .88 <.001 Cognitive interference .37 .13 <.001 Processing fluency .57 .33 <.001 Note. N = 192. Factor loadings are standardized.

Outcome Variables

In-lab study affective reactions. During the in-lab portion of the study

participants completed several affective reactions measures—satisfaction with the

training program, perceived utility of the training program, self-efficacy to manage one’s finances, and intentions to use the learned material. Because all of these subscales were significantly correlated (and are also all classified as affective-based training outcomes;

Gully & Chen, 2010), their overall factor structure was examined.

First, all subscale items were submitted to a four-factor CFA. The model fit the

data reasonably well, χ² (183, N = 192) = 441.31, p < .001, RMSEA = .086 (.076; .096),

CFI = .97, SRMR = .065. Several significant modification indices were suggested, and where they made theoretical sense, item disturbance terms were allowed to covary (item

pairs 4, 5 and 5, 6 for the self-efficacy subscale; item pairs 3, 5 and 4, 6 for the

satisfaction subscale). The revised four-factor model fit the data well, χ² (179, N = 192) =

342.22, p < .001, RMSEA = .069 (.058; .080), CFI = .98, SRMR = .060, and the improvement in fit was statistically significant, Δχ² = 100.09, Δdf = 4, χ²crit = 18.47, p <

.001). An alternative one-factor solution, allowing the same disturbances to covary, fit the

data poorly, χ² (185, N = 192) = 952.63, p < .001, RMSEA = .15 (.14; .16), CFI = .93,

SRMR = .083, and more importantly, the model fit was significantly worse than that of

the four-factor model, Δχ² = 610.42, Δdf = 6, χ²crit = 22.46, p < .001. Therefore, the four-

factor solution was accepted. As noted in Table 4.7 (next page) and Figure J.5, all factor

loadings were strong (all >.41) and significant; correlations between the latent variables

ranged in values from .58 to .82 and were all statistically significant. Due to the high

intercorrelations among the subscales, the presence of a higher-order factor solution was

examined (mean scores for subscales were calculated and used as first-order indicators;

item-level indicators were not included). The model fit the data very well (χ² (2, N = 192)

= 2.44, p = .29, RMSEA = .034 (.00; .15), CFI = 1.00, SRMR = .014), proving support to

the notion that a second-order affective outcomes factor is present (see Table 4.8 and

Figure J.6 for standardized factor loadings).

Table 4.7

Results from the CFA of In-lab Affective Reactions Items

Factor Subscales and Items Loadings R² p Satisfaction (α = .889) 1. I am satisfied with the content of the money management training program .79 .63 <.001 2. I am satisfied with the overall quality of the money management training .83 .68 <.001 program. 3. Overall, I found the content of the training program valuable. .65 .43 <.001 4. Overall, I am very satisfied with the presentation of the content of the .77 .60 <.001 training program. 5. Overall, I had a very positive learning experience. .78 .62 <.001 6. I am satisfied with the instructor’s ability to keep my interest. .72 .52 <.001 7. I am satisfied with the instructor’s pace of presenting. .57 .33 <.001

Utility (α = .708) 1. I believe the course objectives closely matched my idea of what I expected .44 .19 <.001 would be taught. 2. This course will help me improve my money management habits. .81 .66 <.001 3. I believe that the course content is relevant to my everyday life. .56 .31 <.001 4. The content of the Money Management training program helped me learn .77 .60 <.001 important concepts.

Self-Efficacy (α = .793) 1. I am confident I can create my personal budget as a result of this training .74 .54 <.001 program. 2. I am confident I can manage my money better as a result of this training .80 .64 <.001 program. 3. I am confident I now possess enough knowledge to manage my money .77 .59 <.001 effectively. 4. I am confident in my ability to better monitor my spending habits. .56 .31 <.001 5. I am confident in my ability to be a smart shopper. .41 .17 <.001 6. I am confident in my ability to be a more efficient driver. .41 .17 <.001

Use intentions (α = .918) 1. I am planning to use in my everyday life the new knowledge and skills I .83 .68 <.001 acquired in this training. 2. I anticipate making every effort in the coming weeks to put into practice .87 .76 <.001 what I learned in this training. 3. My objective is to apply in my everyday life as much of the learning from .88 .77 <.001 this training as I can. 4. As soon as it is feasible, I intend to use all that I learned in this training in .86 .75 <.001 my daily life. Note. N = 192. Factor loadings are standardized.

Table 4.8

Second-order In-lab Affective Reactions Factor Loadings

Subscale Factor loadings R² p Satisfaction .80 .63 <.001 Utility .84 .70 <.001 Self-Efficacy .80 .65 <.001 Use Intent .72 .52 <.001 Note. N = 192. Factor loadings are standardized.

Follow-up survey affective reactions. A total of six scales (effort exerted during

the money management training, self-efficacy to manage finances, helpfulness of the training program, satisfaction with the training program, extent to which participants are currently applying learned information, and intentions to use it in the future) were

included in the follow-up survey. To investigate the factor structure of the final stage

outcomes, all subscale items were submitted to a six-factor CFA. The model provided a

reasonable fit to the data, χ² (390, N = 172) = 907.27, p < .001, RMSEA = .088 (.081;

.096), CFI = .96, SRMR = .069. Based on theoretical and statistical justification, several

disturbance terms were allowed to covary. The revised model resulted in a good fit, χ²

(385, N = 172) = 624.72, p < .001, RMSEA = .060 (.052; .071), CFI = .98, SRMR = .059,

which was significantly better than the fit of the initial model, Δχ² = 282.55, Δdf = 5,

χ²crit = 22.46, p < .001. An alternative one-factor model, allowing the same disturbances

to covary fit the data poorly, χ² (400, N = 172) = 2705.91, p < .001, RMSEA = .18 (.18;

.19), CFI = .90, SRMR = .11, and significantly worse than the six-factor solution, Δχ² =

1798.64, Δdf = 10, χ²crit = 29.59, p < .001). The factor loadings for the six-factor solution

are presented in Table 4.9 (and Figure J.7). As demonstrated, they were all strong and

statistically significant (all > .40); the correlations among the latent constructs were also all statistically significant and ranged in values from .48 to .90.

Table 4.9

Results from the CFA of Follow-up Survey Affective Reactions Items

Factor Subscales and Items Loadings R² p Effort (α = .877) 1. I put a lot of effort to learn and understand the material presented during .85 .73 <.001 the money management training program. 2. I tried very hard to learn the material presented during the training .90 .80 <.001 program. 3. It was important to me to learn as much as possible during the training .70 .50 <.001 program. 4. I didn’t put much energy into the money management training program. .62 .39 <.001 (R) 5. I didn’t try very hard to learn and understand the material presented during .61 .38 <.001 the training program. (R)

Satisfaction (α = .909) 1. I was satisfied with the content of the money management training .83 .69 <.001 program 2. I was satisfied with the overall quality of the money management training .79 .63 <.001 program. 3. Overall, I found the content of the training program valuable. .78 .62 <.001 4. Overall, I was very satisfied with the presentation of the content of the .83 .68 <.001 training program. 5. Overall, I had a very positive learning experience. .87 .77 <.001 6. I was satisfied with the instructor’s ability to keep my interest. .75 .57 <.001 7. I am satisfied with the instructor’s pace of presenting. .57 .33 <.001

Helpfulness (α = .676) 1. I believe the money management training objectives closely matched my .49 .24 <.001 idea of what I expected would be taught. 2. The money management training program helped me improve my money .75 .57 <.001 management habits. 3. I believe the money management training content is relevant to my .52 .27 <.001 everyday life. 4. The content of the money management training program helped me learn .71 .51 <.001 important concepts.

Self-Efficacy (α = .849) 1. I am confident I can create my personal budget as a result of this training .83 .70 <.001 program. 2. I am confident I can manage my money better as a result of this training .79 .63 <.001 program. 3. I am confident I now possess enough knowledge to manage my money .75 .57 <.001 effectively. 4. I am confident in my ability to better monitor my spending habits. .70 .49 <.001 5. I am confident in my ability to be a smart shopper. .63 .40 <.001 6. I am confident in my ability to be a more efficient driver. .40 .16 <.001

Using now (α = .897) .76 .58 <.001 1. I am using in my everyday life the knowledge and skills I acquired during .88 .77 <.001 the MMT. 2. I make every effort to put into practice what I learned in the MMT. .91 .83 <.001 3. My objective is to apply in my everyday life as much of what I learned .75 .57 <.001 during the training as I can. 4. I use what I learned in the MMT every time it is feasible.

Use intentions (α = .940) 5. I am planning to use in my everyday life the knowledge and skills I .89 .80 <.001 acquired during the MMT. 6. I anticipate making every effort in the coming weeks to put into practice .93 .86 <.001 what I learned in the MMT. 7. My objective is to apply in my everyday life as much of what I learned .93 .87 <.001 during the training as I can. 8. As soon as it is feasible, I intend to use what I learned in the training. .82 .68 <.001 Note. N = 172. Factor loadings are standardized.

Because all subscales were strongly correlated, the presence of a higher-order factor was investigated (mean scores for the six subscales were used as indicators of the second-order factor; no item-level indicators were included). Because the ‘Intentions to use’ and ‘Using now’ subscales are very similar in content (the only difference is that one is in present, the other in future tense), the error terms of the corresponding items were allowed to covary. Although model fit was mediocre, χ² (8, N = 172) = 30.88, p < .001,

RMSEA = .13 (.083; .18), CFI = .96, SRMR = .057, the second-order factor model was retained as it made theoretical sense and no theoretically appropriate modification indices were suggested (see Table 4.10 and Figure J.8 for standardized solution).

Table 4.10

Second-order Follow-up Affective Reactions Factor Loadings

Subscale Factor loadings R² p Satisfaction .90 .80 <.001 Effort .60 .36 <.001 Self-Efficacy .70 .50 <.001 Use Intent .62 .39 <.001 Helpfulness .79 .63 <.001 Note. N = 172. Factor loadings are standardized.

Video Framing Manipulation Checks

Two self-report measures were utilized in the current study to attest the merit of

the experimental manipulation. First, a 12-item explicit measure was created for the current study, with six items designed to be more consistent with the promotion-framed

video (e.g., “A budget will help me set money aside for my dream home.”) and six items

designed to be more consistent with the prevention-framed video (e.g., “Having a budget will help me live a safer and free-of-worry life.”). To confirm the factor structure of the scale, the 12 items were submitted to a two-factor CFA. The resulting model demonstrated poor fit to the data, χ² (53, N = 192) = 236.32, p < .001, RMSEA = .135

(.12; .15), CFI = .79, SRMR = .10. After a close examination of the modification indices and the content of the items, it was determined to remove all driving-related statements.

This resulted in four promotion-framed and three prevention-framed items. These seven items were submitted to a two-factor CFA and the resulting solution provided a satisfactory fit, χ² (13, N = 192) = 36.78, p < .001, RMSEA = .098 (.061; .14), CFI = .91,

SRMR = .074. As suggested by the only modification index and because it made theoretical sense, the error covariance between two of the items (promotion item 1 and prevention item 5) was freed, which resulted in a good fit, χ² (12, N = 192) = 22.92, p = 96

.03, RMSEA = .069 (.022; .11), CFI = .95, SRMR = .059, which was significantly better than the fit for the initial model, Δχ² = 13.86, Δdf = 1, χ²crit = 10.83, p < .001. An alternative one-factor model, allowing the same disturbance terms to covary, resulted in poor fit, χ² (13, N = 192) = 63.88, p < .001, RMSEA = .143 (.11; .18), CFI = .83, SRMR

= .096, which was also significantly worse than the two-factor model, Δχ² = 40.96, Δdf =

1, χ²crit = 10.83, p < .001). Table 4.11 lists standardized factor loadings, which were all significant. The correlation between the two latent constructs was moderate in size, r =

.42, and also statistically significant.

Table 4.11

Results from the CFA of Promotion/Prevention Consistency Items

Factor Scales and Items Loadings R² p Promotion-framed items (α = .665) 1. A budget will help me achieve my long-term hopes and dreams. .38 .14 <.001 2. A budget will help me set money aside for my dream home. .72 .51 <.001 3. Having a budget will allow me to go to my dream holiday. .84 .71 <.001 4. Having a budget will allow me to live a more exciting and full of .44 .19 <.001 adventures life.

Prevention-framed items (α = .553) 1. A budget will prevent me from failing my long-term duties and .43 .18 <.001 responsibilities. 2. Having a budget will help me avoid going into debt. .34 .12 <.001 3. Having a budget will help me live a safer and free-of-worry life. .82 .68 <.001 Note. N = 192. Factor loadings are standardized.

After the factor structure of the measure was determined, participants’ ratings on the promotion- and prevention-framed items were averaged and compared. A 2 X 2 mixed model ANOVA, with video as the between-subjects factor and ratings of consistency items as the within-subjects factor was conducted. The results revealed a significant interaction effect, F (1, 190) = 33.528, p < .001, η² = .18. As expected,

participants who watched the promotion-framed video rated the promotion-framed items

as significantly more consistent with the video than the prevention-framed items (Mpro items = 2.54, SDpro items = 1. 24, Mpre items = 2.94, SDpre items = 1.51; higher values indicate

less consistency). The reverse was true for the participants who watched the prevention-

framed video. They rated the promotion-framed items as significantly less consistent with

the video than they did the prevention-framed items (Mpro items = 3.22, SDpro items = 1.44,

Mpre items = 2.36, SDpre items = 1.30). Thus, this consistency scale provided initial support

that participants who watched the prevention/promotion-framed videos had perceived the

video presentations differently, in the intended manner.

In addition to this explicit measure, two implicit indicators were utilized as

manipulation checks. The first was based on the time participants needed to rate the

consistency items described above. Specifically, it was expected that participants would

take less time to rate the items, framed in a manner consistent with the frame of the

video. In order to test this, response times for each rating were recorded. Because

reaction times usually cause certain issues in statistical analysis, a few modifications of

the data were warranted. First, following Bargh and Chartrand (2000) recommendations,

unrealistic or extreme response times (either too quick or two slow) were noted. A total of 13 reaction times were below 500 ms (300 ms is the rule of thumb lower limit for lexical decision tasks, but since these were entire statements, the reasonable lower bound

response time was increased). Four additional responses were below 1000 ms. A closer examination of these response times indicated that they all belonged to the same two participants. Thus, both of these cases were removed from further analysis as these

individuals were clearly not doing the task properly. Within-person response time

standardization did not reveal any other problematic response times (no RT exceeded two

standard deviations beyond the within-person mean). Next, values were log-transformed

(natural logarithm function) to ensure homogeneity of variance. Response times for the

items regarding driving and car expenses were not included in the analysis.

A 2 X 2 mixed model ANOVA, with video frame as the between-subjects factor,

and response time as the within-subject factor, revealed a significant interaction, F(1,188)

= 13.189, p < .001, η² = 07. In line with expectations, participants who watched the

promotion-framed video rated the promotion-framed items significantly faster than they

did the prevention-framed items (Mpro items = 5833.97, SDpro items = 1822.46, Mpre items =

6242.22, SDpro items = 2225.92; untransformed response times are reported for ease of interpretation), while the participants who watched the prevention-framed video rated the

prevention-framed items significantly faster than they did the promotion-framed ones

(Mpro items = 6679.67, SDpre items = 2301.05, Mpre items = 6111.06, SDpre items = 2987.60).

Thus, participants not only rated the promotion/prevention-framed items as more

consistent in content with the promotion/prevention-framed videos, but they also did so

faster than they did the mismatching items (promotion-framed items for prevention-

framed video and prevention-framed items for promotion-framed video). This attests to the fact that the experimental manipulation worked not only on an explicit content level, but on an implicit, unconscious level as well.

As a final manipulation check, a Lexical Decision Task (LDT) was used, where

participants had to identify whether a given letter string represented a real English word

or not. The “real words” letter strings included promotion- and prevention-relevant

words. For each participant, only correct responses to words were used in analysis, thus

omitting any “0” response times (a “0” response time indicated a given letter string was

identified as a nonword). Overall, the error rate was relatively low, with 149 participants

not making any mistakes in identifying the real English words, 30 participants making

one mistake, and 13 participants making 2 or more mistakes. Participants who reported

that English was not their first language and several participants who did not understand

the directions of the task (they did not provide a correct answer to any “real word” letter

string within the allotted time) were dropped from analysis, resulting in N = 182. As was

done with the response times for the consistency items, extreme times (below 300 ms and

two standard deviations above the within-person mean) were omitted from analysis here

as well. The remaining response latencies were log transformed (natural logarithm

function) to ensure homogeneity of variance.

A 2 X 2 mixed-model ANOVA, with video frame as the between-subjects factor

and log-transformed response time as the within-subjects factor did not reveal a significant interaction, F (1, 180) = .003, p = .985. Overall, participants who watched the promotion-framed video did not respond to the promotion- vs. prevention-related words differently (Mpro words = 701.347, SDpro words = 153.907; Mpre words = 703.841, SDpre words =

128.286). Additionally, participants who watched the prevention-framed video did not

respond to the prevention- vs. promotion-related words significantly differently (Mpro words

= 667.718, SDpro words = 127.884; Mpre words = 671.660, SDpre words = 123.814). Results were

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similar after controlling for word frequencies and average response times found on the

English Lexicon Project website (http://elexicon.wustl.edu/).

Descriptive Statistics and Correlations

Descriptive statistics and correlations between focal study variables, as well as

several covariates are presented in Tables 4.12 and 4.13 (next page). Scale reliabilities are

presented in the diagonal, where applicable. Most scales presented good psychometric

properties (α’s > .793). Only the RFQ promotion subscale (α = .675) and the helpfulness

scale (α = .676) demonstrated lower, yet acceptable reliabilities. Both of these measures

have been established in the literature and were not altered much for the current study,

deeming their use in analysis appropriate. All variables were also examined for normality

by following common rules of thumb (i.e., skew < 3, kurtosis < 8; Kline, 2005). No

concerns about normality were raised. Finally, because most indicators of study variables

were highly correlated, before each set of models was tested, Measurement models were

carefully specified to ensure appropriate factor structure.

Hypothesis Testing Procedure

Based on theory and preliminary analyses, it was determined that the RFQ

promotion and prevention scores (Higgins et al., 2001) would be used to test hypotheses

(responses were collected during the in-lab portion of the study, prior the experimental

manipulation). One reason for this choice was that a lot of the regulatory fit effects

discussed in the literature reviewed in Chapter II have been obtained through this

measure. Also RFQ score have been shown to not be affected by common regulatory

focus manipulations (Latimer et al., 2008), a characteristic that renders them especially

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Table 4.12

Correlations and Descriptive Statistics of In-lab Study Variables

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1. Video --- 2. Pro Score .08 (.68) 3. Pre Score .05 .19** (.80)

Mediators 4. Rightness -.07 .27** .08 (.82) 5. Enjoyment -.08 .11 -.07 .63** (.93) 6.Liking .01 .24** .15* .50** .57** (.93) 7.Att Focus .01 .20** .05 .58** .61** .41** (.86) 8.Proc F .06 .23** .08 .56** .38** .38** .53** (.89) 9.CI .05 .30** .06 .25** .21** .12 .39** .25** (.84) 10.Mot -.06 .14 -.13 .42** .60** .42** .55** .44** .04 (.82) 11.LMot -.04 .16* -.13 .50** .76** .49** .67** .42** .21** .71** (.91)

Outcomes 12.Sat -.06 .16* -.03 .58** .70** .48** .66** .55** .27** .58** .62** (.89) 13.Utility .01 .06 -.10 .44** .60** .38** .47** .50** .14 .65** .60** .67** (.71) 14.SE -.02 .25** .01 .50** .54** .45** .47** .50** .23** .52** .58** .65** .66** (.79) 15.Use Intent -.02 .12 -.09 .37** .61** .34** .54** .30** .12 .66** .78** .54** .62** .60** (.92) 16.Declare1 .08 .24** .13 .16* -.02 .04 .08 .14 .05 .02 .00 .02 -.04 .01 -.07 (.95) 17.Declare2 -.02 .17* .02 .15* .09 .14 .20 .10 .13 .12 .15* .06 .09 .06 .11 .25** (.92) 18.Declare3 -.11 .18* .10 .13 .07 .11 .23** .20** .17* .16* .15* .15* .17* .08 .17* .27** .39** (.95)

Covariates 19.Ethnicity .01 .01 -.05 -.16* .00 -.06 -.09 -.18* .09 -.08 -.08 -.09 -.01 .01 .00 -.17* -.13 -.15* --- 20.$$ Concern -.06 -.13 -.22** .07 .09 -.09 .06 .00 -.15* .16* .15* -.01 .16* .00 .16* -.07 .00 .09 -.01 --- 21.Pos Affect .00 .50** -.01 .33** .24** .30** .27** .29** .18* .36** .33** .27** .26** .37** .25** .05 .06 .07 .02 .02 (.89) 22.Pos Mood -.08 .37** .07 .35** .31** .30** .32** .28** .26** .24** .32** .28** .28** .37** .23** .09 .11 .08 .01 -.16* .39** (.81) Mean --- 3.83 3.31 5.95 5.02 5.30 5.49 6.37 3.94 5.93 5.45 3.95 4.05 3.99 5.15 2.28 3.05 3.90 --- 4.76 3.68 3.38 SD --- .60 .81 .87 1.05 1.15 1.05 .67 .57 .74 .96 .61 .54 .53 1.18 .86 .96 1.47 --- 1.47 .71 .72 Note. Video = Video frame, Pro Score = Promotion Score, Pre Score = Prevention Score, Mediators = Intervening Mechanisms, Att Focus = Attentional Focus, Proc F = Processing Fluency, CI = Cognitive Interference, Mot = Motivation to Budget, LMot = Motivation to Learn, Sat = Satisfaction, SE = Self-Efficacy, $$ Concern = Concern with Money, Pos Affect = General Positive Affectivity, Pos Mood = Current Positive Mood; Video Frame coded as 1 = promotion-framed, 2 = prevention-framed; Ethnicity coded as 1 = Majority, 2 = Minority; N = 192; *p < .05, **p < .001.

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Table 4.13

Correlations and Descriptive Statistics of In-lab Study and Follow-up Survey Variables

Variables 19 20 21 22 23 24 1.Video Frame -.02 -.09 -.14 -.12 -.08 -.09 2.Promorion Score .03 .01 .16* -.01 .20** .16* 3.Prevention Score -.16* -.10 .01 -.13 -.13 -.11

Intervening Mechanisms 4.Rightness .40** .50** .42** .38** .44** .36** 5.Enjoyment .64** .59** .41** .46** .45** .48** 6.Likeness .40** .31** .37** .34** .36** .33** 7.Attention .59** .57** .45** .42** .50** .48** 8.Processing Fluency .18* .31** .38** .34** .41** .34** 9.Cog Interference .20** .22** .13 -.00 .15 .08 10.Mot to Budget .49** .37** .35** .48** .54** .57** 11.Mot to Learn .72** .51** .45** .49** .64** .68**

In-Lab Affect Outcomes 12.Satisfaction .53** .65** .52** .55** .55** .49** 13.Utility .46** .49** .41** .55** .48** .49** 14.Self-efficacy .38** .46** .59** .47** .54** .48** 15.Use Intentions .58** .49** .45** .47** .63** .68**

Covariates 16.Ethnicity -.05 -.03 -.01 -.07 -.08 -.15 17.Concern with Money .20** .19* .08 .21** .19* .23** 18.Positive Affect .32** .11 .21** .14 .37** .31**

Follow-up Affect Outcomes 19.Effort (.88) .55** .45** .44** .52** .57** 20.Satisfaction 2 (.91) .64** .71** .47** .43** 21.Self-Efficacy 2 (.85) .58** .48** .41** 22.Helpfulness (.68) .56** .51** 23.Using Now (.90) .84** 24.Use Intentions2 (.94) Mean 3.86 3.88 4.04 3.97 4.93 5.26 SD .67 .62 .52 .51 1.10 1.18 Note. N = 172; *p < .05, **p < .001

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useful for examining interaction effects between chronic regulatory focus and

promotion/prevention framing manipulations. Furthermore, preliminary analysis using

the GRFM promotion and prevention scores (Lockwood et al., 2002) did not support

focal hypotheses (see Appendix L for details, as well as a discussion of regulatory focus measures and relationships among them).

Participants’ chronic regulatory focus was calculated by subtracting their mean

RFQ prevention score from their mean RFQ promotion score. Thus, the resultant regulatory focus value represents dominant regulatory focus, with higher values indicating a stronger promotion focus and lower values indicating a stronger prevention focus. Although difference scores are used in hypothesis testing (necessitated by the examination of interactive effects among numerous variables in LISREL), it should be noted that difference scores are problematic and do not provide a full picture of the existing relationships (Edwards, 1994). Thus, additional hierarchical regression analyses are presented in Appendix M, where promotion and prevention scores were treated as separate independent variables. Following Aiken and West’s (1991) recommendations, participants’ regulatory focus difference scores were mean centered and the centered values were used in analyses. Additionally, an interaction variable was created by multiplying video frame (1 = promotion; 2 = prevention) by the centered regulatory focus difference score.

The hypothesized model was tested with structural equation modeling, using maximum likelihood estimation based on covariance matrices, utilizing LISREL v. 8.80

(Jöreskog & Söbom, 2003). Models were tested in two stages. During the first stage of

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the analysis, only variables collected at the time of the video training program were used.

During the second stage of data analysis, variables collected during the follow-up survey were also included. For each stage, a Measurement model was first specified. Next, an

Interaction model examining the interactive effects of video frame X regulatory focus on focal dependent variables was tested, followed by a Mediated model, where hypothesized mediation paths were added. Specific model testing is described next.

Hypothesis Testing

In-lab Variables Models

In-lab variables measurement model. Before proceeding with hypothesis testing,

the factor structure of all focal variables was examined to develop a sound In-lab

variables measurement model. Following results of confirmatory factor analyses

described earlier, a five-factor model (emotional fit, cognitive fit, motivation, affective

outcomes, and declarative knowledge) was tested. This model (with the disturbance of

the “rightness” indicator allowed to covary with the disturbances for the “processing

fluency” and “attention” indicators) provided mediocre fit to the data, χ² (78, N = 192) =

245.07, p < .001, RMSEA = .11 (.091; .12), CFI = .95, SRMR = .06. After careful

investigation of modification indices and items’ content, where theoretically appropriate, additional pairs of disturbances were allowed to covary (i.e., the disturbances for ‘use

intentions’ with the disturbances for ‘motivation to budget’ and ‘motivation to learn’ and

the disturbances for ‘utility’ and motivation to budget). The revised In-lab variables

measurement model resulted in a reasonable fit, χ² (75, N = 192) = 155.05, p < .001,

RMSEA = .075 (.058; .091), CFI = .98, SRMR = .051, which was significantly better

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than the fit of the initial model, Δχ² = 90.02, Δdf = 3, χ²crit = 16.27, p < .001). See Figure

N.1 located in Appendix N for details.

In-lab variables interaction model—Hypotheses 1-4, 5a, and 6a. After the measurement model was revised and clarified, hypothesis testing was in progress. First an

In-lab variables interaction model was tested, where the five latent variables (emotional fit, cognitive fit, motivation, affective outcomes, and declarative knowledge) were regressed on video condition, dominant regulatory focus, an interaction term (frame X regulatory focus product term) as well as on three covariates—current positive mood,

general positive affectivity (responses collected prior the in-lab study), and participants’ ethnicity. This model fit the data well, χ² (141, N = 192) = 239.89, p < .001, RMSEA =

.061 (.047; .074), CFI = .98, SRMR = .051 and relevant estimated parameters were used to test the basic research hypotheses. Figure 4.1 (next page) depicts the In-lab variables interaction model, with indicator variables omitted and paths between endogenous variables and covariates listed in table below the model, for clarity of the figure.

To restate, Hypotheses 1 through 4 proposed that a match between one’s dominant chronic regulatory focus and the frame of the training video would result in enhanced subjective feelings (“rightness,” liking, and enjoyment), processing fluency, attentional focus, and motivation. Additionally, Hypotheses 5a and 6a stated that such a match would also enhance affective outcome indicators of training effectiveness (e.g., satisfaction, perceived utility) and declarative knowledge of the training material.

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Regulatory Video-Frame Video X RF Focus

-.07

-.06 .07 -.04 -.01

.39^ .62** .69** .60** -.05

-.38^ -.64** -.62** -.57** .15

Affective Emo Fit Cog Fit Motivation Recall Reactions

.57** .64** .61** .13

.66** .63** .13

.66** .29*

.09

Path Coefficients (β) Between Control Variables and Endogenous Variables Endogenous Variables Covariates Emotional Fit Cognitive Fit Motivational Affective Declarative Engagement Outcomes Knowledge Positive Affect .18* .18* .26** .22** --- Current Pos Mood .28** .28** .22** .25** --- Ethnicity ------.33*

Figure 4.1. In-lab Variables Interaction Model Note. N = 192. Interaction term coefficients in bold for emphasis; Video Frame coded as 1 = promotion-framed, 2 = prevention-framed; Ethnicity coded as 1 = Majority, 2 = Minority; ^p ≤ .10, *p ≤ .05, **p ≤ .01

Examination of path coefficients revealed that the video X regulatory focus interaction term predicting subjective regulatory fit feelings was marginally significant, β

= -.38, t = -1.69, p = .093, R² = .19. Thus, Hypothesis 1 received only marginal support.

The interaction was plotted to explore its nature (using chronic regulatory focus

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difference values one standard deviation above and one standard deviation below the mean). As can be noted in Figure 4.2, the interaction is in the expected direction.

Specifically, those who watched the promotion-framed video had more positive subjective feelings the higher their dominant chronic regulatory focus score was (i.e., dominant promotion focus). On the other hand, those who watched the prevention-farmed video experiences more positive feelings the lower their chronic regulatory focus score was (i.e., dominant prevention focus).

0.05 0 -0.05 -0.1 pro vid -0.15 pre vid Emotional Fit Fit Emotional -0.2 -0.25 + SD - SD Regulatory Focus

Figure 4.2. Regulatory Focus X Video Frame Interaction Effect on Emotional Fit

It was discussed earlier that processing fluency and attentional focus were combined into one overall cognitive fit/non-fit construct and therefore Hypotheses 2 and

3 were collapsed into one Hypothesis 2-3 (see Appendix O for individual Hypothesis 2 and Hypothesis 3 tests). The path coefficient of the interaction term predicting cognitive processes was significant, β = -.64, t = -2.77, p = .006, R² = .21, providing support for

Hypothesis 2-3. Again, the interaction was plotted to explore its nature. As illustrated in

Figure 4.3, the interaction is in the expected direction, with participants who watched the promotion video reporting enhanced cognitive processes the higher their regulatory focus

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was (i.e., promotion focus) and participants watching the prevention video reporting

enhanced cognitive processes the lower their chronic regulatory focus was (i.e.,

prevention focus).

0.5 0.4

0.3 0.2 pro vid 0.1 pre vid Cognitive Fit Cognitive 0 -0.1 + SD - SD Regulatory Focus

Figure 4.3. Regulatory Focus X Video Frame Interaction Effect on Cognitive Fit

In order to test Hypothesis 4, the path coefficient of video frame X regulatory

focus product term associated with motivation was examined. In support of Hypothesis 4

it was significant, β = -.62, t = -2.99, p = .003, R² = .25. Figure 4.4 (next page) illustrates the nature of the interaction which is, again, consistent with expectations. Specifically, those who watched the promotion-framed video rated their motivation as stronger, the higher their regulatory focus scores were (i.e., promotion focus) while those who watched the prevention focused video rated their motivation as stronger, the lower their chronic regulatory focus scores were (i.e., prevention focus).

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0.15 0.1 0.05 0 -0.05 pro vid -0.1 pre vid -0.15 -0.2 -0.25 Motivational Engagement + SD - SD Regulatory Focus

Figure 4.4. Regulatory Focus X Video Frame Interaction Effect on Motivation

Next, in support of Hypothesis 5a, the path coefficient between the video X regulatory focus interaction term and affective reactions was significant, β = -.57, t = -

2.62, p = .010, R² = .21. Figure 4.5 depicts the nature of the interaction. As expected, those who watched the promotion-framed video reported more positive affective reactions to the training program, the higher their regulatory focus scores were (i.e., promotion focus) while those who watched the prevention focused video reported more positive reactions, the lower their chronic regulatory focus scores were (i.e., prevention focus).

0.15

0.1 0.05 0 pro vid -0.05 pre vid

AffectiveReactions -0.1 -0.15 + SD - SD Regulatory Focus

Figure 4.5. Regulatory Focus X Video Frame Interaction Effect on Affective Reactions

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Finally, Hypothesis 6a was tested. To that end, three questions from the in-lab portion of the study were used as indicators of declarative knowledge (“What is a budget?”, “What steps do you need to go through when creating a budget?” and “List as many of the things presented in the lecture that can help you reduce your driving expenses.”). Participants’ answers were coded by two independent raters and average recall scores were created. To test Hypothesis 6a, the interaction coefficient associated with declarative knowledge in the In-lab variables interaction model was explored.

Unfortunately, results did not lend support for Hypothesis 6a as the interaction term was not significant, β = .16, t = .56, p = .576, R² = .12. Thus, the dominant chronic regulatory focus X training video frame match/mismatch, did not seem to affect recall of presented information.

In addition to the recall questions given to participants after the training program, they were asked to complete a 60-second task, where they had to list specific ways in which they planned to apply in their daily lives the information they had just learned.

Based on theory, it was expected that a match between the video framing and one’s regulatory focus would lead to a larger number of points (or implementation intentions) listed by participants. To test this, a regression analysis was performed where number of listed points was regressed on video frame, regulatory focus, and their product. No significant effect was observed for this dependent variable, either (F(3,191) = .317, p =

.813).

In-lab variables mediated model—Hypotheses 5b, 5c, 6b-d. As a next step, an In- lab variables mediated model was tested, where paths were added from emotional fit,

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cognitive fit and motivation to affective outcomes and declarative knowledge. This model, equivalent to the In-lab variables interaction model, had very similar fit indices, indicating good fit to the data, χ² (141, N = 192) = 236.73, p < .001, RMSEA = .060

(.046; .073), CFI = .98, SRMR = .048. Figure 4.6 depicts the model, where only significant paths are illustrated for clarity.

Emo Fit .54** .38^ Video-Frame Affective Outcomes -.37^ .47* Regulatory .60* Cog Fit Focus -.63** .69* Recall

Video X RF -.61** .68**

Motivation

Path Coefficients (β) Between Control Variables and Endogenous Variables Endogenous Variables Covariates Emotional Fit Cognitive Fit Motivational Affective Declarative Engagement Outcomes Knowledge Positive Affect .18* .20* .26** .05 --- Current Pos Mood .29** .29** .23** -.02 --- Ethnicity ------.33*

Figure 4.6. In-lab Variables Mediated Model Note. N = 192. Direct, non-mediated paths in bold for emphasis; Non-significant paths are omitted for clarity; Video Frame coded as 1 = promotion-framed, 2 = prevention- framed; Ethnicity coded as 1 = Majority, 2 = Minority; ^p ≤ .10, *p ≤ .05, **p ≤ .01

To restate, Hypotheses 5b and 5c proposed that subjective feelings and processing fluency would partially mediate the relationship between video frame X chronic

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regulatory focus match and affective training outcomes. In order to test these

hypothesized mediating effects, the Sobel test was utilized (Hayes, 2009). Because of

statistical considerations and potential power issues associated with the Sobel test (e.g., it assumes that the sampling distribution of the indirect effect is normal, which is often not the case) (Hayes, 2009), a decision was made to use a more liberal p-value of p = .10 when testing mediated interaction effects.

First, the indirect effect of the match between video and regulatory focus on

affective outcomes through emotional fit was tested, resulting in a non-significant test

statistic, αβ = -.20, Sobel test = -1.419, p = .156, and a lack of support for Hypothesis 5b.

Next, the indirect effect from the interaction term to affective outcomes, through

cognitive indicators of fit was tested. Hypothesis 5c received support, as the indirect

effect was significant, αβ = -.30, Sobel test = -1.879, p = .060. Although not

hypothesized, the indirect effect of regulatory focus X video frame match on affective

outcomes through motivation was also calculated and results demonstrated that it was not

significant, αβ = .04, Sobel test = 0.25, p = .79. When all of the intervening mechanisms

were considered together as a set, there was a significant indirect effect from

video/regulatory focus match to affective outcomes, αβ = -.46, t = -2.30, p = .023,

indicating that this set of three intervening mechanisms (emotional fit, cognitive fit and

motivation) accounted for the video/regulatory focus—affective reactions relationship

(which became non-significant after the mediating paths were added).

To further investigate the unique importance of each intervening mechanism,

three additional models were tested where each proposed mediator was included in a

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model by itself (see Appendix P for detailed depiction of the three models). The results demonstrated that both cognitive fit (αβ = -.53, t = -2.67, p = .008) and motivation (αβ =

-.50, t = -2.87, p = .005) were significant individual mediators of the regulatory focus/training frame match and affective outcomes. The mediating role of emotional fit approached conventional levels of significance (αβ = -.33, t = -1.73, p = .085). Taken together, these analyses show that when examined individually, each proposed mediator plays an important role in explaining regulatory fit effects. When they are all included in one model (hypothesized mediated model), however, the unique contribution of each proposed mediator does not reach conventional levels of significance; still, as a set, the three proposed intervening mechanisms account for fit effects on affective reactions. One potential reason for such findings might have been partial redundancy among the proposed mediators due to their high intercorrelations (remofitXcogfit = .68, remofitXmotivation =

.85, rcogfitXmotivation = .77).

Hypotheses 6b, 6c, and 6d were tested next. They stated that processing fluency,

attentional focus, and motivation would partially mediate the relationship between

video/regulatory focus match and declarative knowledge. Recall that processing fluency

and attentional focus were combined into a single cognitive fit construct. Thus,

Hypotheses 6b and 6c were combined into one Hypothesis 6bc and tested jointly. A Sobel test demonstrated a significant video frame X regulatory focus interaction indirect effect on declarative knowledge through cognitive fit, αβ = -.43, Sobel test = - 1.66, p = .096, providing support for Hypothesis 6bc. Although this mediation analysis may have appeared unnecessary given the lack of direct association between the interaction term

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and declarative knowledge, recent developments in the area of mediated effects have

noted that the conditions required for a mediation effect to be present outlined by Baron

and Kenny (1986) should not be interpreted in such absolute terms (Hayes, 2009).

To test Hypothesis 6d, the significance of the indirect effect from the interaction

term to declarative knowledge through motivation was calculated. The results of the

Sobel test did not support this hypothesis, αβ = .17, Sobel test = .691, p = .490. Finally,

the indirect effect of regulatory focus/video frame match on declarative knowledge,

through the set of intervening mechanisms did not reach statistical significance, αβ = -

.22, t = -1.46, p = 1.46. Thus, among the intervening mechanisms, only cognitive

processes seem to have an impact on information recall.

Follow-up Variables Model

Before proceeding with testing the full hypothesized model including the follow-

up survey variables, several preliminary analyses were conducted to ensure that the

twenty participants who did not complete the follow-up survey were generally similar to

those who did. Specifically, a series of independent samples t-tests were conducted, comparing the two groups on all focal dependent variables’ indicators. None of the t-tests

were significant (all p’s > .266), indicating that the 20 participants who dropped out did not seem to be different from those who did complete the follow-up survey. Thus, the

Follow-up variables measurement model specification proceeded.

Follow-up variables measurement model. The measurement model was specified using subscale means as indicators for a six-factor model, comprising the following latent constructs: emotional fit, cognitive fit, motivation, in-lab affective reactions, in-lab

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information recall, and follow-up affective reactions. In addition to allowing the same pairs of disturbances to covary as the ones discussed in the earlier preliminary CFAs, the disturbance covariances for several pairs of identical scales used in both the in-lab experiment and the follow-up survey were also freed. The model resulted in a reasonable fit, χ² (163, N = 172) = 414.34, p < .001, RMSEA = .095 (.084; .11), CFI = .96, SRMR =

.072. Figure Q.1 in Appendix Q depicts the Follow-up variables measurement model.

Follow-up variables interaction model. An interaction model where all latent variables (emotional fit, cognitive fit, motivation, in-lab affective reactions, in-lab information recall, and follow-up affective reactions) were regressed on the manipulated video-frame condition, participants’ chronic regulatory focus, and the video X regulatory focus interaction term, as well as on two covariates (general positive affectivity and concern with money) was tested next. This model fit the data relatively well, χ² (244, N =

172) = 522.82, p < .001, RMSEA = .082 (.072; .091), CFI = .96, SRMR = .073. Relevant path coefficients were examined to address the study hypotheses (see Figure 4.7 on the next page).

The path coefficients of this model were consistent with those observed in the In- lab variables interaction model, though somewhat stronger. One notable difference was that the video/regulatory focus interaction term associated with emotional fit was significant, proving some support for Hypothesis 1, β = -.52, t = -2.17, p = .031, R² = .16.

The remaining interaction terms associated with cognitive fit, β = -.79, t = -3.15, p = .002,

R² = .16, motivation, β = -.67, t = -2.98, p = .003, R² = .21, and in-lab affective reactions,

β = -.65, t = -2.78, p = .006, R² = .17, were all significant and in the expected direction.

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As in the in-lab variables model, the interaction term associated with declarative

knowledge was not significant, β = .02, t = -.07, p = .944, R² = .01

Regulatory Video-Frame Video X RF Focus

-.10 -.01 -.10 -.07 -.12 -.11

.54* .79** .76** .67** .07 .59*

-.52* -.79** -.67** -.65** .02 -.53*

Follow-up In-lab Affective Emo Fit Cog Fit Motivation Recall Affective Outcomes Outcomes

.65** .69** .67** .15 .15

.68** .68** .18 .69**

.72** .29* .59**

.11 .61**

.61**

Path Coefficients (β) Between Control Variables and Endogenous Variables Endogenous Variables Covariates Emotional Cognitive Motivational In-lab Declarative Follow-up Fit Fit Engagement Affective Knowledge Affective Outcomes Outcomes Pos Affect .32** .29** .34** .33** --- .22* Money Con ------.15**

Figure 4.7. Follow-up Variables Interaction Model Note. N = 172. Interaction term coefficients in bold for emphasis; Video Frame coded as 1 = promotion-framed, 2 = prevention-framed; ^p ≤ .10, *p ≤ .05, **p ≤ .01

Hypothesis 7a, which stated that a match between video frame and one’s regulatory focus would enhance follow-up affective reactions, was tested by examining

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the relevant path coefficient in the Follow-up variables interaction model. In support of

Hypothesis 7a, the interaction term associated with follow-up affective reactions was statistically significant, β = -.53, t = -2.16, p = .03, R² = .13. Figure 4.8 depicts the interaction. Consistent with expectations, among the participants who watched the promotion-framed video, those who had higher regulatory focus scores (i.e., dominant

promotion focus) also had more positive reactions to the training program later. In

contrast, among those who watched the prevention-framed video, participants with lower

regulatory focus scores (i.e., dominant prevention focus) reported more positive reactions

two to three weeks later.

0 -0.05

-0.1 -0.15 pro vid up Affective -

Reactions -0.2 pre vid

Follow -0.25 -0.3 + SD - SD Regulatory Focus

Figure 4.8. Regulatory Focus X Video Frame Interaction Effect on Follow-up Affective Reactions

Follow-up variables mediated model. Finally, a mediated model (equivalent to the

interaction model) was tested. In this model, paths were added from emotional fit,

cognitive fit and motivation to in-lab affective reactions, declarative knowledge, and

follow-up affective reactions, and from in-lab affective reactions and declarative

knowledge to follow-up affective reactions. This mediated model fit the data adequately,

χ² (244, N = 172) = 520.36, p < .001, RMSEA = .081 (.072; .091), CFI = .96, SRMR =

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.071. Figure 4.9 depicts the mediated model, showing standardized values of the statistically significant path estimates.

Emo Fit .54* .48**

Video-Frame In-Lab Affective Outcomes .69** -.52* .28^ Follow-up Regulatory .78** Cog Fit Affective Focus Outcomes -.79** .63^ Recall

Video X RF -.67** .75**

Motivation

Figure 4.9. Follow-up Variables Mediated Model Note. N = 172. Direct, non-mediated paths in bold for emphasis; Non-significant paths are omitted for clarity; Video Frame coded as 1 = promotion-framed, 2 = prevention- framed; ^p ≤ .10, *p ≤ .05, **p ≤ .01.

Again, Sobel tests were used to test the significance of separate indirect effects. In contrast with results obtained when the in-lab variables only model was tested, here the indirect path from video/regulatory focus interaction to in-lab affective reactions through emotional fit was significant, αβ = -.250, Sobel test = -1.77, p = .077, while the indirect effect through cognitive fit was not, αβ = -.221, Sobel test = -1.49, p = .137. Considering

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the model as a whole, an overall significant indirect effect from the interaction term to in- lab affective reactions through the set of intervening mechanisms was revealed, αβ total =

-.60, t = -2.86, p = .005.

Regarding declarative knowledge, the indirect effect from the interaction term to recall through cognitive fit was non-significant, αβ = .498, Sobel test = -143, p = .153, as

was the one though motivation, αβ = .027, Sobel test = .11, p = .912. Overall, however,

the indirect effect from video/regulatory focus match to declarative knowledge was

significant, αβ total = -.31, t = -1.75, p = .082.

Finally, Hypothesis 7b proposed that there would be a significant indirect effect from the video/regulatory focus interaction to follow-up affective reactions. To test this hypothesis the overall indirect effect coefficient was examined. In support of Hypothesis

7b, the indirect effect estimate was significant, αβ total = -.57, t = -2.78, p = .006, indicating that the set of intervening mechanisms, in-lab affective reactions, and declarative knowledge mediated or accounted for the relationship between video/regulatory focus match and follow-up affective reactions (which became non- significant after the mediation paths were added). Table 4.14 (next page) summarizes

hypothesis testing results.

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Table 4.14

Summary of Hypothesis Testing Results

Interaction Hypotheses Supported/Not Supported Hypothesis 1 A match between one’s chronic regulatory orientation Partially Supported and the frame and presentation style of the training • Marginal in In-lab model program will enhance subjective feelings of • Significant in Follow-up model “rightness,” enjoyment, and liking.

Hypothesis 2-3 A match between one’s chronic regulatory orientation Fully Supported and the frame and presentation style of the training program will enhance processing fluency and attentional focus.

Hypothesis 4 A match between one’s chronic regulatory orientation Fully Supported and the frame and presentation style of the training program will enhance participants’ motivation to budget and learn about money management.

Hypothesis 5a A match between one’s chronic regulatory orientation Fully Supported and the frame and presentation style of the training program will enhance affective reactions towards the training program.

Hypothesis 6a A match between one’s chronic regulatory orientation Not Supported and the frame and presentation style of the training program will enhance training-related information recall.

Hypothesis 7a A match between one’s chronic regulatory orientation Fully Supported and the frame and presentation style of the training program will enhance affective reactions towards the training program two to three weeks after the in-lab session.

Mediation Hypotheses Supported/Not Supported Hypothesis 5b Subjective feelings of “rightness,” enjoyment, and Not Supported liking will partially mediate the relationship between • Not significant in In-lab model fit and affective outcomes. • Weak in Follow-up model

• When part of the set of three mediating mechanisms, set is significant

Hypothesis 5c Processing fluency partially will mediate the Partially Supported relationship between regulatory fit and affective • Weak in In-lab model outcomes. • Not sig in Follow-up model

• When part of the set of three mediating mechanisms, set is significant

Hypothesis 6bc Processing fluency and attentional focus will partially Partially Supported mediate the relationship between regulatory fit and • Weak in In-lab model information recall. • Not sig in Follow-up model

Hypothesis 6d Motivation will partially mediate the relationship Not Supported between regulatory fit and information recall.

Hypothesis 7b There will be a significant indirect effect from fit to Fully Supported follow-up affective outcomes, through the intervening mechanisms and in-lab affective reactions.

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Additional Analyses

Several additional indicators of training program utility and effectiveness were

collected during the follow-up survey. These include whether participants had created

their own budget as a result of the training program (and if not, whether they intended to

create one) and whether they had used some of the expense-reduction strategies presented

during the video (and if yes, which ones).

Creating a Budget

To investigate whether participants who experienced regulatory fit were more

likely to create a budget than those experienced regulatory non-fit, a binary logistic

regression was conducted in SPSS v. 19. Only participants who reported they did not

have a budget at the time of the experimental manipulation were included in the analysis

(N = 102). Overall, of the 102 who did not have a budget, 62 created one after the money-

management training and 40 did not. Only 3 participants of the 40 (7.7%) who had not

yet created a budget stated they did not plan to do so in the future. To examine whether

the difference in budget creation was attributable to people’s regulatory focus and the

version of the video they had watched, a dichotomous ‘created a budget’ variable (0 = no,

did not create; 1 = yes, created) was regressed on video-frame, dominant regulatory focus

(RFQ difference score), their product, as well as on concern with money and age which

served as covariates. The model chi-square was χ²(5, N = 102) = 22.40, p < .001 and the deviance score -2log likelihood (Dk) was 108.33. The logistic regression coefficients for

each predictor in this model are listed in Table 4.15 (next page).

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Table 4.15

Regression Coefficients for Binary Logistic Regression Predicting Budget Creation

Predictor B S.E. Wald df p Constant .183 1.388 .017 1 .895 Video -.958 .506 3.583 1 .058 Regulatory Focus 2.252 .958 5.519 1 .019 Video X Regulatory Focus -1.079 .555 3.785 1 .052 Controls Concern with Money .564 .180 9.795 1 .002 Age -.041 .044 .867 1 .352 Note: N = 102. DV = Created a budget, 0 = No, 1 = Yes.

Because Cohen, Cohen, West, and Aiken (2003) express concern about using the

Wald test reported in most statistical software as a logistic regression coefficient significance test, a deviance scores (Dk) difference test was conducted (Cohen et al.,

2003). When the interaction term was removed from the regression model, the deviance score of the new model was significantly higher, Dk-i = 112.513, χ² (1) = 4.187, p = .041, indicating that the video frame X regulatory focus interaction effect is significant. Figure

4.10 (next page) depicts the interaction. In line with expectations, of the participants who watched the promotion-framed video, those with higher regulatory focus scores (i.e., dominant promotion focus) were more likely to create a budget than those with lower scores. This pattern was not observed for participants who watched the prevention video.

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1 0.9 0.8

0.7 0.6 Pro Video 0.5 Pre Video 0.4 0.3

Probability ofProbability success 0.2 0.1 0 Low Regulatory Focus High Regulatory Focus

Figure 4.10. Binary Logistic Regression for Regulatory Focus X Video Frame Interaction Effect on Budget Creation Note. N = 102.

Additionally, a series of independent samples t-tests were conducted comparing those who created a budget to those who did not on other focal dependent variables. The t-tests revealed that those who created a budget also experienced more emotional (t(100)

= -2.917, p = .004) and cognitive fit (t(100) = -2.847, p = .005), reported higher motivation (t(100) = -4.086, p = < .001) and had more positive affective reactions both during the training (t(100) = -3.908, p < .001) and several weeks later (t(100) = -4.359, p

< .001). Table 4.16 (next page) lists means and standard deviations for each unit-

composite variable across groups.

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Table 4.16

Means and (Standard Deviations) for Focal Dependent Variables by “Created a Budget” Group

Unit composite Created a budget Did not created a budget

Emotional Fit .230 -.247 (.792) (.830) Cognitive Fit .173 -.241 (.614) (.852) Motivational Engagement .336 -.387 (.876) (.867) In-lab Affective Reactions .298 -.328 (.823) (.736) Follow-up Affective Reactions .216 -.404 (.678) (.735) Note. N created budget = 62, N did not create budget = 40. All group differences are statistically significant.

Using Expense-Reduction Strategies

Next, the extent to which participants used the expense-reduction strategies presented during the training video was investigated. Of the 172 participants who completed the follow-up survey, 120 (62.5%) reported they had used some strategies they learned during the training. To investigate whether this difference could be attributed to the experience of regulatory focus/video frame match, a dichotomous ‘strategy use’ variable (0 = did not use, 1 = used) was regressed on video version, dominant regulatory focus, and the product term. As shown in Table 4.17 (next page), none of the binary logistic regression coefficients were statistically significant. The deviance score for the overall model was Dk = 204.257.

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Table 4.17

Regression Coefficients for Binary Logistic Regression Predicting Strategies Use

Predictor B S.E. Wald df p Video -.326 .342 .907 1 .341 Regulatory Focus -.581 .596 .945 1 .331 Video X Regulatory Focus .184 .386 .228 1 .633 Constant -.393 .538 .534 1 .465 Note. N = 171; DV = Strategies Use, 0 = No, 1 = Yes.

As suggested by Cohen et al. (2003), to determine the unique contribution of the interaction term, a second model was tested, with the interaction term excluded. The new deviance score (Dk-i) was 204.485. The chi-square difference test comparing this model to the previous one did not reach conventional levels of significance (χ² (1) = .228, p =

.633).

However, results were in an expected direction when those who used the strategies were compared to those who did not on key dependent variables. Specifically, those who used the strategies also experienced significantly more emotional (t(169) = -

2.031, p = .044) and cognitive fit (t(169) equal variances not assumed = -2.801, p = .007), as well as more motivation (t(169) = -2.637, p = .012), and reported significantly more positive affective reactions both during the training (t(169) = - 2.019, p = .045) and a few weeks later (t(169) equal variances not assumed = -2.187, p = .032). Table 4.18 (next page) lists the means and standard deviations for each unit-composite variable across groups.

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Table 4.18

Means and (Standard Deviations) for Focal Dependent Variables by “Strategies Used” Groups

Unit composite Used strategies Did not use strategies

Emotional Fit .094 -.197 (.841) (.890) Cognitive Fit .129 -.261 (.655) (.898) Motivational Engagement .120 -.293 (.920) (.974) In-lab Affective Reactions .087 -.205 (.866) (.859) Follow-up Affective Reactions .010 -.227 (.680) (.969) Note. N used strategies = 120, N did not use strategies = 50; Values in parentheses are Standard Deviations. All group differences are statistically significant.

To further examine the extent to which participants used the trained material, the number of specific strategies they had used was regressed on video frame, regulatory focus scores (both promotion and prevention), and on three two-way and one three-way interaction terms in a hierarchical linear regression (only participants who reported they had used the strategies were included, N = 120). The Step 2 model (with the three two- way interaction terms added) was statistically significant (F(6, 100) = 2.189, p = .05) with only the prevention X video coefficient term being significant, β = .770, t = 2.499, p

= .014, indicating a significant video frame X prevention score interactive effect on number of strategies used. Figure 4.11 (next page) illustrates the plotted interaction which is in line with expectations. Specifically, among participants who watched the prevention-framed video, those with higher prevention scores reported using more strategies from the video in their lives after the completion of the training program. No such effect was observed for those who watched the promotion-framed video. 127

2.5

1.5 pro vid 1 pre vid 0.5 Number of Strategies 0 +SD -SD Prevention Score

Figure 4.11. Regulatory Focus X Video Frame Interaction Effect on Number of Strategies Used

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CHAPTER V

DISCUSSION

Summary of Results

The purpose of the current study was twofold. On the one hand, it sought to

explore a novel case of an attribute-treatment interaction effect (i.e., regulatory fit) on training effectiveness outcomes in the context of Gully and Chen’s (2010) theoretical framework. On the other, it applied research on regulatory fit theory (Higgins, 2000) to a training situation, an area not yet fully investigated by regulatory focus and regulatory fit researchers.

To accomplish these goals, a promotion-framed and a prevention-framed version of the same training program were video-taped and presented to participants in a between-subjects quasi-experimental design. Participants’ chronic regulatory focus was assessed prior the experimental manipulation and their reactions to the money management training program and their memory of the training content were assessed both immediately after the video presentation and a few weeks later.

It was hypothesized that a match between the participant’s dominant regulatory focus and the framing of the training video would result in enhanced subjective feelings regarding the training, enhanced ease of processing and attentional focus, as well as stronger motivation. These proposed mediating variables in turn were expected to lead to

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more positive affective reactions towards the training as a whole, and to result in better recall of the trained material. Finally, it was expected that these effects would extend beyond the in-lab training session and last for at least a few weeks, resulting in tangible behavioral change (e.g., creating a budget, applying learned material in daily life).

The hypothesized model was generally supported by the data, with a few exceptions (see Table 4.14). A match between trainee chronic regulatory focus and the training framing significantly affected the proposed intervening mechanisms, as indicated by the learners’ enhanced processing fluency, attentional focus, and motivational engagement. Although the interaction effect on the proposed mediating factor of subjective feelings only approached statistical significance, the path coefficient was in the expected direction. That is, participants experiencing fit had more positive subjective feelings than those who experienced non-fit. Additionally, the regulatory focus/video frame interaction had a significant direct effect on both in-lab and follow-up affective reactions but not on recall of training content.

Although the mediation hypotheses were not as uniformly supported, there is evidence that the proposed attribute-treatment interaction had a significant indirect effect on both in-lab affective reactions and declarative knowledge. Even though the mediating effect of cognitive processes on the fit-affective reactions relationship was only marginally significant, when the set of intervening mechanisms was considered, there was a significant indirect effect from regulatory focus/video match on in-lab affective reactions. Thus it might be the case that even though each explanatory mechanism is not a unique significant mediator when the three processes are included in the same model,

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the three proposed mediating variables work as a set to explain regulatory focus/training frame fit effect on affective outcomes. What is more, even though regulatory fit was not significantly related to declarative knowledge, its indirect effect on recall through cognitive processes was significant, suggesting that regulatory fit might play a role in information recall, though not directly.

Finally, the follow-up mediation model revealed a significant indirect effect of fit on follow-up affective reactions, providing evidence that the training video had long- lasting positive effects on participants, especially when the video framing and presentation matched participants’ chronic regulatory focus. Additional analyses further showed that participants who experienced fit were also more likely to create a budget after the training session and also applied in their own lives a greater number of strategies they had learned during the training, as indicated by specific applied strategies listed by participants. Thus, the current study supports the general conclusion that the better the match between one’s chronic regulatory focus and the framing and presentation style of a training initiative, the more effective that training is, with observable effects on a variety of effectiveness indicators, including cognitive, affective, and behavioral constructs assessed both immediately after the training and a few weeks later. Specific implications for theory and practice are considered next.

Contributions and Implications

A major contribution of the current study is that it integrates two bodies of literature which have examined similar constructs and processes but have rarely been combined. In particular, research on attribute-treatment interactions has noted that

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learners’ characteristics (ability, personality, etc.) might interact with characteristics of

the training context (design, content, structure, presentation style, etc.) to affect training

outcomes. At the same time, regulatory fit theory has demonstrated that people’s

regulatory focus (a basic motivational individual difference) interacts with environmental

characteristics (type of goal, type of expected reward, content framing, etc.) to impact

different performance indicators and affective reactions. Thus, both literatures have been

interested in the interplay between human characteristics and contextual demands and

how this interplay affects subsequent behavior; both have studied similar processes which

they have given different names.

In this study, the phenomenon of regulatory fit described by Higgins (2000) was

interpreted as an example of an attribute-treatment interaction as defined in the training

literature, allowing for the integration of theory and research from both the training and

regulatory fit literatures, and consequently enriching our understanding of both. Thus, the

current research findings begin to fill gaps in the training literature noted by Gully and

Chen (2010) by empirically testing a framework through which attribute-treatment interactions affect training outcomes. At the same time, the regulatory focus and regulatory fit literature is expanded by examining the regulatory fit process as a whole, applying it to a new field, and exploring its effects across a more extended period of time than had typically been empirically studied by researchers within this theoretical perspective. The more applied domains of human resource management and of learning and development (L & D) practice, can also benefit from the results of this research

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project as understanding how and why a certain training program works is a crucial first

step for creating a better-trained workforce.

Training

In their review of the training literature, Gully and Chen (2010) urge researchers

to spend more time empirically exploring attribute-treatment interactions and especially

to focus on answering questions as to why and how certain variables independently and

interactively affect training outcomes. In fact, a quick Google Scholar search shows that

Gully and Chen’s chapter has already spurred some research, as several papers have

already cited it. The current study followed suit and empirically tested a model depicting

the interactive effects of a training video frame and an individual’s regulatory focus on a

series of training outcomes. By doing so, it makes several valuable contributions to the

training literature.

Value of considering ATI’s. First, the current study attests the value of attribute- treatment interactions by showing that the predictive validity of regression models for training outcomes increases significantly when an interaction term is added. In other words, when a training program is designed in a way that matches the learner’s basic motivational orientation, training outcomes including both declarative knowledge and affective reactions, are enhanced and so are training transfer indicators such as follow-up

affective reactions and behavioral change.

Although the trainee-centered perspective on training was introduced more than a

half century ago (Cronbach, 1957), the real value of customizing training initiatives to

learner characteristics seems not yet fully appreciated by L & D professionals. One

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reason for this might be the relatively sparse number of empirical studies that show the

utility of spending the time to customize training programs (Campbell & Kuncel, 2002;

Gully & Chen, 2010). Often, if practitioners do not see the immediate monetary value in

designing for the learner, they are reluctant to invest money for training initiatives that

are likely to be initially more expensive and effortful (e.g., designing at least two versions

of the same program to match individually varying characteristics of the trainees). By

demonstrating that promotion vs. prevention framing of a training session affects people

differently based on their regulatory foci and has the potential to enhance important

effectiveness indicators, the current study clearly shows the value of training

customization for optimal results. Even though presenting a single version of a training

program might be a bit easier and less time-consuming, the time and effort to tailor it to learners’ motivational orientation is easily justifiable when large numbers of individuals are trained over time.

Explanatory mechanisms. Importantly, the current study also empirically

investigated explanatory mechanisms through which training outcomes may be affected

by characteristics of the training, the trainee, and the interplay of those characteristics. In

the current study, the ATI’s effect on affective reactions right after the training, and a few

weeks later, was fully mediated by the set of proposed explanatory mechanisms

consisting of subjective feelings, processing fluency, attentional focus, and motivation.

Additionally, there was some evidence for an indirect effect of video frame X regulatory

focus match on declarative knowledge through the cognitive processes of processing

fluency and attentional focus. Thus, investigating exploratory mechanisms proved to be

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very informative because without assessing them, important indirect links would have

been missed. What is more, by understanding how a phenomenon works and by knowing

more about the underlying processes that drive it, we can develop specific strategies

targeted towards improving the mediating processes as well as the end result.

Regulatory focus as an individual difference. Although most research on ATIs has

focused on ability-related constructs, such as cognitive ability, learners’ control, and

metacognition, the importance of other personality and motivational characteristics has

been noted as well. Goal orientation (in particular, the distinction between performance

and mastery goal orientation) is probably one of the most widely researched motivational

characteristics in the training literature and some important advances in understanding its

role in the training context have been made. For example, Schmidt and Ford (2003)

showed that goal orientation is a motivation-based individual characteristic that

differentially affects learning outcomes, based on training design. In their study,

participants with a low performance-avoidance goal orientation benefited more from meta-cognitive training than did participants with a high performance-avoidance goal orientation. Thus, these authors demonstrated the need for considering motivational characteristics in training contexts and the potential importance of training customization.

Interestingly, there is some conceptual and empirical overlap between regulatory focus and goal orientation. The conceptual overlap occurs because of the further subdivision of performance and mastery goals according to whether they are pursued with an approach or an avoidance orientation (Elliot & McGregor, 2001). A closer look at measurement instruments suggests that the construct of goal orientation as it is typically

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measured more closely matches Lockwood et al.’s (2002) GRFM operationalization of regulatory focus than Higgins’ (1997) RFQ measure. Both goal orientation measures and

Lockwood’s GRFM include statements regarding positive and negative outcomes related to current goal-directed behavior. In contrast, the RFQ focuses on past experiences and general concerns about ideals or duties. The similarities and differences between the measures are empirically illustrated by the stronger positive correlations of performance and mastery approach with the GRFM promotion subscale, and of performance and mastery avoidance with the GRFM prevention subscale, compared to much weaker correlations of the promotion and prevention subscales of the RFQ with the goal orientation scales. In the current dissertation, additional data on this and related issues of measurement were collected for exploratory purposes. A more extensive treatment of these issues is contained in the material of Appendix L.

From a conceptual perspective, as opposed to goal orientation, which is especially relevant to achievement situations like training, the construct of regulatory focus is much more basic and broader. Regulatory focus theory distinguishes between two fundamentally different motivational orientations which are associated with different emotional, cognitive, and behavioral experiences (Higgins, 1997). As a result, promotion and prevention focused individuals respond differently to environmental stimuli and have different natural preferences. Because regulatory focus distinguishes people on such a core self-regulatory level, this individual characteristic should be central in all person- centered frameworks of behavior. The advertising literature has already noted the role of regulatory focus in consumer choices (e.g., Florack & Scarabis, 2006; Lee & Aaker,

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2004; Wang & Lee, 2006), and the literature on leadership demonstrates that regulatory

focus may play an important role in leadership processes (Benjamin & Flynn, 2006; Lord

& Brown, 2004).

However, very few studies have examined the role of regulatory focus when it

comes to learning (see Zhao, 2006 for an exception). The current study does just that—it

presents evidence for the importance of regulatory focus for the field of learning and

development as well. In short, when a training program emphasizes goals and values

consistent with one’s regulatory focus (at least, as assessed with the RFQ), the training

content is processed more easily, people pay more attention to the material being

presented, they are more motivated to learn it, and consequently have more positive

training outcomes including overall affective reactions and training transfer. Specific

implications for the regulatory fit literature are discussed next.

Regulatory Fit Theory

This research project makes important contributions to regulatory fit research as

well. In addition to applying regulatory fit to a the novel context, i.e., training, the current study also empirically tests the regulatory fit process in its entirety (through a chain of

mediated paths) and over time and thus deepens our understanding of this process.

The “fit” process. Regulatory fit effects on different outcomes have been demonstrated in numerous contexts already (see Molden et al., 2008 for a review).

However, to fully understand something, one needs to understand the underlying

processes through which it works. In his work, Higgins (2000, 2005, 2006) has argued

that regulatory fit enhances feelings of “rightness” and engagement, and creates a sense

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of value, and that these psychological states in turn affect the favorable outcomes

associated with regulatory fit. However, only a few empirical studies have

mathematically tested for mediation effects (e.g., Cesario & Higgins, 2008; Ritchie,

2009) and a lot more work is needed to fully understand the causal relationships during a

regulatory fit experience. By looking at the regulatory fit process as a whole—that is,

spanning from a manipulation which creates a fitting or non-fitting situation, through assessing several potential intervening mechanisms, to looking at a series of cognitive and affective outcomes, both at the time of the training and after some time has passed, the current study makes an effort to identify causal links among different processes and outcomes. The empirical results suggest that regulatory fit is a process that first influences a series of emotional, cognitive, and motivational processes which in turn have an impact on emotional and behavioral change.

Even though a number of studies have demonstrated fit effects on feelings of

“rightness” and enjoyment (e.g., Camacho et al., 2003; Cesario & Higgins, 2008; Freitas

& Higgins, 2002), such results were only marginally supported here, after the effects of general positive affect and current positive mood were controlled. Although in the current study support for the mediating role of subjective feelings was only marginal, the mediating role of cognitive processes and motivational engagement were well supported.

One potential interpretation of this pattern of results is that regulatory fit is generally a non-affective process that acts on our cognition and motivation and has weaker impact on emotional experiences. This interpretation is also supported by findings that participants’ mood, assessed shortly after the experimental video was presented, was not affected by

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regulatory fit, as demonstrated by supplementary hierarchical multiple regression analyses, which are reported in Appendix R. As a whole, by investigating a full regulatory fit model, the current study goes a step further than past research and its results suggest that attentional focus, cognitive fluency, and motivation are at the core of regulatory fit effects. Indeed, it is interesting that Higgins himself downplays the role of emotions in the regulatory fit experience.

It is not clear, however, the extent to which this pattern of results generalizes to the application of regulatory fit to other types of contexts, whether they are training contexts or other types of situations to which the theory might be applied. The content of the particular type of training used in this study is unlikely to be highly emotionally engaging to trainees. This might not be the case for training efforts that are directed at changing more deeply seated and potentially emotionally charged attitudes or values, rather than imparting information. It is an open question whether feelings would play a stronger mediating role in those types of training situations.

Fit effects over time. Surprisingly, regulatory fit effects over time have not been investigated much (see Spiegel et al., 2004 for an exception) and the duration of this phenomenon has been unknown. Thus, one of the most important contributions of this project is that it demonstrates that the effects of regulatory fit are not merely transient but initial differences associated with fit are maintained outside the lab and demonstrate an impact on daily behaviors some time after the experimental manipulation has taken place.

Specifically, the positive outcomes of video/regulatory focus match were observed not only during the training session, but also two to three weeks after it was complete.

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Additionally, participants who watched a video that matched their chronic regulatory

focus were more likely to create their own budget and also utilized more of the learned

material. In this way, the current results validate the significance of “regulatory fit” and

its potential utility for the learning and development field, where behavioral change is of

key importance.

Learning and Development Practice

Taken together, the contributions described thus far have great potential to impact the field of learning and development and of human resource management practice in general. As human capital is often the most important asset of a company, and because continuous training and development are vital for a competitive advantage, understanding how to design an optimally effective training is of crucial importance. Before effective training can be designed, however, one needs to understand how and why certain methods work and others do not; one needs to understand the underlying processes that go into a training initiative. The current study helps to do just that.

By empirically testing a complete model of an attribute-treatment interaction, the current research provides an understanding of the full training process and it can thus provide a framework for future developments in the area. For example, by knowing that cognitive processes like processing fluency and attentional focus are of crucial importance for training effectiveness, efforts can be focused on particularly enhancing them. Additionally, the current study demonstrates the value of regulatory focus and regulatory fit theories for the training field, as well as for applied psychology in general.

Although research utilizing these theories has flourished over the past two decades, they

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have not yet been fully applied by HR professionals. By showing that a training program can be designed to match one’s basic motivational concern and that this fit results in a variety of training effectiveness indicators, including training transfer, the current study clearly demonstrates the practical potential of these constructs (i.e., regulatory focus and regulatory fit).

Limitations and Future Research

Although the current study makes important contributions to both theory and practice, several limitations deserve attention. In what follows, design and analysis issues are discussed as well as some general gaps in the relevant literature. Directions for further research that can remedy some of the existing problems are suggested.

Study Design

In-lab experimentation. Overall, in-lab experimental designs have often been criticized for their lack of ecological validity. However, efforts were undertaken in this research project to design the in-lab portion of the study in a way that closely matches a real-life training situation. Specifically, learners listened to a 15-minute lecture—a time span which is typical for in-class presentations in which instructors break up lecture material into 10- to 15-minute chunks interrupted by other activities like discussions, problems, etc. Next, learners completed a series of questions about the lecture content.

These questions included recalling some important points and generating ways to apply the lecture content — again, activities that usually occur during a typical training session.

To further make the experience realistic, at the end of the experiment participants were encouraged to use what they had learned in their daily lives. The extent to which they did

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so was assessed a few weeks later. Even though conducting a similar experiment in a real-life training context (e.g., during an actual class or as a part of a real training initiative) might have been even better, results from the current study can still be generalized to real-world situations, as special care was taken to create a typical learning environment.

Because the proposed model received general support, future research should further explore this training framework and apply it to different types of training contexts, both in educational institutions and in organizations. Additionally, it could be interesting to see whether any topic can be framed in a promotion vs. prevention way. Perhaps some topics like driving or other dangerous behaviors should be trained with greater caution and emphasis should be placed on issues of vigilance and safety. For such topics it might be better to situationally induce a prevention regulatory focus (see Molden et al., 2008 for a review of different manipulation strategies) so that all participants experience “fit” and benefit equally well from such a training initiative.

Regulatory focus measurement issues. It was discussed in Chapter IV that

Higgins’ RFQ was used to test for regulatory fit effects in this study, although the

Lockwood et al.’s GRFM was also administered to participants. There were two main reasons for this decision. First, the RFQ seemed conceptually more appropriate in terms of its emphasis on past experiences with promotion and prevention goal pursuit strategies, as well as its emphasis on a broader concern with hopes and aspirations vs. duties and obligations. Related to that, because there is evidence that it is not impacted much by regulatory focus manipulations (Latimer et al., 2008), the RFQ seemed more

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suitable for a situation where promotion vs. prevention framing was used as part of the

experimental manipulation. The second reason for choosing the RFQ over the GRFM

was that preliminary analysis did not show much support for the hypotheses when the

GRFM was used as an indicator of regulatory focus. Additional analyses and

relationships between the two regulatory focus measures and other related constructs are

discussed in Appendix L.

As noted in Appendix L, the RFQ promotion and prevention subscales seem to be

conceptually different from the GRFM promotion and prevention subscales. For example,

although the correlations are statistically significant, the two promotion scales correlate

only modestly with each other (r = .45) and the two prevention scale scores are even negatively correlated (r = - .23). This lack of consistency across regulatory focus measures has been noted by others (Summervile & Roese, 2008) and has been attributed to differences in how regulatory focus is defined. While the GRFM takes an end-point perspective, where the presence or absence of positive and negative outcomes are of main importance, the RFQ is based more on the values and ideals that promotion and prevention focused individuals have (Summervile & Roese, 2008). Even though this difference in definitions might be the reason why these measures are so weakly related, such an inconsistency between scales that are assumed to assess one overall construct creates a disconnect in the regulatory focus literature and makes it difficult to talk about a single regulatory focus construct when different measures are associated with different results. Efforts should be taken to differentiate the constructs these measures are

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assessing and to better understand when and why certain effects occur in research utilizing either one of them.

Additionally, the differences in operationalization should be taken in consideration when developing hypotheses and deciding on the appropriate regulatory focus measure. For example, if rewards and punishments will be emphasized during a task, the GRFM might be the better option. However, if a concern for general ideals and hopes or duties and obligations will be emphasized, the RFQ may prove more fruitful. A necessary next step in the regulatory focus and fit literatures is the development of an assessment tool which encompasses the regulatory focus construct as a whole.

Implicit measures. Another measurement issue in this study was the limited number of implicit measures. In particular, there was only one implicit measure assessing processing fluency. This measure was based on participants’ response times in rating each of 12 video-consistent or inconsistent items (see Appendix K). Although the results based on this measure were not consistent with hypotheses, an interesting effect was revealed. Specifically, among those who watched the prevention video, participants with stronger promotion focus rated the promotion-framed items faster than those with a weaker promotion focus suggesting that a match between one’s chronic regulatory focus and the framing of the items impacted participants’ response times. Unfortunately, such effects were not observed for the prevention-framed items, and thus no specific conclusions can be drawn based on this implicit measure.

No implicit measures were used to assess attentional focus and motivational engagement. Demonstrating that the model supported here works on an implicit level as

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well, would have been an even stronger argument for the validity of the intervening

mechanisms presumed to be at work here. Unfortunately, implicit measures are usually

difficult to administer and obtained data is cumbersome to analyze and interpret. Future

research should focus on making implicit measures more user-friendly so that they are more readily incorporated by a larger number of researchers, as implicit data may provide invaluable information regarding underlying explanatory processes.

Issues Related to Choice of Statistical Analysis

Hypothesis testing was conducted through structural equation modeling utilizing

LISREL v. 8.80 (Jöreskog & Söbom, 2003). Because of the complexity of the regulatory focus construct, a decision was made to calculate dominant chronic regulatory focus by subtracting participants’ RFQ prevention scores from their RFQ promotion scores and use that score as a predictor. Although such a decision was made, the issues associated with difference scores are noted and additional analyses were conducted to further explore the nature of the fit effects presented in Chapter IV. Specifically, Appendix M

presents a series of hierarchical regression analyses where participants’ promotion and

prevention scores were used as separate predictors. While revealing some interesting

results, these findings raised some questions about the interplay of promotion and

prevention focus, as well as the relative importance people’s promotion and prevention

foci played during the regulatory fit experience examined here.

For example, when looking at cognitive processes, a significant three-way

interaction effect was observed, suggesting that both promotion and prevention foci

played important roles. However, while the promotion-video interaction affecting

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cognitive processes was in the expected direction (when participants watched the

promotion-framed video, they reported better cognitive processing the higher their

promotion score was; the reverse was true for those who watched the prevention-framed

video), the prevention X video interaction was somewhat inconsistent. When watching

the promotion video, participants had better cognitive fit the lower their prevention scores

were. However, among those who watched the prevention-framed video, lower

prevention scores were again associated with slightly better cognitive processes. Thus,

looking at promotion and prevention scores separately, presents a slightly different

picture from what the simple difference score analysis has shown.

When motivation was regressed on the three main effects and the four interaction

terms (as well as the covariates), only the promotion X video interaction was significant

(and in the expected direction). In contrast, when regression models were estimated for

in-lab and follow-up affective reactions, only the prevention X video interaction was

significant (and in the expected direction). Taken together, these analyses demonstrate

the importance of including all available information when testing hypotheses and

examining interactive effects, as a difference score approach presents only a limited view

of the effects that are going on.

Experimental manipulation. Two video presentations based on the same information were developed herein to examine the interactive effect of regulatory focus and training design. Specifically, one version was framed in promotion terms, emphasizing hopes, ideals, and dreams while the other was framed in prevention terms, emphasizing duties, obligations, and safety. Additionally, during the promotion-framed

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presentation, the presenter engaged in more open and forward gestures while during the prevention-framed presentation, the presenter engaged in more limited gestures and a backward-leaning body position.

Two manipulation checks were employed to attest the merit of the experimental manipulation—a 12-item consistency measure (which included consistency ratings and reaction times) and a lexical decision task (LDT). As discussed in Chapter IV, both of the indicators based on the 12-item consistency measure provided evidence that the experimental manipulation worked. Specifically, those who watched the promotion video rated the promotion-framed items as more consistent with it than they rated the prevention framed items. Additionally, they took less time to rate the promotion-framed items than the prevention ones. In contrast, those who watched the prevention-framed video rated the prevention-framed items as more consistent with it and did so faster than they did the promotion-framed items. Because the consistency measure has both an explicit and an implicit component, it was concluded with some certainty that the video- framing was successful.

However, the LDT did not work in the expected direction and participants responded faster to the prevention words regardless of video-frame condition. These results were similar, even after several participants were dropped for making too many mistakes and after controlling for word frequency in the English language and average response times for the different words.

An additional limitation regarding manipulation checks is that a measure of participants’ direct perceptions of the presentation style was not collected. In other words,

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there is no way of knowing whether the framing of the content would have been enough

to produce the observed fit effects or whether nonverbal cues made a unique contribution

as well. In Cesario and Higgins’ (2008) study, a promotion-consistent and a prevention- consistent presentations were developed and shown to have an effect, however the content of the presentations was kept identical (in contrast with the current study) and

only non-verbal cues were manipulated. Thus, we can assume from Cesario and Higgins’

results that nonverbal cues during a presentation are key in driving fit effects. However,

by having both verbal and non-verbal cues in the same experiment (as done in the current

study) and not separating the effects of the two, we cannot make firm conclusions about

the relative importance of each.

Theoretical Considerations

Subjective feelings and mood. The regulatory fit effect on subjective feelings

received only marginal support. Because feelings of “rightness” and value derived from

the fit experience are such important constructs in the regulatory fit literature, more

discussion of the observed effects is needed. To further explore the nature of fit effects on

subjective feelings, three additional hierarchical regression analyses were conducted,

where “rightness,” enjoyment, and liking feelings were investigated separately. Both

promotion and prevention scores were used as unique predictors, rather than the

regulatory focus difference score, in addition to controlling for the effects of general

positive affect and current positive mood. In these models, only the general positive

affect and current positive mood control variables significantly related to feelings of

“rightness,” enjoyment, and liking of the instructor, and an additional marginally

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significant regression coefficient was observed for the prevention X video interaction

predicting “rightness” feelings (β = .368, t = 1.706, p = .090).

These findings are generally inconsistent with previous findings reported by

Higgins and colleagues (Camacho et al., Cesario & Higgins, 2008; Freitas & Higgins,

2002; Higgins et al., 2006; Ritchie, 2009). However, other researchers have noted that

participants tend to have trouble responding to questions regarding feelings of “rightness”

as they might not be necessarily aware of them (Vaughn, Schwartz, Malik, Petkova,

Trudeau, & Graber, 2006). In regards to enjoyment and liking, there seems to be a slight

contradiction in Higgins’ work and theorizing. In general, he argues that the regulatory fit

experience is non-affective in the sense that it does not and is not affected by affective

feelings like mood. At the same time, liking and enjoyment have a strong affective

component, and participants might have had a hard time distinguishing them from their

overall positive feelings which were measured and included as a control variable in the

analyses. Future research on regulatory fit effects should further explore the specific

feelings associated with it and especially focus on operationalizing feelings of

“rightness.” Since people in general do not have a clear awareness as to what these

feelings are, rather than “it just feels right,” psychometric efforts should be taken to

capture them.

To further discuss emotion-related findings, regulatory fit effects on specific high and low intensity positive and negative emotions were examined. Overall, these results

were not particularly clear, as none of the interaction models were significant, but some

interaction coefficients did approach significance (see Appendix R for details). The lack

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of clear findings associated with specific moods should be expected, as high and low intensity positive and negative emotions have been associated with positive and negative feedback regarding certain behaviors (Idson et al., 2004). There were no evaluative tasks in the current study so participants could not get any feedback as to how they were doing.

Therefore, accurate predictions about the specific emotions they would be feeling could not be made.

Other “fit” experiences. One final suggestion for future research is to explore

“fit” experiences in general. It was noted in Chapter II that the general concept of regulatory fit is not limited to regulatory focus theory. In fact, there are other motivational orientations proposed to interact with environmental demands to result in a

“fitting” experience. For example, Kruglanski et al. (2000) looked at the regulatory mode of assessment vs. the regulatory mode of locomotion and Bianco et al. (2003) worked with participants’ expectations regarding the “funness” or importance of a task. Both of these research projects demonstrated that when participants mode or expectations, matched the demands of the task, participants experienced fit and this fit resulted in favorable outcomes. Future research efforts might find it interesting to investigate the differences and similarities between the characterizations of such “fit” experiences and the “fit” experience associated with regulatory focus.

Along this line of thought, it is reasonable to expect that other types of “fit” like person-job fit, person-organization fit, and person-environment fit share some common characteristics with regulatory fit. As a number of authors note, fit on the job is not yet

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very well understood (Kristof-Brown et al., 2005) and integrating that body of research with research on regulatory fit theory might prove productive.

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CHAPTER VI

SUMMARY

By integrating training and regulatory fit theory research, this dissertation demonstrated empirically the importance of considering both individuals’ characteristics and contextual characteristics (i.e., lexical framing and presentation style) when designing a training program. Results clearly showed that when a video-taped training program was designed in a manner consistent with trainees’ motivational orientation (i.e., regulatory focus), training effectiveness indicators were enhanced. Specifically, the trainee/training fit resulted in favorable affective, cognitive, and behavioral outcomes both right after the training and a few weeks later.

Overall, this study makes several important contributions to the literature. First, it demonstrates that studying attribute-treatment interactions is both needed and productive, as customizing training programs to fit learners’ specific needs and characteristics might be the key to a more effective and efficient learning and development practice. Second, the current study shows initial empirical support for Gully and Chen’s (2010) theoretical model of the training process. Thus, it starts to address the lack of understanding as to the how and why certain training initiatives work and some do not.

Additionally, this project attests the need to apply regulatory focus (Higgins,

1997) and regulatory fit (Higgins, 2000) theories to more real-world situations. As evident here, one can design a training program to better fit promotion or prevention 152

focused learners and this fit in turn can affect important effectiveness outcomes. What is

even more interesting, these positive effects hold for at least a few weeks, as

demonstrated by the results from the follow-up survey. Thus the current study makes a

great contribution to the regulatory fit literature as it demonstrates the prolonged effect of the “fit” phenomenon.

In conclusion, this dissertation speaks to the experience of a match or “fit” between people’s characteristics and the characteristics of the environment. It empirically shows that we interact with the world around us and our perceptions, experiences, and consequent behaviors are shaped by this interaction. Considering or studying people or situations in isolation provides an incomplete and often misleading picture of the processes we are investigating. Although not novel, this point is often forgotten or ignored.

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REFERENCES

Ackerman, P. L., & Kanfer, R. (2004). Cognitive, affective, and conative aspects of adult intellect within a typical and maximal performance framework. In D. Y. Dai & R. J. Sternberg (Eds.) Motivation, emotion, and cognition: Integrated perspectives on intellectual functioning (pp. 119-141). Mahwah, NJ: Erlbaum.

Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.

Appelt, K. C., Zou, X., Arora, P., & Higgins, E. T. (2009). Regulatory fit in negotiation: Effects of “prevention-buyer” and “promotion-seller” fit. Social Cognition, 27, 365-384.

Avnet, T., & Higgins, E. T. (2003). Locomotion, assessment, and regulatory fit: Value transfer from “how” to “what.” Journal of Experimental Social Psychology, 39, 525-530.

Avnet, T., & Higgins, E. T. (2006). How regulatory fit affects value in consumer choices and opinions. Journal of Marketing Research, 43, 1-10.

Baldwin, T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41, 63–105.

Bargh, J. A. (1982). Attention and automaticity in the processing of self-relevant information. Journal of Personality and Social Psychology, 43, 425-436.

Bargh, J. A., & Chartrand, T. A. (2000). Studying the mind in the middle: A practical guide to priming and automacity research. In Reis, H., & Judd, C. (Eds), Handbook of Research Methods in Social Psychology. New York: Cambridge University Press.

Bargh, J. A., & Pratto, F. (1986). Individual construct accessibility and perceptual selection. Journal of Experimental Social Psychology, 22, 293-311.

Baron R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.

154

Beal, D. J., Weiss, H. M., Barros, E., & MacDermin, S. M. (2005). An episodic process model of affective influences on performance. Journal of Applied Psychology, 90, 1054-1068.

Beier, M. E. & Kanfer, R. (2010). Motivation in training and development: A phase perspective. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, Training, and Development in Organizations (pp. 65-97). New York, NY: Routledge/Taylor & Francis Group.

Bell, B. S., & Kozlowski, S. W. (2008). Active learning: Effects of core training design elements on self-regulatory processes, learning, and adaptability. Journal of Applied Psychology, 93, 296-316.

Bell, B. S., & Kozlowski, S. W. J. (2010). Toward a theory of learner-centered training design: An integrative framework of active learning. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, Training, and Development in Organizations (pp. 263- 300). New York, NY: Routledge/Taylor & Francis Group.

Benjamin, L., & Flynn, F. (2006). Leadership style and regulatory mode: Value from fit? Organizational Behavior and Human Decision Processes, 100, 216-230.

Bianco, A. T., Higgins, E. T., & Klem, A. (2003). How “fun/importance” fit affects performance: Relating implicit theories to instructions. Personality and Social Psychology Bulletin, 29, 1091-103.

Bodenhausen, G. V., & Lichtenstein, M. (1987). Social stereotypes and information- processing strategies: The impact of task complexity. Journal of Personality and Social Psychology, 52, 871-880.

Brockner, J., & Higgins, E. T. (2001). Regulatory focus theory: Implications for the study of emotions at work. Organizational Behavior and Human Decision Processes, 86, 35-66.

Brodscholl, J.C., Kober, H., & Higgins, E.T. (2007). Strategies of self-regulation in goal attainment versus goal maintenance. European Journal of Social Psychology, 37, 628–648.

Cable, D. M., & Edwards, J. R. (2004). Complementary and supplementary fit: A theoretical and empirical integration. Journal of Applied Psychology, 89, 822-834.

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of personality and Social Psychology, 42, 116-131.

155

Camacho, C. J., Higgins, E. T., & Luger, L. (2003). Moral value transfer from regulatory fit: What feels right is right and what feels wrong is wrong. Journal of Personality and Social Psychology, 84, 498-510.

Campbell, J. P., & Kuncel, N. R. (2002). Individual and team training. In N. Anderson, D. S. Ones, H. K. Sinangil, & C. Viswesvaran (Eds.), Handbook of Industrial, Work and Organizational Psychology, 1: Personnel psychology. (pp. 278-312). Thousand Oaks, CA: Sage Publications Ltd.

Campbell, N. (1988). Correlates of computer anxiety of adolescent students. Journal of Adolescent Research, 3, 107-117.

Carver, C. S. & Scheier, M. F. (2000). On the structure of behavioral self-regulation. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 41-84). San Diego, CA: Academic Press.

Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality and Social Psychology, 67, 319-333.

Cesario, J. F. (2006). Regulatory fit from nonverbal behaviors: How source delivery style influences message effectiveness (Doctoral dissertation, Columbia University, 2006). Dissertation Abstracts International, 67, 2276.

Cesario, J., & Higgins, E. T. (2008). Making message recipients “feel right”: How nonverbal cues can increase persuasion. Psychological Science, 19, 415-420.

Cesario, J., Grant, H., & Higgins, E. T. (2004). Regulatory fit and persuasion: Transfer from “feeling right.” Journal of Personality and Social Psychology, 86, 388-404.

Cesario, J., Higgins, E. T., & Scholer, A. A. (2008). Regulatory fit and persuasion: Basic principles and remaining questions. Social and Personality Psychology Compass, 2, 444-463.

Cohen, J., Cohen, P., West, S. J., & Aiken, L.S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge Academic.

Colquitt, J. A., Lepine, J. A., & Noe, R. A. (2000). Toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85, 678-707.

Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671-684. 156

Cronbach, L. J., & Snow, R. E. (1969). Individual Differences in Learning Ability as a Function of Instructional Variables: Final Report. Stanford University, CA: School of Education.

Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. Oxford, England: Irvington.

Crowe, E., & Higgins, E. T. (1997). Regulatory focus and strategic inclinations: Promotion and prevention in decision-making. Organizational Behavior and Human Decision Processes, 69, 117-132.

Debowski, S., Wood, R. E., & Bandura, A. (2001). Impact of guided exploration and enactive exploration on self-regulatory mechanisms and information acquisition through electronic search. Journal of Applied Psychology, 86, 1129-1141.

Deci, E. L., & Ryan, R. M. (2003). Intrinsic Motivation Inventory. Self-Determination Theory [On-line]. Available: http://www.psych.rochester.edu/SDT/measures/IMI_description.php

Edwards, J. R. (1994). Regression analysis as an alternative to difference scores. Journal of Management, 20, 683-689.

Edwards, J. R., Cable, D. M., Williamson, I. O., Lambert, L. S., & Shipp, A. J. (2006). The phenomenology of fit: Linking the person and environment to the subjective experience of person–environment fit. Journal of Applied Psychology, 91, 802- 827.

Elliot, A. J., & McGregor, H. A. (2001). A 2 X 2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519.

Elliot, A. J., & Thrash, T. M. (2002). Approach—avoidance motivation in personality: Approach and avoidance temperaments and goals. Journal of Personality and Social Psychology, 82, 804-818.

Epitropaki, O., & Martin, R. (2005). From ideal to real: A longitudinal study of the role of implicit leadership theories on leader–member exchanges and employee outcomes. Journal of Applied Psychology, 90, 659-676.

Evans, L. M. & Petty, R. E. (2003). Self-guide framing and persuasion: Responsibly increasing message processing to ideal levels. Personality and Social Psychology Bulletin, 29, 313-324.

157

Fiske, S. T., & Neuberg, S. L. (1990). A continuum of impression formation, from category-based to individuating processes: Influences of information and motivation on attention and interpretation. In M. P. Zanna (Ed.), Advances in Experimental Social Psychology, 23, San Diego, CA: Academic Press.

Florack, A., & Scarabis, M. (2006). How advertising claims affect brand preferences and category-brand associations: The role of regulatory fit. Psychology and Marketing, 23, 741-755.

Ford, J. K., Kraiger, K., & Merritt, S. M. (2010). An updated review of the multidimensionality of training outcomes: New directions for training evaluation research. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, Training, and Development in Organizations (pp. 135-165). New York, NY: Routledge/Taylor & Francis Group.

Ford, J. K., Smith, E. M., Weissbein, D. A., Gully, S. M., & Salas, E. (1998). Relationships of goal orientation, metacognitive activity, and practice strategies with learning outcomes and transfer. Journal of Applied Psychology, 83, 218-233.

Forgas, J. P., & George, J. M. (2001). Affective influences on judgments and behavior in organizations: An information processing perspective. Organizational Behavior and Human Decision Processes, 86, 3–34.

Förster, J., & Higgins, E. T. (2005). How global versus local perception fits regulatory focus. Psychological Science, 16, 631–636.

Förster, J., Grant, H., Idson, L. C., & Higgins, E. T. (2001). Success/failure feedback, expectancies, and approach/avoidance motivation: How regulatory focus moderates classic relations. Journal of experimental Social Psychology, 37, 253- 260.

Förster, J., Higgins, E. T., & Bianco, A. T. (2003). Speed/accuracy decisions in task performance. Built-in trade-off or separate strategic concerns? Organizational Behavior and Human Decision Processes, 90, 148-164.

Förster, J., Higgins, E. T., & Idson, L. C. (1998). Approach and avoidance strength during goal attainment: Regulatory focus and the “goal looms larger” effect. Journal of Personality and Social Psychology, 75, 1115-1131.

Freitas, A. L., & Higgins, E. T. (2002). Enjoying goal-directed action: the role of regulatory fit. Psychological Science, 13(1), 1-6.

158

Freitas, A. L., Liberman, N., & Higgins, E. T. (2002). Regulatory fit and resisting temptation during goal pursuit. Journal of Experimental Social Psychology, 38, 291-298.

Friedman, R. S., & Förster, J. (2001). The effects of promotion and prevention cues on creativity. Journal of Personality and Social Psychology, 81, 1001-1013.

Fuglestad, P. T., Rothman, A. J., & Jeffery, R. W. (2008). Getting there and hanging on: The effects of regulatory focus on performance in smoking and weight loss interventions. Health Psychology, 27, 260-270.

Geller, V., & Shaver, P. (1976). Cognitive consequences of self-awareness. Journal of Experimental Social Psychology, 12, 99-108.

Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In I. Mervielde, I. Deary, F. DeFruyt, & F. Ostendorf (Eds.), Personality Psychology in Europe, 7 (pp. 7-28). Tilburg, The Netherlands: Tilburg University Press.

Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. C. (2006). The International Personality Item Pool and the future of public-domain personality measures. Journal of Research in Personality, 40, 84- 96.

Goldstein, I. L., & Ford, J. K. (2002). Training in organizations: Needs assessment, development, and evaluation. Belmont, CA: Wadsworth.

Grant, H. & Higgins, E. T. (2003). Optimism, promotion pride, and prevention pride as predictors of quality of life. Personality and Social Psychology Bulletin, 29, 1521- 1532.

Gray, J. A. (1990). Brain systems that mediate both emotion and cognition. Motivation and Emotion, 4, 269-288.

Green, M. C., & Brock, T. C. (2000). In the mind’s eye: Transportation-imagery model of narrative persuasion. In M. C. Green, J. J. Strange, & T. C. Brock (Eds.), Narrative impact: Social and cognitive foundations (pp.315-341). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Green, M. C., & Brock, T. C. (2002). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79, 701-721.

159

Gully, S. M., Payne, S. C., Kiechel, K. L., & Whiteman, J. K. (1999). Affective reactions and performance outcomes of error-based training. Paper presented at the meeting of the Society for Industrial and Organizational Psychology, Atlanta, GA.

Gully, S., & Chen, G. (2010). Individual differences, attribute-treatment interactions, and training outcomes. In Kozlowski, S. W. J., & Salas, E. (Eds.), Learning, Training, and Development in Organizations (pp. 3-64). New York, NY: Routledge/Taylor & Francis Group.

Haaga, D., Friedman-Wheeler, D., McIntosh, E., & Ahrens, A. (2008). Assessment of individual differences in regulatory focus among cigarette smokers. Journal of Psychopathology and Behavioral Assessment, 30, 220-228.

Hayes, A. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408-420.

Hicks, W. D., & Klimoski, R. J. (1987). Entry into training programs and its effects on training outcomes: A field experiment. Academy of Management Journal, 30, 542-552.

Higgins, E. T. (1987). Self-discrepancy: A theory relating self and affect. Psychological Review, 94, 319–340.

Higgins, E. T. (1996). Knowledge activation: Accessibility, applicability, and salience. In E. T. Higgins & A.W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 133–168). New York: Guilford.

Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 1280-1300.

Higgins, E. T. (1998). Promotion and prevention: Regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 30, 1–46.

Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55, 1230-1234.

Higgins, E. T. (2005). Value from regulatory fit. Current Directions in Psychological Science, 14, 209-213.

Higgins, E. T. (2006). Value from hedonic experience and engagement. Psychological Review, 113, 439-460.

Higgins, E. T. (2008). Regulatory fit. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp. 356-372). New York: Guilford Press.

160

Higgins, E. T., Bond, R. N., Klein, R., & Strauman, T. (1986). Self-discrepancies and emotional vulnerability: How magnitude, accessibility, and type of discrepancy influence affect. Journal of Personality and Social Psychology, 51, 5-15.

Higgins, E. T., Friedman, R. S., Harlow, R. E., Idson, L. C., Ayduk, O. N., & Taylor, A. (2001). Achievement orientations from subjective histories of success: Promotion pride versus prevention pride. European Journal of Social Psychology, 31, 3–23.

Higgins, E. T., Idson, L. C., Freitas, A. L., Spiegel, S., & Molden, D. C. (2003). Transfer of value from fit. Journal of Personality and Social Psychology, 84, 1140-1153.

Higgins, E. T., Pittman, T., & Spiegel, S. (2006). Regulatory fit effects on the attractiveness of redoing an activity. Unpublished manuscript, Columbia University.

Higgins, E. T., Roney, C., Crowe, E., & Hymes, C. (1994). Ideal versus ought predilections for approach and avoidance: Distinct regulatory systems. Journal of Personality and Social Psychology, 66, 276-286.

Higgins, E. T., Shah, J., & Friedman, R. (1997). Emotional responses to goal attainment: Strength of regulatory focus as a moderator. Journal of Personality and Social Psychology, 72, 515-525.

Higgins, E.T., & Tykocinski, O. (1992). Self-discrepancies and biographical memory: Personality and cognition at the level of psychological situation. Personality and Social Psychology Bulletin, 18, 527-535.

Hogue, M. & Lord, R. G. (2007). A multilevel, complexity theory approach to understanding gender bias in leadership. The Leadership Quarterly, 18, 370-390.

Holton, E. F., Bates, R. A., & Ruona, W. E. A. (2000). Development of a generalized learning transfer system inventory. Human Resource Development Quarterly, 11, 333-360.

Holton, E., Bates, R., & Ruona, W. (2000). Development of a generalized learning transfer system inventory. Human Resource Development Quarterly, 11, 333-360.

Hong, J., & Lee, A. Y. (2008). Be fit and be strong: Self-regulation through regulatory fit. Journal of Consumer Research, 34, 682-695.

Idson, L. C., Liberman, N., & Higgins, E. T. (2000). Distinguishing gains from nonlosses and losses from nongains: A regulatory focus perspective on hedonic intensity. Journal of experimental Social Psychology, 36, 252-274.

161

Idson, L. C., Liberman, N., & Higgins, E. T. (2004). Imagining how you‘d feel: The role of motivational experiences from regulatory fit. Personality and Social Psychology Bulletin, 30, 926-937.

Jain, S. P., Agrawal, N., & Maheswaran, D. (2006). When more may be less: The effects of regulatory focus on responses to different comparative frames. Journal of Consumer Research, 33, 91-98.

Kanfer, R., & Ackerman, P. L. (2004). Work competence: A person-oriented perspective. In A. J. Elliot, & C. S. Dweck (Eds.), Handbook of Competence and Motivation, (pp. 336-353). New York, NY: Guilford Publications.

Kanfer, R., & Heggestad, E. D. (1997). Motivational traits and skills: A person centered approach to work motivation. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behavior (Vol. 19, pp. 1–56). Greenwich, CT: JAI.

Kanfer, R., Ackerman, P. L., & Heggestad, E. D. (1996). Motivational skills and self- regulation for learning: A trait perspective. Learning and Individual Differences, 8, 185–209.

Keith, N. & Frese, M. (2005). Self-regulation in error management training: Emotion control and metacognition as mediators of performance effects. Journal of Applied Psychology, 90, 677-691.

Kettanurak, V., Ramamurthy, K., & Haseman, W. D. (2001). User attitude as a mediator of learning performance improvement in an interactive multimedia environment: An empirical investigation of the degree of interactivity and learning styles. International Journal of Human-Computer Studies, 54, 541-583.

Kline, R. B. (2005). Principles and practice of structural equation modeling. New York, NY: The Guilford Press.

Koenig A. M., Cesario, J., Molden, D. C., Kosloff, S., Higgins, E. T. (2009). Incidental experiences of regulatory fit and the processing of persuasive appeals. Personality and Social Psychology Bulletin, 35, 1342-1355.

Kolenikov, S., & Bollen, K. A. (2008). The negative error variances: Is a Heywood case a symptom of misspecification? Unpublished manuscript. University of Missouri, Columbia, MO.

Kozlowski, S. W. J., & Bell, B. S. (2006). Disentangling achievement orientation and goal setting: effects on self-regulatory processes. Journal of Applied Psychology, 91, 900-916.

162

Kozlowski, S. W. J., Gully, S. M., Brown, K. G., Salas, E., Smith, E. M., & Nason, E. R. (2001). Effects of training and goal orientation traits on multidimensional training outcomes and performance adaptability. Organizational Behavior and Human Decision Processes, 85, 1-31.

Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78, 311-328.

Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals’ fit at work: A meta-analysis of person–job, person– organization, person– group, and person–supervisor fit. Personnel Psychology, 58, 281–342.

Kruglanski, A. W., Thompson, E. P., Higgins, E. T., Atash, M. N., Pierro, A., Shah, J. Y., & Spiegel, S. (2000). To “do the right thing” or to “just do it”: Locomotion and assessment as distinct self-regulatory imperatives. Journal of Personality and Social Psychology, 79, 793-815.

Latimer, A. E., Rivers, S., E., Rench, T. A., Katulak, N. A., Hicks, A., Hodorowski, J. K., Higgins, E. T., & Salovey, P. (2008). A field experiment testing the utility of regulatory fit messages for promotion physical activity. Journal of Experimental Social Psychology, 44, 826-832.

Lee, A. Y. & Aaker, J. L. (2004). Bringing the frame into focus: The influence of regulatory fit on processing fluency and persuasion. Journal of Personality and Social Psychology, 86, 205-218.

Lee, A. Y., & Labroo, A. A. (2004). The effect of conceptual and perceptual fluency on brand evaluation. Journal of Marketing Research, 41, 151-165.

Liberman, N. Idson, L. C., Camacho, C. J., & Higgins, E. T. (1999). Promotion and prevention choices between stability and change. Journal of Personality and Social Psychology, 77, 1135-1145.

Liberman, N., Molden, D. C., Idson, L. C., & Higgins, E. T. (2001). Promotion and prevention focus on alternative hypotheses: Implications for attributional functions. Journal of Personality and Social Psychology, 80, 5-18.

Lim, B., & Ployhart, R. E. (2006). Assessing the convergent and discriminant validity of Goldberg's International Personality Item Pool: A multitrait-multimethod examination.” Organizational Research Methods, 9, 29-54.

163

Lockwood, P., Jordan, C. H., & Kunda, Z. (2002). Motivation by positive or negative role models: Regulatory focus determines who will best inspire us. Journal of Personality and Social Psychology, 83, 854-864.

Lord, R. G., & Brown, D. J. (2004). Leadership processes and follower self-identity. Mahwah, NJ: Lawrence Erlbaum Associates.

Lord, R. G., Brown, D. J., Harvey, J. L., & Hall, R. J. (2001). Contextual constraints on prototype generation and their multilevel consequences for leadership perceptions. The Leadership Quarterly, 12, 311-338.

Macrae, C. N., Milne, A. B., & Bodenhausen, G. V., (1994). Stereotypes as energy- saving devices: A peek inside the cognitive toolbox. Journal of Personality and Social Psychology, 66, 37-47.

Markus, H. (1977). Self-schemata and processing information about the self. Journal of Personality and Social Psychology, 35, 63-78.

McClelland, D. C., Koestner, R., & Weinberger, J. (1989). How do self-attributed and implicit motives differ? Psychological review, 96, 690-702.

Molden, D. C., & Higgins, E. T. (2004). Categorization under uncertainty: Resolving vagueness and ambiguity with eager versus vigilant strategies. Social Cognition, 22, 248-277.

Molden, D. C., Lee, A. Y., & Higgins, E. T. (2008). Motivations for promotion and prevention. In J. Shah & W. Gardner (Eds.), Handbook of motivation science (pp. 169-187). New York: Guilford Press.

Morgan, R., & Casper, W. J. (2000). Examining the factor structure of participant reactions to training: A multidimensional approach. Human Resource Development Quarterly, 11, 301-317.

Naidoo, L. J. (2005). Effects of leaders on follower goal striving processes: Cognitive and emotional sensemaking mechanisms. Unpublished doctoral dissertation, The University of Akron, Akron, Ohio.

Noe, R. A. (1986). Training attributes and attitudes: Neglected influences of training effectiveness. Academy of Management Review, 11, 736-749.

Noe, R. A., & Schmitt, N. (1986). The influence of trainee attitudes on training effectiveness: test of a model. Personnel Psychology, 39, 497-523.

164

O’Keefe, P. A. (2010). The situational adaptiveness of implicit theories of intelligence and achievement goal orientations (Doctoral dissertation, Duke University, 2009). Dissertation Abstracts International, 70, 4537.

O’Leonard, К. (2009). The corporate learning factbook, 2009. Bersin & Associates Factbook Report.

Pennington, G. L., & Roese, N., J. (2003). Regulatory focus and temporal distance. Journal of Experimental Social Psychology, 39, 563-576.

Pintrich, P. R., Cross, D. R., Kozma, R. B., & McKeachie, W. J. (1986). Instructional psychology. Annual Review of Psychology, 37, 611-651.

Postman, L., Bruner, J. S., & McGinnies, E. (1948). Personal values as selective factors in perception. Journal of Abnormal and Social Psychology, 2, 142-154.

Reber, R., Winkielman, P., & Schwarz, N. (1998). Effects of perceptual fluency on affective judgments. Psychological Science, 9, 45-48.

Ree, M. J., Carretta, T. R., & Teachout, M. S. (1995). Role of ability and prior knowledge in complex training performance. Journal of Applied Psychology, 80, 721-730.

Ritchie, S. A. (2009). The impact of leader-follower regulatory focus congruence on regulatory fit and relationship quality (Doctoral dissertation, The University of Akron, 2009). Dissertation Abstracts International, 70, 6594.

Roney, C. J., Higgins, T., & Shah, J. (1995). Goals and framing: How outcome focus influences motivation and emotion. Personality and Socials Psychology Bulletin, 21, 1151-1160.

Ross, M., & Sicoly, F. (1979). Egocentric biases in availability and attribution. Journal of Personality and Social Psychology, 37, 322-336.

Salas, E., & Cannon-Bowers, J. A. (2001). The science of training: A decade of progress. Annual Review of Psychology, 52, 471-499.

Sarason, I. G., Sarason, B. R., Keefe, D. E., Hayes, B. E., & Shearin, E. N. (1986). Cognitive interference: Situational determinants and traitlike characteristics. Journal of Personality and Social Psychology, 51, 215-226.

Schmidt, A. M., & Ford, K. (2003). Learning within a learner control training environment: The interactive effects of goal orientation and metacognitive instruction on learning outcomes. Personnel Psychology, 56, 405-429.

165

Schwarz, N. & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45, 513-523.

Seamon, J. G., Williams, P. C., Crowley, M. J., Kim, I. J., Langer, S. A., Orne, P. J., Wishengrad, D. L. (1995). The mere exposure effect is based on implicit memory: Effects of stimulus type, encoding conditions, and number of exposures on recognition and affect judgments. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 711-721.

Seibt, B., & Förster, J. (2004). Stereotype threat and performance: How self-stereotypes influence processing by inducing regulatory foci. Journal of Personality and Social Psychology, 87, 38-56.

Semin, G. R., Higgins, T., de Montes, L. G., Estourget, Y, & Valencia, J. F. (2005). Linguistic signatures of regulatory focus: how abstraction fits promotion more than prevention. Journal of Personality and Social Psychology, 89, 36-45.

Shah, J., & Higgins, E. T. (1997). Expectancy X value effects: Regulatory focus as a determinant of magnitude and direction. Journal of Personality and Social Psychology, 73, 447-458.

Shah, J., & Higgins, E. T. (2001). Regulatory concerns and appraisal efficiency: The general impact of promotion and prevention. Journal of Personality and Social Psychology, 80, 693-705.

Shah, J., Higgins, E. T., & Friedman, R. S. (1998). Performance incentives and means: How regulatory focus influences goal attainment. Journal of Personality and Social Psychology, 74, 285-293.

Smith, E. R. (1996). What do connectionism and social psychology offer each other? Journal of Personality and Social Psychology, 70, 893-912.

Snow, R. E. (1991). Aptitude-treatment interaction as a framework for research on individual differences in psychotherapy. Journal of Consulting and Clinical Psychology, 59, 205–216.

Spiegel, S., Grant-Pillow, H., & Higgins, E. T. (2004). How regulatory fit enhances motivational strength during goal pursuit. European Journal of Social Psychology, 34, 39-54.

Summerville, A., & Roese, N. J. (2008). Self-report measures of individual differences in regulatory focus: A cautionary note. Journal of Research in Personality, 42, 247- 254.

166

Tabachnick, B.G. & Fidell, L.S. (2007). Using multivariate statistics. New York: Allyn and Bacon.

Taylor, S. E., & Fiske, S. T. (2007). Social cognition: From brains to culture. McGraw- Hill Humanities Social.

Thagard, P., & Kunda, Z. (1998). Making sense of people: Coherence mechanisms. In S. J. Read & L. C. Miller (Eds.), Connectionist models of social reasoning and social behavior (pp.3-26). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

VandeWalle, D. (1997). Development and validation of a work domain goal orientation instrument. Educational and Psychological Measurement, 57, 995-1015.

Vaughn, L. A., Baumann, J. & Klemann, C. (2008). Openness to experience and regulatory focus: Evidence of motivation from fit. Journal of Research in Personality, 42, 886-894.

Vaughn, L.A., Hesse, S., Petkova, Z., & Trudeau, L. (2009). “This story is right on”: The impact of regulatory fit on narrative engagement and persuasion. European Journal of Social Psychology, 39, 447-456.

Vaughn, L.A., Schwartz, S., Malik, J., Petkova, Z., Trudeau, L., Graber, L. (2006) Regulatory fit as input for stop rules. Journal of Personality and Social Psychology, 91, 601-611.

Wang, J., & Lee, A. (2006). The role of regulatory focus in preference construction. Journal of Marketing Research, 43, 28-38.

Watson, D., & Clark, L. A. (1994). The PANAS-X: Manual for the positive and negative affect schedule – expanded form. Unpublished manuscript. The University of Iowa.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063-1070.

Whittlesea, B. W. A. (1993). Illusions of familiarity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 1235-1253.

Yi, M. Y., & Davis, F. D. (2003). Developing and validating an observational learning model of computer software training and skill acquisition. Information Systems Research, 14, 146-169.

167

Yi, S., & Baumgartner, H. (2008). Motivational compatibility and the role of anticipated feelings in positively valences persuasive message framing. Psychology & Marketing, 26, 1007-1026.

Zhao, X. (2006). Self-regulatory focus, organizational climate and training effectiveness (Doctoral Dissertation, The Pennsylvania State University, 2006). Dissertation Abstracts International, 70, 259.

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APPENDICES

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APPENDIX A

MONEY MANAGEMENT TRAINING PROGRAM

VERSION I

Promotion Focused Video Script

Screen 1

You will now watch a training video on Money Management. Please put your headphones on now.

Screen 2

(Two simultaneous windows are open. The presenter is in the left window. A Power Point presentation is in the right window)

(Instructor): When you hear the word “budget,” what does it mean to you? If you’re like most people, you probably think of it as penny-pinching and crunching numbers—a process that will lead to missing out on all that fun spending like the occasional coffee or dinner out, or even the trip to grandma’s for the holidays. This couldn’t be further from the truth! While you may find that you could be more proactive about magnifying your savings and expanding your investments, without actually sitting down and creating a budget, it is impossible to know what expenses can be reassessed, if any.

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Screen 3

(Instructor): A budget is nothing more than an encompassing and comprehensive outline and plan of how much money you have coming in and where it goes. What good does it do to find out, after the fact that you really can't afford that new TV you just bought! Or that you splurged on eating out and going to the movies! With a budget, you will always be one step ahead of your bills and will be able to ensure the satisfaction of your cravings.

If you get upset or worried when you hear the word "budget," think of it as a process that (1) summarizes how you spend your income and (2) creates guidelines to ensure appropriate spending habits. To ensure your success, you better not think of a budget as a financial diet! A budget is simply (1) a tool to increase your awareness of how and where you spend your money, and (2) a guideline that will allow you to spend your money on the things that you want.

Screen 4

(Instructor): Remember that your budget isn’t created to make your life void of happiness; it is simply a guide of how to manage your money in a better way. We all have income, and we all have expenses, and to be successful in achieving your goals, you need to be sure your 171

money is allocated where YOU want it to be. The purpose of a budget is to lay a healthy foundation for determining what portion of your income to allocate to each expense. By making a budget, you create an outline for your money which ensures the achievement of you goals and allows you to engage in occasional exciting splurges.

A budget allows you to readily follow where your money is going which can consequently make you better at readjusting your expenses so you can pay for the things you really want. By having a budget, you can note your monthly expenditures and develop strategies to make sure everything goes right while achieving your short- and long-term goals. After creating your budget, you may find that about 5-10% of your total spending is for superfluous and excessive purchases. Think about it. What can you do with that extra 5-10%? Having a budget will help you recognize what and when items can be purchased. This further will help you set and support your financial goals, which may include buying a car or simply setting aside cash for a dream trip.

Screen 5

(Instructor): The most daunting part of creating a budget is sitting down and actually creating one. It’s like worrying about that big project you need to complete over the weekend. However, like any other project, if the process of creating a budget is seen as composed of a few smaller and easy to follow steps, things become much easier! As you begin to create your budget, remember that for your endeavor to be successful, you would want as much and as comprehensive information about your income and expenditures as possible. Ultimately, your budget will outline where your money is coming from, how much is there and where it is all going.

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Screen 6 (Points on slide appear on at a time, as the Instructor talks about each)

(Instructor): (1) Set up categories (written on board by instructor) The first step is setting up income and expense categories to track. A common mistake is to try to fit your spending into somebody else's categories and include too many and unnecessary categories. While basic categories such as housing, utilities, insurance, and food apply to all of us, we each have expenses that are unique to our personal lives. Thus, you need to be creative and flexible and include categories that fit YOUR lifestyle. For example, if you enjoy eating lunch with your friends on campus, you'll want a subcategory under "Food" for "Dining Out." Think about YOUR hobbies (for example: music, fashion, crafts, photography) and YOUR habits (like: smoking, drinking, buying a cup of coffee every day) to identify your spending categories. Such an approach will set the foundation for a successful budget and you will be able to see where your money goes.

Here are some examples of potential categories (Several points from table below are written on board). If it helps, you can start by using some of these, but in creating your budget, you will be better off customizing it to your own habits, desires, and ambitions.

INCOME EXPENSES From jobs Rent or Room & Board Gasoline/Oil From parents Utilities Entertainment From student loans Telephone Eating out From scholarships Groceries Books From financial aid Car payment/transportation School fees Miscellaneous income Insurance Miscellaneous expenses…

(2) Calculate income and expenses (written on board by instructor)—Once you have created a tentative list of income and expense categories, you can start calculating.

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a. Calculate income (written on board by instructor) Start calculating your average monthly pay by adding the pay on your pay stubs. Next, record ALL your other sources of income like scholarships, parents, grandparents, etc. Include everything you can think of!

b. Calculate expenses (written on board by instructor) As a next step, create a list of your monthly expenses. Gather as many bills and receipts as you can find from the past month or two and list your expenses on your budget worksheet under the corresponding category. Ensure your categories are comprehensive enough to include the wide range of your expenditures. Yet, remember that this has to be something YOU will stick with for the long term, so create it in a user-friendly way so it doesn’t consume too much of your time. Surely, you will include rent payment, car payments, insurance, groceries, utilities, entertainment, dry cleaning, and essentially everything you spend money on. Also, record ALL of your cash expenditures. It might be helpful to develop a habit of jotting them down in a little notebook as you spend the cash so you have a complete record of your cash expenditures. You might get pretty upset and disappointed once you realize how much cash you are actually spending. However, once you start taking a note of it, you can be happy again!

(3) Total your monthly income and monthly expenses (written on board by instructor) Once you have an idea of how much money is coming in and how much is going out, you can calculate the difference. If you have a positive value (OR more income), that’s exciting news as you are off to a good start. You can use this extra money towards your short- and long-term goal. For example, you can put it aside for you dream house or you can go to that exciting summer cruise across the Caribbean! If you have a negative value (OR higher expense column), some changes will have to be made. Don't get discouraged, though! Often times, success can be achieved by just making several adjustments to your spending.

(4) Make adjustments to expenses and set budget goals (written on board by instructor) Once you have an idea about how much money is coming in and how much is going out, you will be better able to decide what values can be adjusted and what purchases can be rethought if necessary. Maybe this means losing two of your Friday nights out a month or not having one of your magazine subscriptions. Typically, just saving a few dollars here and there can be enough to not only make sure you spend less than you earn, but also ensure the accomplishment of your long-term goals.

(5) Don’t forget to review your budget monthly (written on board by instructor) It is important to eagerly review your budget on a regular basis so you stay on track. The more you stick with the budgeting process, the more you’ll be able to adjust your budgeted amounts for each category so you can painlessly save money and ensure the success of your exciting future goals. Cutting costs becomes a stimulating challenge that can be very rewarding, especially as the accomplishment of you financial goals seems more and more plausible.

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Screen 7

(Instructor): In this next section, I will give you an idea about how you can lessen the expenses column in your budget. Try this out in your everyday life and see how it works for you and your budget!

Use cash instead of credit or debit cards to limit excessive and unnecessary expenditures. Even though swiping cards has become incredibly easy because we can be in and out with a purchase for seconds, by using our cards, we can get so excited and enthusiastic about our purchase that we lose track of how much money is actually being spent. Sure, 2 dollars here, 4 dollars there, doesn’t seem like much, but if we are too careless, they can add up quickly! Using cash instead will make you a more aware and a better shopper! It will help you visualize how much money you’re actually spending.

Give this a try! See for yourself how much better you can be at keeping track of your spendings by using cash! Before your regular routine next Monday create a budget for how much money you might want throughout the week. If you regularly buy lunch out, count that, or if you stop for coffee on the way to school include that as well. Include everything you spend money on! Once you have a pretty good idea of how much money you will spend throughout the week, only take that much cash at the start of the week. Whether this is $20 or $100, only have the amount of cash that you have budgeted and use this cash for all of those everyday expenses. After one week, see how you did. Were you successful? Did you find that you have money left over or did you have to pass on your Friday lunch with your classmates because you ran out of money the day before? Regardless of the outcome, you will have a very real sense of where your money is going.

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Screen 8

(Instructor): Drive more efficiently for better gas mileage and safe travels! • First of all, take a note of your driving habits. Aggressive driving like speeding, rapid acceleration and braking wastes gas. It can lower your gas mileage by 33 percent at highway speeds and by 5 percent around town. Less aggressive driving is also safer for you and others, so you may gain more than just gas money. • You also need to follow the speed limit. While each vehicle reaches its optimal fuel economy at a different speed, gas mileage usually decreases rapidly at speeds above 60 mph. You can assume that each 5 mph you drive over 60 mph is like paying an additional $0.24 per gallon for gas. Consistent with the previous point, observing the speed limit is also safer. • Additionally, ensure your car is as light as it can be! Unload extra and unnecessary items from your vehicle, especially heavy ones. Carrying an extra 100 pounds in your vehicle could reduce your mile per gallon by up to 2 percent. This effect is stronger for smaller vehicles. • Use cruise control: Using cruise control on the highway helps you maintain a constant speed and, in most cases, will save gas. Be eager in keeping your car in shape! • Keep your engine properly tuned to ensure its health and proper operation. Fixing a car that is noticeably out of tune or hasn’t passed an emissions test can improve its gas mileage by an average of 4 percent. Fixing serious maintenance problems, such as a faulty oxygen sensor, can also increase your gas mileage significantly. • Ensure your tires are properly inflated: You can improve your gas mileage by keeping your tires inflated to the proper pressure. Properly inflated tires are safer and last longer. The proper tire pressure for your vehicle is usually found on a sticker in the driver's side door jamb or the glove box and in your owner's manual. Do not use the maximum pressure printed on the tire's sidewall.

Another way to ensure savings is to use the proper fuel for you vehicle. More than 90% of vehicles use the low octane fuel, which can average about $0.20 cents less per gallon.

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Related to that, shop around for cheaper gas. Search and compare gas prices. Many non- brand stations use the same gasoline as brand names, just at a lower price! Also, membership warehouses (Sams Club, BJs, Costco) often offer reduced gasoline prices for its members. Check for a club membership in your area.

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VERSION II

Prevention Focused Video Script

Screen 1

You will now watch a training video on Money Management. Please put your headphones on now.

Screen 2

(Two simultaneous windows are open. The presenter is in the left window. A Power Point presentation is in the right window)

(Instructor): When you hear the word “budget,” what does it mean to you? If you’re like most people, you probably think of it as penny-pinching and crunching numbers—a process that will lead to never-ending worrying about each purchase. This couldn’t be further from the truth! While you may find that you do need to be more careful about your spendings, without actually sitting down and making a budget, it is impossible to know what expenses need to be cut, if any.

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Screen 3

(Instructor): A budget is nothing more than a careful, detailed breakdown and plan of how much money you have coming in and where it goes. What good does it do to find out, after the fact, that you can’t afford that new TV you just got and that you will go into debt because you can’t afford your monthly car payments? OR, that you will fail to cover your credit card payment because there isn't enough money in your account! Without a budget, many of us just muddle through, struggling to stay one step ahead of our bills and responsibilities.

If the word "budget" makes you anxious, think of the process as (1) summarizing how you spend your income and (2) creating guidelines to prevent against inappropriate spending habits. Thinking of a budget as a financial diet is a sure way to set yourself up for failure. A budget is simply (1) a tool to increase your conscientiousness of how and where you spend your money, and (2) a guideline to help you spend your money on the things that you need.

Screen 4

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(Instructor): It is important not to forget that your budget isn’t created to make your life miserable; it is simply a guide to help you manage your money. We all have income, and we all have expenses, and without careful allocation of the money we might fail to meet our goals. The purpose of a budget is to lay the foundation for determining what portion of your income to allocate to cover each expense. When you create a budget, you take control of your money so that it doesn’t control you. The goal is to simply create an outline for your money that puts YOU in control so you don’t fall short of your goals.

A budget allows you to carefully keep track of where your money is going and it helps you discipline yourself. By having a budget, you can track your monthly expenditures so that you can plan key savings strategies for important short- and long-term goals. After creating your budget, you may find that about 5-10% of your total spending is for unnecessary purchases and thus can be cut. Think about it. You can add these 5-10% to pay for things you really need to. Your budget will set guidelines on what and when items can be purchased and thus it can help limit your spending. This further helps you identify expenses that can be cut so that you can meet your goals which may include paying off debt or preparing for emergencies!

Screen 5

(Instructor): The hardest part of creating a budget is actually sitting down and making one! It’s like the anxiety you get from staring at a blank piece of paper when you need to write a 5- page essay! If the budgeting process is broken down into a few easy steps, however, things become much easier! To make sure your budgeting doesn’t fail, it is important to have at hand as much specific and detailed information about your income and expenditures as possible. Ultimately, the end result will show precisely where your money is coming from, how much is there and where it is all going.

180

Screen 6 (Points on slide appear on at a time, as the Instructor talks about each)

(Instructor): (1) Set up categories (written on board by instructor) The first step is setting up income and expense categories to track. Beware that a common mistake is to try to fit your spending into somebody else's categories and consequently fail to include important personal categories. While basic categories such as housing, utilities, insurance, and food apply to all of us, we each have expenses that are unique to our personal lives. Thus, you need to think carefully so you don’t omit categories that are important for your own lifestyle. For example, if you regularly buy lunch on campus, you'll want a subcategory under "Food" for "Dining Out." Carefully think about YOUR hobbies (music, fashion, crafts, photography) and YOUR habits (smoking, drinking, buying a cup of coffee every day) to identify your spending categories. The idea is to become more aware of where your money goes so you can make conscious decisions about spending it.

Here are some examples of potential categories (Several points from table below are written on board). If it helps, you can start by using some of these, but when you create your own budget, be sure to customize it to YOUR own needs and responsibilities.

(2) Calculate income and expenses (written on board by instructor)—Once you have created a tentative list of income and expense categories, you can start calculating.

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a. Calculate income (written on board) Start by calculating your average monthly pay by adding the pay on your pay stubs. Next, diligently record all your other sources of income like scholarships, parents, grandparents, etc. Be careful not to forget anything!

b. Calculate expenses (written on board by instructor) As a next step, create a list of your monthly expenses. Diligently gather your bills and receipts from the past month or two and list your expenses on your budget worksheet, carefully placing each expense under the appropriate category. Make sure you adjust your categories to be detailed enough. However, don’t forget that this has to be something YOU will stick with for the long term, so you would need to organize it thoughtfully and avoid making it too much of chore. Don’t forget to include rent payment, car payments, insurance, groceries, utilities, entertainment, dry cleaning, and essentially anything you spend money on. Also, don't forget to record your cash expenditures. It might be useful to develop a habit of jotting them down in a little notebook as you spend the cash so you have a precise record of your cash expenditures. You might get pretty worked up and uneasy once you realize how much cash you are actually spending. Keeping track of it, however, will help you regain your serenity.

(3) Total your monthly income and monthly expenses (written on board by instructor) Once you have an idea of how much money is coming in and how much is going out, you can calculate the difference. If you have a positive value (OR more income), you can calm down as you are off to a good start. You should carefully think about this unspent money and allocate it in the needed categories. For example, you can use it to pay off credit card or loan payments or put it aside for emergencies. If you have a negative value (OR higher expense column) this means some changes will need to be made. Don't get too nervous! Often times just cutting a few small expenses is enough to prevent your budget from crashing.

(4) Make adjustments to expenses and set budget goals (written on board by instructor) Only after you've carefully tracked your actual spendings for a month or two, you can accurately identify what values can be cut and what purchases can be eliminated if necessary. Maybe this means staying in two Friday nights a month or catching the bus to get to school. Typically, just saving a few dollars here and there can be enough to not only make sure you spend less than you earn, but also save money and guard against the failure of your long-term goals.

(5) Don’t forget to carefully review your budget monthly (written on board by instructor) It is important to carefully review your budget on a regular basis so you stay on track. The more you stick with the budgeting process, the more you’ll be able to adjust your budgeted amounts for each category so you can painlessly save money and prevent from failing of your important future goals. Following a budget can help you avoid the burden of debt and the constant worrying that you will fail your goals.

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Screen 7

(Instructor): In this next section of the program, I will give you several ideas about how you can lessen the expenses column in your budget. You should try at least a few of these in your everyday life and see how they work for you and your budget!

Use cash instead of credit or debit cards to avoid carelessly emptying your bank account. Even though swiping cards has become incredibly easy because we can be in and out with a purchase for seconds, by using our cards, we might stop thinking and begin to lose track of how much money is actually being spent. Sure, 2 dollars here, 4 dollars there, doesn’t seem like much, but if we are not careful, they can add up quickly! Paying with cash will help you visualize precisely how much money you’re actually spending and help you resist things you don’t need.

You NEED to give this a try! You have to see whether using cash makes you more thoughtful and keeps your spending under control. Before your regular routine next Monday create a budget for how much money you will need throughout the week. If you regularly buy lunch out, count that, or if you stop for coffee on the way to school be sure to include that as well. Don’t forget anything you spend money on! Once you have a pretty good idea of how much money you will spend throughout the week you should have that much cash on you at the start of the week. Whether this is $20 or $100, only have the precise amount of cash that you have budgeted and use this cash for all of those everyday expenses. After one week, see how you did. Did you fail? Did you have to pack a snack lunch on Friday because you spent your last dollar on Thursday or were you able to prevent this hassle? Regardless of the outcome, you will have a very real sense of where your money is going.

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Screen 8

Avoid driving inefficiently to avoid high gas mileage and prevent accidents! • First of all, you need to drive slowly and cautiously! Driving slowly and accelerating smoothly will help you in not wasting gas. More careful driving can result in better gas mileage by increasing it by 33 percent at highway speeds and by 5 percent around town. Careful driving is also safer and will help you ensure against accidents. • You also need to obey the speed limit: While each vehicle reaches its optimal fuel economy at a different speed, gas mileage usually decreases rapidly at speeds above 60 mph. You can assume that each 5 mph you drive over 60 mph is like losing $0.24 per gallon for gas. Consistent with the previous point, obeying the speed limit is also safer. • Additionally, you need to avoid carrying excess weight in your car. Don’t keep unnecessary items in your vehicle, especially heavy ones. Getting rid of 100 pounds from your vehicle could increase your mile per gallon by up to 2 percent. This effect is stronger for smaller vehicles. • Don’t forget to use cruise control: Using cruise control on the highway helps you maintain a constant speed and, in most cases, will save gas.

Be thorough in keeping your car in shape! • Keep your engine properly tuned to prevent it from failing and wasting gas. Fixing a car that is noticeably out of tune or has failed an emissions test can prevent an average of 4 percent loss in gas mileage. Avoiding serious maintenance problems, such as a faulty oxygen sensor, can prevent you from wasting a ton of gas. • Avoid improper tire inflation: You can avoid wasting gas by keeping your tires inflated to the proper pressure. Properly inflated tires are safer and last longer. The proper tire pressure for your vehicle is usually found on a sticker in the driver's side door jamb or the glove box and in your owner's manual. Do not use the maximum pressure printed on the tire's sidewall.

Another way to avoid spending more than you have to for gas is to carefully check the octane requirements for your vehicle. Less than 10% of vehicles require higher octane fuel, which can average about $0.20 cents more per gallon. 184

Related to that, avoid buying expensive gas. Search and compare gas prices. Beware that many non-brand stations use the same gasoline as brand names, just at a lower price! Also, membership warehouses (Sams Club, BJs, Costco) often offer reduced gasoline prices for its members. Check for a club membership in your area.

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Lowering Grocery and Food Costs Video Script (Script is identical for both experimental conditions)

A food budget is one of the easiest expenses to reduce. Start by changing your shopping habits! 1. (Promotion) Develop a comprehensive food budget for the week and plan to shop for food and groceries weekly. Include all the groceries you would want to have in a healthy and rewarding weekly diet. 2. (Prevention) Carefully build your own personal detailed price book. Note and systematically compare item prices among brands and stores as you shop so you can avoid spending more than you need to. This book will help you make your weekly budget plans even more precise. 3. (Promotion) Shop a wide variety of stores. Find the store that offers the overall best price and incentive bargains such as double coupons, bulk sales, and the like rewards for shopping there. 4. (Promotion) Shop wisely with coupons, especially on days when coupons will be doubled. Use coupons for items that you want. Compare the coupon-reduced price with store brand prices. The store brand may be cheaper. 5. (Prevention) Avoid shopping on an empty stomach. Those "expensive" goodies are too tempting to resist and don’t forget you probably need to stay away from them to protect both your wallet and health! 6. (Prevention) Never go shopping without a hand calculator. Begin with your weekly budget and carefully count down as you place items in the basket. 7. (Promotion) Buy in bulk. If storage allows it, shop in large quantities for non-food items such as paper products, cleansers, bathroom supplies and the like. Many member warehouses offer a lot of reward points and sale opportunities for these items. 8. (Prevention) Stay away from snacks. Replacing them with more healthy and inexpensive selections such as vegetable and fruits will prevent you from being unhealthy and will also prevent your budget from failing.

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APPENDIX B

REGULATORY FOCUS QUESTIONNAIRE (HIGGINS ET AL., 2001)

INSTRUCTIONS: This set of questions asks you about specific events in your life. Please indicate your answer to each question by circling the appropriate number below it.

1 = Never or seldom/Certainly false 2 = 3 = Sometimes 4 = 5 = Very often/Certainly true

1. Compared to most people, are you typically unable to get what you want out of life? 2. Growing up, would you ever ``cross the line'' by doing things that your parents would not tolerate? 3. How often have you accomplished things that got you ``psyched'' to work even harder? 4. Did you get on your parents' nerves often when you were growing up? 5. How often did you obey rules and regulations that were established by your parents? 6. Growing up, did you ever act in ways that your parents thought were objectionable? 7. Do you often do well at different things that you try? 8. Not being careful enough has gotten me into trouble at times. 9. When it comes to achieving things that are important to me, I find that I don't perform as well as I ideally would like to do. 10. I feel like I have made progress toward being successful in my life. 11. I have found very few hobbies or activities in my life that capture my interest or motivate me to put effort into them.

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APPENDIX C

GENERAL REGULATORY FOCUS MEASURE (LOCKWOOD, JORDAN, & KUNDA, 2002)

INSTRUCTIONS: Please rate your agreement with the following statements using the 7-point scale below.

1 = Not at all true of me 5 = Somewhat true of me 2 = Untrue of me 6 = True of me 3 = Somewhat untrue of me 7 = Very true of me 4 = Neither true nor untrue of me

1. In general, I am focused on preventing negative events in my life. 2. I am anxious that I will fall short of my responsibilities and obligations. 3. I frequently imagine how I will achieve my hopes and aspirations. 4. I often think about the person I am afraid I might become in the future. 5. I often think about the person I would ideally like to be in the future. 6. I typically focus on the success I hope to achieve in the future. 7. I often worry that I will fail to accomplish my academic goals. 8. I often think about how I will achieve academic success. 9. I often imagine myself experiencing bad things that I fear might happen to me. 10. I frequently think about how I can prevent failures in my life. 11. I am more oriented toward preventing losses than I am toward achieving gains. 12. My major goal in school right now is to achieve my academic ambitions. 13. My major goal in school right now is to avoid becoming an academic failure. 14. I see myself as someone who is primarily striving to reach my “ideal self” – to fulfill my hopes, wishes, and aspirations. 15. I see myself as someone who is primarily striving to become the self I “ought” to be – to fulfill my duties, responsibilities, and obligations. 16. In general, I am focused on achieving positive outcomes in my life. 17. I often imagine myself experiencing good things that I hope will happen to me. 18. Overall, I am more oriented toward achieving success than preventing failure.

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APPENDIX D

BIS/BAS SCALE (CARVER & WHITE, 1994)

INSTRUCTIONS: Please rate your agreement with the following statements using the 7-point scale below.

1 = Not at all true of me 2 = Untrue of me 3 = Somewhat untrue of me 4 = Neither true nor untrue of me 5 = Somewhat true of me 6 = True of me 7 = Very true of me

BIS 1. If I think something unpleasant is going to happen I usually get pretty “worked up”. 2. I worry about making mistakes. 3. Criticism or scolding hurts me quite a bit. 4. I feel pretty worried or upset when I think or know somebody is angry at me. 5. Even if something bad is about to happen to me, I rarely experience fear or nervousness. 6. I feel worried when I think I have done poorly at something. 7. I have very few fears compared to my friends.

BAS Reward Responsiveness 8. When I get something I want, I feel excited and energized. 9. When I’m doing well at something, I love to keep at it. 10. When good things happen to me, it affects me strongly. 11. It would excite me to win a contest. 12. When I see an opportunity for something I like, I get excited right away.

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BAS Drive 13. When I want something, I usually go all-out to get it. 14. I go out of my way to get things I want. 15. If I see a chance to get something I want, I move on it right away. 16. When I go after something I use a “no holds barred” approach.

BAS Fun Seeking 17. I will often do things for no other reason than that they might be fun. 18. I crave excitement and new sensations. 19. I’m always willing to try something new if I think it will be fun. 20. I often act on the spur of the moment.

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APPENDIX E

MANIPULATION CHECKS

CONSISTENCY MEASURE

INSTRUCTIONS: Please carefully read the statements below and use the seven-point scale below to indicate how consistent each statement is with the video training program you just watched:

1 2 3 4 5 6 7 Very consistent Not consistent at all

Promotion video-consistent items: 1. A budget will help me achieve my long-term hopes and dreams. 2. A budget will help me set money aside for my dream home. 3. Being proactive about keeping my car in shape will increase potential car-related savings. 4. Driving more efficiently will help me achieve better gas mileage. 5. Having a budget will allow me to go to my dream holiday. 6. Having a budget will allow me to live a more exciting and full of adventures life.

Prevention video-consistent items; 1. A budget will help me avoid missing car payments. 2. A budget will prevent me from failing my long-term duties and responsibilities. 3. Avoiding driving inefficiently will help me save gas. 4. Being meticulous about keeping my car in shape will help me avoid unnecessary spendings. 5. Having a budget will help me avoid going into debt. 6. Having a budget will help me live a safer and free-of-worry life.

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LEXICAL DECISION TASK

INSTRUCTIONS: In this task, you will have to identify whether the letter strings you see on the computer screen are real English words or not. If the letter string is a word, press the right arrow key on the keyboard. If it is not a word, don’t do anything. Once the ‘Continue’ button appears, press it to continue on to the next word. The first four strings are practice trials.

Practice words: kaat, desk, pogn, chair

Promotion words: earn, gain, wish, ideal, obtain, reward, success, excite, adventure, opportunity

Prevention words: loss, safe, duty, fail, relax, ought, punish, anxious, careful, responsible

Non-words: leil, mern, gekt, cifh, maft, tuge, baih, guins, nager, venil, feilen, hellum, teined, vehocle, peilin, velougs, deining, penerable, venjilating, pentilation

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APPENDIX F

INTERVENING MECHANISMS

INSTRUCTIONS: Please rate your agreement with the following statements regarding your thoughts and experiences during the Money Management Training program using the 7-point scale below.

1 = Strongly disagree 2 = Disagree 3 = Somewhat disagree 4 = Neither disagree nor agree 5 = Somewhat agree 6 = Agree 7 = Strongly agree

FEELINGS OF RIGHTNESS (created for current study, based on Cesario & Higgins, 2008) 1. It felt ‘right’ while listening to the information presented in the money management training program. 2. I felt uneasy while watching the money management training program. 3. I felt comfortable during the training program. 4. Sitting on the money management training program felt ‘wrong.’

ENJOYMENT (adapted from Freitas & Higgins, 2002) 1. It was interesting to sit on this training program. 2. It was enjoyable to sit on this training program. 3. It was exciting to sit on this training program. 4. I liked this training program very much. 5. Sitting on this training program was a pleasure.

LIKING (adapted from Ritchie, 2009) 1. I think this instructor would make a good friend. 2. I think would get along well with this instructor.

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PERCEIVED EASE OF PROCESSING (adapted from Kettanurak et al., 2001; Lee & Aaker, 2004) 1. The Money Management training program was clear. 2. I could easily understand the content presented during the Money Management training program. 3. The material presented in the training program was comprehensive. 4. Overall, the information presented during the training program was easy to process.

ATTENTION PROCESSES (Yi & Davis, 2003) 1. I paid close attention to the video demonstration. 2. I was able to concentrate on the video demonstration. 3. The video demonstration held my attention. 4. During the video demonstration, I was absorbed by the demonstrated information.

MOTIVATION TO BUDGET (Yi & Davis, 2003) 1. The training provided information that motivated me to create my own budget. 2. The training helped me see the usefulness of a budget. 3. The training increased my intention to master my budget management skills. 4. The training showed me the value of having a budget.

MOTIVATION TO LEARN (based on Hicks & Klimoski, 1987; Noe & Schmitt, 1986) 1. I was very excited to attend the money management training program. 2. I was interested in learning the material that was covered in this training program. 3. I tried to learn as much as I can from the money management program. 4. I was motivated to learn the training material that was emphasized in this program. 5. I was willing to exert considerable effort to learn the content of the money management training program. 6. I gave 100% effort to learn as much possible during the training.

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COGNITIVE INTERFERENCE QUESTIONNAIRE (adapted from Sarason, et al., 1986)

INSTRUCTIONS: This questionnaire concerns the kinds of thoughts that go through people's heads at particular times, for example, while they are listening to a lecture. The following is a list of thoughts, some of which you might have had while listening to the training program you just watched. Please indicate approximately how often each thought occurred to you while watching.

1 = Never 2 = Once 3 = A few times 4 = Often 5 = Very often

1. I thought about how I should listen more carefully. 2. I thought about the purpose of the experiment. 3. I thought about how much time there was left of the training. 4. I thought about how often I got confused. 5. I thought about other activities (for example, assignments, work). 6. I thought about members of my family. 7. I thought about friends. 8. I thought about something that made me feel guilty. 9. I thought about personal worries. 10. I thought about something that made me feel tense. 11. I thought about something that made me feel angry. 12. I thought about something that happened earlier today. 13. I thought about something that happened in the recent past (last few days, but not today). 14. I thought about something that happened in the distant past. 15. I thought about something that might happen in the future.

Please circle the number on the following scale which best represents the degree to which you felt your mind wandered during the Budget Management training session you have just watched.

1 2 3 4 5 6 7 Not at all Very much

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APPENDIX G

OUTCOME VARIABLES

INSTRUCTIONS: Please rate your agreement with the following statements regarding your thoughts and experiences during the Money Management Training program using the 5-point scale below.

1 = Strongly disagree 2 = Disagree 3 = Neither disagree nor agree 4 = Agree 5 = Strongly agree

SATISFACTION WITH TRAINING PROGRAM (adapted from Kettanurak et al., 2001; Morgan & Casper, 2000) 1. I am satisfied with the quality of the course content. 2. I am satisfied with the overall quality of this training program. 3. Overall, I found the content of the training program valuable. 4. Overall, I was very satisfied with the presentation of the content of the training program. 5. Overall, I had a very positive learning experience. 6. I am satisfied with the instructor’s ability to keep my interest. 7. I am satisfied with the instructor’s pace of presenting.

PERCEIVED UTILITY OF THE TRAINING PROGRAM (Morgan & Casper, 2000) 1. I believe the course objectives closely matched my idea of what I expected would be taught. 2. This course will help me improve my money management habits. 3. I believe that the course content is relevant to my everyday life. 4. The content of the Money Management training program helped me learn important concepts.

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PERCEIVED EFFORT (Deci & Ryan, 2003) 1. I put a lot of effort into this training program. 2. I tried very hard on this training program. 3. It was important to me to do well in this training program. 4. I didn’t put much energy into this training program. 5. I didn’t try very hard to do well in this training program.

MONEY MANAGEMENT SELF-EFFICACY 1. I am confident I can create my personal budget as a result of this training program. 2. I am confident I can manage my money better as a result of this training program. 3. I am confident I now possess enough knowledge to manage my money effectively. 4. I am confident in my ability to better monitor my spending habits. 5. I am confident in my ability to be a smart shopper. 6. I am confident in my ability to be a more efficient driver.

INTENTION TO USE INFORMATION (Holton, et al., 2000)

INSTRUCTIONS: Please use the 7-point scale below to indicate your agreement with the following statements

1 2 3 4 5 6 7 Definitely no Definitely yes

1. I am planning to use in my everyday life the new knowledge and skills I acquired in this training. 2. I anticipate making every effort in the coming weeks to put into practice what I learned in this training. 3. My objective is to apply in my everyday life as much of the learning from this training as I can. 4. As soon as it is feasible, I intend to use all that I learned in this training in my daily life.

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DECLARATIVE KNOWLEDGE

INSTRUCTIONS: Based on the information presented to you during the Money Management training program, please answer the following questions in the space provided.

1. What is a budget? 2. Why would you want to have a budget? 3. What steps do you need to go through when creating a budget? 4. What are some mistakes that can hinder your budget management? 5. List as many of things presented in the lecture that can help you reduce your driving expenses. 6. List as many of things presented in the lecture that can help you reduce your grocery/food costs.

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APPENDIX H

DECLARATIVE KNOWLEDGE CODING RUBRIC

Question: What is a budget?

Relevant information presented during video presentation: 1. An encompassing and comprehensive outline/a careful, detailed breakdown of how much money you have coming in and where it goes 2. Increases your awareness/conscientiousness of how and where you spend your money 3. Helps you to be one step ahead of your bills; lets you ensure the satisfaction of your cravings/stay one step ahead of our bills and responsibilities 4. A process that summarizes how you spend your income 5. Creates guidelines to ensure appropriate spending habits/to prevent against inappropriate spending habits

The points given are based on the following criteria: 1 point: Not much from video, common sense, or just speaks of a general purpose (e.g., “helps you manage/track money”)

2 points: A brief description, mentions both income and expenses (e.g., “A way to track income/expenses”)

3 points: Describes in detail what a budget is (without mentioning a purpose), mentions both income and expenses (e.g., “A budget is an organized account of how much money is earned about how much money is spent.”)

4 points: Defines budget AND mentions purpose (e.g., “Comprehensive plan and record of income and expenses in order to keep a positive balance of finances.”)

5 points: Provides extra detail (no one had an exceptional answer)

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Question: What steps do you need to go through when creating a budget?

There are five main steps one needs to take when creating a budget: 1. Setting up categories (that match your lifestyle) 2. Calculate average income and expenses (make sure you include everything) 3. Total monthly income and expenses; calculate difference 4. Make adjustments if necessary 5. Review budget monthly

The answers were coded on a 5-point Likert scale with one point given for each correct step.

Example 1: “Make categories of income and expenses 2. Calculate income and expenses 3. Find the difference between your income and expenses 4. Make adjustments 5. Review your budget monthly”

This answer should receive 5 points as it clearly lists all 5 steps of the budgeting process.

Example 2: “You need to make lists of your income and expenses and compare their difference. You then need to make adjustments accordingly to be sure you don't over spend.”

This answer should receive 3 points as it lists three of the steps presented in the video.

Example 3: “make a list of incomes and expenses” This answer should receive 1 point as it only lists 1 point made in the video presentation.

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Question: List as many of the things presented in the lecture that can help you reduce your driving expenses.

There is a total of eight points made during the video presentation: 1. driving efficiently (includes driving slowly, carefully, accelerating smoothly; don’t drive aggressively; don’t break/accelerate rapidly) 2. obey/follow the speed limit 3. avoid carrying excess weight in your car; don’t keep unnecessary items, esp. heavy ones 4. use cruise control; maintain constant speed 5. keep your car in shape (includes engine tuned up, faulty oxygen sensor) 6. keep tires inflated to the proper pressure 7. use the proper octane fuel 8. don’t buy expensive gas; shop around for gas; use club membership cards (fuel perks)

For each point made by the participant, one point is given. A 7-point scale was used with participants listing zero or one strategies receiving one point, and participants listing seven or eight strategies, receiving 7 points. For each additional strategy from two to six, one additional point is given.

Example 1: “-Be sensible when driving, accelerating steadily and coasting instead of laying on the brakes - Reducing the weight in my car (removing things inside the car) -Keeping active with tune-ups for the car, such as checking tires -Researching the cheapest gas”

This answer should receive 4 points, as it makes four unique points, made during the video.

Example 2: “correct tire pressure. 2. regularly caring for your cars engine 3. using the correct gas 4. comparing gas prices 5. correct tire pressure 6. driving at a constant speed 7. using cruise control on the high way 8. going the speed limit 9. not making sudden stops or accelerations.”

This answer should receive 7 points, as it makes seven unique points, made during the video.

Example 3: “Fixing your car. E check”

This answer should receive 1 point, as it makes one unique point.

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APPENDIX I

CONTROL AND EXPLORATORY VARIABLES

MOOD (based on Naidoo, 2005; Shah & Higgins, 2001)

INSTRUCTIONS: The following is a list of words that describe different feelings and emotions. For each word, indicate to what extent you feel this way right now.

1 = Very slightly or not at all 2 = A little 3 = Moderately 4 = Quite a bit 5 = Extremely

1. Happy 2. Excited 3. Satisfied 4. Sad 5. Disappointed 6. Discouraged 7. Relaxed 8. Calm 9. Peaceful 10. Tense 11. Anxious 12. Agitated

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TRAIT AFFECT (Watson & Clark, PANAS-X, 1994)

INSTRUCTIONS: The following is a list of words that describe different feelings and emotions. For each word, indicate to what extent you generally feel this way.

1 = Very slightly or not at all 2 = A little 3 = Moderately 4 = Quite a bit 5 = Extremely

1. Interested 2. Distressed 3. Excited 4. Upset 5. Strong 6. Guilty 7. Scared 8. Hostile 9. Enthusiastic 10. Proud 11. Irritable 12. Alert 13. Ashamed 14. Inspired 15. Nervous 16. Determined 17. Attentive 18. Jittery 19. Active 20. Afraid

Positive Affect = 1, 3, 5, 9, 10, 12, 14, 16, 17, 19 Negative Affect = 2, 4, 6, 7, 8, 11, 13, 15, 18, 20

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PREVIOUS KNOWLEDGE AND EXPERIENCE WITH MONEY MANAGEMENT

INSTRUCTIONS: Please answer the following questions about your knowledge and experience with money management.

1. Have you taken any classes on money management before? If yes, how many? 2. Have you read any books on money management? If yes, which ones? 3. Do you currently have a personal budget? If yes, for how long have you had it? 4. Do you currently keep track of your spendings and earnings in any way? If yes, how?

INSTRUCTIONS: Please rate your agreement with the following statements regarding your thoughts and experiences during the Money Management Training program using the 7-point scale below.

1 = Strongly disagree 2 = Disagree 3 = Somewhat disagree 4 = Neither disagree nor agree/Unsure 5 = Somewhat agree 6 = Agree 7 = Strongly agree

PRE-TRAINING MOTIVATION TO LEARN (based on Hicks & Klimoski, 1987; Noe & Schmitt, 1986) 1. I will be very excited to attend a money management training program. 2. I am interested in learning the material that will be covered in the money management training program. 3. I will try to learn as much as I can from the money management program. 4. I am motivated to learn the training material that will be emphasized in this program. 5. I am willing to exert considerable effort to learn the content of the money management training program. 6. I will give 100% effort to learn as much possible during the training. 7. Taking training courses and seminars is not a high priority for me. (reverse scored)

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GOAL ORIENTATION (Elliot & Macgregor, 2001) 1. It is generally important for me to do better than others in training contexts. 2. It is important for me to do well compared to others at in training programs. 3. My goal during training programs is to perform better than most other students. 4. I worry that I might not learn all that I possibly could during trainings. 5. Sometimes I’m afraid I may not understand the content of the training as I’d like. 6. I am often concerned that I may not learn all there is to learn during training courses. 7. I want to learn as much as possible during training courses. 8. It is important for me to understand the content of the training program as thoroughly as possible. 9. I desire to completely master the tasks I complete during training programs. 10. I just want to avoid doing poorly during training programs. 11. My goal during training is to avoid performing poorly. 12. My fear of performing poorly during training is often what motivates me.

BIG FIVE FACTOR MEASURE (Goldberg, 1999, IPIP)

INSTRUCTIONS: In this section, there are phrases describing people's behaviors. Please use the rating scale below to describe how accurately each statement describes you. Describe yourself as you generally are now, not as you wish to be in the future. Please read each statement carefully and use the 5-point scale below to indicate your answer.

1 = Very inaccurate 2 = Moderately inaccurate 3 = Neither inaccurate nor accurate 4 = Moderately accurate 5 = Very accurate

NEUROTICISM 1. Often feel blue. 6. Rarely get irritated. 2. Dislike myself. 7. Seldom feel blue. 3. Am often down in the dumps. 8. Feel comfortable with myself. 4. Have frequent mood swings. 9. Am not easily bothered by things. 5. Panic easily. 10. Am very pleased with myself.

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EXTRAVERSION 1. Feel comfortable around people. 7. Keep in the background. 2. Make friends easily. 8. Would describe my experiences as 3. Am skilled in handling social somewhat dull. situations. 9. Don't like to draw attention to 4. Am the life of the party. myself. 5. Know how to captivate people. 10. Don't talk a lot. 6. Have little to say.

OPENNESS TO EXPERIENCE 1. Believe in the importance of art. 6. Am not interested in abstract ideas. 2. Have a vivid imagination. 7. Do not like art. 3. Tend to vote for liberal political 8. Avoid philosophical discussions. candidates. 9. Do not enjoy going to art museums. 4. Carry the conversation to a higher 10. Tend to vote for conservative level. political candidates. 5. Enjoy hearing new ideas.

AGREEABLENESS 1. Have a good word for everyone. 6. Have a sharp tongue. 2. Believe that others have good 7. Cut others to pieces. intentions. 8. Suspect hidden motives in others. 3. Respect others. 9. Get back at others. 4. Accept people as they are. 10. Insult people. 5. Make people feel at ease.

CONSCIENTIOUSNESS 1. Am always prepared. 6. Waste my time. 2. Pay attention to details. 7. Find it difficult to get down to work. 3. Get chores done right away. 8. Do just enough work to get by. 4. Carry out my plans. 9. Don't see things through. 5. Make plans and stick to them. 10. Shirk my duties.

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NEED FOR COGNITION (Cacioppo & Petty, 1982, IPIP)

1 = Very inaccurate 2 = Moderately inaccurate 3 = Neither inaccurate nor accurate 4 = Moderately accurate 5 = Very accurate

1. Like to solve complex problems. 2. Need things explained only once. 3. Can handle a lot of information. 4. Love to think up new ways of doing things. 5. Am quick to understand things. 6. Love to read challenging material. 7. Have difficulty understanding abstract ideas. 8. Try to avoid complex people. 9. Avoid difficult reading material. 10. Avoid philosophical discussions.

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DEMOGRAPHIC INFORMATION

Personality Assessment Questions

INSTRUCTIONS: Please answer the following questions by selecting the appropriate option or indicating your response in the space provided.

1. Age: ____

2. Gender (select one): Male Female

3. Ethnicity (select one): Caucasian African American Native American Asian/Pacific Islander Hispanic/Latino Two or more races Other (please specify) ______

4. Major: ______

5. Year in college: ______

6. Number of credits completed toward degree: ______

7. Are you currently employed? Yes No

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Experimental Session Questions

1. Is English your first language? Yes No

2. Have you had Karen Marando as an instructor in the University of Akron? Yes No

3. Please indicated you socioeconomic status: Lower class Middle class Upper middle class Upper class

4. Please rate your current concern with money:

1 2 3 4 5 6 7 Not at all concerned Very concerned

209

APPENDIX J

LATENT VARIABLES CFA MODELS

.77**

.60** .64**

Rightness Enjoyment Liking

.78** .69** .75** .69** .78** .85** .79** .91** .84** .90** .95**

R1 R2 R3 R4 E1 E2 E3 E4 E5 L1 L2

.39** .52** .44** .52** .40** .27** .38** .17** .30** .18** .09**

.10** .08** Figure J.1. Emotional Fit Factor Structure Note. N = 192. Standardized solution. **p < .001

.83**

Motivation to Motivation to budget learn

.86** .53** .84** .63** .75** .77** .79** .94** .78** .69**

M1 M2 M3 M4 ML1 ML2 ML3 ML4 ML5 ML6

.26** .72** .30** .61** .43** .40** .37** .12** .39** .52**

.14** .19** .24** Figure J.2. Motivation Factor Structure Note. N = 192. Standardized solution. **p < .001 210

.30**

.45** .61**

Processing Attentional Cognitive fluency focus interference

.80** .87** .76** .91** .88** .74** .77** .69** .46** .14 .58** .45** .64** .53** .54** .51** .71** .63** .43** .54** .61** .43** .55**

PF1 PF2 PF3 PF4 AF1 AF2 AF3 AF4 CI1 CI2 CI3 CI4 CI5 CI6 CI7 CI8 CI9 CI10 CI11 CI12 CI13 CI14 CI15

.35** .25** .42** .18** .23** .45** .40** .52** .79** .98** .66** .79** .59** .72** .71** .74** .50** .60** .82** .71** .63** .81** .70**

.22** .29** .27** -.27** -.30**

Figure J.3. Cognitive Fit Factor Structure Note. N = 192. Standardized solution. **p < .001

211

.85**

.68** .77**

Emo Fit Cog Fit Motivation

.66** .95** .61** .94** .37** .57** .76** .94**

Rightness Enjoyment Liking Attntion CI Proc Fluen Mot LMot

.57** .09* .63** .12 .87** .67** .42** .12**

.16** .30**

Figure J.4. Intervening Mechanisms Factor Structure Note. N = 192. Standardized solution. **p < .001

212

.59**

.79** .78**

.77** .82** .68**

Satisfcation Utility Self-effiacy Use intentions

.79** .83** .65** .77** .78** .72** .57** .44** .81** .56** .77** .74** .80** .77** .56** .41** .41** .83** .87** .88** .86**

S1 S2 S3 S4 S5 S6 S7 U1 U2 U3 U4 SE1 SE2 SE3 SE4 SE5 SE6 UI1 UI2 UI3 UI4

.37** .32** .57** .40** .38** .48** .67** .81** .34** .69** .40** .46** .36** .41** .69** .83** .83** .32** .24** .23** .25**

.33** .12** .12** .20** Figure J.5. In-lab Affective Reactions Factor Structure Note. N = 192. Standardized solution. **p < .001

213

In-lab Affective Reactions

.80** .84** .80** .72**

Self- Satisfaction Utility Use intent efficacy

.37** .30** .35** .48**

Figure J.6. Second-Order In-lab Affective Reactions Model Note. N = 192. Standardized solution. **p < .001

214

.57**

.51** .49**

.52** .53** .64**

.60** .78** .68** .48**

.63** .87** .72** .53** .90**

Effort Satisfaction Helpfulness Self-efficacy Using now Use intentions

.85** .90** .70** .62** .61** .83** .79** .78** .83** .87** .75** .57** .49** .75** .52** .71** .83** .79** .75** .70** .63** .40** .76** .88** .91** .75** .89** .93** .93** .82**

.83 .83 E1 E2 E3 E4 E5 S1 S2 S3 S4 S5 S6 S7 H1 H2 H3 H4 SE1 SE2 SE3 SE4 SE5 SE6 UI1 UI2 UI3 UI4 UI1 UI2 UI3 UI4

.27** .20** .50** .61** .62** .31** .37** .38** .32** .23** .43** .67** .76** .43** .73** 49** .30** .37** .43** .51** .60** .84** .42** .23** .17** .43** .20** .14** .13** .32**

.48** .12** .33** .12** .16** Figure J.7. Follow-up Affective Reactions Factor Structure Note. N = 172. Standardized solution. **p < .001

215

Follow-up Affective Reactions

.64** .85** .73** .81** .64** .60**

Self- Effort Satisfaction Helpfulness Use intent Use intent efficacy

.59** .28** .47** .35** .59** .64**

.45** Figure J.8. Second-Order Follow-up Affective Reactions Model Note. N = 192. Standardized solution. **p < .001

216

APPENDIX K

IMPLICIT PROCESSING FLUENCY

An additional implicit measure of processing fluency was embedded in one of the

manipulation checks. Specifically, when participants rated how consistent each of the 12

statements was with the training video they had watched, their response times were

recorder. It was expected that when participants watched the promotion-framed video, they would respond faster to the promotion-consistent statements and that when they watched the prevention-framed video, they would respond faster to the prevention- framed video. Additionally, it was expected that there would be an interaction effect, such that a video/chronic regulatory focus match would result in an even faster response, indicating that the match enhances ease of processing.

Before testing the merit of this proposed interaction, response times were

explored for odd values. Several responses were noted as very quick (13 were below 500

ms and four additional ones were below 1000 ms). Because all of these response times

belonged to only two individuals, both cases were excluded from analysis as those

participants must have not taken the task seriously. Next, response times were

standardized within-person to look for any exceptionally slow responses. No such

responses were noted.

In order to explore the video/regulatory focus interactive effects on response

latencies, the average log-transformed (natural logarithm) promotion and prevention

217

(non-driving related items—see explanation in Manipulation Checks section of Chapter

IV) were regressed (one at a time) on video frame, RFQ promotion score, RFQ

prevention score, three two-way interaction terms, and one three-way interaction term.

There was a significant video frame main effect (discussed in Chapter IV), qualified by a

promotion X video interaction for the average response time for the promotion items (β =

-.596, t = -2.501, p = .013; R2 = .084, F(6, 183) = 2.802, p = .012). Specifically, participants who watched the prevention video responded to the promotion-framed items

faster when their promotion scores were high. No such difference was observed for those

who watched the promotion-framed video (see Figure K.1). Although not in the expected

direction, the observed interaction effect represents an interesting effect. It shows that

among participants who watched the prevention-framed video, those with high promotion

scores responded more quickly to promotion-framed items which suggests that even

though they were experiencing non-fit, they rated the promotion-framed items more

quickly as they matched their own self-regulatory orientation.

8.6

8.5

8.4 pro vid 8.3

Pro Items pre vid 8.2

Log Transformed RT for for RT Transformed Log 8.1 + SD -SD Promotion Focus

Figure K.1. Interaction of Regulatory Focus and Video Frame and Rating Response Times for Promotion-framed Items

218

When prevention video-consistent items were regressed on the predictors, none of the regression coefficients were significant. Therefore, even though there was some

evidence for a video/regulatory focus interaction effect on implicit cognitive ease, this

effect was only present for the promotion-framed items and thus this implicit measure of

processing fluency was not included in further hypothesis testing.

219

APPENDIX L

REGULATORY FOCUS MEASURES AND HYPOTHESIS TESTING WITH GRFM

It was discussed in Chapter II that there are different measures of chronic

regulatory focus, which arguably assess somewhat different aspects of the regulatory

focus construct. To briefly review, on the one hand, the Regulatory Focus Questionnaire

(RFQ) developed by Higgins (Higgins et al., 2001) is based on the assumption that past

successes and failures associated with promotion-related eagerness and prevention- related vigilance have had an impact on our general preferences for promotion/prevention goal pursuit strategies. Additionally, because it is based on Higgins’ (1987) original self-

discrepancy and self-guide theories, this measure defines promotion focus in terms of a

concern with achieving personally valuable ideals, hopes, and aspirations, and prevention

focus in terms of a concern with meeting duties, obligation, and responsibilities. On the

other hand, Lockwood et al.’s (2002) General Regulatory Focus Measure (GRFM)

assesses people’s endorsement of a series of promotion and prevention-related goals and

behaviors. Thus, it is based on a “reference-point” definition of regulatory focus, which

distinguishes between different end states that promotion vs. prevention focused people

strive to achieve (presence/absence of positive outcomes vs. presence/absence of negative

outcomes, respectively). Unfortunately, however, there is no clear empirical support for

these theoretical assumptions, other than the fact that the promotion and prevention

subscales of the RFQ and the GRFM are weakly correlated (Summerville & Roese,

2008).

220

Because of this general lack of clarity regarding the differences between the RFQ

and the GRFM, as well as their different relationships with other personality constructs,

information regarding these regulatory focus measures and other relevant scales was

collected here. Specifically, participants completed both the RFQ and the GRFM, as well as Carver and White’s (1994) BIS/BAS scale, Watson and Clark’s (1994) trait affect scale, Elliot and Macgregor’s (2001) goal orientation scale, and Godlberg’s (1999) Big

Five Factor Measure. Table L.1 (next page) lists relationships among the subscales of these measures.

An inspection of Table L.1 reveals that the RFQ promotion subscale is strongly positively correlated with the GRFM promotion subscale (r = .448, p < .001) and negatively correlated with the GRFM prevention subscale (r = -.362, p < .001). In contrast, the relationships between the RFQ prevention subscale and the GRFM promotion and preventions subscales are much weaker. These results suggest that even though one might argue that the RFQ promotion subscale has some overlap with the

GRFM, the RFQ prevention subscale seems to be assessing something conceptually different. Similar to findings reported in the literature, the relationships between the RFQ promotion and prevention subscales (r = .199, p < .001) and the GRFM promotion and

prevention subscales (r = .069, p < .05) are positive and weak.

221

Table L.1

Correlations Among Regulatory Focus Measures and Related Constructs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1.RFQ Pro (.62) 2.RFQ Pre .20** (.79) 3.GRFM Pro .45** .09** (.87) 4.GRFM Pre -.36** -.23** .07* (.77) 5.BIS -.24** .01 .06 .54** (.80) 6.BAS RR .35** .04 .69** .12** .18** (.86) 7.BAS Drive .30** -.10** .45** .04 -.07* .52** (.82) 8.BAS Fun Seek .14** -.26** .25** .06 -.10** .36** .40** (.88) 9.Perf App .22** .03 .32** .07* .07* .31** .27** .13** (.91) 10.Perf Avoid -.10** -.03 .11** .39** .26** .22** .08* .14** .25** (.89) 11.Mast App .29** .02 .42** -.00 .03 .38** .28** .13** .39** .25** (.88) 12.Mast Avoid -.24** -.13** -.05 .38** .31** .03 -.01 .04 .14** .36** .17** (.82)

13.Pos Affect .54** .05 .54** -.19** -.25** .45** .45** .29** .26** .05 .36** -.11** (.89) 14.Neg Affect -.37** -.20** -.20** .46** .45** -.12** -.10** -.07* -.06 .16** -.11** .31** -.24** (.88) 15.Openness .15** -.05 .21** .01 .01 .22** .14** .17** .03 .00 .26** -.04 .21** -.06 (.79)

16.Neuroticism -.46** -.21** -.29** .47** .52** -.15** -.18** -.14** -.07* .17** -.16** .31** -.50** .69** -.06 (.87) 17.Agreeable .31** .23** .32** -.12** .01 .29** .04 .08* .06 .04 .29** -.10** .29** -.35** .25** -.38** (.82) 18.Extraversion .41** -.08** .31** -.17** -.27** .30** .43** .42** .16** .04 .20** -.14** .52** -.29** .22** -.44** .23** (.90)

19.Conscien .55** .29** .44** -.27** -.17** .34** .29** .02 .22** -.06 .33** -.22** .52** -.35** .18** -.45** .37** .29** (.87) Note. N = 949. All responses collected during the personality assessment phase of the study.

222

In relation to other measures, in line with the Summervile and Roese’s (2008)

paper, the GRFM was more associated with the BIS/BAS measure, than the RFQ was.

Specifically, the positive relationships between the GRFM promotion scale and BAS

reward responsiveness (r = .687, p < .001), BAS drive (r = .450, p < .001), and BAS fun

seeking (r = .245, p < .001) were stronger than those between the RFQ promotion subscale and the BAS subscales (r BAS RR = .350, p < .001; r BAS Drive = .295, p < .001; r

BAS Fun Seek = .139, p < .001). Additionally, there was a strong positive relationship

between GRFM prevention and BIS (r = .542, p < .001) and no relationship between

RFQ prevention and BIS (r = .005, n.s.). Thus, there is some clear overlap between the

GRFM and the BIS/BAS scales, and a weaker association between the RFQ and

BIS/BAS scale, especially with the RFQ prevention subscale, suggesting that the GRFM

might speak more to individuals’ general approach/avoidance motivation rather than their

regulatory foci.

Both the RFQ promotion and the GRFM promotion subscales were strongly

positively correlated with general positive affect (r RFQ = .543, p < .001; r GRFM = .535, p

< .001). The RFQ prevention subscale was negatively correlated with general negative

affect (r = -.198, p < .001), while the GRFM prevention subscale was positively

correlated with negative affect (r = .464, p < .001). Thus, although there seems to be a

positive affectivity nuance in both of the promotion subscales, only the GRFM

prevention subscale was associated with negative affectivity. The lack of a strong

association between the RFQ prevention subscale is more in line with Higgins’ (1997)

223

theorizing, as neither promotion nor prevention focus is supposed to be associated with overall positive or negative affectivity.

Information regarding goal orientation was also collected in an exploratory manner. As can be noted in Table L.1, the GRFM subscales were more strongly correlated with performance and mastery approach and avoidance orientations.

Specifically, the promotion subscale was positively associated with performance (r =

.316, p < .001) and mastery approach (r = .422, p < .001) while the prevention subscale was related to performance (r = .389, p < .001) and mastery avoid (r = .375, p < .001).

This overlap between goal orientation and the GRFM should not be surprising, as items from both measures are concerned with one’s preference for different end states and as well as overall approach and avoidance tendencies.

Finally, when associated with the Big Five personality traits, the RFQ promotion subscale was most strongly positively correlated with conscientiousness (r = .550, p <

.001) and extraversion (r = .407, p < .001), and strongly negatively correlated with neuroticism (r = -.462, p < .001). The GRFM promotion subscale was also strongly positively correlated with conscientiousness (r = .444, p < .001), as well as with extraversion (r = .310, p < .001) and agreeableness (r = .319, p < .001) while the GRFM prevention subscale was strongly associated with neuroticism (r = .464, p < .001).

Based on Table L.1 and the discussion above, one can note that there is a bit of overlap between the GRFM subscales and the RFQ promotion subscale. However, further investigation of the RFQ prevention subscale is needed to clearly define the construct of

224

prevention focus it is presumed to assess and distinguish it from the construct assessed by

the GRFM prevention subscale.

General Regulatory Focus Measure—Hypothesis Testing

The interactive effects between video frame and regulatory focus match/mismatch

on focal dependent variables were tested using GRFM scores as well. Specifically,

GRFM promotion and prevention scores were both mean centered, three two-way

interaction product terms were calculated, as well as one three-way interaction product

term. To test each potential interaction effect, unit-weighted composite scores were used

to indicate emotional fit, cognitive fit, motivational fit, in-lab affective reactions,

declarative knowledge, and follow-up affective reactions. Non-English speakers were

excluded from analyses, resulting in 193 participants who completed the in-lab portion of

the study and 172 who completed all parts of the research project.

Emotional Fit

When emotional fit was regressed on the regulatory focus scores, video frame,

and the four interaction terms, a significant main effect for promotion score was revealed

(β = .505, t = 2.038, p = .043), qualified by a three-way interaction (β = .602, t = 2.215, p

= 028, R² = .084, F(7,185) = 2.419, p = .022). Figures L.1 and L.2 (next page) illustrate the nature of this interaction.

225

0.6

0.4

0.2

0 pro + SD -0.2 pro - SD Emotional Fit Emotional -0.4

-0.6 + SD -SD Prevention Score

Figure L.1. GRFM Regulatory Focus and Video Frame and Emotional Fit—Promotion

Video

0.3 0.2

0.1 0 -0.1 pro + SD pro - SD Emotional Fit Emotional -0.2 -0.3 -0.4 + SD -SD Prevention Score

Figure L.2. GRFM Regulatory Focus X Video Frame Interaction Effect on Emotional

Fit—Prevention Video

Consistent with expectations, among participants who watched the promotion- framed video, those with high promotion score and low prevention score reported the most enhanced emotional fit. However, among participants who watched the prevention- framed video, those with high prevention scores and low promotion scores reported the

226

lowest levels of emotional fit, a finding which contradicts the expected relationship

direction.

Cognitive Fit and Motivation

Next, cognitive fit was regressed on the independent variables and the product

terms. This regression revealed a significant main effect for promotion focus (β = .227, t

= 3.218, p = .002) and a significant main effect for prevention focus (β = -.145, t = -

2.037, p = .043; R² =.084, F(3,189) = 5.803, p = .001). Overall, participants with higher

promotion scores and participants with lower prevention scores reported experiencing

more cognitive ease and attentional focus. Regarding motivation, only a significant main

effect for promotion focus score was revealed (β = .311, t = 4.441, p < .001; R² = .099,

F(3,189) 6.905, p < .001), indicating that higher promotion focus was associated with

higher motivation.

Affective Reactions

Only a main effect for promotion focus was observed when in-lab affective

reactions were regressed on the independent variables and the product terms (β = .292, t =

-4.1392, p < .001; R² = .087, F(3,189) = 6.001, p = .001). The same effect was observed

when the video X regulatory focus interaction effect on follow-up affective reactions was examined. Specifically, participants with higher promotion scores had significantly more positive reactions than those with lower promotion scores, regardless of video version they watched (β = .274, t = 3.700, p < .001; R² = .090, F(3,169) = 5.573, p = .001).

227

Declarative Knowledge

Finally, information recall was regressed on video, regulatory focus scores, and the four product terms. Here, only a significant main effect for prevention focus was revealed (β = -.210, t = -2.906, p = .004; R² =.048, F(3,189) = 3.155, p = .026). Overall, higher prevention scores were associated with less recall.

228

APPENDIX M

RFQ ADDITIONAL HYPOTHESIS TESTING

In addition to testing hypotheses through structural equation modeling in LISREL

v. 8.80 (Jöreskog & Söbom, 2003), SPSS v. 19 was utilized to further explore the nature of

the observed effects. Specifically, instead of using a difference score indicating dominant

promotion or prevention focus, participants’ promotion and prevention scores were used

as distinct predictors. Hypotheses were tested through hierarchical regressions where

covariates (trait positive affect, current positive mood, concern with money, and

ethnicity) were entered at Step 1, the three main effects (video frame, promotion score,

and prevention score) were entered at Step 2, the three two-way interactions (promotion

X prevention, promotion X video, and prevention X video) were entered at Step 3, and the three-way interaction (promotion X prevention X video) was entered at Step 4 (where promotion and prevention scores were mean centered following Aiken and West’s, 1991 recommendations). For the dependent variables, unit-weighted composite scores were calculated for emotional fit, cognitive fit, motivation, declarative knowledge, in-lab affective reactions, and follow-up affective reactions.

Emotional Fit

When emotional fit was regressed on the controls, independent variables, and the product terms, only the regression coefficients for the controls were significant (R² =

.193, F(5,186) = 8.90, p < .001). To further explore potential interaction effects, the

229

emotional fit dependent variable was decomposed into its indicator subscales

(“rightness,” enjoyment, and liking) and each subscale was used as a dependent variable.

When feelings of “rightness” were regressed on the predictors, there was a marginally

significant prevention X video interaction (β = .3.68, t = 1.706, p = .090; R² = .192,

F(8,183) = 5.430, p < .001). Figure M.1 illustrates the nature of the interaction. As can be

noted, among participants who watched the promotion-framed video, those with lower

prevention scores reported feeling more “right” than those who had higher prevention scores. When liking was regressed on the predictors, there was only a marginally significant main effect for prevention focus (β = .128, t = 1.843, p = .067). No significant

coefficients were revealed when enjoyment was being predicted.

2 pro vid

1 pre vid Rightness Feelings Rightness 0 + SD - SD Prevention Score

Figure M.1. Prevention Score X Video Frame Interactive Effect on Feeling “Right”

Cognitive Fit

A hierarchical linear regression with cognitive fit as the dependent variable

reveled a significant promotion X video (β = -.614, t = -2.804, p = .006) and a significant

prevention X video interaction (β = .437, t = 2.094, p = .038; R² = .243, F(8, 183) =

7.337, p < .001). The two interactions are plotted in Figures M.2 and M.3 (next page) to

230

explore their nature. As expected, among participants who watched the promotion-framed video, those who had higher promotion scores experienced more cognitive fit than those who had lower promotion score. When prevention scores were considered, those who watched the promotion video reported more cognitive fit the lower their prevention scores were. However, unexpectedly, among those who watched the prevention video, those who had lower prevention scores had a stronger cognitive fit experience.

-2.2

-2.4

-2.6 pro vid

Cognitive Fit Cognitive -2.8 pre vid

-3 + SD -SD Promotion Score

Figure M.2. Promotion Score X Video Frame Interactive Effect on Cognitive Fit

0 -0.5

-1 -1.5 -2 pro vid

Cognitive Fit Cognitive -2.5 pre vid -3 -3.5 + SD -SD Prevention Score

Figure M.3. Prevention Score X Video Frame Interactive Effect on Cognitive Fit

231

Motivation

A hierarchical regression analysis with motivation as the dependent variable revealed a significant main effects for promotion score (β = .684, t = 1.981, p = .049), qualified by a significant promotion X video interaction (β = -.481, t = -2.172, p =.031;

R² = .226, F(8,183) = 6.667, p < .001). Figure M.4 depicts the interaction. As expected, among those who watched the promotion-framed video, the higher one’s promotion score was, the stronger their motivational engagement was.

-0.2

-0.4 pro vid -0.6

Motivation pre vid -0.8

-1 + SD - SD Promotion Focus

Figure M.4. Promotion Score X Video Frame Interaction Effect on Motivation

In-lab Affective Reactions

When in-lab affective outcomes were regressed on the predictors, a significant prevention X video interaction effect was revealed (β = .449, t = 2.087, p = .038; R² =

.197, F(8, 183) = 5.605, p < .001). As expected, the higher one’s prevention score was, the more positive reactions he/she had towards the prevention-framed video and the more negative reactions he/she had towards the promotion-framed video (see Figure M.5).

232

-1.7

-1.8

-1.9

-2 pro vid -2.1 pre vid

lab Affective Reactions -2.2 - In -2.3 + SD -SD Prevention Focus

Figure M.5. Prevention Score X Video Frame Interaction Effect on In-lab Affective Reactions

Declarative Knowledge

Only a main promotion score effect was observed when declarative knowledge was regressed on the predictors (β = .266, t = 3.783, p < .001; R² = .120, F(4,185) =

6.318, p < .001). Regardless of experimental condition, people who had higher promotion scores were able to recall more information presented during the training.

Follow-up Affective Reactions

Finally, when follow-up affective outcomes were regressed on the covariates

(general positive affect and concern with money), the three main effects, and the four interaction terms, a significant prevention X video interaction was revealed (β = .476, t =

2.062, p = .041; R² = .185, F(8,163) = 4.639, p < .001). Consistent with expectations, the

higher people’s preventions scores were, the better they responded to the prevention-

framed video. In contrast, among those who watched the promotion-framed video, the

higher their prevention scores were, the worse affective reactions they had towards it (see

Figure M.6).

233

-0.5

-1

up Affective pro vid - Reaction pre vid -1.5 Follow

-2 + SD - SD Prevention Score

Figure M.6. Prevention Score X Video Frame Interaction Effect on Follow-up Affective Reactions

234

APPENDIX N

IN-LAB VARIABLES MEASUREMENT MODEL

.16

.86* .35*

.87* .83* .21*

.75* .85* .83* .19

Emo Fit Cog Fit Motivation Outcomes Recall

.69* .92* .63* .85* .36* .65* .78* .92* .86* .79* .77* .72* .40* .56* .70*

Self- Rightness Enjoyment Liking Attntion CI Proc Fluen Mot LMot Sat Utility Use Intent Item1 Item2 Item3 efficacy

.52* .15* .60* .28* .87* .58* .40* .16* .27* .38* .41* .48* .84* .68* .50*

.22* .12* .13* .24* .16* Figure N.1. In-lab Variables Measurement Model Note: Figure illustrates the standardized solution; *p < .05.

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APPENDIX O

HYPOTHESES 2 AND 3 SEPARATELY

Additional analyses were conducted in SPSS to demonstrate that both attentional

focus and processing fluency were impacted by the video frame X regulatory focus

interaction. First, the mean score of the processing fluency subscale was regressed on

video frame, chronic regulatory focus, and their product. The regression analysis revealed

a significant main effect for regulatory focus (β = .622, t = 2.808, p = .006), qualified by a significant interaction (β = -.571, t = -2.576, p = .011). Next, the means for the attention and CI subscales were standardized and their unit-weighted mean was calculated. This score was regressed on video frame, chronic regulatory focus, and their product. The regression revealed a significant main effect for regulatory focus (β = .698, t = 3.169, p =

.002), qualified by a significant interaction (β = -.591, t = -2.684, p = .008). Figures O.1

and O.2 (next page) depict the nature of both interactions and demonstrate they are in the

expected direction. Specifically, for both dependent variables, when participants watched the promotion-framed video, they reported higher scores the higher their regulatory focus scores were (i.e. dominant promotion focus). Although differences were not as large, the reverse effect was observed among those who watched the prevention-framed video.

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8.5

7.5 pro vid 7 pre vid 6.5 Processing Fluency

6 + SD - SD Regulatory Focus

Figure O.1. Regulatory Focus X Video Frame Interaction Effect on Processing Fluency

0.3

0.2 0.1 0 -0.1 pro vid -0.2 pre vid Attentional Focus -0.3 -0.4 + SD - SD Regulatory Focus

Figure O.2. Regulatory Focus X Video Frame Interaction Effect on Attentional Focus

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APPENDIX P

ALTERNATIVE MEDIATED MODELS

Sat

Video-Frame .51** .43** Utiity Affective .42** Outcomes SE .83** .85**

Regulatory .38^ Use I Emo Fit Focus R1 .18^ .37* -.39^ .63** .92** .75** .61** Recall R2 .89** Right Enjoy Like R3 Video X RF

Figure P.1. In-lab Mediated Model—Emotional Fit Note: Figure illustrates the standardized solution; χ² (78, N = 192) = 122.00, p = .001, RMSEA = .05 (.035; .073), CFI = .98, SRMR = .043; *p < .05, **p < .001.

Sat

Video-Frame .51** .43** Utiity Affective .42** Outcomes SE .80** .84**

Regulatory .62* Use I Cog Fit Focus R1 .34* .37* -.66* .84** .46** .23** .58** Recall R2 .94** Att Proc F CI R3 Video X RF

Figure P.2. In-lab Mediated Model—Cognitive Fit Note: Figure illustrates the standardized solution; χ² (79, N = 192) = 129.93, p < .001, RMSEA = .054 (.034; .072), CFI = .97, SRMR = .047; *p < .05, **p < .001.

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Sat

Video-Frame .49** .44** Utiity Affective .43** Outcomes SE .81** .85**

Regulatory .68* Use I Motivation Focus R1 .21* .37* -.61* .61** .84** .61** Recall R2 .89** Mot LMot R3 Video X RF

Figure P.3. In-lab Mediated Model—Motivation Note: Figure illustrates the standardized solution; χ² (61, N = 192) = 60.99, p = .48, RMSEA = .00 (.00; .043), CFI = 1.00, SRMR = .036; *p < .05, **p < .001.

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APPENDIX Q

FOLLOW-UP VARIABLES MEASUREMENT MODEL

.75*

.17 .74*

.88* .34* .75*

.86* .83* .25* .83* .79* .86* .86* .20 .20

Follow-up In-lab Affective Emo Fit Cog Fit Motivation Recall Affective Outcomes Outcomes

.71 .93* .66* .86 .63* .32* .79 .92* .87 .81* .77* .74* .37 .56* .71* .69 .83* .72* .78* .67* .63*

Right Enjoy Like Att PF CI MB LM Sat Utility SE UseI DK1 DK2 DK3 Effort Sat2 SE3 Helpful UseN UseI2

.50* .14* .57* .27* .60* .90* .37* .15* .24* .35* .40* .45* .86* .69* .50* .53* .31* .48* .39* .56* .61*

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Disturbance pair Covariance Rightness-Attention .11* Rightness-Processing Fluency .24* Motivation to Budget-Utility .13* Motivation to Budget-Intentions to Use .12* Motivation to Learn – Intentions to use .15* Motivation to Learn-Effort .14* Satisfaction-Satisfaction2 .06* Self-effiacy-Self-Efficacy2 .17* Intentions to Use-Intentions to Use 2 .14* Intentions to Use-Using Now .09* Intentions to Use2-Using Now .42*

Figure Q.1. Follow-up Variables Measurement Model. Note. N = 172. Standardized solution is presented. Disturbance terms’ covariances presented in table below picture for clarity.

241

APPENDIX R

REGULATORY FIT AND MOOD

When discussing regulatory fit theory, Higgins (2000, 2005, 2006) argues that the experience of “fit” is different from the mere experience of positive and negative affect.

In support of this claim, numerous studies have controlled for positive and negative mood and have noted that regulatory fit effects are independent of them (e.g., Cesario et al.,

2004). Positive and negative mood effects were also controlled for in the current study and most regulatory fit effects on the outcomes variables were significant even after mood was added in the prediction models (see Chapter IV for details). Here, a few additional analyses are presented to explore whether a match between one’s chronic regulatory focus and the video presentation frame would impact participants’ current mood. To do that, several hierarchical regressions were conducted where overall current positive mood, overall current negative mood, high intensity positive mood, high intensity negative mood, low intensity positive mood, and low intensity negative mood were regressed on general positive and negative affectivity (controls), video frame, promotion focus, and prevention focus, three two-way interaction terms and one three-

way interaction term. Results are briefly discussed next.

Positive and Negative Mood

Table R.1 (next page) summarizes results for hierarchical regressions with overall

positive and negative mood as the dependent variables. The analyses revealed only a

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significant main effect for promotion focus for both positive (β = .199, t = 2.387, p =

.018) and negative mood (β = -.331, t = -3.895, p < .001). Overall, regardless of

experimental condition, the higher participants chronic promotion focus was, the more

positive and less negative mood they reported. No other regression coefficients reached

conventional levels of significance.

Table R.1

Hierarchical Regression Results Predicting Positive/Negative Mood

Change Statistics Model R² ΔR² ΔF df1 df2 Sig. ΔF DV: Positive Mood Controls .176 .176 20.157 2 189 .000 Main effects .210 .034 2.699 3 186 .047 Two-way interactions .227 .017 1.313 3 183 .272 Three-way interaction .227 .000 .104 1 182 .748

DV: Negative Mood Controls .104 .104 11.021 2 189 .000 Main effects .174 .070 5.247 3 186 .002 Two-way interactions .195 .021 1.561 3 183 .200 Three-way interaction .201 .006 1.361 1 182 .245

Specific Promotion/Prevention Emotions

Higgins (2000) also argues that when promotion vs. prevention focused people

experience regulatory fit, they experience different types of emotions in response to successful/unsuccessful goal pursuit. On the one hand, promotion focused individuals tend to experience high intensity positive emotions like elation and low intensity negative emotions like sadness. On the other, prevention focused individuals tend to experience low intensity positive emotions like calmness and high intensity negative emotions like

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anxiety. To explore whether promotion and prevention focused individuals reported

experiencing different types of emotions, positive and negative moods were further

divided based on intensity level.

Promotion focus positive and negative feelings. First, emotions associated with

promotion focus were examined (high intensity positive and low intensity negative

emotions). Table R.2 lists models’ significance levels. For the high intensity positive

emotions, the controls-only model is the only one which reached significance, with

general positive affect having a positive significant main effect on high intensity positive

feelings (β = .424, t = 6.347, p < .001). Even though the other models did not

significantly improve power of prediction, there was an expected significant main effect

for promotion focus (β = .183, t = 2.201, p = .029) with promotion focused people experiencing more high intensity positive feelings.

Looking at the low intensity negative emotions models reveals that adding the

three-way interaction term significantly improved the prediction model. First, a significant effect for general negative affectivity was present (β = .169, t = 2.281, p =

.024) with people high on general negative affectivity experiencing more low intensity

negative reactions. Additionally, a main effect for both promotion (β = -2.627, t = -2.873, p = .005) and prevention foci (β = -2.980, t = -1.995, p = .048) were revealed as well as a

two-way promotion-prevention interaction (β = 4.048, t = 2.151, p = .033). These effects

were qualified by a significant three-way interaction (β = -.525, t = -2.373, p = .019).

This interaction is plotted in Figures R.1 and R.2 (next page).

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Table R.2

Hierarchical Regression Results Predicting High Intensity Positive Emotions and Low Intensity Negative Emotions

Change Statistics Model R² ΔR² ΔF df1 df2 Sig. ΔF DV: High intensity pos emo Controls .183 .183 21.216 2 189 .000 Main effects .208 .025 1.941 3 186 .124 Two-way interactions .219 .011 .825 3 183 .481 Three-way interaction .219 .000 .043 1 182 .836

DV: Low intensity neg emo Controls .099 .099 10.368 2 189 .000 Main effects .165 .066 4.914 3 186 .003 Two-way interactions .183 .018 1.374 3 183 .252 Three-way interaction .208 .024 5.629 1 182 .019

-2

-4

-6 pro + SD -8 pro - SD

-10

Low Intensity Neg Emotions Intensity Low -12 +SD -SD Prevention Focus

Figure R.1. Promotion X Prevention Interaction Effect on Low Intensity Negative Emotions—Promotion Video

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0 -2 -4 -6 -8 pro + SD pro - SD -10 -12

Low Intensity Neg Emotions Intensity Low -14 + SD -SD Prevention Focus

Figure R.2. Promotion X Prevention Interaction Effect on Low Intensity Negative Emotions—Prevention Video

Prevention focus positive and negative feelings. Next, emotions associated with prevention focus were examined (low intensity positive and high intensity negative emotions). Table R.3 lists models’ significance levels. When looking at low intensity positive emotions, there were main effects for both positive and negative affectivity, with people with higher positive affectivity experiencing more low intensity positive emotions

(β = .207, t = 2.958, p = .003) and people with higher negative affectivity experiencing less low intensity positive emotions (β = -.206, t = -2.931, p = .004). Although the two- way interactions model did not improve model fit significantly, there was a significant promotion X video interaction (β = -.559, t = -2.412, p = .017), depicted in Figure R.3.

As can be noted, participants who watched the promotion-framed video experienced more low intensity positive emotions the higher their promotion scores were. In contrast, those who watched the prevention-framed video experienced more low intensity positive emotions, the lower their promotion scores were.

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Table R.3

Hierarchical Regression Results Predicting Low Intensity Positive Emotions and High Intensity Negative Emotions

Change Statistics Model R² ΔR² ΔF df1 df2 Sig. ΔF DV: Low intensity pos emo Controls .101 .101 10.589 2 189 .000 Main effects .125 .024 1.705 3 186 .168 Two-way interactions .152 .027 1.943 3 183 .124 Three-way interaction .152 .000 .093 1 182 .760

DV: High intensity neg emo Controls .078 .078 7.976 2 189 .000 Main effects .135 .057 4.103 3 186 .008 Two-way interactions .160 .025 1.820 3 183 .145 Three-way interaction .160 .000 .000 1 182 .988

3.6

3.4

3.2 pro vid 3 Emotions pre vid 2.8 Low Low Pos Intensity

2.6 + SD - SD Promotion Focus

Figure R.3. Promotion Focus X Video Frame Interaction Effect on Low Intensity Positive Emotions

For the high intensity negative emotions, there was a significant positive relationship with negative affectivity, such that higher general negative affectivity was associated with more high intensity negative emotions (β = .165, t = 2.186, p = .030). 247

Additionally, there was a significant main effect for promotion focus (β = -.300, t = -

3.447, p = .001) such that weaker promotion focus was associated with an enhanced experience of high intensity negative emotions. Although the two-way interactions model was not significant, there was a significant prevention X video interaction effect (β

= -.488, t = -2.218, p = .028), plotted in Figure R.4. Among participants who watched the

prevention-framed video, those who had stronger chronic prevention focus experienced fewer high intensity negative feelings.

3.5 3 2.5 2 1.5 pro vid 1 pre vid 0.5 0 High Intensity Neg Emotions Neg Intensity High + SD - SD Prevention Focus

Figure R.4. Prevention Focus X Video Frame Interaction Effect on High Intensity Negative Emotions

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APPENDIX S

IRB APPROVAL

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250