Understanding the Nature of Adaptive Events: a Qualitative and Quantitative Exploration of the Adaptation Process at Work

UNDERSTANDING THE NATURE OF ADAPTIVE EVENTS: A QUALITATIVE AND QUANTITATIVE EXPLORATION OF THE ADAPTATION PROCESS AT WORK

Tara A. Rench

A DISSERTATION

Submitted to Michigan State University in partial fulfillment of the requirements for the degree of

Psychology - Doctor of Philosophy

2014

ABSTRACT

UNDERSTANDING THE NATURE OF ADAPTIVE EVENTS: A QUALITATIVE AND QUANTITATIVE EXPLORATION OF THE ADAPTATION PROCESS AT WORK

Tara A. Rench

Over the last few decades, research has overwhelmingly demonstrated that adaptation is

required for success across all types of organizations – corporate, government, military, and at all

levels – individual, team, organizational. While the extant literature has led to many critical

advances that have pushed the field forward significantly over the last few decades, substantial

gaps still exist in our understanding of the adaptation phenomenon. The current study begins to

address three of these critical limitations, which include: (1) an over-reliance on static

conceptualizations and empirical examinations of adaptation; (2) a lack of attention to the nature

of the changes or adaptive events to which individuals are responding in the real world; and (3) a

limited understanding about how cognitive, motivational, and affective processes and reactions

influence the adaptation process. Specifically, to address these gaps, the current study presents a

conceptual model of the adaptation process, which posits that individuals move through three

phases of adaptation (situation assessment, planning and strategy selection, and execution and

evaluation) as they adapt to a new or changing situation. To track the behaviors that individuals are actually engaging in during each phase, several theoretically-relevant variables (e.g., contingency planning behaviors) were identified and assessed during the study. Additionally, the adaptive events reported by study participants were carefully coded using three existing frameworks (i.e., task complexity type [Wood, 1986]; adaptive performance dimension [Pulakos et al., 2000]; reactive versus proactive change [e.g., Ployhart & Bliese, 2006]). Together, these frameworks allowed for reported events to be meaningfully categorized based on the nature of

the change being encountered and the type of adaptation that was required. Several hypotheses

were explored to determine the extent to which the type of event impacts the adaptation process.

Furthermore, individuals reported their cognitive, affective, and motivational reactions to the

events, which were analyzed to understand how these states may impact, or be impacted by, the

adaptation process. Finally, several individual differences factors, including goal orientation, openness to experience, trait adaptability, and perceived autonomy, were examined to determine the extent to which characteristics about the individual may help or hinder individuals during the

adaptation process. Using an event-based sampling methodology, data were collected on 218 adaptive events from 51 employees at a small research and development company. A wide-range of event types was collected, with findings supporting the hypothesis that the nature of the event does impact an individual’s behaviors and effectiveness when adapting to that event.

Specifically, the most challenging event types tended to be those that were reactive in nature and

that resulted in an increased workload (i.e., do more in less (or the same amount) of time). When

responding to these events, individuals often reported more challenged and threatened appraisals,

higher levels of anxiety and frustration, less contingency planning, and different, often less

effective, behavioral strategies. Individual differences also impacted the adaptation process to

some degree, although performance avoid goal orientation and trait adaptability tended to have

the most impact (negative and positive, respectively) on behaviors and effectiveness. Together,

the findings highlight the importance of looking at the process of adaptation, as well as carefully

assessing the type of adaptive event. The failure to do so could mask critical patterns that provide

insight into the conditions under which individuals may be more or less successful adapting.

I would like to dedicate this work to all of my family and friends who have stood by my side and provided encouragement throughout this process. Your love and support gave me the strength and confidence to persevere through the challenges that arose along the way. And most importantly, I would like to dedicate this work to my Lord and Savior, Jesus Christ. It is only because of His provision and grace that this work could be accomplished.

ACKNOWLEDGEMENTS

I would like to thank Dr. Steve Kozlowski for his commitment as my dissertation advisor.

Steve, thank you for all of the time, effort, and guidance you provided throughout the duration of this effort. Thank you for helping me identify strategies for overcoming the challenges I encountered, and a huge thank you for your encouragement, support, and patience when the timeline was slower than planned. I would also like to thank Dr. Rick DeShon, Dr. Kevin Ford, and Dr. Brent Scott for serving on my committee and for providing suggestions and encouragement along the way. A special thank you to Rick for providing guidance on the analysis plan to help deal with the unique challenges of the data. And finally, I would like to thank the participants of this study, who took time out of their work days to provide invaluable data for better understanding the adaptation process.

TABLE OF CONTENTS

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xi

INTRODUCTION ...... 1 Adaptation Review ...... 5 Domain General Approach ...... 6 Domain Specific Approach ...... 11 Adaptation Review Summary ...... 19 The Adaptation Process ...... 19 A Conceptual Model of Individual Adaptation ...... 21 Adaptive Cycle Phase 1: Situation Assessment ...... 25 Adaptive Cycle Phase 2: Planning and Strategy Selection (Problem Solving) ...... 32 Adaptive Cycle Phase 3: Execution and Evaluation ...... 43 Outcomes of the Adaptation Process ...... 44 Inputs into the Adaptation Process ...... 45 Summary of Introduction ...... 58

METHOD ...... 59 Participants ...... 59 Description of Methodology ...... 59 Recruitment Procedure ...... 61 Selection of the Company ...... 61 Sample Recruitment ...... 62 Study Procedure ...... 62 Measures ...... 66 Background Survey ...... 66 Daily Journal Surveys ...... 68

RESULTS ...... 78 Analysis Plan ...... 78 Hypotheses Results ...... 79 Hypothesis 1 ...... 79 Hypothesis 2 ...... 82 Hypothesis 3 ...... 83 Hypothesis 4 ...... 84 Hypothesis 5 ...... 86 Hypothesis 6 ...... 87 Hypothesis 7 ...... 88 Hypothesis 8 ...... 89 Hypothesis 9 ...... 90 Hypothesis 10 ...... 91

vii

Hypothesis 11 ...... 94 Hypothesis 12 ...... 94 Hypothesis 13 ...... 95 Hypothesis 14 ...... 99 Hypothesis 15 ...... 103 Supplementary Event-Analyses...... 106 Event-Level Summary ...... 107 Person-Level Summary ...... 108

DISCUSSION ...... 110 Summary of Findings ...... 114 Event-Level Hypotheses ...... 115 Person-Level Hypotheses ...... 129 General Research Questions ...... 135 Theoretical Contributions and Implications ...... 137 Practical Contributions and Implications ...... 139 Limitations and Future Research ...... 141 Sample ...... 141 Study Methodology and Timing ...... 143 Analysis Approach ...... 146 Coding Challenges ...... 147 Future Research: Next Steps ...... 148 Conclusion ...... 150

APPENDICES ...... 151 Appendix A: Participant Guidelines: Adaptive Event Description and Examples ...... 152 Appendix B: Email Including Link to Consent Form and Background Survey ...... 153 Appendix C: Email Reminder about Consent and Background Survey ...... 154 Appendix D: Background Questionnaire ...... 155 Appendix E: Daily Journal Surveys ...... 162 Appendix F: Adaptive Events Coding Rules ...... 168 Appendix G: Tables ...... 171 Appendix H: Figures ...... 217

REFERENCES ...... 222

viii

LIST OF TABLES

Table 1. Overview of the Theoretical Perspectives of Adaptation ...... 171

Table 2. Categorization Framework for Adaptive Events ...... 172

Table 3. Summary of Gaps and Contributions ...... 174

Table 4. Event Coding Framework ...... 175

Table 5. Background Survey Correlations ...... 184

Table 6. Average Event Ratings and Outcome Variables (Quantitative Variables Only) ...... 186

Table 7. Breakdown of Hypotheses by Type ...... 189

Table 8. Hypothesis 1 Variance Accounted For ...... 190

Table 9. Hypothesis 2 Variance Accounted For ...... 190

Table 10. Hypothesis 3 Variance Accounted For ...... 191

Table 11. Hypothesis 4 Variance Accounted For ...... 191

Table 12. Primary Behavioral Strategy Use (percentage) by Coping Strategy Type ...... 192

Table 13. Effects of Learning Goal Orientation (Hypothesis 6) ...... 193

Table 14. Effects of Performance Goal Orientation (Hypothesis 7) ...... 193

Table 15. Effects of Performance Avoid Goal Orientation (Hypothesis 8) ...... 194

Table 16. Effects of Openness (Hypothesis 9) ...... 194

Table 17. Correlations between Individual Adaptability and Effectiveness by Phase and Overall ...... 195

Table 18. Effects of Trait Adaptability (Hypothesis 10) ...... 196

Table 19. Correlations between Individual Adaptability and Situation Assessment Effectiveness by Event ...... 197

Table 20. Correlations between Individual Adaptability and Planning & Strategy Selection Effectiveness by Event ...... 198

Table 21. Correlations between Individual Adaptability and Execution and Evaluation Effectiveness by Event ...... 199

Table 22. Correlations between Individual Adaptability and Overall Adaptation Effectiveness by Event ...... 200

Table 23. Effects of Autonomy (Hypothesis 11) ...... 201

Table 24. Effects of Autonomy (Hypothesis 12) ...... 201

Table 25. Hypothesis 13 Variance Accounted For ...... 202

Table 26. Primary Behavioral Strategy Use (percentage) by Reactive, Mixed, and Proactive Event Types ...... 203

Table 27. Hypothesis 14 Variance Accounted For ...... 204

Table 28. Primary Behavioral Strategy Use (percentage) by Adaptive Performance Dimension ...... 205

Table 29. Hypothesis 15 Variance Accounted For ...... 206

Table 30. Primary Behavioral Strategy Use (percentage) by Type of Complexity Change ...... 207

Table 31. Supplementary Event-Level Analyses Results ...... 208

Table 32. Supplementary Analyses Variance Accounted For ...... 212

Table 33. Summary of Event-Level Relationships ...... 214

Table 34. Example Snippets from Collected Stories Reflecting Different Event Types ...... 216

LIST OF FIGURES

Figure 1. Full dynamic process heuristic of the individual adaptation process ...... 217

Figure 2. Heuristic of the adaptive cycle phases ...... 218

Figure 3. Multiple adaptive event cycle heuristic ...... 219

Figure 4. Participant flow chart...... 220

Figure 5. Representational model of key study variables ...... 221

INTRODUCTION

Today’s organizations, and the nature of the jobs and roles within them, can no longer be characterized as stable and predictable. Instead, organizations, and those individuals working within them, are facing changing conditions on a daily basis (Bell & Kozlowski, 2008; Chan,

2000; Ilgen & Pulakos, 1999; Smith, Ford, & Kozlowski, 1997). The onus falls on organizational leaders to identify and determine the appropriate response to volatility stemming from a variety of sources, including economic and political changes, expanding globalization efforts (resulting in social and cultural changes), organizational restructuring and strategic changes (e.g., team- based work; Burke et al., 2006), and technological advances and integrations (Bell & Kozlowski,

2008; Chan, 2000; Ilgen & Pulakos, 1999; Ployhart & Bliese, 2006; Smith, Ford, & Kozlowski,

1997; Stokes, Schneider, & Lyons, 2010). As organizations make efforts to adjust to these changes, individuals within these organizations face increasingly ill-defined, complex, novel, and often unpredictable conditions (Chan, 2000; Kozlowski, Toney et al., 1999). Effectively adapting to these changes is critical to long-term organizational and individual effectiveness (Burke et al.,

2006; Chan, 2000; Kozlowski, Watola, Nowakowski, Kim, & Botero, 2009).

As a result of the clear implications of adaptation on workplace effectiveness, there has been a surge in research focused on identifying the drivers, processes, and performance indicators that constitute adaptation in the workplace (Bell & Kozlowski, 2008; Ployhart &

Bliese, 2006; Pulakos et al., 2000; 2002). The conceptual and empirical studies focused on understanding adaptation have primarily been driven by one of two goals: 1) identifying individual differences that predict adaptive performance or that comprise a trait of “adaptability”

(e.g., Ployhart & Bliese, 2006; Pulakos et al., 2000; 2002), or 2) identifying, developing, and evaluating training interventions that enhance the adaptive capabilities (e.g., knowledge and

1 skill) of individuals within a specific domain, which should in turn positively impact adaptive performance (e.g., Bell & Kozlowski, 2002; 2008; Keith & Frese, 2005; Smith, Ford, &

Kozlowski, 1997). While these research efforts have resulted in several noteworthy conceptual and empirical advances, substantial gaps still exist in our understanding of the adaptation phenomenon. First, adaptation has typically been operationalized as a relatively static construct or performance shift after a change, which provides little insight into what individuals are actually doing in response to a change (i.e., what activities are they engaging in?, how do these activities differ over time or across different kinds of changes?). A performance change does not just happen – it is an end result of a sequence of activities that individuals engage in as they try to respond and adjust to changes in their environment. While the notion of an underlying process is implicitly assumed in some of the conceptualizations of adaptation, the process itself has only been explicitly discussed in a few studies (e.g., Burke et al., 2006). Understanding the “black box” that represents the adaptation process is critical as it can begin to shed light onto why certain individual differences or training mechanisms are useful in promoting adaptive performance. Without understanding the process, we are essentially shooting in the dark when it comes to identifying the most powerful predictors and effectively leveraging interventions to maximize adaptive performance.

Another area that has received limited attention in the adaptation literature pertains to the nature of the changes or “adaptive events” that individuals face at work. The two key issues underlying this gap are: 1) the changes that individuals are responding to in most of the studies are poorly defined, and often are not driven from a theoretical framework (Baard, Rench, &

Kozlowski, 2014), and 2) the majority of the adaptation literature (especially the works coming from a training perspective) is laboratory-based, which provides limited insights into the types of

changes individuals are actually facing in their natural environments. Regarding the first issue,

there are some good exemplars in the literature of how to better define and theoretically ground the changes explored in adaptation work. For example, Pulakos and colleagues (2000) rigorously

developed an adaptive performance taxonomy that can serve as a guide for understanding the

different types of changes one may incur in the workplace and for directing change

manipulations in the lab. Additionally, Kozlowski and colleagues (e.g., Bell & Kozlowski, 2002;

Kozlowski et al., 2001) use Wood’s (1986) taxonomy of task complexity to inform the nature of

the changes they use in their laboratory-based experiments. By doing so, they are able to more

clearly describe and defend the specific changes made in the task environment and better map

how different training interventions should benefit performance after the change. Furthermore,

by thinking about how a task is changing and how one type of change is different from another

type of change, we can begin to discern how and why individuals differ in how effectively they

respond to different types of changes in their environments.

Finally, while a substantial stream of research conducted by Kozlowski and colleagues

(e.g., Bell & Kozlowski, 2002; 2008; Kozlowski & Bell, 2006; Kozlowski et al., 2001) has provided insight into how self-regulation mechanisms can impact individuals’ behaviors during the learning phase (i.e., pre-change), little is known about how cognitive, affective, and motivational states as well as self-regulatory processes and strategies may impact individuals’ behavior and performance during the adaptive phase (i.e., post-change). Initial conceptual work has begun to hypothesize what the role of regulatory mechanisms may be in team adaptation contexts (e.g., Rosen et al., 2011); however, this work is currently all conceptual and contained at the team level. Further conceptual developments at the individual and team levels, as well as empirical evaluations of the conceptual frameworks that are proposed, are needed to refine our

3 understanding of how self-regulation can inform adaptation. However, the first two gaps highlighted above need to be addressed first or at least in tandem with this gap, as we cannot predict how self-regulatory mechanisms can impact the adaptation process if we do not have a good idea of what the adaptation process looks like and the types of changes individuals are facing in their environments.

The overarching purpose of the current paper is to begin to address these three gaps in the adaptation literature. Specifically, the first objective of this paper is to expand our understanding of adaptation by building upon existing work to develop a model of the adaptation process. The adaptation process model proposed assumes that there are specific activities individuals engage in when faced with a change in their environment. Based in part in control theory (e.g., Carver &

Scheier, 2001) and existing adaptation work that embraces a process perspective (e.g., Rosen et al., 2011), this model will present a dynamic set of adaptation activities that unfold from the time a situation is assessed and a change is detected (e.g., situation assessment) through the problem solving process (e.g., identifying strategies for responding to a change) and finally ending in an execution and evaluation period (e.g., executing and evaluating the effectiveness of a response to the change). It is unclear whether these activities are distinguishable and tractable in the real world, so the second objective of the current study is to employ a descriptive event-based sampling methodology that will allow individuals to report and describe the types of adaptive events they are facing in their natural work environments over an extended period of time and provide an avenue for empirically evaluating the tenets of the proposed adaptation process. The third objective of this paper is to record individuals’ cognitive, affective and motivational states across events to begin to reveal how these states relate to individuals’ behaviors and effectiveness after a change. Finally, a few conceptually relevant individual differences (e.g.,

cognitive ability, personality, adaptability) will be examined and relationships with adaptive

responding will be explored.

Adaptation Review

While the term adaptation has been used in the organizational literature for several

decades (e.g., Trites, 1960), it has only been over the past two decades that researchers have

begun to take a more rigorous, systematic approach to studying the adaptation phenomenon (e.g.,

Smith, Ford, & Kozlowski, 1997). During this time, research has emerged from different

theoretical perspectives, each with their own strengths and weaknesses. While the diversity in

research streams is natural, and often beneficial as a literature develops, the lack of an organizing

framework for mapping the extant literature detracts from our ability to draw inferences and

identify synergies within the literature (Baard et al., 2014; Kozlowski & Rench, 2009). To this

end, Kozlowski and Rench (2009) identified a high level bifurcation in the adaptation literature –

domain general and domain specific - which allows extant research to be meaningfully

categorized into different approaches. Baard and colleagues (2014) further dissected these two

approaches to adaptation, clearly distinguishing them on several factors including their: theoretical foundation, underlying assumptions, conceptualization, typical research design and methodology, intended applications, and strengths and weaknesses (see Table 1 for a side-by- side comparison of each of these factors). Each of these approaches (and perspectives within the approaches) will be described below, and the literature falling under these approaches will be discussed in turn. By organizing the literature review within this framework, the impact of the review will be maximized by providing: 1) a clearer picture of how the current study’s perspective is situated within the larger literature; and 2) a solid conceptual and empirical basis for the gaps identified above.

Domain General Approach

Based in the individual differences literature, the domain general approach to adaptation conceptualizes adaptation as a relatively stable, typically multidimensional, construct that is

generalizable across domains (e.g., Ployhart & Bliese, 2006; Pulakos et al., 2000; 2002).

Researchers embracing this perspective seek to inform and improve the selection and

performance assessment practices within organizations through the development of relatively

broad-band, reliable and valid predictor or criterion instruments. The need for new, stable

measurement tools that predict adaptation arose from the evolution of jobs in organizations,

whereby the role-specific performance predictors of the past were no longer reliable given the shifting demands comprising today’s jobs. As a result, the majority of the work in this area has focused on instrument development and validation through correlational studies relying on subjective (self- or supervisor-) ratings of adaptation (e.g., Dokko, Wilk & Rothbard, 2009;

Oswald, Schmitt, Kim, Ramsay, & Gillespie, 2004)

Two separate, but often overlapping, streams of research have emanated from this area,

one conceptualizing adaptive performance as a performance construct (e.g., Pulakos et al.,

2000), and the other conceptualizing adaptability as an individual difference construct (e.g.,

Ployhart & Bliese, 2006). From the performance construct perspective, researchers have made

efforts toward understanding how adaptive performance fits into the broader performance criterion space, suggesting that adaptation can be defined as a unique set of performance dimensions requiring individuals or teams to respond adaptively (e.g., Pulakos et al., 2000).

Early performance models (e.g., Borman & Motowidlo, 1993; Campbell et al., 1993) did not incorporate an adaptive performance component, but as performance requirements began to shift and become more dynamic, this missing element became evident. In response, researchers began

to make efforts geared at addressing this gap (e.g., Allsworth & Hesketh, 1999; Griffin &

Hesketh, 2003, 2004, 2005). However, the work by Pulakos and colleagues (2000; 2002)

epitomizes the performance construct approach, taking a rigorous approach to conceptually

defining the adaptive performance dimensions and empirically evaluating the underlying factor

structure and providing initial criterion validity work. Specifically, Pulakos and colleagues

(2000) built their conceptual taxonomy of adaptive performance upon a thorough review of the relevant literature, initially identifying six possible dimensions of adaptive performance. After an expert-led content analysis of several hundred unique critical incidents collected from individuals across 21 different jobs, the initial six dimensions were supported and an additional two dimensions were discovered. The final adaptive performance taxonomy is comprised of eight dimensions, including: handling emergencies or crisis situations; handling work stress; solving problems creatively; dealing with uncertain and unpredictable work situations; learning work tasks, technologies, and procedures; demonstrating interpersonal adaptability; demonstrating cultural adaptability; and demonstrating physically oriented adaptability.

In a series of empirical evaluation efforts, Pulakos and colleagues (2000) developed and refined (2002) the Job Adaptive Inventory (JAI), a behavioral-based inventory based on the critical incidents. In its original form, results supported the psychometric soundness of the

instrument, demonstrating good internal consistency and high interrater agreement within jobs.

Additionally, exploratory and confirmatory factor analyses revealed that an eight-dimensional

model fit the data better than a one- or two-dimensional model, providing initial evidence for an

eight-dimension factor structure; however, moderately high correlations (rs ranging from .30 to

.69) were found among factors suggesting that the dimension distinctions may not be clean. In a

follow-up study, Pulakos and colleagues (2002) used a reduced version of the JAI (cut from 68

items to 24 items) to collect behaviorally-based performance ratings on each of the eight dimensions for a sample of 739 military personnel. Only a one-factor solution emerged from the

data, suggesting adaptive performance may not be multi-dimensional as originally hypothesized.

Later work by Griffin and Hesketh (2003) also supported a one-factor structure for adaptive

performance.

While the stream of research coming from Pulakos and colleagues (2000; 2002) has

provided a useful framework for categorizing the types of adaptive performance situations

individuals may encounter, the discrepant empirical findings raise questions regarding the

robustness of the conceptual structure of adaptive performance and create ambiguity in terms of

the true underlying dimensionality of the adaptive performance domain. It is unclear whether

adaptive performance is truly one dimensional or if the methodology (supervisor ratings) has

contributed to the inability to find the multidimensional construct proposed. The current study

intends to address this gap by having individuals report and describe the types of adaptive events

they encounter at work over the course of a week or more. By using an event-based descriptive

methodology, it is hoped that a more representative, natural set of adaptive event types emerge,

and the behaviors and effectiveness of those behaviors can be tracked across events within and

between individuals to provide a more rigorous exploration of the adaptive performance

framework. Additionally, while the work in the performance construct approach has contributed

to our understanding of the performance context, it has neglected to examine the processes that

yield adaptive performance. The current study will also target this gap, examining the processes

underlying adaptive performance across the different adaptive events, which can inform our

understanding of the robustness of the process as well as highlight performance situations where

individuals are more or less effective in their behaviors. Together, these contributions can help us

better understand the nature of adaptive performance and more clearly target individual differences and performance measurement systems to predict and capture variations, respectively, in adaptive performance over time.

While researchers situated in the performance construct perspective aim to map the criterion space, those adopting an individual difference construct perspective target the predictor

space, focusing on identifying a relatively broad-band adaptability construct (either a composite

of several constructs or a meta-competency) that can predict variance in performance in

dynamic, changing situations. As one example of an adaptability composite, Pulakos and

colleagues (2002) were among the first to describe and create a measure of individual

adaptability comprised of three multidimensional individual difference measures: past

experience with, interest in, and self-efficacy for adapting, which were based on the dimensions

of their adaptive performance taxonomy. After more than 700 military personnel provided self-

report responses to each of these scales, a series of confirmatory factor analyses found that an

eight dimensional structure fit the data best for each of the three measures. Additionally, the past

experience indicator of adaptability positively predicted performance over and above other

individual difference measures (cognitive ability and personality), suggesting that this

operationalization may have some utility. The work by Griffin and Hesketh (2003, 2004, 2005)

represent another line of work focused on identifying an adaptability composite predictor. For

example, in their 2005 study, Griffin and Hesketh operationalized adaptability as a set of three

multidimensional constructs, which they labeled: personality adaptability (behavioral rigidity,

variety seeking, openness, proactivity, and change receptiveness); self-efficacy for behaving

adaptively (confidence in behaving adaptively in adaptive performance situations); and behavioral adaptability (past experience in adaptive performance situations). While this study

did not explore how these predictors related to adaptive performance, their operationalization

provides another clear example of how some researchers are trying to capture the construct of

“adaptability” as a composite of adaptability-related individual differences.

As a key exemplar of the metacompetency approach, Ployhart and Bliese (2006) defined adaptability as “an individual’s ability, skill, disposition, willingness, and/or motivation to change or fit different task, social, and environmental features” (2006, p.13), distinguishing it from more distal KSAOs such as openness to experience. Conceptualizing adaptability as a more proximal and malleable multi-dimensional construct, Ployhart and Bliese developed a self-report

measure, called the I-ADAPT, based on Pulakos and colleagues (2000) taxonomy dimensions to

assess the eight dimensional adaptability construct. Additionally, Ployhart and Bliese contributed

to our understanding of adaptability by taking a more theoretically-driven approach, proposing a set of mediating mechanisms - knowledge acquisition; situation perception and appraisal; strategy selection; self-regulation and coping - through which adaptability is likely to influence performance.

While this avenue of adaptability research has helped inform selection practices, it is still unclear whether adaptability as a predictor construct is unique from existing individual differences, and if so, if it has any utility in predicting performance in changing situations. The I-

ADAPT is the most theoretically driven operationalization, and given the theoretical framework

proposed by Ployhart and Bliese, has the most potential to inform how a trait adaptability

construct may impact performance. Specifically, the proposed mediating mechanisms provide

viable pathways for explaining why and how adaptability may predict performance, which serves

as a guide for identifying the adaptation process activities individuals move through during a

change. Thus Ployhart and Bliese’s (2006) work will contribute to the development of the

adaptation process proposed in the current paper, which will help address the process gap

identified earlier. Additionally, the utility of the I-ADAPT measure will be evaluated in the

current study to determine if it relates to effective behaviors during adaptive events.

In summary, while a domain general perspective elicits the strengths of generalizability and widespread application of results across domains, the broad-band nature of the work in this approach limits the precision with which we can predict performance, thus reducing the variance explained. Additionally, the operationalizations of adaptation emerging from this approach are often not based in theory, and likely as a result of this, a large degree of variation exists in how the individual difference and performance constructs are defined and measured. Furthermore, the majority of the work comprising the domain general literature is absent of process, leaving questions regarding why, how and if the identified constructs are relevant to our understanding of adaptation.

Domain Specific Approach

While researchers adopting a domain general approach conceptualize adaptation as a generalizable construct, researchers with a domain specific view propose that adaptation cannot be defined outside of a specific context. This perspective comes out of the expertise (e.g.,

Holyoak, 1991) and skill acquisition literatures (e.g., Kanfer & Ackerman, 1989). With a strong training and development focus, researchers have focused on identifying and evaluating training interventions and manipulations that can induce effective learning cycles that promote the development of the knowledge, skills, and abilities (adaptive expertise) that are needed to respond to changes within a specific domain. This perspective diverges from traditional training methods as it became evident that traditional training approaches which promote routine expertise (e.g., rote memorization) were less effective at aiding performance in more complex,

dynamic, or novel conditions. The majority of the work in this approach has been theoretical or

lab-based empirical research, where experimental control over the task conditions allows for

increased precision in both the manipulation of experimental factors as well as in the

measurement of learning processes and performance change.

Two conceptualizations of adaptation are subsumed under the domain specific umbrella: a performance change or a dynamic process. While specific operationalizations vary by study

(given the assumptions of the domain specific approach), those embracing a performance change

perspective generally operationalize adaptation as a performance change from a routine

environment (or that of a training environment) to a novel environment (Baard et al., 2014; see

Chen, Thomas & Wallace, 2005; Ivancic & Hesketh, 2000; Kozlowski, Gully et al., 2001;

LePine, 2003, as a few examples). The specific nature of the change varies across studies and is

often not explicitly noted; however, the novel environment is typically characterized as more

complex, with individuals who are best able to apply and adapt what they learned during the

training or routine context to the more complex, novel context experiencing better performance

in the newly changed environment. Within the performance change perspective, Baard and

colleagues (2014) have identified three relatively distinct operationalizations of adaptation:

input-output (IO; examines how different inputs (e.g., ability or training) explain performance

after a task change); learning process (IPO; investigates processes during the (pre-change)

learning phase); and longitudinal performance change (LP; explores how performance changes

over time after a change).

A stream of research by Frese and colleagues (e.g., Dormann & Frese, 1994; Frese &

Altmann, 1989; Frese et al., 1991; Heimbeck et al., 2003) best represents the IO approach,

operationalizing adaptation as performance in a novel or more complex scenario within the

context of various computer software training programs (e.g., WordStar, Excel, SPSS). Error training was used as the input in these studies, as Frese and colleagues attempted to examine how various error training conditions impacted individuals’ outcomes (e.g., behaviors, performance) as compared to error avoidant conditions. Across studies, the results were mixed, with some studies reporting no significant differences in performance across error conditions (e.g., Frese et al., 1991), while others identified error training characteristics that had a significant positive relationship with adaptive performance (e.g., Heimbeck et al., 2003). However, the missing element in IO studies is the processes through which inputs impact performance, which yields ambiguity in terms of why and how these inputs are impacting performance.

The IPO approach begins to address the gap in the IO approach, by assessing and manipulating processes that occur during initial learning and identifying how those processes, along with various inputs (e.g., training inductions or individual difference), predict both routine and adaptive performance (Baard et al., 2014). As in the IO approach, successful adaptation is evident when performance decrements are minimized after an increase in task complexity; however, the primary objective of the IPO approach is to explore how adaptive expertise is developed during the learning phase, and in turn, how adaptive expertise impacts performance after the change. As key contributors to this approach, Kozlowski, Toney et al. (2001) presented a self-regulatory model of motivation, learning, and performance, called the Adaptive Learning

System (ALS), based on previous work that has suggested that self-regulatory mechanisms are particularly relevant to learning complex tasks (Bandura & Wood, 1989; Kozlowski, Gully et al.,

2001). Based on this model, a systematic study of adaptation conducted by Kozlowski and colleagues has examined the role of three self-regulatory pathways during learning: cognitive

(Bell & Kozlowski, 2002a, 2008; Kozlowski & Bell, 2006), motivational (Bell & Kozlowski,

2002a, 2002b; 2008), and affective (Bell & Kozlowski, 2008; Kozlowski & Bell, 2006).

Utilizing a computer-based simulation task, Kozlowski and colleagues conducted a series of

studies examining the impact of key individual differences (e.g., mastery orientation and

cognitive ability; Bell & Kozlowski, 2002a; Kozlowski, Gully et al., 2001) and training

inductions (e.g., adaptive guidance (Bell & Kozlowski, 2002b); goal framing (Kozlowski & Bell,

2006); guided exploration, error framing, and emotion-control (Bell & Kozlowski, 2008)) on

these self-regulatory processes, which in turn predicted adaptive performance. Together, these

studies (Bell & Kozlowski, 2002a, 2002b, 2008; Kozlowski & Bell, 2006; Kozlowski, Gully et

al., 2001) have established how adaptive performance is driven by cognitive mechanisms (e.g.,

metacognitive activity), motivational mechanisms (e.g., self-efficacy and intrinsic motivation),

affective components (e.g., anxiety) and behavioral indicators (e.g., self-evaluative activity and

effort allocation). However, it remains unclear how these self-regulatory mechanisms and

adaptive expertise impact adaptation processes and performance over time, as these studies focus

on a single adaptive performance trial and only imply that an adaptive process may unfold after

the change. Additionally, ambiguity exists when determining how self-regulatory mechanisms

play out in the adaptive phase as they are not explored beyond learning. However, it is possible

that the findings from the learning phase can help guide the development of a model of self-

regulatory processes in the adaptive phase. The current study will draw upon the basic tenets and

findings of this approach when developing the proposed adaptation process model.

The LP approach can be distinguished from the other performance change approaches

based on the longitudinal design that it employs which allows for performance to be measured

over multiple time periods (Baard et al., 2014). Within this approach, adaptation is

operationalized as how quickly effective behaviors are adopted and performance improves over

time. As a good exemplar of this line of work, LePine has paved the empirical path toward

examining adaptive performance over time. In a series of empirical studies (LePine, 2003; 2005;

LePine et al., 2000) using a computer-based multiple cue probability learning task, LePine

manipulated the duration and type of task changes (e.g., rule shifts, communication breakdowns)

that occurred in individuals’ and teams’ task environments, examining performance across

multiple pre-change and post-change trials. LePine (2003) found that the number of trials it took

for a team to adopt the newly effective communication strategy positively predicted performance after a change. Similarly, in 2005, LePine found that as more post-change trials passed, more teams had adopted the appropriate communication strategy. Lang and Bliese (2009) provide another example of a longitudinal performance approach to adaptation. Examining the relationship between cognitive ability and performance, Lang and Bliese found that across the learning and adaptive (post-change) trials, higher cognitive ability individuals performed better than lower cognitive ability individuals. However, during the adaptive phase (after an increase in scenario complexity), individuals with higher cognitive ability had a steeper decrease in performance as compared to those with lower cognitive ability. Together with LePine’s (2003;

2005) findings, these studies demonstrate the richness inherent in longitudinal adaptive

performance data which would have been missed had a single performance episode been used.

However, while longitudinal designs are a big step toward understanding adaptation over time,

some researchers have failed to fully exploit the data, instead resorting to aggregation-based

analyses. Additionally, while this approach assumes that an adaptive process takes place after a

change, the specific process mechanisms are not explicitly examined. As emphasized in the

current study, it is important to assess the process and performance in tandem as adaptive events

occur to determine how the process impacts performance.

While different operational approaches have been adopted within the performance change perspective, each approach contributes to our understanding of adaptation and provides insights

into how to extend the literature further. However, the work comprising this area is primarily

laboratory-based. While lab-based work is critical to the development of science, the current study aims to build upon the basic principles comprising this work by extending the study to the field. By doing so, it is my hope that the nature of the changes reported in a naturalistic setting can be used to inform subsequent laboratory manipulations and that the use of an ESM design will allow for multiple process-performance event cycles to be captured over time to help contribute to our understanding of how process and performance operate in relation to one another across different adaptive events.

As the second of the domain specific perspectives, the dynamic process perspective shifts the focus on process from the learning phase (as in the performance change perspective) to the adaptive phase, with the intent of identifying measurable components of the process that individuals and/or teams move through when trying to effectively respond to a change in their environment. Whereas the performance change approach conceptualized adaptation as effective performance in a changed task situation, the process approach defines adaptation as a series of activities that an individual (or team) engages in from the time a change occurs until performance stabilizes. Although work has been scarce, researchers first started to theorize about the adaptation process over a decade ago (Kozlowski et al., 1999; Kozlowski, Gully, Salas &

Cannon-Bowers, 1996), with recent conceptual work making great strides in explicating how the hypothesized process components comprising the adaptive cycle unfold over time at the team level (e.g., Burke et al., 2006;; Rosen et al., 2011). More specifically, Burke and colleagues

(2006) proposed a conceptual adaptive cycle (situated within a larger model of team

performance) comprised of four activities or elements: situation assessment, plan formulation,

plan execution, and team learning. The first element, situation assessment, consists of

recognizing that a change has occurred through constantly scanning the environment to check for

indications that something has shifted that may impact a team’s (or individual’s) goals. Once the

situation is assessed and a change is detected, the next adaptive cycle element is plan

formulation, which consists of setting goals, assigning member duties, prioritizing activities, and

sharing important task-related information to ensure everyone involved has a shared

understanding of the situation and the proposed plan. Once a plan is formulated, the next step is

to execute that plan. Plan execution encompasses several activities targeted at improving team

coordination during adaptation, including monitoring performance to ensure task completion,

communicating new information, assisting others and performing leadership activities. The final

element of the adaptive cycle, team learning, consists of reflecting on and developing a deeper

understanding about the nature of the change that occurred and the response taken to ensure that

teams or individuals can be better prepared to handle a similar change in the future. More

recently, Rosen, Bedwell, Wildman, Frizsche, Salas and Burke (2011) built upon Burke and

colleagues (2006) framework, retaining the four adaptive cycle elements, but incorporating

additional processes proposed to be unique to each adaptive cycle element (e.g., the situation

assessment phase incorporates cue recognition, meaning ascription and team communication

processes). These processes are then proposed to yield several emergent states, including mutual

trust, motivation, shared mental models, team situation awareness, and psychological safety,

which then influence the next element of the adaptive cycle, which in turn lead to the same

emergent states, and so on. While this framework is much more complex than discussed here, the

main contribution of Burke and colleagues work (2006; Rosen et al., 2011) is their careful

conceptualization of the adaptive cycle which has opened up the black box of adaptation,

allowing for more grounded consideration of how and why individuals and teams differ in their

responses and effectiveness in adaptive situations. However, currently the process approach is all conceptual in nature and is primarily focused on the team level. The main theoretical contribution of the current paper is to build upon the groundwork laid by Burke and colleagues to describe an individual level adaptation process, which I will then begin to empirically evaluate in a real world context, across multiple adaptive event types. Given that the conceptual process has not been evaluated empirically yet, it is not clear if the identified elements are tractable. The current study will attempt to measure each of the proposed adaptive cycle elements for each adaptive event experienced and reported by individuals. While this presents a methodological challenge, it is a critical next step in moving our understanding of the adaptation process forward.

In summary, research from the domain specific approach has been critical in advancing our understanding of the adaptation phenomena. While the domain specific perspectives benefit from increased predictive precision as a result of the contextual parameters confining the operationalization of adaptation, especially the performance change perspective, the primary weakness of this approach is the limited applicability and generalizability of findings across domains given the diverging operationalizations and context-specific processes. Additionally, the process approach is limited given the methodological difficulties facing researchers as they not

only try to provide a theoretical framework for the process, but take steps toward empirically

validating that process.

Adaptation Review Summary

By organizing the review above into a meaningful framework, it becomes clear that

researchers have tackled the adaptation phenomenon from different angles with different

intended implications. While all four theoretical perspectives have made significant contributions

in the adaptation literature, it is evident that there are still substantial gaps in our knowledge

when it comes to: 1) the adaptation process (i.e., what are the components? how does it unfold

over time?); 2) the types of adaptive events or changes that individuals are facing in the

workplace; and 3) the role of mechanisms and strategies geared toward regulating cognitive,

affective, and motivational states in the adaptive cycle. To address these gaps, the current paper

adopts a dynamic process perspective that will inform the development of the individual level

conceptual adaptive cycle model, described below. This model serves as the guide for the design

of the proposed event-based sampling study which is intended to examine the feasibility of

measuring the adaptive cycle components and explore the robustness of the proposed process.

The Adaptation Process

As the dynamic process perspective highlights above, conceptualizing adaptation as a process suggests that adaptation is more than a performance outcome; rather, adaptation is best represented as a set of behaviors and related cognitive, affective, and motivational states that individuals engage in overtime with the ultimate goal of meeting the demands of the newly changed environment (i.e., successfully adapting to the change). The focus of the adaptation work falling outside of the process perspective typically relies on performance outcomes to determine whether or not an individual has been able to meet the demands of the new environment, thus indicating successful adaptation; however, by solely focusing on the performance outcome, these approaches fail to explain why or how individuals are able to

achieve (or not achieve) acceptable performance levels. While other approaches only imply an

underlying process, the perspective adopted here attempts to explicitly identify and map the

process with the assumption that there is a set of process components that are able to be

measured empirically. Given the methodological challenges involved when trying to measure a

process unfolding over time, researchers have not yet attempted to test this assumption.

However, one of the goals of the current study is to begin to examine the feasibility of capturing

the process components empirically. Another assumption of the current approach is that if the

process components are able to be measured they will provide unique and valuable information

about how to maximize performance in changing situations (i.e., does understanding the process

buy us anything that simply knowing the performance outcome does not?). It is expected that

understanding the process will provide new insights into selection, training, and possibly even

leadership practices by helping researchers and organizations target more specific behaviors and

responses shown to be effective in dynamic situations that could be trained, monitored, or

prompted as an individual performs in his or her work environment. Additionally, based on the

logic of Campbell (1990) who stated that “performance is not the consequence(s) or result(s) of

action; it is the action itself” (p. 704), I argue that to understand the phenomenon of adaptation,

and differences in adaptation performance, a good model should not solely focus on the results of

an individual’s actions, but instead focus on the actions themselves – that is, what is an

individual doing? An individual’s decisions, actions, and reactions to a changing environment

should best capture the nature of adaptation. Together, these actions should be geared toward

aligning the current state of a situation with the desired or goal state. Therefore, for the purposes

of the current paper, I have defined adaptation as:

A dynamic process of assessing the situation (Situation Assessment), planning and

deciding on a course of action (Planning & Strategy Selection), and executing and

evaluating the selected strategies (Execution & Evaluation) with the goal of aligning

one’s behaviors with the demands of the changed environment (Reactive Adaptation),

or adjusting one’s behaviors in anticipation of a change (Proactive Adaptation) in

order to minimize or eliminate performance decrements, with all steps impacting and

subsequently being impacted by one’s cognitive, motivational, and affective states;

see Figure 1 for a heuristic of the dynamic process model).

Before diving into each of the adaptive steps highlighted in the definition above, it is first important to distinguish the individual adaptation process from the individual learning process.

In line with Burke and colleagues (2006) who discussed team level learning and adaptation, learning is proposed to be “an essential but insufficient condition” for adaptation (p. 1190).

Many adaptation researchers from the performance change approach (e.g., Bell & Kozlowski,

2002; 2008; Kozlowski et al., 2001) focus on the pre-change learning phase of a task

performance model, targeting ways to enhance the development of adaptive expertise and skills that can then be used to guide performance after a change. While this is a critical element of understanding adaptation and the drivers of adaptation, this learning occurs before the individual engages in adaptation. Additionally, the information gained by going through the stages of the adaptation process can also result in learning outcomes. However, the behavioral steps identified above which represent adaptation, or the adaptive cycle, are not learning in and of themselves.

A Conceptual Model of Individual Adaptation

While researchers have not devoted much attention to mapping the dynamic process of

adaptation at the individual level, there are existing dynamic process models in the self-

regulatory literature that can help guide thinking about adaptation as a dynamic process. Control

theory, which was brought to the psychological literature in 1960 by Miller, Galanter and

Pribham (1960) and later by Powers (1973), is the self-regulatory theory that is most consistent

with the underlying assumptions of the adaptation process. While variations exist, the most

prominent conceptualizations (e.g., Carver & Scheier, 1981; Kanfer, 1990) include several key

features: 1) goals, 2) monitoring activity, 3) discrepancy detection, 4) planning and action

execution geared toward reducing the discrepancy, and 5) a negative feedback loop that tells

people if the discrepancy has been reduced. If one accepts the premise that adaptation is

essentially goal-driven behavior – that is, the purpose of adaptation is to bring a current or

potential future state in line with the desired goal state – then using control theory as the general

underlying heuristic upon which to build a dynamic process model of adaptation makes

conceptual sense as this is the core of control theory as well as other conceptualizations of self-

regulation (e.g., DeShon & Rench, 2009; Kanfer, 1990; Vancouver, 2000). Control theory thus

provides insights into some critical steps that may be relevant for understanding behavior in an

adaptation process. First, monitoring or scanning of the environment is necessary to detect cues

that signal that a discrepancy exists. If a discrepancy does exist, an individual must decide what

actions to take to reduce the discrepancy and then retrieve feedback from the environment to

evaluate whether the actions were effective. This process is dynamic as it unfolds iteratively over

time, which is also true of the adaptation process.

While control theory is helpful in guiding thinking about adaptation as a process, the

current conceptualization of adaptation diverges in some significant ways from control theory

models of self-regulation. First, control theory assumes a closed system environment (i.e., goals

and strategy effectiveness remain fairly stable over time), while the current conceptualization of

adaptation assumes an open system environment, with environmental conditions and goals

subject to change over time as a result of individuals’ behaviors and task and environmental

factors. Second, the current view of adaptation suggests that individuals are not only motivated

to behave in response to an existing performance discrepancy, but may be motivated to act

proactively in anticipation of a change that may or may not have a negative impact on

performance. Third, control theory provides little description about how individuals appraise

cues or discrepancies that they encounter, and speak little about the specific nature of the strategy

selection process. And finally, control theory and other regulatory theories do not really discuss

how the process or contributing factors may be different depending on the nature of the

discrepancy and the specific situational conditions (e.g., new process? cultural shift? increased

workload?). The model proposed below retains some of the control theory elements that are

consistent with adaptation, but goes beyond control theory to incorporate the unique elements

highlighted above: open system, proactive behavior, appraisal process, and different situational

conditions.

Extending beyond the tenets of control theory, the model of individual adaptive cycle

displayed in Figure 1 was constructed based on a review of the adaptation literature, both at the

individual and team levels (e.g., Burke et al., 2006; Kozlowski, Gully, Nason, & Smith, 1999;

Ployhart & Bliese, 2006; Rosen et al., 2011), as well as related literatures focused on situation

awareness (e.g., Endsley, 1995) and strategy selection (e.g., Lovett & Schunn, 1999). Adopting a

dynamic process theoretical perspective, adaptation was defined (above), and the core elements

of the adaptation process (adaptive cycle) were identified: 1) situation assessment; 2) planning &

strategy selection; and 3) execution & evaluation. These elements are in line with the general

activities underlying team adaptation proposed by Burke and colleagues (2006) – detect and

frame (Situation Assessment) and act (Plan & Problem Solve; Execute & Evaluate) – which are

proposed to unfold in a dynamic, cyclical fashion. Additionally, Burke and colleagues include

cognitive, affective, and motivational emergent states as a key element of the adaptation process.

While these states manifest at a different level in Burke’s model, cognitive, affective, and

motivational states are expected to continue to play a key role in the adaptation process at the

individual level as well, resulting from and impacting the steps of the adaptive cycle over time.

Furthermore, while the purpose of the current paper is to take a descriptive look at the core

process elements, a parsimonious set of individual and situational inputs were also identified to

begin to examine how more stable factors may impact this cycle. While many input factors likely

play a role, only those factors that have strong conceptual and/or empirical ties to adaptation were included in the model (see Figure 1).

While much of the existing conceptual work on the adaptation process has been conducted at the team level, the current paper will focus on the individual level adaptation process. While the theoretical framework guiding the current model was based in some of the team-level theoretical work, only those elements that were relevant to the individual level were included. However, team and organizational level adaptation research was reviewed to help shed light onto the individual level process. It is acknowledged that restricting the model to the individual level provides an overly simplistic view of the process, as individuals are nested within teams, multiteam systems, and organizations, all of which are likely to exert top-down influences on the process (multilevel theory; see Klein & Kozlowski, 2000). However, given the lack of theoretical development at the individual level, it is argued that a simplified model is

needed as the first step in theory building with the additional complexities of the multi-level

system being incorporated in future work. Given that this model is based in part on team-level

models, it will be interesting to see if comparable constructs (e.g., situation assessment) operate

similarly at the individual and team levels, which could actually inform future work geared at

multi-level process models.

Figure 1 displays the dynamic process model adopted for the current paper. A traditional

Input-Process-Output (I-P-O) model underlies the proposed adaptation model; however, the

proposed model highlights the dynamics of the process that move beyond a temporal progression

from inputs to process to outputs. Instead, the current model highlights the dynamics within the

process itself (digging into the “black box” that typically comprises the “P”) and incorporates a feedback loop that allows for outputs to influence process as well as the process to influence outputs. A more detailed examination of the “P” – that is, the adaptive cycle – is provided in

Figure 2. This figure expands upon each of the adaptive cycle phases and depicts the dynamic relationships between each of these phases.

In the following sections, each of the three phases comprising the adaptive cycle will be described. Additionally, the conceptual relationships between the processes within and between phases, as well as between the phases and the cognitive, motivational, and affective states, will be discussed.

Adaptive Cycle Phase 1: Situation Assessment

Consistent with Burke and colleagues’ (2006) team-level model, the first phase of the individual level adaptive cycle is labeled situation assessment. This step is critical as it ultimately results in the detection, diagnosis, and appraisal of a cue that signals that there is an

existing or potential issue to which an individual needs to respond. Without situation assessment,

the remaining adaptation phases (i.e., planning/strategy selection and execution/evaluation)

would not occur as they are dependent on an individual becoming aware of a cue in the

environment that signals that a response may be needed. In her theory of situation awareness in

dynamic systems, Endsley (1995) describes situation assessment as the process through which

individuals develop situational awareness (i.e., the state of knowledge about current and future

situational conditions). Endsley proposed that in order to develop situation awareness individuals

must not only scan and identify the critical elements and cues in their environment (Level 1), but

also synthesize the cues in a way that is comprehensible (Level 2) and use this synthesized

knowledge to make projections regarding future situations (i.e., is there a change on the

horizon?; Level 3).

In line with the tenets of this theory, situation assessment is defined as a cognitively-

based process that is focused on gathering, integrating, and making sense of information from the

environment to ensure an individual has a complete and accurate picture of the situation (i.e.,

situation awareness) to maximize one’s ability to detect cues that a change has occurred or a

change may be occurring in the future (Burke et al., 2006; Endsley, 1995; Ployhart & Bliese,

2006). Specifically, situation assessment is proposed to be comprised of four general activities:

1) scanning/monitoring, 2) cue detection, 3) diagnosis, and 4) appraisal. These activities are not static; rather, they unfold over time and impact one another in dynamic ways. The speed at which individuals are able to move through these four process activities and move forward into the planning and strategy selection phase has an impact on subsequent performance outcomes and serves as a good indicator for how effective an individual is at adapting (Rosen et al., 2011).

Scanning/monitoring and detection. As mentioned above, self-monitoring is a critical feature of self-regulatory models, as it serves to focus attention on individuals’ behaviors

(Kanfer, 1990). Complementary to this is self-evaluation, which essentially provides individuals with feedback from the environment about the progress one is making toward his/her goals

(Kanfer, 1990). Together, these sources of information – monitoring one’s own actions and

receiving feedback from the task or environment about one’s current state – represent the

scanning function in the situation assessment phase. However, to successfully scan and detect changes, individuals must have some level of basic declarative and procedural knowledge regarding the task, situation, or environment in which they are operating (Chen et al., 2005;

Kozlowski et al., 2001). Declarative, or factual knowledge (e.g., knowledge of basic concepts, definitions, etc.), and procedural knowledge (e.g., knowledge about the if-then connections between elements and actions) represent the foundational knowledge that is necessary for performance (Anderson, 1982). Together these types of knowledge provide a framework from which to scan for and detect cues, as individuals should be able to notice cues that deviate from the expected conditions given what they know about the task situation.

While a solid basic knowledge base is critical for effectively scanning for and detecting cues, there are multiple types of cues possible in any environment, and depending on the nature of these cues and the situational conditions under which one is operating, these cues may be more or less difficult to detect. For example, if the environment is highly volatile, effective scanning may be difficult as too many things are changing too frequently for individuals to have a complete and accurate representation of what is going on. If cues, and relationships between cues, are constantly changing, this results in a situation with high levels of dynamic complexity

(Wood, 1986), which is proposed to make a change more difficult to detect and appraise in the environment. Conversely, if an individual is operating in a highly stable situation where procedures are highly routinized, he/she may not regularly search for or recognize cues in the environment that may indicate a change is coming or that a change is needed to move things to the next level. At the organization level, Ford and Baucus (1987) discussed a similar

phenomenon labeled organizational inertia – or the habituation of routines and processes – which

is manifested as the inability or unwillingness to react to cues signaling the need for change.

Individuals operating within a stagnant organization, or those individuals that have highly

routinized jobs characterized by very little change, may be unprepared to notice and react to cues

that indicate change is needed. The expertise literature makes a similar argument, suggesting that

experts are no better at performing tasks than novices when the configuration of cues changes

and no longer fit the well-known structures, yet experts continue to use the same routine

procedures (Devine & Kozlowski, 1995).

Additionally, the manner in which feedback is provided or information is made available

can impact situation assessment. For example, Endsley (1995) noted that inaccurate or

incomplete information from the task or the system (e.g., cockpit displays) can prevent

individuals from developing an accurate assessment of the situation. Additionally, delayed or

intermittent feedback may also mitigate one’s chances of assessing the situation in an accurate

and timely fashion.

The specific nature of the cue is also an important factor in situation assessment, as cues

range in their salience levels (Endsley, 1995). In dynamic systems, individuals must be able to

scan for and detect not only cues that indicate that there are existing problems that need

addressed (reactive), but also cues that signal that a problem may occur at some point in the

future (proactive; Ployhart & Bliese, 2006; Rosen et al., 2011). Additionally, while some cues

are easily identified (e.g., a loud alarm that sounds when an aircraft loses altitude suddenly or

direct feedback from a supervisor that performance levels have dropped), others are more subtle

(e.g., a gradual drop off in sales over the course of a month; a mid-way point in a project plan),

and at times, may be completely off an individual’s radar (e.g., focusing on the short-term, day-

to-day, instead of considering long term conditions). Finally, the cue may not indicate that

something has changed, but rather that something has not changed that should have.

Given the complexities of most organizational systems and the multiple types of cues that

are competing for individuals’ attention, it is clear that change detection is not always easy even

if individuals are diligently scanning and monitoring their environment for changes. Louis and

Sutton (1991) have identified three situational factors that may enhance individuals’ ability to

detect cues in their environments. First, when an individual encounters a new or atypical

situation, this serves as a pretty strong indicator that adaptation is needed. For example, if an

individual typically completes reports on paper, and now must complete them in a new online

system, it is clear that some activities will require adaptation. Second, adaptation should be

triggered if an unexpected discrepancy, disruption or failure occurs in one’s environment. For

example, if a previously effective strategy is no longer producing the same results, this should

serve as a cue that something has changed or needs to change. Finally, clear verbal (e.g., a boss

telling you a change is on the horizon or that you won’t be getting a bonus because your

performance levels are sub-par) or non-verbal (e.g., a warning light coming on) indicators can be

used to prime an individual to change in a situation. All of these situational conditions aid

change detection by making cues more salient to individuals.

Diagnosis and appraisal. As cues are detected, individuals are expected to evaluate the

significance and meaning of the cues using two complementary processes: diagnosis and

appraisal. Diagnosis is a cognitively-driven process, whereby individuals rely on their

knowledge and understanding about the environment to determine how and why the situation or

environment may have changed (or may potentially change) given the cue(s) detected (e.g.,

Ackerman, 1992). Proper diagnosis of the meaning of the cue is critical for effectively engaging

in the planning and problem solving process that follows. Specifically, individuals must

determine if the cue is signaling: 1) a potential opportunity to “stay ahead of the game” by

adjusting plans and strategies before (in anticipation of) future conditions change or problems occur which would require adaptation, or 2) an existing threat or problem that needs to be responded to immediately. The way the cue is diagnosed will frame how the individual then plans for and makes decisions about how to proceed in the next phase (Endsley, 1995;

Manktelow & Jones, 1987). Specifically, if a cue is perceived as a potential opportunity to stay ahead of the game, individuals should adopt a more proactive planning and problem solving approach; however, if a cue is perceived to signal an existing threat or problem, individuals

should adopt a more reactive planning and problem solving strategy.

In addition to the cognitively-driven diagnosis process, individuals are also expected to

engage in a cognitive appraisal process that has affective undertones. Lazarus and Folkman

(1984) suggest that appraisals are individuals’ evaluations of a specific situation in light of their

beliefs, values, and/or goals, which have implications for individuals’ emotional experiences and

subsequent behaviors. According to Lazarus’ transactional theory of stress, individuals engage

in two appraisal evaluations when facing a new or stressful situation (Lazarus & Folkman, 1984;

Lazarus, 1991). First, individuals make a primary appraisal to determine the relevance of the

situation – that is, how relevant or meaningful is this situation or cue to me? Second, individuals

make a secondary appraisal which is an evaluation of the resources one has to cope with the situation. The combination of these evaluations leads to either a challenged (“yes, this is relevant, but I can handle it”) or threatened (“yes, this is relevant, but I can’t handle it”) reaction to the situation. Depending on the response, individuals will have different emotional and behavioral reactions with more productive responses coming out of challenge appraisals than

threat appraisals. Integrating this logic into the situation assessment process, it can be argued that

if cues are detected that are relevant to one’s performance or situation, an individual will engage in this appraisal process to evaluate how to move forward. If the cue is appraised as a challenge, individuals will more likely engage in active, productive planning and problem solving activities.

However, if the cue is perceived as a threat, individuals may ignore the cue (continue scanning) or engage in more emotion-focused planning and problem solving strategies that are less productive. This is in line with Folkman and Lazarus (1980) who noted that individuals who feel like a situation can be dealt with constructively (more challenged) will engage in more problem- focused coping strategies, while those who feel they cannot effectively deal with the stressor

(more threatened) will invest in more emotion-focused coping strategies.

Summary of the situation assessment phase. Individuals are expected to continue to cycle through the processes involved in situation assessment until a cue or set of cues is detected that are diagnosed and appraised to be relevant and important to address at which time they will cycle into the next phase of planning and problem solving. Until that point, individuals should continue to scan their environments for additional cues or information and when a cue is deemed irrelevant and is ignored, individuals are expected to cycle back into scanning for additional

cues. However, the scanning, detecting, and appraisal components are so intertwined and are

expected to happen so quickly that it is unclear whether individuals can actually distinguish the three elements. Additionally, if the elements are empirically distinguishable, it is unknown whether or not any value is gained from gathering data on each individual element versus having a more general measurement of situation assessment. Given the exploratory and descriptive nature of these questions, I propose two general research questions:

Research Question 1: Is it feasible to empirically tease apart the scanning, detecting,

diagnosing, and appraising cognitive processes through the use of targeted self-report

questions?

Research Question 2: If unique data can be gathered on each of the three elements of the

situation assessment process, does this provide additional value above and beyond

collecting a more general measure of situation assessment?

Adaptive Cycle Phase 2: Planning and Strategy Selection (Problem Solving)

The second phase of the adaptive cycle focuses on the planning and strategy selection

(problem solving) activities that occur following the detection of a viable cue. The goal of the

planning and strategy selection phase is to develop a plan of action and decide on a set of

strategies for effectively responding to the change or potential change that the situation

assessment phase identified, such that the current state is (re-) aligned to fit the expected or

desired current or future state. However, dynamic environments are not always conducive to

carefully planning out a course of action, as conditions may change too quickly for sufficient

planning to take place. Instead, under these conditions, planning may occur as brief episodes of activity that occur over time, with plans being updated and refined as the situation unfolds.

While at the team level, Marks, Mathieu, and Zaccaro (2001) provide a theoretical framework that helps inform both the nature of the activities that are likely to occur in the

planning and problem solving stage, and the temporal model depicting how these planning and

action cycles may manifest over time. Specifically, as individuals engage in planning, they are

expected to develop goals/expectations regarding the situation and create plans of action (or

strategies) for meeting these goals (Chen et al., 2005; DeShon et al., 2004). These activities will

be driven largely by how the cue was diagnosed and the problem was framed during the situation

assessment phase. While all individuals engaged in planning and strategy selection should be

engaged in the same activities of goal setting and establishing a course of action, the content and

timing of the goals and strategies will vary based on how the situation was framed in the

previous adaptive phase. Additionally, Marks and colleagues propose that as teams move

through an event, they are likely to engage in multiple planning and action phases over time as opposed to one long planning phase followed by one long action phase. This model is proposed to be most consistent with how individuals’ planning and strategy selection activities will take place over the course of an adaptive event. The four components – goal setting, strategic planning, behavioral strategy selection and self-regulatory strategies - within the planning and strategy selection phase are discussed below.

Goal setting. In any theory of motivated behavior, goals play a central role in driving subsequent behavior (Carver & Scheier, 1981; Chen et al., 2005; DeShon et al., 2004; DeShon &

Rench, 2009; Kanfer, 1991; Kanfer et al., 1994; Marks et al., 2001). Acknowledging the importance of goals, Rosen and colleagues (2011) incorporated goal setting as one of the primary planning activities in their team level model of adaptation. When individuals (or teams) set goals or expectations for a specific event or situation they are encountering, Rosen and colleagues propose that the goals should address three issues: 1) identify what needs to be done, 2) determine the time line or time frame in which actions need to be accomplished, and 3) set a standard (level of quality) that is acceptable. For example, if a human resources assistant had previously been using an excel spreadsheet to track the status of applicants, but is now faced with adapting that knowledge to the use of a new online applicant tracking system, a goal may be: 1) I need to transfer all of the existing applicant records into the new system and learn how to enter and track new applicants in the online system, 2) this must be accomplished within the next

two weeks, and 3) all records need to be complete and accurate (no mistakes or omissions). The

purpose of goals in planning and strategy selection is to translate the information gained during the situation assessment phase into a more targeted understanding of what needs to be done in

response to the cue, which in turn provides a clear roadmap for identifying a course of action, or

set of strategies, that can be used to meet that goal, and ultimately, address the problem identified during situation assessment.

Strategic planning. By definition, adaptation requires a new or revised plan of action – one that is not already known and used by the individual facing the situation. Therefore, an

individual must use existing knowledge or strategies to discern a new strategic plan that is fitting

for the situation. Rosen and colleagues provide a description of strategic plan development that is

consistent with the premise of the current model. Specifically, Rosen and colleagues suggest that

strategic planning may look different if an individual is planning for a future event (i.e., a change

is anticipated, but not yet present) than for a current event (i.e., a change has occurred already).

Based on this conceptualization, it is assumed that most individuals operate under “Plan A,” or the initial course of action determined during planning (deliberate planning; Marks et al., 2001), until a cue is detected that suggests that Plan A is no longer feasible, or at least is no longer the most effective plan given the new information gathered. Depending on how the cue is diagnosed, individuals are likely to utilize one of two types of strategic planning: 1) if the cue suggests a potential future change, then an individual should engage in contingency planning, or 2) if the cues suggests a change already occurred, then an individual should engage in reactive strategy planning adjustment (Marks et al., 2001; Rosen et al., 2011).

Contingency planning sets the person up for a proactive strategic response through the development of an “a priori formulation and transmission of alternative plans and strategy

adjustments in response to anticipated changes in the performance environment” (Marks et al.,

2001; p. 366). Given that strategic formulation is occurring before a change occurs, individuals

should have more time to plan and develop a course of action than if responding to an immediate problem. In this case, strategy formulation or adjustment often occurs in the absence of immediate problems or performance problems, but is instead driven by the anticipation of a

change or a perceived opportunity to change something to make a process more effective

(Mullins & Cummings, 1999). That is, rather than waiting for the environment to impose a change, an individual may impose a change on the system in anticipation of a future change

(Ployhart & Bliese, 2006). Conversely, reactive strategy planning occurs when an individual encounters an unexpected change in the environment that has already occurred or is currently in motion, which requires the current strategic plan to be adjusted (Ployhart & Bliese, 2006; Rosen et al., 2011; Salas, Sims, et al., 2005). Specifically, Rosen and colleagues define reactive strategy planning as the “adjustment of strategies based on information gathered from the environment and performance feedback” (Rosen et al., 2011; p. 112), which is consistent with how Marks and colleagues conceptualized it in 2001. The majority of the adaptation literature focuses on this type of situation (see LePine, 2003; 2005; LePine et al., 2000; Kozlowski et al., 2001 as a few examples). It is expected that reactive strategy planning will need to occur on a shorter time frame as performance decrements or other problems are likely already in the works, therefore requiring shorter time periods for strategic planning adjustment. It is expected that:

Hypothesis 1: The more proactive (versus reactive) an event is and the more it is viewed

as a potential opportunity to make improvements before a change is required, individuals

will engage in (a) more contingency planning, (b) less reactive strategic planning and (c)

report longer planning time frames.

Regardless of the type of strategic planning that is dictated by the situation, the ultimate

goal of strategic planning is to develop a course of action that leads to the appropriate strategy to

be selected for the given conditions, which is the final step in the planning and strategy selection

phase.

Behavioral strategy selection. Strategy selection is critical to the adaptation process as it

determines how behaviors, cognitions, and affect will be adjusted or regulated during the

execution phase. Up until this point in the adaptation process, the goal has been to gather,

interpret, and structure information from the task and environment with the goal of appropriately

framing the situation so effective strategies can be identified. Newell and Simon (1972) argue

that the framing of a situation is what determines the problem solving strategies that individuals

choose. If the problem space is framed wrong, then ineffective strategies are likely to be adopted

and executed which could be associated with poor performance as well as negative cognitive,

motivational and affective states (Ployhart & Bliese, 2006). However, while their research

focused on identifying how individuals develop, choose, and implement strategies, Schunn and

colleagues (e.g., Lovett & Schunn, 1999; Schunn & Reder, 1998; 2001) found that people vary in

how adaptive they are when selecting strategies even when individuals frame a situation

similarly and have access to the same set of strategies. Ineffective strategy selection is often

attributed to an individual’s inability to depart from a previously effective strategy, which

requires reframing the situation and abandoning old ways of thinking and acting (LePine et al.,

2000; Newell, Shaw, & Simon, 1962). These findings suggest that it is important not to only

examine performance outcomes, but also to track strategy choices to understand how or why individuals differ in their actions and performance during an adaptive event. The following

section describes some of the behavioral strategy choices that an individual will make when

tackling an adaptive event.

From a behavioral adaptation perspective, individuals will have to choose how to adjust

their behavioral strategy when conditions change. Specifically, it is proposed that individuals

will have to choose between qualitative and quantitative changes in their behaviors (Rench,

2009). While a qualitative strategic change implies that the underlying mechanisms of the task or

situation have changed, a quantitative strategic change (or effort adjustment) implies that the

mechanisms of the task are the same, but an effort adjustment is necessary. While the word

“strategy” is typically applied to the former situation, the current paper suggests that deciding to

increase or decrease effort during a situation can also be considered a strategic choice. While

either of these paths could be appropriate, the effectiveness will depend on how the behavioral

adaptation fits the demands of the situation.

A qualitative change in behavior is represented by adopting a different behavioral

strategy or course of action, without it necessarily changing the effort level one is investing. For

example, suppose every week a restaurant manager orders a food shipment for his store, and he

typically orders from Supplier A because they have the lowest prices. However, a new supplier

(Supplier B) has entered the market and has substantially lower prices on some of the products

that the manager orders. A qualitative change in behavior would be represented by the manager

shifting his typical ordering plan, such that he stops completely or lessens the number of products he orders from Supplier A and instead orders from Supplier B (chose a different path).

While this is a simple example, an individual who determines that a qualitative shift in behavior is necessary must then decide how to identify the “right” new behavioral strategy to use. Rench

(2009) proposed two possible strategies for identifying the “right” qualitative change: 1) choose

an alternative and exploit, or 2) explore multiple alternatives (Fowler, 1965; Kaehlbling,

Littman, & Moore, 1996). If an individual embraces the “choose an alternative and exploit”

method, they will identify an alternative course of action for tackling the problem and exploit

that alternative without considering other alternatives. If this happens to be the most effective

alternative, then performance is maximized because an individual is not wasting time or

resources exploring less viable options. However, if it is an ineffective, or at least not the most

effective, strategy then this represents a poor adaptation strategy choice and performance will

likely suffer. The other qualitative change method, “explore alternatives,” consists of an

individual identifying and testing out multiple alternative strategies to determine which

alternative appears to be the most effective before settling on one. This can be a valuable process

if it is unclear what alternative has the biggest payoffs and is the most relevant for addressing the

demands of the adaptive event. Additionally, this strategy may be adopted more frequently in a

proactive adaptation situation, as an individual will have time to explore alternatives before the

change is demanded. While these two qualitative changes require individuals to shift away from

their previous behavioral strategy, a quantitative strategy adjustment is one where individuals

continue to pursue their current strategy, but change the amount of effort they are investing.

Specifically, if an individual determines that a cue is only a temporary disturbance, then an

individual may choose to persist with the same strategy but work harder or faster to overcome

the current situation. Additionally, an individual may attribute the changing conditions to be a

result of a lack of effort rather than a change in the task or environmental conditions, and thus

increase effort and pace as a means for improving outcomes. In this case, if the problem was

framed accurately, then a quantitative change in strategy may be the most adaptive response as

persisting with more effort may be appropriate. However, if the situation truly does require a

qualitatively different response, then a quantitative strategy adjustment will likely not be effective. Finally, if an individual interprets the problem situation as being too overwhelming or out of their control, the quantitative shift in behavior may be to withdraw effort from the task

completely. This strategy is often adopted as the result of ineffective motivational or emotion

control (self-regulatory or coping) strategies, which are also an important strategy choice in an

adaptive situation. These will be discussed in the next section.

State reactions and regulatory strategies. Over the course of the adaptation process,

individuals are likely to experience several cognitive, affective, and motivational states in

response to the information they are gathering and their interpretations of that information. For

example, individuals may increase their level of attention and metacognitive awareness when a

cue is identified in the environment, as it triggers the individual to increase monitoring activity

and information gathering activities. Additionally, if an adaptive event is appraised as

threatening, then an individual is likely to experience negative emotional states, such as

frustration or anxiety, which can impact their ability to appropriately frame a situation and select

an appropriate strategy as negative emotions can pull attention away from the task (Kanfer &

Ackerman, 1996). Similarly, motivation can waver as one moves through the adaptation process

due to issues of boredom, a lack of commitment, or negative emotions that reduce self-efficacy.

Cognitive, motivational, and affective states will likely impact and be impacted by each of the

adaptation phases, so while self-regulatory strategy choice is contained within the problem

solving phase, self-regulatory strategies may be necessary throughout the adaptation process as

they help keep individuals focused on the task at hand.

Self-regulatory strategies are used to guide purpose-driven behavior by maintaining

attention and effort on the task at hand (Kanfer & Ackerman, 1996; Kuhl, 1985). Similarly,

coping researchers have identified several problem- and emotion-focused coping strategies that

individuals may adopt when facing stressful conditions (Carver et al., 1989). While these

approaches originate from different literatures, the purpose of both is to identify effective and

ineffective strategies that individuals may use to regulate their cognitions, motivations, and

emotions as they engage in a course of action. The general consensus is that effective strategies will be those that help individuals focus and devote attentional resources to solving the problem or task at hand, while ineffective strategies are those that take attentional resources away from the problem, instead focusing resources on internal feelings or other events (e.g., distractions).

For example, Kanfer and Ackerman (1996) endorsed two of Kuhl’s (1985) self- regulatory strategy types: motivation control and emotion control strategies. The purpose of motivation control strategies is to fortify on-task attention even when task or performance conditions are such that motivation is likely to decrease (i.e., performance levels are stable; a task is boring, repetitive). While motivation control strategies are important regardless of task conditions, motivational factors are likely to be even more important when an adaptive event has been addressed and conditions are stable. However, it is proposed that motivational strategies may be less critical (although certainly still important) when situational conditions are changing and dynamic (Kuhl, 1985), as these situations may have more “situation strength” that result in high levels of motivation for everyone.

However, emotion control strategies are proposed to be especially critical during the adaptation process, as they focus on inhibiting negative emotional states that may undermine action (Kanfer & Ackerman, 1996). Negative emotion states (e.g., anxiety) can often draw attention away from the task (Kanfer & Ackerman, 1996) which can be detrimental during adaptation when attentional demands are high. The coping literatures propose several strategies

for dealing with stressful or emotion-laden situations which vary in their level of effectiveness

(Carver et al., 1989; Lazarus & Folkman, 1984). Problem-focused coping strategies are the most

effective, as they direct action and resources at the problem rather than at the individual or off- task activities (Lazarus & Folkman, 1984). Carver and colleagues (1989) propose several strategies that fall under the umbrella of problem-focused strategies: active coping (taking direct action to address a problem), planning (thinking about and devising action steps for tackling a situation), minimizing distractions (ignoring or suppressing activities that may distract from the task at hand), restraint coping (waiting for the appropriate time to act rather than acting impulsively), and seeking social support for instrumental reasons (seeking people out for help or advice related to the problem). All of these strategies are expected to help individuals handle and alleviate the negative emotions that come with stressful situations by focusing attentional resources on the problem at hand.

Conversely, passive- or emotion-focused coping strategies tend to refocus attentional resources on one’s internal state or distractions in the environment as a way to avoid or indirectly deal with the problem (Carver et al., 1989; Gross & Thompson, 2007). Carver and colleagues

(1989) suggest that while emotion-focused strategies are likely to be employed in response to a threat appraisal, some individuals are able to positively reappraise the situation as a challenge, thus reducing their need to engage in emotion-focused strategies. However, if a threat appraisal persists, then individuals are more likely to engage in emotion-focused strategies as a threat appraisal indicates that an individual does not have the resources to address the problem itself.

Therefore it is expected that:

Hypothesis 2: The use of problem-focused coping strategies will be associated with (a)

higher levels of on-task attention, (b) lower levels of anxiety, and (c) lower levels of

frustration than emotion-focused strategies.

Hypothesis 3: The coping strategies (problem vs. emotion-focused) used by individuals

will be associated with their appraisals of the event, such that problem-focused

strategies will be associated with (a) higher challenge appraisals, and (b) lower threat

appraisals than emotion-focused strategies.

Emotion-focused strategies include: focusing on and venting emotions (focusing on the negative emotions or stress one is feeling and sharing those feelings with others), behavioral disengagement (discussed above; reducing effort or completely giving up on goals), and mental disengagement (daydreaming, focusing on other off-task activities to take mind off of current situation, sleeping). These activities are generally ineffective as they divert resources so individuals fail to deal with the problem they are facing. Within an adaptive event context, emotion-focused coping strategies could lead individuals to miss important cues, incorrectly frame a situation, and choose ineffective behavioral strategies (e.g., withdraw effort), which all represent poor adaptation. Therefore, the following hypothesis is proposed:

Hypothesis 4: Problem-focused coping strategies will be associated with (a) different and

(b) more effective behavioral strategy choices than emotion-focused strategies.

Summary of the planning and strategy selection phase. While many factors may impact the effectiveness with which one plans and selects strategies during an adaptive event, the description above focuses on some of the key situational factors believed to impact individuals’ goal setting, planning, and strategy selection activities. Additionally, the role of cognitive, motivational, and emotional states was explored at a high level. While a much more detailed

theoretical framework could be developed to identify exactly what states impact specific

behaviors at specific times during the adaptation process, this paper is focused on developing a

more general, descriptive model that can be explored in a natural field setting. The function of

the planning and strategy selection phase is to use the information gathered and interpreted

during the situation assessment phase to develop a targeted plan of action and to determine

strategies that will be executed and evaluated in the final phase of the adaptation process.

Adaptive Cycle Phase 3: Execution and Evaluation

While the previous phase focused on the planning, or transition processes (Marks et al.,

2001), the execution and evaluation stage focuses on the actual behavioral, affective, and

cognitive actions individuals engage in during an adaptive event (Burke et al., 2006; Rosen et al.,

2011). While execution and evaluation are two separate activities, I argue that it makes the most

sense to include them in a single phase of the adaptation model as they are proposed to operate

together in a dynamic cycle. Consistent with self-regulatory models based in control theory (e.g.,

DeShon et al., 2004), after an individual makes a decision about how to act, he will engage in

behaviors consistent with that course of action and then seek feedback from the environment to

evaluate whether or not the behaviors are successful in bringing his current state in line with the

desired state he is targeting. Depending on the feedback, the evaluation process can yield

different next steps. If the feedback suggests that the executed actions are bringing an individual

closer to the intended goal state (e.g., situation is stabilizing; performance is rebounding; other

indicators suggesting that behavior is on course), then individuals should continue to execute

those actions (and continue to alternate between execution and evaluation as long as this

continues to be the result of the evaluation process). If instead the feedback suggests that the

executed behaviors are ineffective, then individuals must cycle back to an earlier phase of the

adaptation process and revise their approach. If an individual determines that the environmental conditions have not changed and that the problem is appropriately diagnosed and framed, then the individual should shift back to the planning and problem solving phase and choose a different strategy or alter the current strategy. However, if an individual determines that the environment

has changed once again or that the problem was not appropriately diagnosed or framed, than the

individual may cycle back to the situation assessment phase to gather additional information and

reframe the problem which will then drive a new plan and strategy, and ultimately, new actions

being executed.

Outcomes of the Adaptation Process

While the focus of the current paper is on understanding the activities that comprise the

adaptive cycle phases described above, the effectiveness of the behaviors and processes engaged

in during an adaptive event are expected to result in two primary outcomes: performance and

learning (refer back to Figure 1).

Performance. Given that the majority of adaptation research relies on performance evaluations to determine how well an individual adapts, it is important to evaluate performance

indicators to keep some connection to the extant literature. Performance indicators can vary in type and depend on what an organization wants to assess. For example, performance can be assessed subjectively through self- or other-ratings of adaptation effectiveness as a whole, or for

each individual phase of the adaptation process (e.g., how effective was Person X at assessing

the situation?). Objective indicators of adaptation can also be used. For example, sales dollars

can be tracked over time to see how low sales dropped after a change before rebounding, or to see how long it took sales dollars to rebound after a change. From a proactive adaptation standpoint, performance can be assessed by examining how effective an individual is at

anticipating and avoiding crises. Regardless of the specific measurement method, performance is

proposed to be an outcome of adaptation in the current model as opposed to representing

adaptation directly.

Learning. Another outcome of the adaptation is learning. As individuals cycle through the adaptation process, evaluating the effectiveness of their choices and actions along the way, they are likely to acquire and structure new information about a situation (Burke et al., 2006;

Rosen et al., 2011). That is, with every evaluation, an individual is learning what is and what is not effective which can be stored as knowledge and used in future situations that present similar circumstances. Additionally, when individuals reflect on an adaptive event as a whole, it is possible that they will be able to identify effective and ineffective patterns of behavior that can be used to extract principles or guidelines for how to approach future adaptive situations.

Inputs into the Adaptation Process

As the adaptive cycle was described above, several situational and environmental characteristics were mentioned that may impact the various phases of the adaptive cycle. This section will highlight more general inputs into the adaptive cycle that may exert influence on any or all of the phases depending on the individual and the situation. Given that the current study will employ a naturalistic methodological approach, the hypotheses proposed in this section are more general and descriptive in nature than would be in a highly controlled experimental study.

As depicted in Figure 1, two types of input will be focused on: individual (person) factors and situational factors.

Individual factors. Previous research on adaptation has lent support for both cognitive and non-cognitive predictors of adaptation, although some of the findings are mixed given the varying operationalizations of adaptation adopted (Baard et al., 2014). Domain general cognitive

predictors (e.g., general cognitive ability, working memory, and attention) have consistently demonstrated significant relationships with adaptive performance. As the most commonly assessed, cognitive ability (g) taps into one’s ability to process information, reason, and learn

(Hunter, 1986), and is one of the most consistently supported predictors of complex task performance within and outside of the adaptation literature (Hunter & Hunter 1984). Within the

adaptation literature, LePine and colleagues (2000) found that cognitive ability had a stronger

relationship with performance after a rule change in a decision making task than before the

change, suggesting that cognitive ability is especially important in novel or changing

environments that require more information processing demands. Similarly, using a computer-

based simulation task, Kozlowski and colleagues (2001) found that cognitive ability predicted

knowledge acquisition during learning which in turn benefited performance after an increase in

complexity. Together, these findings support the notion that cognitive ability is important for

situations characterized by high levels of information processing demands. The more

cognitively-laden an adaptive event or situation is, the more likely it is that cognitive ability will

influence one’s ability to effectively assess and diagnose the situation and plan and problem

solve effective strategies. Therefore, cognitive ability is expected to play a critical role in the

adaptation process for the majority of, if not all, adaptive events, but it should demonstrate the

greatest effects in highly cognitively demanding situations. Therefore:

Hypothesis 5: Cognitive ability will be positively related to effective behavioral strategy

use and self-reported adaptation effectiveness for each phase and overall, especially in

cognitively-driven adaptive events.

While g represents a trait level indicator of mental ability, cognitively-based states

(primarily developed through learning in a specific domain) are also expected to exert influence

on adaptive behaviors. For example, Endsley and Smith (1996) suggested that individuals’ prior

experience and learned cognitive structures (e.g., mental models) play a critical role in helping

individuals identify what the critical elements or cues are that they should be monitoring during

this phase, as they are likely different across events and domains (e.g., firefighting vs. flying an

aircraft). This proposition is in line with the conceptual and empirical work conducted under the

performance change theoretical perspective of adaptation that was reviewed earlier (e.g.,

Kozlowski et al., 2001). Kozlowski, Gully, Brown, Salas, Smith, and Nason (2001) found that

better knowledge structures predicted higher performance in a more complex scenario of a

computer-based simulation task. While specific adaptive processes such as situation assessment

were not measured, it can be assumed that those with better knowledge structures were better

able to detect critical cues in their environment due to their deeper understanding of the task

structure and the relationships between elements. Similarly, those individuals with more task- or

situation- related basic and strategic knowledge tend to perform better in more complex

simulation environments as well (e.g., Bell & Kozlowski, 2002). Together, these findings lend

credence to the benefits of developing adaptive expertise (Smith, Ford & Kozlowski, 1997), or

flexible knowledge structures, during learning as it helps guide the decisions one makes during

an adaptive event. While cognitive structures are expected to be beneficial to the adaptive cycle,

the nature of the current study will not allow for specific hypotheses to be tested.

In addition to cognitive predictors, several non-cognitive predictors have been examined

in their relation to adaptive performance. However, for the purposes of the current study only

three factors will be explored: goal orientation, openness to experience, and trait adaptability. As

a motivational construct, goal orientation has been defined as having three dimensions: learning

goal orientation (LGO), performance-prove goal orientation (PPGO) and performance avoid goal

orientation (APGO; Vandewalle, 1997). Individuals exhibiting high LGO focus on learning

rather than performance outcomes, and as a result, they are more likely to engage in exploratory

behavior, seek more feedback, and experience more positive outcomes (e.g., self-efficacy,

adaptive performance; Ford et al., 1998; Kozlowski et al., 2001; Payne, Youngcourt & Beaubien,

2007; Vandewalle, 1997). Additionally, individuals with a learning goal orientation tend to seek

out situations that are challenging and novel and are also expected to appraise situations as more

challenging than threatening, as those high in LGO usually view errors or setbacks as learning

opportunities. Therefore it is expected that:

Hypothesis 6: Individuals higher in LGO are expected to (a) engage in more exploratory

qualitative strategies, (b) experience less anxiety and (c) experience less frustration, and

(d) feel more challenged by the event than those low in LGO.

Conversely, individuals high in PPGO and APGO tend to be focused on performance outcomes and avoiding failure as opposed to learning the environment (Vandewalle, 1997).

Typically, individuals with performance orientations are motivated by situations that will allow them to demonstrate positive performance (minimize errors) and are threatened by situations that

may make them look incompetent. These individuals are likely to be motivated to avoid the

exploratory strategies as exploration often results in errors as individuals engage in trial and error learning. Instead, individuals high in PPGO are expected to be more comfortable identifying and exploiting a strategic option that currently appears to be the most viable (choose alternative and exploit), whereas individuals high in APGO are likely to engage in less risky or avoidant strategy choices, such as behavioral disengagement and emotion focused coping.

Hypothesis 7: Individuals higher in PPGO are expected to engage in more exploitation

strategies than those lower in PPGO.

Hypothesis 8: Individuals higher in APGO are expected to (a) engage in more avoidant

behavioral strategies, (b) use more emotion-focused coping strategies, (c) appraise more

situations as threatening, (d) experience more anxiety, and (e) experience more

frustration than those lower in APGO.

Similar to LGO, openness refers to the tendency to be more open and curious about new

alternatives and new surroundings and to seek out situations that are novel and intellectually

challenging (Barrick & Mount, 1991; Costa & McCrae, 1992). While openness is typically not a

strong predictor of performance, LePine and colleagues (2000) found that openness to

experience was a good predictor of performance after a change, suggesting that openness is

especially important in situations that are novel or dynamic. Additionally, individuals higher in

openness have been found to engage in more self-monitoring behaviors (e.g., Blickle, 1996),

which suggests that they may exhibit more effective situation assessment behaviors than those

lower in openness. Furthermore, LePine (2003) found that openness was positively related to

role structure adaptation, operationalized as the number of trials it took teams to switch to the

most effective communication structure after a change, suggesting that openness may also aid

individuals when they are selecting and executing adapted strategies. Adaptive events that are

conducive to exploration and are relatively unexpected and ambiguous are likely to be most

impacted by openness. The current study will explore how openness impacts adaptation, and

specifically expects that:

Hypothesis 9: Individuals higher in openness are expected to (a) report more effective

situation assessment activities, (b) engage in more exploratory qualitative strategies, (c)

experience less anxiety, (d) experience less frustration, and (e) make more challenge

appraisals than those low in openness.

Earlier in the paper, I reviewed the adaptability work conducted by Ployhart & Bliese,

2006 who conceptualize adaptability as a trait individual difference mirroring the eight

dimensions of adaptive performance identified by Pulakos and colleagues (2000). Specifically,

Ployhart and Bliese developed the I-ADAPT measure to capture one’s self-reported adaptability on each of the eight dimensions, which should then predict critical mediating processes (e.g., strategy selection, self-regulation and coping) relating to performance. While the theoretical perspective adopted in this paper proposes that adaptation is a process, and not an individual difference, the current study will try to evaluate whether or not the dimensions of the I-ADAPT relate to adaptation activities across different types of events. More specifically, the current study will categorize the adaptive events reported into the eight dimensions (described in the next section), and the scores for the I-ADAPT will be examined to determine if individuals scoring higher on adaptability dimensions relevant to the adaptive event they are facing engage in more effective behaviors. If the I-ADAPT is a valid measure of adaptability, it is expected that:

Hypothesis 10: Higher scores on the adaptability dimensions will be related to more

effective ratings of the adaptation phases and overall adaptation when the situational

characteristics of the adaptive event mirror that dimension.

While not a trait like the factors listed above, perceived autonomy is also expected to impact individuals’ actions during an adaptive event, as autonomy can more or less constrain how much an individual can do to adjust to changes. Autonomy is defined as the independence/freedom to determine how and when to act and/or coordinate activities (Burke et al., 2006; Hackman & Oldham, 1980). In an environment that is low in autonomy, individuals may have to ask and wait for approval before actions can be taken or ideas can be pursued and are likely to engage in fewer important activities and feel less ownership over their behavior and

performance than those with high levels of autonomy (Hackman & Oldham, 1980). Conversely,

individuals with high levels of autonomy typically feel more empowered to make immediate and

important decisions on their own, which is expected to be beneficial in adaptive environments.

Autonomy is proposed to be especially important during adaptive events that are time-pressured,

since quick action is necessary to be adaptive in those situations. If this is the case, it is also

possible that individuals in low autonomy environments may experience more negative reactions

in the face of a change, as they may feel less able to deal with the change. Additionally,

individuals in low autonomy environments may be more likely to adopt the problem-focused

coping strategy focused on seeking out social support for instrumental reasons, as they will likely

have to seek out their supervisors for permission and feedback before engaging in a course of

action. Therefore, autonomy is expected to impact adaptation in the following ways:

Hypothesis 11: Individuals reporting lower levels of autonomy will (a) report more

seeking social support strategies, (b) experience more anxiety, and (c) experience more

frustration than those in high autonomy environments.

Hypothesis 12: Individuals reporting higher levels of autonomy will report (a) more

effective behavioral strategy use and (b) higher levels of adaptation effectiveness overall

and by phase than those in low autonomy environments.

Adaptive event (situation) factors. The individual adaptation process model proposed

above is expected to provide a fairly generalizable set of adaptive cycle phases that should occur

regardless of the nature of the adaptive event being faced. However, there has been minimal

work conducted that examines how the type of adaptive situation an individual encounters may

influence their behaviors and performance, as many studies only explore adaptation within a

single situation or domain. Additionally, the majority of the work on adaptation has been

conducted within a laboratory context, which limits our ability to identify the types of adaptive events are actually experiencing at work. To address this gap, the current study will use a descriptive field study approach to begin to categorize the types of adaptive events reported over an extended period of time and begin to explore how the adaptation process, or at least the specific strategies employed and key drivers of effectiveness, may vary depending on the specific characteristics of the adaptive event.

For the purposes of the current paper, adaptive events are defined as confined situations that begin when a cue is identified in one’s work or task environment that indicates a change or potential change in conditions that diverge from normal routine or expectations, and requires an individual to use new or adapted responses. Depending on the volatility of one’s work environment, individuals may experience very few to several adaptive events each day. By capturing data at the event level, it may help advance our understanding of adaptation by allowing for events to be captured over time and categorized into two existing frameworks

(Pulakos et al., 2000; Wood, 1986). By doing so, it is possible to start identifying patterns of

effective and/or ineffective strategies within each event type that can be used to inform

subsequent training processes. Additionally, capturing event-level data allows for an examination

of within-person, between-event differences (e.g., same person behaving in a cultural adaptive

event versus a stress adaptive event), as well as between-person, within-event differences (e.g.,

different people behaving in a cultural adaptive event), although these differences will be

primarily descriptive in nature for the current paper. While beyond the scope of the current

paper, an event-based approach could also be used to examine how process-performance cycles

vary across events, and explore how the process-performance cycle in one event may influence

the process-performance cycle in a subsequent event (see Figure 3 for an example of what this

may look like).

For the current study, the goal was to utilize existing theoretical frameworks to categorize the adaptive events reported as suggested by Baard and colleagues (2014). Specifically, adaptive events will be categorized into one of the dimensions of Pulakos and colleagues (2000) adaptive performance taxonomy, as well as categorized based on the task complexity dimensions proposed by Wood (1986). Both of these frameworks have strong conceptual foundations and are proposed to separate the reported events into meaningful adaptive clusters. Additional categorizations will be more exploratory in nature and will focus on breaking events into reactive vs. proactive events and into the primary underlying driver (cognitive or affective). All of these categories are depicted in Table 2.

The two primary frameworks will be described in the next section, but first, the smaller breakdowns will be described. It is possible that complexity types or adaptive dimensions may naturally align with reactive vs. proactive or cognitive vs. affective categories. While the expected alignment is proposed in Table 2, this categorization has not been tested. The first of these, reactive vs. proactive events, was identified as a meaningful breakdown given the reactive vs. proactive adaptation component addressed in the adaptation model. Reactive events are those events that unfold as a result of an existing change, while proactive events are those that arise due to a potential future change. It is reasonable to expect that there will be differences in one’s appraisal of these two types of events, as well as differences in the strategies they employ (and how effective they are) when responding to the event.

Hypothesis 13: The extent to which an event is characterized as reactive or proactive will

determine (a) how the event is appraised (perceived challenge and threat); (b) what

behavioral strategies are used; and (c) the effectiveness of one’s strategies.

The cognitive versus affective distinction is intended to help categorize events by the

primary paths that are likely to influence behaviors and performance within those events.

Specifically, events that require more cognitive effort or attention (e.g., learning a new

procedure) will be classified as a cognitive event, whereas events that require more affective or emotional regulation (e.g., completing a task under increased time pressure) will be classified as an affective event. It is acknowledged that this distinction may be difficult to make and there is

likely to be a combination of cognitive and affective factors within several events. However, the

current study will attempt to determine whether this distinction can be made effectively.

As described earlier in the review, Pulakos and colleagues’ (2000) adaptive performance

taxonomy was generated and revised through both theoretical and empirical techniques. Through

a careful review of the literature on adaptive performance, an original set of dimensions were

identified. Subsequently, several hundred critical incident reports were collected and sorted by

SMEs, which resulted in the eight adaptive performance dimensions listed in Table 2: handling

emergencies or crisis situations; handling work stress; solving problems creatively; dealing with

uncertain and unpredictable work situations; learning work tasks, technologies, and procedures;

demonstrating interpersonal adaptability; demonstrating cultural adaptability; and demonstrating

physically oriented adaptability. Each of these dimensions is characterized by different

situational demands, which could have implications for strategy use, cognitive and affective

reactions, and effectiveness both within- and between-individuals. For example, effective

adaptation during events characterized as “handling work stress” is expected to be driven by the

use of appropriate emotion-control strategies that focus individuals on the problem instead of the

stress and anxiety that is likely to occur as a result of the situational demands (as indicated by the

“Affective” label in the Construct sub-category in Table 2). Conversely, an adaptive event

categorized as “solving problems creatively” is more likely to be driven by the effective use of

exploratory behavior strategies geared toward identifying the best new way to tackle the situation and is expected to be influenced most heavily by cognitive factors, including cognitive ability and creative problem solving, rather than affective factors. For the current study, the categorization and pattern examination will be descriptive and exploratory in nature, with the expectation that:

Hypothesis 14: The adaptive performance dimension into which an event is categorized

will determine (a) how the event is appraised (perceived challenge and threat); (b) what

behavioral strategies are used; and (c) the effectiveness of one’s strategies.

From a slightly different perspective, some adaptive events can also be categorized according to how the task changed – that is, at a more fine-grained level, how did the elements of the task change? While it is reasonable to try to categorize all events based on Wood’s task complexity breakdown, the taxonomy may not be appropriate for some types of changes. For example, those events classified as “demonstrating interpersonal adaptability”; “demonstrating cultural adaptability” and “demonstrating physical adaptability” based on Pulakos and colleagues’ (2000) framework are likely to broad in nature for Wood’s (1986) classification to be appropriate. However, it is possible that the specific changes characterizing the remainder of the events may be meaningfully classified based on Wood’s three levels of task complexity: component complexity, coordinative complexity, and dynamic complexity. If an event is categorized as having a change in component complexity (often equated with task difficulty), the

change should require that more elements or activities be processed and responded to (i.e., more behaviors to engage in) in order to perform the task than were required previously, and typically

within the same amount of time. Comparing this categorization to Pulakos’ dimensions, it is

clear that “handling work stress” or “handling emergencies or crises” may include events that are

characterized by component complexity changes, as often times the stress or emergency results

in conditions of having to do more in the same amount of time or with fewer resources.

Individuals encountering this type of change during an adaptive event may be more likely to

engage in a quantitative strategy shift, employing the "work harder and faster" approach. The

second category of task complexity change is represented by a change in coordinative

complexity, or a change in stimulus-response relationships. In other words, coordinative

complexity changes occur when the form, strength, or sequencing of behaviors resulting in

outputs changes, requiring a novel strategy or course of action to be enacted to effectively handle the task. Going back to Pulakos’ framework, events categorized as “solving problems creatively” or “learning new work tasks, technologies and procedures” may be most likely to stem from coordinative complexity changes. As mentioned, individual encountering this type of change should be more likely to engage in qualitative strategy adjustments focused on identifying a different course of action, rather than using the same strategy and exerting more effort. Finally,

the third type of complexity change, a change in dynamic complexity, results from a dynamic

shifting in the nature of the relationships between task inputs (behaviors) and the types and

effectiveness of the outcomes. While changes of this nature are more difficult to map onto

Pulakos’ framework, it is expected that changes in dynamic complexity will require more

monitoring and strategy revision as the event unfolds if the nature of the relationships between

inputs and outputs continue to change.

Hypothesis 15: The type of complexity change that represents an event will determine (a)

how the event is appraised (perceived challenge and threat); (b) what behavioral

strategies are used; and (c) the effectiveness of one’s strategies.

In sum, one of the main gaps in the adaptation literature is our lack of knowledge

regarding the types of adaptive events individuals experience at work and how the characteristics

of those events may influence the adaptation process. As described above, to begin to address

this gap, the current study will attempt to gather descriptive data about events encountered

throughout the work day across several days for multiple individuals in multiple roles. To

meaningfully organize the events, established taxonomies will be used to categorize these events.

While there is likely to be some overlap between Pulakos’ and Wood’s categories, it is not

expected that the overlap will be clean or consistent. Instead categorizing events using the two

taxonomies provides two different perspectives for understanding how the adaptation process

may vary across situations. Additionally, using both frameworks to categorize events will allow

the current study to best connect to and inform future research in the other adaptation theoretical

perspectives as both taxonomies have been used within the adaptation literature (e.g., Bell &

Kozlowski, 2008; Kozlowski et al., 2001; Pulakos et al., 2000). Together, these frameworks are

proposed to provide valuable insight into the generalizability of the adaptation process and

should begin to shed light on effective or ineffective cognitive, affective, and behavioral patterns

that may emerge within specific event types that can be used to inform future studies as well as

training practices. Finally, the data from the current study will attempt to explore the relative

impact of the event factors (e.g., type of complexity change) versus the individual on the

adaptation process by investigating the following exploratory research question:

Research Question 3: Will within-event/between-person OR within-person/between-event

differences be more prominent given the proposed categorization scheme?

Summary of Introduction

Together, the research questions and hypotheses identified above are proposed to address

the three gaps identified in the current paper (see Table 3 for a summary). The exploratory nature

of this study also leaves the door open to further supplementary analyses where appropriate to allow for a better understanding of the adaptation process.

METHOD

Participants

Participants were recruited from a small research and development company with

employees distributed across multiple offices throughout the United States. All individuals

employed at the company at the time of recruitment – one hundred and twenty-three individuals

– received the initial study invite. Sixty-four (52%) individuals consented to participate in the

study, and sixty-two (50%) individuals completed the background survey1. Forty (64%) of the participants completing the background survey were male. The age of the participating individuals was fairly diverse, with 13% (n = 8) under 25; 40% (n = 25) between 25-34; 19% (n

= 12) between 35-44; 18% (n = 11) between 45-54; and 10% (n = 6) 55 and over. Nearly all

(98%) of the sample indicated that they were not Hispanic or Latino, with 94% (n = 58) indicating they were white. Fifty percent (n = 31) identified their primary job responsibility as research, 24% (n = 15) as software development, 10% (n = 6) as Senior/Exec Management, and

16% (n = 10) as Administration/Finance. Finally, 18% reported being at the company for less than a year, 23% (n = 14) between 1-2 years, 18% (n = 11) between 3-4 years, 13% (n = 8) between 5-6 years, and 29% (n = 18) 7 years or longer. The characteristics of the study sample were representative of the demographic makeup of the company.

Description of Methodology

The current study utilized an experience sampling methodology (ESM) to address the study’s research questions. ESM techniques were developed to overcome the limitations of

1 Two individuals consented but completed less than half of the background survey and did not provide any daily journal entries. These two individuals were excluded from all analyses. One individual completed the majority of the background survey (all but the IPIP), so their data for the remaining background scales was retained. All other individuals who started the background survey completed it. Those individuals completing the background survey were retained for analyses, regardless of how many daily journal surveys they completed. The sample size varied by the analysis being conducted.

cross-sectional methods by providing a platform for capturing momentary assessments of the

phenomena of interest as individuals operate in their natural environment (Alliger & Williams,

1993; Beal & Ghandour, 2011; Beal, Weiss, Barros, & MacDermid, 2005). By tracking

individuals’ reactions (including cognitions, affect, motivations, and behaviors) to different

events over time within their natural context, it is possible to examine within-person variations in

individuals’ responses to different situational conditions (i.e., different events; Beal & Weiss,

2003; Hormuth, 1986; Uy, Foo, & Aguinis, 2010).

Several ESM sampling techniques have been identified and used within the ESM

literature, including interval-, signal-, and event-contingent sampling protocols (Alliger &

Williams, 1993; Beal & Weiss, 2003; Uy et al., 2010; Wheeler & Reis, 1991). Each of these

sampling approaches has strengths and weaknesses, and some are better suited for certain

questions than others. Given that the current study is interested in capturing the nature of and

responses to specific events in one’s work environment, the event-contingent protocol was

adopted. The key to obtaining valid data in an event-contingent study is providing participants

with a clear understanding (i.e., an unambiguous operational definition) of the “event” of interest

(Beal & Weiss, 2003). For the current study, individuals were provided with guidelines for

identifying adaptive events in their work environment (see Appendix A for the full set of

guidelines). Specifically, adaptive events were defined as situations that you encounter during

your work day that deviate from your normal routine or expectations due to changes in your

work or task environment. Each day, individuals were asked to identify one adaptive event they

had experienced that day and respond to a series of questions about that event, either

immediately following the occurrence of the event, or at the end of the day. While responses

occurring closer to the event are likely to be more reliable (i.e., less susceptible to bias due to

time elapsing after the event), the added flexibility to respond about an event at the end of the

work day is necessary in a real-world environment.

Recruitment Procedure

Selection of the Company

One of the biggest challenges facing researchers using ESM is securing an organizational

field sample (Beal & Weiss, 2003; Uy et al., 2010). For the current study, the target sample size was 50-60 individuals within one organization, which is about average for a typical experience sampling study (Uy et al., 2010). Experience sampling studies often have smaller sample sizes than the typical research study given the time commitment demanded of participants (Beal &

Weiss, 2003). However, each individual provides responses at multiple time points, thus allowing the actual n for within-person analyses to be quite large (i.e., actual n = number of individuals*number of time points).

Given the focus of the current research study, it was important that the organization had the following characteristics: (1) a moderate quantity of tractable challenges/changing situations;

(2) variety in the types of adaptive events being faced by employees; (3) an organizational structure that allows for individuals to respond autonomously to events in their organization; (4) enough flexibility in job roles that allow individuals to face and respond to somewhat ambiguous situations without having to follow pre-determined steps; and (5) organizational support for, and interest in, the training, leadership and/or systems design (e.g., monitoring/behavior tracking) implications that may result from understanding how natural adaptive events are experienced.

Together, these contextual characteristics increase the likelihood that there will be adequate amounts of variance in the experiences reported to identify differences and detect effects.

To recruit an organization that met these requirements, the researcher leveraged personal

contacts as the point of entrée into different companies. After exploring multiple options, a small

research and development firm with over one hundred employees agreed to participate in the

study. The company met all of the requirements described above. Additionally, the timing of the

study was ideal, as the company was going through some organizational changes and leadership

was interested in understanding how the day-to-day challenges, as well as the organizational

level changes, were impacting employees. The company agreed to open up the study to all

employees and conveyed their support for this study to the other employees.

Sample Recruitment

Once the organization was selected, the potential study participants (employees) were

initially informed of the study through an informal announcement given by the researcher at a

company-wide meeting. During the announcement, an overview of the study was provided and

employees were told they would receive an email with additional information following the

company meeting. The email provided information about: (1) the nature of the study; (2) the

importance of the study (i.e., the benefits); (3) who could participate (everyone); (4) the tasks

participants would be asked to complete; (5) the timeline for the study; and (6) contact

information for the study researcher. Additionally, employees were informed that if they

completed at least 8 daily journal entries, they would be entered into a drawing to win a $50 gift

card to a local restaurant, which is on par with incentives used in previous ESM research (Brent

Scott – 6/26/12 – personal communication; Uy et al., 2010).

Study Procedure

The company at which the study took place had their own internal Institutional Review

Board (IRB). Therefore, the procedure and materials for this study were approved by both the

Michigan State University IRB and the company’s IRB. Once IRB approval was obtained and

the initial recruitment efforts were undertaken (described above), an email was sent out first

thing in the morning to all employees that included a link to the consent form and background

survey, which were hosted on Survey Monkey (see Appendix B for the email text). If an

individual selected no, that they did not consent to participate, the survey ended and the

participant was not directed to the background survey. If the participant agreed to participate in

the study, the participant was immediately taken to the background survey, which took about 10-

15 minutes to complete and consisted of a series of questions about their demographics, experiences, and individual characteristics (e.g., personality, preferences). A reminder email was sent out later the same day to those individuals who had not yet responded to the informed consent and background survey (see Appendix C for the email text for the reminder).

Individuals who consented to participate in the study received their first daily journal entry the first business day after completing the background survey. To maximize the sample size, the procedure allowed for a staggered start date. While the majority of participants completed the informed consent and background survey the day it was sent out, the researcher sent out reminders to those who had not yet reviewed the consent form over the next several days to encourage additional participation. As a result, there were four “groups” moving through the study, each with different start dates (i.e., the day they received the first daily journal survey).

The first group (n = 54) completed the consent form the day it was sent out; the second group (n

= 8) completed the consent form the following day; the third group (n = 1) completed the consent form two days after it was sent; and the fourth group (n = 1) completed the consent form six days after it was sent. To allow all individuals to have the opportunity to provide data on at least 10

events (one a day for 10 business days), the survey period was extended to 17 business days2 to accommodate the individual in the fourth group. This meant that individuals in the first group had 17 days (up to 17 events) that they could report during the survey period. The number of participants entering the daily journal survey each day is provided in Figure 4, with the number of participants actually providing data about an event for a particular day in parentheses. For example, on Day 1, forty-three Group 1 participants entered the survey, while 33 of those individuals provided data about an actual event. Four hundred and seventy-eight entries were made, but only 218 actual events were reported across all days and all individuals.

The daily journal survey provided the guidelines (discussed above) necessary for an individual to identify an adaptive event about which to respond and included a series of open- ended and close-ended questions about that event. Individuals were asked to type in the date for that day and indicate whether or not they had an adaptive event about which to respond. If they said no, they were directed to further prompts that encouraged them to really think about their day and try to find an event that would be appropriate. If they still felt they had nothing to report, they were able to select “no” and they could exit that day’s survey. If they indicated that they did have an adaptive event to talk about, they were directed to a series of questions that asked them to describe the nature of the event and how they responded to the event. To help improve the response rate, individuals who had not yet completed a daily journal for a particular day received two follow up emails (one mid-day, one in the late afternoon) reminding them to complete the day’s survey. Unless a participant requested to be removed from the study, a new daily journal survey link was sent out to the participant every business day first thing in the morning for the duration of the study period.

2 The study period spanned across the holidays (Christmas and New Year’s) and included the day during which the company announced the plans for the reorganization. As a result, there were several events that focused on balancing work with holiday plans as well as reactions to the reorganization.

The survey system was set up such that each individual received a unique link to the surveys they were asked to fill out (including the background survey and all daily journal surveys). The unique link was associated with a unique custom ID (a number from 1 to 123) that was stored in the data file when they submitted their surveys. All survey data submitted by an individual included this unique ID, which allowed the participant’s data to be merged across surveys without requiring any personally identifying information to be tracked and while also reducing the burden on the individual (that is, they were not asked to remember a number that they had to enter for each survey). The researcher had a list (kept separately) of the email addresses linked to this unique ID, that was only used to identify the winner of the $50 gift card.

This list was not stored with the actual survey data.

At the conclusion of the study, the data were examined to identify the subset of individuals who were eligible to be entered into the drawing for the $50 gift card. Data on a total of 218 events were collected, with fifty-one individuals providing valid data on at least one event. Of the 57 participants who entered the daily journal survey, 11% (n = 6) did not provide any valid entries; 21% (n = 12) provided 1 valid entry; 9% (n = 5) provided 2 valid entries; 11%

(n = 6) provided 3 valid entries; 14% (n = 8) provided 4 valid entries; 7% (n = 4) provided 5 valid entries; 5% (n = 3) provided 6 and 7 valid entries, respectively; 7% (n = 4) provided 8 valid entries; 9% (n = 5) provided 9 valid entries; and 2% (n = 1) provided 10 valid entries. A random number generator was used to select the winner out of the ten eligible individuals (those completing at least 8 daily journal surveys). Once the survey was closed and the winner was selected, all employees were sent an email notifying them of the conclusion of the study and thanking those who participated. Additionally, the winner of the certificate was announced.

Measures

Background Survey

The background survey was completed upon consenting to the study. This survey included items targeting an individual’s demographics and experience, trait adaptability, perceived job autonomy, trait goal orientation, and personality characteristics. Each of these measures is described below, and the full measures are included in Appendix D. The data from these measures were used to explore the possibility that adaptation strategies vary based on an individual’s demographic make-up, experience level, or individual characteristics.

Demographics and experience. The background questionnaire included six items that assessed various demographic characteristics, including gender, age, ethnicity, race, job category, and tenure at the company. The data from these items were used as controls where appropriate.

Trait adaptability. Individual adaptability was assessed via the 55-item I-ADAPT self- report measure (Ployhart & Bliese, 2006). Individuals rated their agreement with each item on a

5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5). The I-ADAPT covers eight dimensions, including: handling emergencies or crises (6 items; α = .87; e.g., “I make excellent decisions in times of crisis”); demonstrating cultural adaptability (5 items; α =

.82; e.g., “It is important to me that I respect others’ culture”); solving problems creatively (5 items; α = .85; e.g., “I see connections between seemingly unrelated information”); learning new tasks, technologies, and procedures (6 items; α = .87; “I quickly learn new methods to solve problems”); dealing with uncertain and unpredictable work situations (9 items; α = .88; e.g.,

“When something unexpected happens, I readily change gears in response”); demonstrating physical adaptability (9 items; α = .68; e.g., “I am adept at using my body to complete relevant

tasks”); demonstrating interpersonal adaptability (7 items; α = .64; “I adapt my behavior to get

along with others”); and handling work stress (5 items; α = .72; “I usually over-react to stressful

news”). For each dimension, all negatively worded items were reverse-scored so that all scale

scores represent greater adaptability. While variations of this scale have been used in a few

empirical studies (e.g., Almahamid et al., 2010; Wang et al., 2011), reliability and factor analyses

have not been documented for the specific 55-item measure developed by Ployhart and Bliese.

However, earlier (shorter) versions of this scale demonstrated solid convergent and divergent

construct validity evidence and conformed to the expected second-order factor structure

(Ployhart & Bliese, 2006). The alphas for each dimension for the current study were strong

overall, with the interpersonal and physical adaptability dimensions demonstrating the weakest

internal consistencies. The correlations between the dimensions ranged from -.04 (cultural and

work stress) to .68 (cultural and interpersonal).

Autonomy. Individuals also completed a six-item job autonomy measure (Barrick &

Mount, 1993). Example items include “The way the job is performed is influenced a great deal

by what others (supervisors, peers, customers, etc.) expect of the incumbent” and “There is a lot

of autonomy in doing the job.” Participants were asked to rate each statement based on how

accurate or inaccurate (7-point scale) the description is for his or her job. Items were scored and

combined so that higher scores indicate more autonomy. In the past, this scale has been shown to

have acceptable psychometric properties, with all items loading onto a single factor and internal

consistency reliability () = .70. However, the current study resulted in poor scale reliability (α =

.48), with inter-item correlations ranging from -.08 to .44.

Goal orientation. Trait goal orientation was assessed using Vandewalle’s (1997) measure

of goal orientation, which is comprised of 13 items measuring three dimensions: learning goal

orientation (LGO), prove performance goal orientation (PPGO) and avoid performance goal

orientation (APGO). LGO refers to a motivational focus on mastering one’s environment (5

items; current study  = .72; previous research  = .89; e.g., “I often look for opportunities to

develop new skills and knowledge”). PPGO refers to a motivational focus on proving one’s

competence in performance situations (4 items; current study  = .67; previous research  = .85; e.g., “I’m concerned with showing that I can perform better than others”). APGO refers to a motivational focus to avoid failure in a performance context (4 items; current study  = .85; previous research  = .88; e.g., “I prefer to avoid situations where I might perform poorly”). The internal consistency of the LGO and PPGO scales was lower than in previous research; however, the alphas were still in an acceptable range. Individuals were asked to rate each item on a 5-point

Likert-type scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. Higher scores represent higher levels of that dimension.

Big Five personality dimensions. The Big Five personality dimensions were assessed via the 20-item IPIP (http://ipip.ori.org). Each dimension was assessed via four items. Individuals rated each item on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5), indicating how well each item represents how they are generally. Items that were negatively worded were reverse-scored so that all scale scores represented higher levels of each

dimension. The five dimensions were: conscientiousness (α = .75), extraversion (α = .83),

neuroticism (α = .70), agreeableness (α = .60) and openness (α = .71). The correlations between

the five dimensions ranged from -.29 to .41.

Daily Journal Surveys

The daily journal survey that participants received each day was comprised of open-

ended questions and single-item measures that were designed to capture relevant aspects of the

adaptive events, an individual’s response to that event, and self-rated effectiveness/performance

(see Appendix E for the full set of daily journal measures). The daily journal survey was

carefully designed to elicit targeted information from participants to maximize the value of the

response, while minimizing the amount of time it took participants to provide the data. The daily

journal was designed to take between 5 and 10 minutes to complete each day.

Qualitative description of adaptive events. At the beginning of each daily journal survey, individuals were asked to provide a brief written description about the nature of the adaptive

event. Similar to the critical incident technique proposed by Flanagan (1954), individuals were

provided with guidelines for how to identify and define an adaptive event (as discussed earlier).

Additionally, rather than asking participants to respond to a single, open-ended question about

the event, targeted questions were included that asked individuals to provide specific types of

information about the event, including: (1) the circumstances leading up to the event; (2) who

was involved in the event; (3) how conditions changed from the routine; (4) when and how it

came to their attention that conditions had changed; (5) the cause of the change; (6) whether the

event was proactive or reactive; (7) what specific actions were taken in response to the event; (8)

the emotional and mental reactions to the event; (9) the coping strategies used to handle the

event; (10) the result or outcome of the event; and (11) the impact of one’s behaviors on the

outcome of the event (effective/ineffective).

Qualitative data coding. The responses gathered to each of these questions provide

insight into an aspect of the event that may be relevant for understanding the conditions under

which individuals may adapt more or less effectively. To be useful for analysis, a coding

framework was developed by the researcher, and tested and refined over time, which was used to

categorize the data meaningfully. The coding categories are each described below, and the full

framework is presented in Table 4. The coding categories were driven by relevant frameworks in

the literature as well as a review of the full data set. Each event was double-coded, once by the

lead researcher and once by a trained master’s level student who had recently taken a qualitative

coding class and had experience in qualitative data coding. To ensure better interrater

reliabilities, the two coders met to walk through the coding framework in depth and a set of coding rules was developed initially and iterated upon over time (see Appendix F). Interrater

reliability (percent agreement) was calculated periodically throughout the coding process to

ensure adequate levels of reliability (> 70%). Discrepancies between coders were identified by

the lead researcher and resolved through further examination of the event and discussions

between the lead researcher and the trained coder. The final data set includes a single final code

for each category for each event.

Number of individuals involved in event. The first coding category was intended to

capture how many people were involved in the adaptive event. The coders indicated whether the

event involved (1) a single individual, (2) a dyad, (3) a group or team, or (4) the full

organization.

Type of people involved in event. The second coding category was intended to capture

information about the type of people involved in the event. The coders indicated whether the

event involved (1) internal (company) personnel only, (2) external personnel only, (3) mixed

personnel (both internal and external), or (4) non-work individuals only (e.g., family).

Level of internal staff involved. The third coding category was intended to understand the

levels of staff involved in the event. The coders indicated whether the event involved (1)

interactions with their subordinates only, (2) with peers only, (3) with supervisors only, or (4)

with a mixed-level group.

Level of external staff involved. The fourth coding category was intended to understand the type of external personnel involved in the event. The coders indicated whether the event involved (1) interactions with clients/customers only, (2) with partners only, or (3) with other external and/or mixed external groups.

Event circumstances. The fifth coding category focused on the circumstances underlying the event. That is, what were the conditions or what happened that led to the event requiring adaptation? There were several coding options for this category, including: (1) people/staffing issue, (2) people/conflict issue, (3) technology issue, (4) workload/labor plan issue, (5) an error/mistake/problem, (6) direction or focus of work, (7) timing issue, (8) personal/non-work related issue, or (9) an organizational level event or change. Coders indicated what the primary circumstances were, but could also code a secondary type of circumstance if relevant.

Type of complexity change. The adaptive events were also categorized according to how the task changed. While all events were attempted to be coded using this framework, it was anticipated that this categorization scheme would only be relevant for some of the adaptive events captured within the current study given the task-based focus of this taxonomy. This coding category was based on Wood’s (1986) three levels of task complexity: (1) component complexity (“work harder”), (2) coordinative complexity (“work smarter”), and (3) dynamic complexity (“chaos”). More detailed descriptions of each of these types of complexity are

available in the coding framework.

Type of situational cue. The next coding category helped identify the type or source of the

cue that the individual used to realize that something new or different was going on that they needed to respond to. Using Louis and Sutton’s (1991) framework for categorizing the nature of a cue as a guide, coders indicated if the cue was (1) encountering a new or atypical situation; (2)

an unexpected performance/timeline discrepancy; (3) an automated warning/indicator (non-

verbal cue); or (4) a direct verbal cue (someone told them).

Detection time. The next coding category was intended to provide insight into when an

individual detected that something had changed (or was about to change). Coders indicated if the

individual detected the change (1) before it occurred (i.e., anticipated that something was going

to happen); (2) immediately after it occurred (i.e., quick detection); or 3) a significant amount of

time after it occurred (i.e., slow detection).

Underlying factors. The next coding category provided insight into what the big picture

underlying factors were that caused or contributed to the adaptive event occurring. That is,

coders determined if the event was caused by (1) situational factors; (2) environmental factors;

(3) organizational factors; (4) political factors; or (5) personal factors.

Reactive versus proactive change. Ployhart and Bliese (2006) argue that adaptation can

be proactive or reactive in nature. Coders indicated if the adaptive event and the response to that

event was (1) primarily reactive; (2) both reactive and proactive; or (3) primarily proactive.

Expectation level. Changes, or adaptive events, can be expected or unexpected. Coders

indicated if the adaptive event was (1) unexpected; (2) neutral or mixed (somewhat expected); or

(3) expected.

Type of self-regulatory change. Bell & Kozlowski (2008) indicate that there are three

pathways – cognitive, affective/motivational, and behavioral – by which people learn and

perform. This coding category is intended to capture which of these pathways was most impacted

by the adaptive event. Specifically, coders indicated if the adaptive event required (1) a primarily

cognitive change; (2) a primarily affective/motivational change; (3) a primarily behavioral

change; or (4) multiple (mixed) pathway changes.

Primary cognitive response. In response to a change impacting the cognitive pathway

(either greatly or in a minor way), individuals may have to make cognitive adjustments to adapt.

Coders indicated what the primary (and secondary, when appropriate) cognitive response was,

including (1) reprioritizing/shifting focus of attention; (2) seeking out information and ideas for

plans from other resources; (3) mentally evaluating/thinking through what to do; or (4) mentally

withdrawing.

Primary affective reaction. Most adaptive events also elicit an affective reaction. The

literature has demonstrated the negative impact of negative emotions or affective reactions on

adaptive performance (e.g., Bell & Kozlowski, 2008). Coders indicated whether individuals

reported affective reactions that were (1) negative; (2) neutral; (3) positive; or (4) mixed.

Primary behavioral response. Different types of events may require different behavioral

adjustments. For this coding category, coders indicated if the primary behavioral response to the

adaptive event was (1) exploitation (i.e., quickly finding an alternative course of action –

performance focus); (2) exploration (i.e., exploring options before determining what to do –

learning focus); (3) quantitative effort increase (i.e., working harder or faster); (4) withdrawing

(i.e., ceasing all action); or (5) staying the course (i.e., continuing to do what one was doing

before the change).

Primary coping strategy. To help manage emotions and maintain focus and motivation in

response to the adaptive event, individuals may use coping strategies. Coping strategies can be

characterized as problem-focused or emotion-focused (Folkman & Lazarus, 1980). However,

there are multiple types of coping strategies that fall under each of these broader categories,

including (1) active coping; (2) mental simulation/planning; (3) minimizing distractions; (4)

restraint coping; (5) seeking social support for instrumental reasons; (6) focusing on/venting

emotions; (7) behavioral disengagement; or (8) mental disengagement. Coders indicated which

of these coping strategies (primary, and secondary if applicable) was used.

Adaptive performance dimension. Coders also categorized each of the adaptive events

into one of Pulakos’ (2000) eight adaptive performance dimensions. As each event may be

characterized by more than one of these dimensions, coders were asked to indicate the primary

dimension as well as the secondary dimension, if applicable. The coding options included (1) handling emergencies or crisis situations; (2) handling work stress; (3) solving problems creatively; (4) dealing with uncertain and unpredictable work situations; (5) learning work tasks, technologies, and procedures; (6) demonstrating interpersonal adaptability; (7) demonstrating cultural adaptability; or (8) demonstrating physically oriented adaptability.

Event result or outcome. Coders also categorized the result or outcome of the event based on participants’ descriptions. Specifically, coders indicated if the result or outcome of the event was (1) negative (bad outcome); (2) neutral; or (3) positive (good outcome).

Behavioral impact on outcome. The final coding category was intended to capture the extent to which an individual’s behaviors impacted the outcome of the event. Specifically, coders indicated if the impact of their behaviors on the outcome was (1) negative; (2) neutral/mixed; or

(3) positive.

In addition to the qualitative data, participants were also asked a series of close-ended survey questions on each daily journal survey. These questions were intended to capture additional data on how participants responded to and felt about the event. Specifically, participants responded to questions about: (1) how they felt about the change (valence); (2) the cognitive adjustment the change required; (3) the affective adjustment the change required; (4) the behavioral adjustment the change required; (5) the extent to which the event stretched the

knowledge, skills, and abilities the participant typically relies on; (6) their appraisal of the

situation (challenge/threat; opportunity/existing problem); (7) their planning efforts; (8) their

emotional response to the event; (9) the behavioral strategies used to adapt; and (10) the

perceived effectiveness of their responses and their ability to adapt across different phases (e.g.,

situation assessment, planning, execution). Single-item and shortened scales were used given the

restricted time allowance (Uy et al., 2010; Williams & Alliger, 1994). Each of the questions is

described below.

Valence. For each event, individuals were asked to respond to a single item asking them

how they felt about the event that they reported about on a five-point scale from negative (1) to

positive (5). This item was used to capture the valence of the change – was this a negative

(harmful) change or a positive (good) change?

Required adjustment. Individuals responded to four items that targeted their perceptions

of how much of an adjustment the change required in terms of (1) their thinking; (2) their

feelings and emotions; (3) their behavioral strategy/approach; and (4) their knowledge, skills,

and abilities. Each of these items was rated on a five-point scale from not at all (1) to a lot (5). It

is expected that some types of change require more adjustments than others.

Diagnosis and appraisal. Individuals responded to three items about their diagnosis and

appraisal of the event. The first two items asked individuals to indicate the extent to which they

found the event to be challenging and threatening, respectively. The third item asked individuals

to indicate the extent to which they perceived the event to be a potential opportunity to make

adjustments or improvements before an actual problem occurred. All three items were rated on a

five-point scale from not at all (1) to a lot (5).

Planning. Individuals were also asked to report the type of planning they engaged in to prepare themselves to respond to the event. Specifically, individuals were asked to indicate the

extent to which they engaged in contingency planning (i.e., considered alternative plans that may be more effective in preventing or addressing the expected change before it occurs) and reactive strategic planning (i.e., immediately adjusted plans in response to the change that had occurred to minimize problems). Individuals were also asked to indicate how much planning time they felt they had when reacting to the detected cue. Each of these items was rated on a five-point scale from not at all (1) to a lot (5).

Cognitive attention. Individuals were asked to rate their level of on-task attention during each adaptive event by responding to the item: “I was able to maintain focus on the important details of the adaptive event,” on a five-point scale from strongly disagree (1) to strongly agree

(5).

Affective reactions. Two types of negative emotions were assessed – anxiety and frustration. To assess anxiety, individuals were asked to respond to the following item: “I found the adaptive event to be anxiety-provoking.” To assess frustration, individuals were asked to respond to the following item: “I was frustrated during the adaptive event.” Both items were rated on a five-point scale from strongly disagree (1) to strongly agree (5).

Behavioral strategy use. Individuals were asked to indicate the extent to which they used different behavioral strategies when responding to the adaptive event. Specifically, individuals were asked the extent to which they: (1) adjusted their current strategy by immediately adopting an alternative strategy and consistently using that strategy (qualitative/choose and exploit); (2) adjusted their current strategy by exploring several alternative strategies that may work better and deciding which one worked best (qualitative/explore alternatives); (3) adjusted their current

strategy by engaging in the same behaviors but working harder and/or faster

(quantitative/increase effort); (4) adjusting their current strategy by withdrawing from the

situation (quantitative/withdraw effort); or (5) making no change in strategy. Individuals rated

their use of each of these five strategies using a five-point scale from strongly disagree (1) to

strongly agree (5).

Behavioral strategy effectiveness. Individuals were asked to rate the effectiveness of their

behavioral strategy adjustments for each adaptive event on a five-point scale (1 = not at all

effective to 5 = extremely effective).

Coping strategy effectiveness. Individuals were asked to rate the effectiveness of their

coping strategy use for each adaptive event on a five-point scale (1 = not at all effective to 5 =

extremely effective).

Situation assessment effectiveness. Individuals were asked to rate the effectiveness of

their situation assessment activities (i.e., detecting, appraising, diagnosing) for each adaptive

event on a five-point scale (1 = not at all effective to 5 = extremely effective).

Planning and strategy selection effectiveness. Individuals were asked to rate the

effectiveness of their planning and strategy selection activities for each adaptive event on a five-

point scale (1 = not at all effective to 5 = extremely effective).

Execution and evaluation effectiveness. Individuals were asked to rate the effectiveness

of their execution and evaluation activities for each adaptive event on a five-point scale (1 = not

at all effective to 5 = extremely effective).

Overall adaptation effectiveness. Individuals were asked to rate overall how effective

they were at adapting to each adaptive event on a five-point scale (1 = not at all effective to 5 =

extremely effective).

RESULTS

Means, standard deviations, intercorrelations, and alpha reliabilities (on diagonal when

applicable) for the background survey variables of interest across the entire sample are shown in

Table 5. Similarly, means, standard deviations, and intercorrelations for the quantitative event- level data (averaged across events within person) and outcome data are provided in Table 6.

Analysis Plan

There are two primary types of hypotheses in this study: (1) event-level hypotheses, and

(2) person-level hypotheses. Additionally, several exploratory (i.e., non-hypothesized) analyses were conducted at the event- and person-levels to further understand the impact of situational and individual difference factors on the adaptation process. An explanation of how these two types of hypotheses were analyzed is provided below. Table 7 lists the hypotheses that fall into the two categories.

The event-level hypotheses in this study primarily examine how situational factors characterizing the change impact various elements of the adaptation process. For example,

hypothesis 1 examines how the nature of the change (i.e., reactive vs. proactive) impacts an

individual’s planning processes during adaptation. As the research design consisted of

individuals providing data on multiple events over the course of the study, it was necessary to

determine whether sufficient variance existed within person to require an analysis plan that

accounted for the nesting of events within person. Intraclass correlation coefficients (ICCs) were

computed for each of the hypothesized continuous dependent variables (DVs), and the results

indicated the need to account for the nesting of events within person (ICCs > .10). As a result,

random coefficient modeling (RCM) was used to analyze the event-level hypotheses to address

the nesting of events within person3. Given that growth was not assumed between events,

random intercept models were used to estimate the variance of the random intercepts across

people for each of the DVs, while also estimating the population regression line (fixed effect) for

each hypothesis. By doing so, it was possible to determine the variance in the DV accounted for

by the independent variable (IV; e.g., type of complexity change) separately from the variance accounted for by the person.

The person-level hypotheses in this study primarily examine how individual differences impact the adaptation process. For example, hypothesis 6 explores how learning goal orientation impacts individuals’ appraisals of and reactions to the adaptive event, as well as their choice of behavioral strategy. For these hypotheses, the dependent variables of interest were aggregated across events to the person level. The specific aggregation method depended on the dependent variable and the hypothesis of interest. Averages and proportions were the most common aggregation strategy. By summarizing across events, it allowed for the examination of how an individual responded across events. Simple linear regression analyses were used to explore these hypotheses.

Hypotheses Results

Hypothesis 1

In general, hypothesis 1 suggested that the nature of the change – that is, the extent to

which the change was reactive or proactive – would impact the planning processes individuals

engage in when adapting. For the most part, this hypothesis was supported. The details, by sub-

3 All of the predictors of interest in the event‐level analyses were categorical (0‐1 or 0‐1‐2). While it may be useful to person‐center the data for the event‐level analyses, person‐centering factors with more than two coding categories remains statistically challenging. For the current study, the predictors were left uncentered. This issue is further discussed in the limitations section.

part, are provided below. Table 8 provides additional details about the variance accounted for in

the relevant dependent variables for each analysis.

Hypothesis 1a predicted that events that were more proactive (anticipatory) in nature

were likely to allow for more contingency planning than reactive events. Two separate random

intercept models were conducted to examine this hypothesis. First, a model was run that used

individuals’ self-reported perception of an event as an “opportunity to improve” (versus an

existing threat) as the independent variable. Consistent with expectations, the more an event was

perceived as an opportunity (versus an existing threat), the more contingency planning an

individual reported (F(1, 210.42) = 7.47, p = .01). Additionally, the category within which the

event was coded (reactive, mixed (both reactive and proactive), or proactive) was used as the IV

in the second random intercept model. The overall model was significant (F(2,209.66) = 5.95, p

< .05), suggesting that the nature of the event does impact the level of contingency planning in

which one engages. Several follow-up contrasts were conducted. When comparing events coded

as reactive versus those coded as either proactive or mixed, it was clear that reactive events were

associated with significantly less contingency planning than those not coded as reactive

(F(1,208.37) = 10.11, p = .00). This provides support for the belief that when individuals

perceive events as opportunities and can respond proactively, they have more time to brainstorm

contingency plans than they do if they are responding reactively to the event. When looking at

the random intercept estimate, the level of contingency planning engaged in was not dependent

on the person (p = .10).

Hypothesis 1b examined the amount of reactive strategic planning individuals engaged in

based on the extent to which an event is more reactive or proactive in nature. Similar to 1a, a few

random intercept models were conducted to explore this hypothesis. Contrary to expectations,

the extent to which an individual felt the event was an opportunity to improve (versus an existing

threat) did not significantly predict the amount of reactive strategic planning (F(1, 212.30) = .78,

p = .38). However, the categorization of the event as reactive, mixed, or proactive did

significantly predict the amount of reactive strategic planning (F(2,197.04) = 6.57, p = .00). All three follow up comparisons revealed significant differences, including reactive versus proactive

(F(1,163.91) = 12.80, p = .00); reactive versus proactive or mixed (F(1,192.57) = 10.02, p = .00); and proactive versus reactive or mixed (F(1, 200.52) = 11.93, p = .00). In all three cases, reactive events were associated with more reactive strategic planning than mixed or proactive events as expected. Reactive strategic planning consists of real-time adjustments to plans as an individual

is adapting. For reactive events that allow for little to no time to plan ahead, it makes sense that

most of the planning is reactive in nature. Additionally, the random intercept estimates were all

significant (p = .00), suggesting that the level of random strategic planning also varies based on

the individual facing the event.

Hypothesis 1c focused on the amount of planning time individuals perceived having as they encountered an adaptive event. As expected, the extent to which an event was perceived as

an opportunity significantly predicted the amount of perceived planning time available

(F(1,204.20) = 10.54, p = .00), with more opportunistic events resulting in more planning time.

The overall model comparing reactive, mixed, and proactive events was significant (F(2,209.20)

= 7.79, p < .00) as well. However, the only significant follow-up model was that comparing

reactive events to both mixed and proactive events (F(1,207.83) = 5.99, p = .02), with reactive

events resulting in significantly less planning time than proactive and mixed events. This

difference was likely primarily driven by the inclusion of mixed events (with proactive events),

as the comparison between just reactive and proactive events was not significant (F(1,180.99) =

.02, p = .88). Together, these results suggest that more opportunistic events, and those with at

least some degree of proactivity, allow for more planning time. Planning time does not appear to

be strongly influenced by the individual based on the random intercept estimate (p = .17).

Hypothesis 2

Hypothesis 2 explored the extent to which the type of coping strategy used – problem- focused or emotion-focused – influenced an individual’s cognitive focus and affective reactions to the adaptive event. Overall, this hypothesis was supported. The details, by sub-part, are

provided below. Table 9 provides additional details about the variance accounted for in the

relevant dependent variables for each analysis.

Hypothesis 2a predicted that events in which problem-focused coping strategies were

used would allow individuals to maintain focus on the important aspects of the event. The

random intercept model testing this hypothesis was not significant, although there was a trend

that emerged (F(1,206.99) = 3.26, p = .07), suggesting that problem-focused coping strategies

may be associated with higher levels of on-task attention than emotion-focused coping strategies.

The trend is in the hypothesized direction. Additionally, the random intercept estimate suggests that on-task attention is also dependent on the individual (p = .02).

Hypothesis 2b predicted that events in which problem-focused coping strategies were used would result in lower levels of anxiety than those in which emotion-focused strategies were used. The random intercept model testing this hypothesis was significant (F(1,206.92) = 5.93, p

= .02), suggesting that problem-focused coping strategies were associated with lower levels of

anxiety than emotion-focused coping strategies. This finding is consistent with the hypothesis

that the use of problem-focused strategies can help individuals effectively manage or reduce

anxiety by shifting their focus to the task at hand. Additionally, the random intercept estimate

suggests that anxiety levels are also dependent on the individual (p = .02).

Similar to hypothesis 2b, hypothesis 2c predicted that events in which problem-focused

coping strategies were used would result in lower levels of frustration than those in which

emotion-focused strategies were used. The random intercept model testing this hypothesis was

significant (F(1,206.57) = 15.02, p = .00), suggesting that problem-focused coping strategies

were associated with lower levels of frustration than emotion-focused coping strategies. This

finding provides support for this hypothesis. The random intercept estimate suggests that

frustration levels are also dependent on the individual (p = .05).

Hypothesis 3

Hypothesis 3 explored the extent to which the type of coping strategy used – problem-

focused or emotion-focused – influenced an individual’s appraisal of the adaptive event.

Hypothesis 3a examined challenge appraisals, while hypothesis 3b focused on threat appraisals.

Table 10 provides additional details about the variance accounted for in challenge and threat

appraisals by coping strategy type. The results were not consistent with expectations.

Specifically, when examining the effect of coping strategy choice on challenge appraisals

(hypothesis 3a), the results indicated that problem-focused coping strategies were associated with

significantly lower challenge appraisals than when emotion-focused coping strategies were used

(F(1,200.76) = 4.00, p = .05). This was opposite of what was expected. While challenge

appraisals have been viewed as healthy and motivating in past research, it is possible that

individuals did not interpret the term “challenge” as a potentially positive motivator, but rather

interpreted it as being representative of a more demanding and difficult situation. Additionally,

when exploring hypothesis 3b, coping strategy type did not yield significant differences in how

threatening an event was appraised (F(1,206.66) = 1.55, p = .21). This was contrary to the

expectation that the use of emotion-focused coping strategies would yield more threatened

appraisals, as emotion-focused strategies typically set attention on one’s emotions and the

negative aspects of an event. While coping strategy type did not impact threat appraisals, threat

appraisals did vary by person (p = .01).

Hypothesis 4

Hypothesis 4 explored the extent to which the type of coping strategy used – problem-

focused or emotion-focused – influenced an individual’s choice of behavioral strategy

(hypothesis 4a) and the effectiveness of their behavioral and coping strategies (hypothesis 4b).

Table 11 provides additional details about the variance accounted for in each of the dependent

variables for each analysis.

Individuals reported the extent to which they used each of five different behavioral

strategies – exploitation, exploration, increase effort, withdraw, and stay the course – when

adapting to the event. In hypothesis 4a, it was expected that problem-focused coping would be

associated with more problem-focused behavioral approaches (exploitation, exploration, or

increase effort), while emotion-focused coping strategies would go hand in hand with more

avoidant behavioral approaches (withdraw or stay the course behaviors). Separate random

intercept models were run for each behavioral strategy. The results were mixed. Specifically, as

expected, problem-focused coping strategies were associated with significantly more increasing

effort behaviors (F(1,204.20) = 5.42, p = .02), with a trend toward more exploration (F(1,206.95)

= 2.91, p = .09) behaviors. Additionally, as expected, emotion-focused coping strategies were associated with more withdraw behaviors (F(1,202.72) = 50.95, p = .00) than problem-focused coping strategies. However, there was no significant difference in stay the course behaviors

based on coping strategy type (F(1,204.34) = .09, p = .77). Aside from exploitation behaviors, the use of the remaining four behavioral strategies also varied based on the person (p’s < .05).

To further explore how coping strategy type may influence the selection of behavioral strategies, the primary behavioral strategy used to respond to each event was identified by coders. Table 12 displays the frequencies with which each behavioral strategy was adopted as the primary behavioral strategy when reacting to an event, organized by coping strategy type.

Specifically, the breakdown reveals that those using problem-focused coping strategies were more likely to adopt behavioral strategies that focused on finding a new solution to the problem – whether that was quickly adopting a new strategy (exploitation) or exploring for a new strategy

(exploration). Additionally, those using emotion-focused coping strategies were more likely to

adopt a behavioral strategy of withdrawing, or removing effort from, the situation or task at

hand. Essentially, these individuals leaned more toward giving up, as opposed to finding new

solutions. In general, this pattern provides support for the results above, suggesting that

individuals using problem-focused coping strategies are able to make more productive, effective

behavioral adaptations than those using emotion-focused coping strategies. The only noticeable distinction is the comparison for increasing effort behaviors. While the random intercept model found that the use of problem-focused coping strategies yielded significantly greater reliance on

increasing effort behaviors than emotion-focused coping strategies, the pattern below suggests

that there is not a strong distinction. There are two likely explanations for this seemingly

contradictory finding. First, the random intercept models used self-reported behavioral strategy use as the DV, while this table looks at the coded primary behavior response. It could be that while individuals relied more on increasing effort strategies (as self-reported) when they used problem-focused coping strategies, this was not necessarily their primary strategy. Additionally,

the analyses took into account the random intercepts across people reporting events, while the

percentages below are just a raw examination of the primary behaviors across events, without

accounting for the person. However, overall, the pattern provides strong convergent evidence for

the relationship between coping strategy use and behavioral strategy use when responding to

adaptive events.

For hypothesis 4b, it was expected that when individuals used problem-focused coping

strategies, their behavioral and coping strategies would be perceived as more effective. To

explore this hypothesis, three separate random intercept models were conducted. Two DVs –

behavioral strategy effectiveness (self-report) and behavior impact (coded) – were used to

examine the effectiveness of an individual’s behavioral strategies. As expected, when individuals

used problem-focused coping strategies, both self-report ratings of behavioral strategy

effectiveness (F(1,202.36) = 19.24, p = .00) and qualitatively coded behavior impact

(F(1,184.76) = 12.31, p = .00) were higher (more effective) than when individuals used emotion-

focused coping strategies. Additionally, consistent with expectations, when individuals used

problem-focused coping strategies, they also reported that their coping strategy was more

effective (F(1,202.63) = 16.30, p = .00). Together these findings provide support for this

hypothesis and suggest that problem-focused coping strategies are related to more effective

adaptive behaviors than emotion-focused coping strategies.

Hypothesis 5

Hypothesis 5 was intended to examine the extent to which cognitive ability influenced

individual’s behaviors and effectiveness when responding to adaptive events. However, due to

the practical constraints during data collection for this study, cognitive ability data was not able

to be collected. Therefore this hypothesis was not examined. The relationship between cognitive

ability and adaptive performance has been well-documented in the research literature, so it is

expected that similar relationships would have been found here. However, there would likely have been range restriction for the cognitive ability factor, as the particular sample the data was collected from was highly educated.

Hypothesis 6

Hypothesis 6 is the first person-level hypothesis that was examined. Hypotheses 6, 7, and

8 examine the role of goal orientation in the adaptation process. The current hypothesis focuses

on learning goal orientation. Specifically, this hypothesis explored the extent to which learning

goal orientation predicted individuals’ behavioral strategy, affective reactions, and appraisals of

the adaptive event. Separate linear regression models were run for each of the sub-parts below.

For each analysis, the Betas, significance levels, and percentage of variance accounted for are presented in Table 13.

For hypothesis 6a, it was expected that learning goal orientation would result in a greater

proportion of events for which individuals chose exploration strategies as their primary behavioral response. However, a simple linear regression did not support this hypothesis

(F(1,49) = .67, p = .42, R2 = .013). In a follow up analysis, a linear regression was run that used

an individual’s average self-reported reliance on exploration strategies as the DV. Once again,

the results were not significant (F(1,49) = .49, p = .49, R2 = .01). Finally, to examine if those

higher in learning goal orientation possibly relied more on any type of qualitative behavioral

strategy (i.e., either exploitation or exploration strategies – both of which require a qualitative

shift in behavior rather than a quantitative (e.g., effort) shift in behavior) as opposed to just

exploration strategies, another linear regression was conducted with the proportion of events for

which an individual used either exploration or exploitation as their primary behavioral strategy

as the DV. The results were not significant, although there was a trend that higher learning goal orientation was associated with the use of more qualitative strategies (F(1,49) = 3.17, p = .08, R2

= .061).

Hypothesis 6b proposed that individuals higher in learning goal orientation would view events as less anxiety-provoking than those lower in learning goal orientation. While the relationship was not significant, there was a trend in the hypothesized direction, such that learning goal orientation was negatively related to anxiety perceptions (F(1,49) = 3.26, p = .08,

R2 = .062). Specifically, those higher in learning goal orientation reported feeling less anxious in

response to the adaptive events.

Similarly, hypothesis 6c proposed that individuals higher in learning goal orientation

would view events as less frustrating than those lower in learning goal orientation. As expected,

learning goal orientation was negatively related to reported frustration (F(1,49) = 4.33, p = .04,

R2 = .081), such that those higher in learning goal orientation reported feeling less frustrated by

the adaptive events.

Finally, contrary to expectations, learning goal orientation was not significantly related to

challenge appraisals (F(1,49) = 2.43, p = .13, R2 = .047).

Hypothesis 7

Hypothesis 7 focuses on performance prove goal orientation. It was hypothesized that

individuals high in performance goal orientation would rely more heavily on exploitation

behavioral strategies as they strive to demonstrate positive performance in the face of a change.

Using a simple linear regression, it was determined that performance prove goal orientation was not significantly related to the proportion of events for which individuals used exploitation strategies as their primary behavioral strategy (F(1,49) = .87, p = .36, R2 = .018). Additionally,

when looking at self-reported reliance on exploitation strategies (not reporting their primary

strategy, but instead, the extent to which they used each of the strategies), no significant results

emerged (F(1,49) = .36, p = .55, R2 = .007). This hypothesis was not supported. For each

analysis, the Betas, significance levels, and percentage of variance accounted for are presented in

Table 14.

Hypothesis 8

Hypothesis 8 focuses on performance avoid goal orientation. It was hypothesized that

individuals higher in avoidant goal orientation would rely to a greater extent on avoidant behavioral strategies (e.g., withdrawing effort, staying the course) and emotion-focused coping strategies, while also reporting more threatened appraisals, more anxiety, and more frustration than those lower in avoidant goal orientation. Separate linear regressions were conducted for each of the targeted DVs. For each analysis, the Betas, significance levels, and percentage of variance accounted for are presented in Table 15. The results are summarized below.

For hypothesis 8a, avoidant goal orientation was significantly related to the proportion of events for which individuals used withdraw strategies as their primary behavioral strategy

(F(1,49) = 16.90, p = .00, R2 = .256), such that higher APGO was associated with the use of

more withdraw strategies. Additionally, the average self-reported reliance on withdraw

behavioral strategies across events reported by an individual was also significantly positively

related to APGO (F(1,49) = 8.14, p = .01, R2 = .143), providing further support for this

relationship.

Consistent with hypothesis 8b, avoidant goal orientation was significantly related to the

proportion of events with which individuals used emotion-focused coping strategies (F(1,48) =

4.51, p = .04, R2 = .086), such that those higher in APGO reported using a higher proportion of

emotion-focused strategies than those lower in APGO.

Contrary to the expectations of hypothesis 8c, avoidant goal orientation was not

significantly related to how threatening individuals perceived the adaptive events to be (F(1,49)

= 1.59, p = .21, R2 = .031).

As expected for hypothesis 8d, avoidant goal orientation was significantly related to how

much anxiety the adaptive events being reported produced (F(1,49) = 6.30, p = .02, R2 = .114), such that on average, those higher in APGO reported higher levels of anxiety in response to the adaptive events than those lower in APGO.

Finally, when exploring hypothesis 8e, avoidant goal orientation was not significantly

related to how frustrating individuals perceived the adaptive events to be (F(1,49) = 1.91, p =

.17, R2 = .037). This hypothesis was not supported.

Hypothesis 9

Hypothesis 9 predicted that openness to experience would influence several aspects of the

adaptation process including how effective an individual is at assessing the situation, what

behavioral strategies individuals choose, individuals’ affective reactions to the event, and how

individuals appraise the event. Separate linear regressions were conducted for each of the

targeted DVs. For each analysis, the Betas, significance levels, and percentage of variance

accounted for are presented in Table 16. None of the expected relationships were supported.

Specifically, contrary to expectations, openness was not related to more effective situation

assessments (F(1,49) = .38, p = .54, R2 = .008), more exploration strategies (F(1,49) = .04, p =

.85, R2 = .001), less anxiety (F(1,49) = .02, p = .89, R2 = .00), less frustration (F(1,49) = .52, p =

.48, R2 = .01), or more challenged appraisals (F(1,49) = 1.57, p = .22, R2 = .031). The findings

largely suggest that trait level openness may be too distally related to the adaptation process to

have much influence on how one reacts and responds to the event.

Hypothesis 10

Hypothesis 10 examined the extent to which individual adaptability, as measured by the

I-ADAPT dimensions, would predict adaptation effectiveness at the different phases and overall.

Additionally, this hypothesis explored the extent to which the pattern of relationships between

individual adaptability and adaptation effectiveness differed based on the nature of the adaptive

event (i.e., which adaptive performance dimension best characterized the event).

Correlations were run to explore the general pattern of relationships between individual adaptability and the various adaptation outcomes (see Table 17). The strongest correlations tend to be with Planning and Strategy Effectiveness. Self-rated learning and cultural adaptability do not appear to impact adaptation effectiveness, by phase or overall. As a result, these dimensions were left out of subsequent analyses. The other dimensions are fairly consistent across adaptation phases, exhibiting moderately strong positive correlations with adaptation effectiveness on average.

To better understand the extent to which individual adaptability impacted adaptation effectiveness, regardless of event type, a series of linear regressions were conducted at the person-level (that is, effectiveness scores were averaged across events within person). For each regression, the six individual adaptability dimension scores (leaving out cultural and learning adaptation, based on the correlations above) were entered into the regression equation as predictors. For each analysis, the Betas, significance levels, and percentage of variance

accounted for are presented in Table 18.

When exploring how individual adaptability predicted situation assessment effectiveness,

individual adaptability accounted for 19% of the variance in situation assessment effectiveness,

although the results were not significant (F(6,44) = 1.67, p = .15, R2 = .185). The creativity,

work stress, and interpersonal dimensions seemed to be the strongest predictors of situation

assessment effectiveness, although none of the Betas were significant.

Looking at planning and strategy selection effectiveness, the six adaptability dimensions

accounted for 36% of the variance. The equation was significant (F(6,44) = 4.08, p = .00, R2 =

.357), providing support for the hypothesis that individual adaptability does predict planning and strategy selection effectiveness. The crisis dimension was the strongest predictor of planning and strategy effectiveness, followed by physical adaptability and work stress. However, again, none of the Betas for the individual dimensions reached significance.

Looking at execution and evaluation effectiveness, the six adaptability dimensions accounted for 28% of the variance, which was significant (F(6,44) = 2.85, p = .02, R2 = .28).

This result provides support for the hypothesis that individual adaptability does predict execution and evaluation effectiveness. Once again, the crisis dimension was the strongest predictor of execution and evaluation effectiveness, followed by physical adaptability, interpersonal adaptability and work stress. None of the Betas for the individual dimensions were significant.

Finally, when examining the extent to which individual adaptability predicts overall adaptation effectiveness, the results were once again significant (F(6,44) = 3.19, p = .01, R2 =

.303), with the six dimensions of adaptability accounting for 30% of the variance in overall adaptation effectiveness. The uncertainty dimension was the strongest predictor of overall adaptation effectiveness, followed by work stress and interpersonal adaptability; however, once again, none of the individual predictors were significant.

The pattern of findings above suggests that (1) the adaptability dimensions, while

positively correlated, do show some evidence of differential effects on adaptation outcomes; and

(2) looking at the phases of adaptability, as opposed to just overall adaptability, may be important as different predictors account for variance in the effectiveness of each of the phases.

This is further supported by the results of the regression equation examining how effectiveness at each phase of the adaptation process predicts overall adaptation effectiveness (F(3,47) = 64.74, p

= .00, R2 = .81). While effectiveness at each phase of the process accounts for 81% of the

variance in overall adaptation effectiveness, only Situation Assessment effectiveness was a

significant contributor, with a trend for Execution and Evaluation. This suggests that Situation

Assessment may be the most critical component of overall adaptation effectiveness. It is possible

that as the first phase in the adaptation cycle, if Situation Assessment is faulty, the rest of the

process may break down as well making it a critical element.

To examine the second piece of hypothesis 10, the pattern of relationships between

individual adaptability and adaptation effectiveness was examined, broken down by event type.

Four correlation tables – one for each DV – were created to examine the general patterns that

emerged. Tables 19 through 22 display the correlations between individual adaptability and

effectiveness of situation assessment, planning and strategy selection, execution and evaluation,

and overall adaptation, respectively.

Overall, the correlations between individual adaptability and adaptation effectiveness for

the learning and interpersonal event types appear to be divergent from expectations and the other

trends. Due to the small sample sizes for these two event types, these relationships are likely unstable, and therefore further examination will not be conducted. For the remaining three event

types, the general pattern suggests that individual adaptability positively relates to adaptation

effectiveness regardless of event type. More specifically, the pattern reveals that the relationship

between a specific individual adaptability dimension (e.g., work stress adaptability) and

adaptation effectiveness may not be stronger if the event is categorized as a work stress adaptive

event (i.e., if there is a match between the individual adaptability dimension and the adaptive

performance dimension characterizing the event). Instead the pattern reveals that uncertainty and

crisis adaptability trait scores appear to be the most consistently predictive of execution and

overall adaptation effectiveness, regardless of event type.

Hypothesis 11

Hypotheses 11 and 12 both examine the role of perceived autonomy on the adaptation

process. Hypothesis 11 focused on how the level of job autonomy an individual has may

influence their choice of coping strategy and their affective reactions to an adaptive event.

Separate linear regressions were conducted for each of the targeted DVs. For each analysis, the

Betas, significance levels, and percentage of variance accounted for are presented in Table 23.

None of the expected relationships were supported. Specifically, contrary to expectations, lower

levels of autonomy were not associated with more social support seeking coping strategies

(F(1,48) = .03, p = .86, R2 = .001) or the experience of more anxiety (F(1,49) = .01, p = .93, R2 =

.00) or more frustration (F(1,49) = .01, p = .94, R2 = .00). The lack of support for this hypothesis

could be due to the poor scale reliability for the autonomy scale, or consistent with the findings

for openness, it is possible that job autonomy is too distally related to the adaptation process to

have much influence on how one reacts and responds to the event.

Hypothesis 12

Hypothesis 12 predicted that job autonomy would influence the effectiveness of an

individual’s behavioral strategies and overall adaptation effectiveness. Separate linear

regressions were conducted for each of the targeted DVs. For each analysis, the Betas,

significance levels, and percentage of variance accounted for are presented in Table 24. None of

the expected relationships were supported. Specifically, contrary to expectations, higher levels of

autonomy were not associated with more effective behavioral strategies (F(1,49) = .62, p = .43,

R2 = .013), situation assessment (F(1,49) = .55, p = .46, R2 = .011), planning and strategy

selection (F(1,49) = .57, p = .46, R2 = .011), execution and evaluation (F(1,49) = 1.82, p = .18,

R2 = .036), or overall adaptation (F(1,49) = .25, p = .62, R2 = .005). Similar to hypothesis 11,

these findings largely suggest that autonomy may be too distally related to the adaptation process

to have much influence on how effective one is in their response to an adaptive event.

Hypothesis 13

The final three hypotheses (13-15) focused on exploring the extent to which situational

factors impacted the adaptation process. These hypotheses were more like research questions, as

specific relationships were not predicted. Instead it was just expected that reactions, responses,

and effectiveness would be different based on the nature of the adaptive event. For hypothesis

13, the focus was on exploring how the extent to which an event was reactive or proactive in

nature impacted how it was appraised, what behavioral strategies were used, and how effective

one’s strategies were at responding to the event. For all hypothesized analyses, random intercept

models were used to account for the random intercepts across subjects. Table 25 provides

additional details about the variance accounted for in each dependent variable for each analysis.

Supplementary exploratory analyses were also conducted to more fully understand how the

reactive versus proactive nature of an event influenced the process more broadly. These results

will also be summarized below.

For hypothesis 13a, it was expected that the extent to which an event was reactive or proactive in nature would influence how an event was appraised (i.e., how challenging or threatening the event was appraised to be). The results for this hypothesis were mixed. Contrary to expectations, how an event was categorized – reactive, mixed, or proactive – did not result in significant differences in how challenging the event was appraised to be (F(2,208.04) = 0.43, p =

.65). Additionally, none of the two category breakdowns (e.g., comparing just reactive to

proactive events) were significant, and challenge appraisals did not significantly vary by subject

as revealed by the random intercept estimate (p = .09). When looking at threat appraisals, there

were significant differences based on the nature of the event, as expected (F(2,203.46) = 3.12, p

= .04). Specifically, reactive events were perceived as more threatening than proactive events (p

= .01). This relationship makes sense, as reactive events are typically unexpected and require

quick responses to mitigate negative performance effects. Additionally, once again, threat

appraisals were also partially driven by the individual reporting the events (p = .00).

Hypothesis 13b examined the extent to which the use of the five behavioral strategies

(exploitation, exploration, increase effort, withdraw, or stay the course) varied depending on how

reactive or proactive the event was. Separate random intercept models were run for each

behavioral strategy. The results were mixed. When looking at the overall models (that is,

including the distinct reactive, mixed, and proactive event categories), no significant differences

emerged for exploration (F(2,207.06) = 1.79, p = .17), increase effort (F(2,201.82) = .21, p =

.81), or stay the course (F(2,197.99) = 1.72, p = .18) behaviors based on the type of event to which one was responding. However, there were significant differences in the degree to which exploitation (F(2,205.17) = 5.25, p = .01) and withdraw (F(2,198.55) = 4.49, p = .01) behavioral strategies were used. Specifically, reactive events were associated with a greater reliance on

exploitation behaviors than proactive events (F(1,180.47) = 8.24, p = .01). This finding is

consistent with expectations, as there is likely limited time available when reacting to an event to

do anything other than quickly identify a solution to exploit. Additionally, proactive events were

associated with significantly fewer withdraw behaviors than the combination of reactive and

mixed events (F(1,194.85) = 5.85, p = .02). This finding suggests that people may feel more

overwhelmed or helpless when reacting to a situation, leading to withdraw, than when they are

able to anticipate changes ahead of time. While the overall examinations of the other behavioral

strategies were not significant, follow-up analyses were conducted to examine if combining or

removing categories from the analysis would allow for significant differences to emerge. When

looking at reactive events versus non-reactive events (i.e., mixed and proactive events

combined), there was not a significant difference in exploration behaviors (F(1,202.56) = 3.15, p

= .08), although there was a trend that suggested that reactive events may be associated with fewer exploration behaviors than either mixed or proactive events. This finding provides some support for the rationale above that suggests that reactive events allow for less time to explore, requiring instead a quick solution to be identified. Finally, when comparing reactive to proactive events, a trend did emerge in stay the course behaviors, although it was not significant

(F(1,170.78) = 3.58, p = .06). Specifically, the results suggested that proactive events may be associated with more stay the course behaviors than reactive events. In all cases, except for exploitation behaviors, behavioral strategy use also varied significantly by person (p’s ranging from .00 to .04).

To further explore how the reactive versus proactive nature of the change may influence the selection of behavioral strategies, the primary behavioral strategy used to respond to each event was identified by coders. Table 26 displays the frequencies with which each behavioral

strategy was adopted as the primary behavioral strategy when reacting to an event, organized by

reactive, mixed, or proactive event types.

When looking at the primary behavioral strategy identified by coders from the qualitative

data in the table, the pattern diverges in some ways from the pattern found in the self-report

analyses above. While the extent to which people reported using exploitation vs. exploration

strategies varied by event type, the primary behavioral strategy identified in the qualitative data

does not appear to vary much based on event type. This could be due to the limited descriptions

provided by the qualitative data – that is, people didn’t fully describe how they approached a

task, thus some elements were missing – or that people tend to use strategies that combine the

different approaches and identifying one primary strategy does not provide as much insight into

what strategies individuals are actually relying on.

For hypothesis 13c, it was expected that the effectiveness of one’s behavioral strategies

would vary based on the extent to which an event was reactive or proactive. When looking at

self-reported behavioral strategy effectiveness as the DV, the overall model was significant

(F(2,191.70) = 3.47, p = .03), suggesting that proactive events yielded more effective behavioral

strategies than reactive (p = .01) or mixed (p = .04) events. Given that reactive events tended to

be associated with a greater reliance on exploitation and withdraw strategies (see hypothesis 13b

above), these findings suggest that exploitation and withdraw strategies may be less effective

than other behavioral strategies. However, regardless of the strategy employed, when responding

reactively to adaptive events, individuals are generally less effective in their behavioral response

than when anticipating and responding to proactive events. However, when using the coded

behavior impact variable as the DV, no significant differences emerged based on how reactive or

proactive an event was perceived to be (F(2,176.59) = .66, p = .52). This discrepancy may be

explained by the different sources of the data being used (self-report versus coder-rated), or by

the way in which the questions were phrased (e.g., how effective was your behavioral strategy? versus “what was the impact of the individual’s behavior?”).

Hypothesis 14

For hypothesis 14, the specific adaptive performance dimension into which an event was categorized was the focus. Specifically, it was expected that the nature of the adaptive event would impact how the event was appraised, what behavioral strategies were used, and how effective one’s strategies were at responding to the event. While there are eight adaptive performance dimensions, the reported events only covered five of the eight (no events for the crisis, cultural, or physical dimensions). Additionally, the bulk of the events fell into one of three dimensions – handling work stress, solving problems creatively, and dealing with uncertainty.

Only a small number of events were coded into the learning and interpersonal categories. For all hypothesized analyses, random intercept models were used to account for the random intercepts across subjects. Table 27 provides additional details about the variance accounted for in each dependent variable for each analysis. Supplementary exploratory analyses were also conducted to more fully understand how the adaptive nature of an event influenced the process more broadly. These results will also be summarized below.

For hypothesis 14a, it was expected that the type of adaptive event would influence how an event was appraised (i.e., how challenging or threatening the event was appraised to be). The results for this hypothesis were mixed. Contrary to expectations, the type of adaptive event did not result in significant differences in how challenging the event was appraised to be

(F(4,206.56) = .23, p = .92). When looking at threat appraisals, there was an overall trend based

on the type of adaptive event, although it did not reach significance (F(4,201.07) = 2.20, p =

.07). A deeper exploration of this relationship revealed a significant difference in threat appraisals across adaptive events that were primarily characterized by an increase in work stress versus those that were primarily characterized by the need for more complex or different thinking (i.e., combination of events categorized as solving problems creatively, learning new tasks, and dealing with uncertainty; F(1,193.67) = 7.04, p = .01). Specifically, when encountering adaptive events characterized by high work stress, individuals appraised these events as more threatening than events characterized by more complex thinking.

Hypothesis 14b examined the extent to which the use of the five behavioral strategies

(exploitation, exploration, increase effort, withdraw, or stay the course) varied depending on the nature of the adaptive event. Separate random intercept models were run for each behavioral strategy. The results were mixed. When looking at the overall models (that is, including the distinct adaptive performance event categories), no significant differences emerged for increase effort (F(4,199.52) = .58, p = .68) or withdraw (F(4,195.03) = 1.03, p = .39) behaviors based on the type of event to which one was responding. However, there was a significant difference in the degree to which exploration (F(4,206.39) = 3.32, p = .01) behavioral strategies were used.

Although not significant, there were trends that suggested that the use of exploitation

(F(4,209.15) = 1.96, p = .10) and stay the course (F(4,193.08) = 1.98, p = .10) behavioral strategies may vary based on the type of event to which one was responding. Specifically, events characterized by increased work stress were associated with a greater reliance on exploitation behaviors (F(1,201.22) = 5.26, p = .02) than events requiring more complex thought, while the reverse was true for exploration (F(1,200.46) = 7.33, p = .01) and stay the course (F(1,185.54) =

6.28, p = .01) behaviors. Digging even deeper, events requiring individuals to solve problems creatively (which is one of the three adaptive dimensions comprising the “increased complex

100 thought” category) were associated with the greatest amount of exploration behaviors

(F(1,211.01) = 9.05, p = .00). Uncertain events may lead to more stay the course behaviors than other event types, although this relationship was not significant (F(1,192.32) = 2.64, p = .11).

These patterns make sense overall. Specifically, work stress events are more likely characterized by higher time demands and higher workload, which may require more effort or a faster pace, but not necessarily require a novel solution that requires exploration. Also, as creative situations often require thinking outside of the box and exploring ideas and solutions that have not been used before, it makes sense that they would require greater exploration. However, when facing uncertain situations, individuals may feel like they need more information before selecting a new strategy, thus they are more prone to stay the current course. Once again, in all cases except for exploitation behaviors, behavioral strategy use also varied significantly by person (p’s ranging from .00 to .04).

To further explore how the nature of the adaptive event may influence the selection of behavioral strategies, the primary behavioral strategy used to respond to each event was identified by coders. Table 28 displays the frequencies with which each behavioral strategy was adopted as the primary behavioral strategy when reacting to an event, organized by the nature of the adaptive event. When looking at the coded primary behavioral strategy used by individuals by adaptive event type, the pattern suggests that individuals respond to events categorized as solving problems creatively, as well as learning events, most frequently with exploration strategies. This makes sense, as events that require a creative solution or learning are likely to have an unknown solution (that is, requiring an unlearned or novel approach). To identify the appropriate strategy, it requires individuals to explore different alternatives first. While the pattern for creative events mirrors the analyses above, the greater use of exploration for learning

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events is not consistent with the analyses above. This discrepancy is likely due to the small

number of learning events available, thus making the analyses less stable. Conversely, events

that require adaptation to work stress are most frequently adapted to with exploitation behaviors.

This also make sense, as work stress typically involves increased workload or time demands, where individuals have more to do and need to quickly figure out the best approach for pushing

forward. When facing events characterized by unpredictability/uncertainty, both exploitation and

exploration strategies are used with relatively equal frequencies, suggesting that in some cases

the best approach is quickly determined, while other times it requires people to explore

alternatives before landing on an approach. Finally, when adapting to interpersonal events, there

is a more even distribution in the types of behavioral strategies used. These events appear to

diverge from the other types of events which are focused more on the nature of the task than on

interactions with other individuals. Additionally, given the small number of events available, the

results for interpersonal events should be interpreted cautiously.

For hypothesis 14c, it was expected that the effectiveness of one’s behavioral strategies

would vary based on the nature of the adaptive event. When looking at self-reported behavioral

strategy effectiveness as the DV, the overall model was not significant (F(4,190.41) = .53, p =

.72), suggesting that effectiveness does not vary based on adaptive event type. However, behavioral strategy effectiveness did vary significantly based on the individual (p = .00). Given that different behavioral strategies were used by event type, the lack of effect on effectiveness suggests that one behavioral strategy is not always the most effective, but rather, different strategies may be more appropriate for different types of events. This finding was further supported when using the coded behavior impact variable as the DV. Behavioral impact did not differ significantly by adaptive event type (F(4,172.80) = .83, p = .51). However, when looking

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at stress versus complex thinking events, a trend did emerge (F(1,163.34) = 2.76, p = .10). While not significant, this trend suggests that behaviors in response to stress events may have a more positive impact on the situation than behaviors in response to complex thinking events.

Hypothesis 15

For hypothesis 15, it was predicted that the type of complexity change required by an

event would impact how the event was appraised, what behavioral strategies were used, and how

effective one’s strategies were for adapting to the event. For all hypothesized analyses, random

intercept models were used to account for the random intercepts across subjects. Supplementary

exploratory analyses were also conducted to more fully understand how the type of complexity

change influenced the process more broadly. Table 29 provides additional details about the

variance accounted for in each dependent variable for each analysis. These results will also be

summarized below.

For hypothesis 15a, it was expected that the type of complexity change would influence

how an event was appraised (i.e., how challenging or threatening the event was appraised to be).

The results provided support for this hypothesis. The type of complexity change was associated

with significant differences in challenge appraisals (F(2,210.46) = 9.89, p = .00) and threat

appraisals (F(2,207.22) = 3.35, p = .04). Specifically, dynamic complexity changes were

associated with more challenged (p = .00) and threatened (p = .03) appraisals than coordinative

complexity changes. Since dynamic events are more complex than coordinative events, often

being associated with “chaos,” it makes sense that these events would be viewed as more

challenging and threatening than the less complex coordinative events.

Hypothesis 15b examined the extent to which the use of the five behavioral strategies

(exploitation, exploration, increase effort, withdraw, or stay the course) varied depending on the

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type of complexity change required by the event. Separate random intercept models were run for

each behavioral strategy. The results were mixed. When looking at the overall models (that is,

including the distinct component, coordinative, and dynamic change conditions), no significant

differences emerged for exploration (F(2,210.95) = 1.31, p = .27) or withdraw(F(2,201.14) = .46,

p = .46) behaviors based on the type of complexity change one was facing. There was not a

significant effect for exploitation behaviors (F(2,191.07) = 1.90, p = .15). Follow-up analyses

comparing dynamic changes to all other changes (component and coordinative, combined)

revealed a trend in the use of exploitation behaviors (F(1,198.97) = 3.73, p = .06), such that

dynamic changes may be associated with fewer exploitation behaviors than events requiring less complex changes. This is likely due to the fact that dynamic events are hard to make sense of, making it less likely that individuals know of and can quickly identify a solution to exploit.

Additionally, the type of complexity change was associated with significantly different amounts

of increase effort behaviors (F(2,203.91) = 5.73, p = .00). Specifically, component changes were

associated with significantly more increase effort strategies than other types of changes

(F(1,205.33) = 11.41, p = .00). While not significant, a trend suggests that coordinative changes

may be associated with fewer increase effort strategies than other types of changes (F(1,202.14)

= 3.02, p = .08). Again, this finding supports the conceptual nature of the task complexity dimensions. Component changes are typically characterized as “do more” or “work harder” changes, thus the tendency for these events to result in behavioral strategies focused on increasing effort, as opposed to finding a novel strategy, makes sense. Finally, the type of complexity change also significantly impacted the use of stay the course behaviors (F(2,200.37)

= 3.28, p = .04). Dynamic changes were associated with the greatest reliance on stay the course behaviors (F(1,207.47) = 4.71, p = .03), while coordinative changes were associated with the

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least amount of stay the course behaviors (F(1,196.65) = 6.23, p = .01). Given that dynamic changes are chaotic and unpredictable, individuals likely rely more on stay the course behaviors as they wait to learn more about the situation. Coordinative changes on the other hand are characterized by the need to work smarter, or identify new or updated solutions to account for changes in the task environment. Thus, it is less likely that someone will stay the course, but will instead try to identify a new strategy. Once again, in all cases except for exploitation behaviors, behavioral strategy use also varied significantly by person (p’s ranging from .00 to .05).

To further explore how the type of complexity change may influence the selection of behavioral strategies, the primary behavioral strategy used to respond to each event was identified by coders. Table 30 displays the frequencies with which each behavioral strategy was adopted as the primary behavioral strategy when reacting to an event, organized by the type of complexity change. The patterns provide additional insights into the relationship between complexity and behavioral strategy choice. Consistent with the analyses above, component complexity changes were associated with by far the largest percentage of “Increase Effort” behaviors, which is consistent with expectations, as component complexity changes often require individuals to work harder (as opposed to changing course or working smarter). Coordinative complexity changes were associated with the largest amount of exploitation behaviors, which suggests that the working smarter strategy was found quickly and exploited. Finally the dynamic complexity change events were associated with the greatest percentage of exploration behaviors, which makes sense as the situation is more chaotic and dynamic, thus the appropriate strategy may be unknown. Additionally, a higher percentage of dynamic events were associated with people withdrawing or staying the course, suggesting that it may be too difficult for individuals to identify a new strategy, so they either do make a change, or they just stop trying.

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For hypothesis 15c, it was expected that the effectiveness of one’s behavioral strategies

would vary based on the type of complexity change. When looking at self-reported behavioral strategy effectiveness as the DV, the overall model was significant (F(2,192.90) = 5.13, p = .01).

When breaking down the analyses to compare each type of complexity change to all other types of changes, additional insight was gained into the differences. Specifically, component changes were associated with significantly less effective behavioral strategies than other change types

(F(1,194.69) = 6.58, p = .01), while coordinative changes were associated with significantly

more effective behavioral strategies than other change types (F(1,188.77) = 8.09, p = .01). This

finding was further supported when using the coded behavior impact variable as the DV. The type of complexity change was not significantly associated with the behavioral impact of one’s strategies, although there was a trend (F(2,179.75) = 2.50, p = .09). Consistent with the analyses looking at self-reported behavioral strategy effectiveness, component changes were associated with behaviors with a less positive impact than other types of changes (F(1,179.52) = 3.79, p =

.05), while a trend suggested that coordinative changes may be associated with behaviors with a more positive impact than other change types (F(1,177.44) = 3.45, p = .07). Together, these findings suggest that, at least for this sample, individuals were more effective at responding behaviorally to “work smarter” events than “work harder” events. This could perhaps be due to the high degree of time and work stress that is part of the normal day-to-day for these individuals, thus any increase in demand (that is, the need to do more in the same or less time) exceeds their available resources.

Supplementary Event-Level Analyses

While the hypotheses above shed light onto some of the critical relationships among individual differences and situational (event) factors and the cognitive, affective and behavioral

106 responses to adaptation, supplementary analyses were conducted to reveal a fuller picture of how the three primary event-related factors (reactive versus proactive, type of complexity change, and adaptive performance dimension) impacted all aspects of the adaptation process (see Figure 5 for a representational model of the key study variables). Specifically, when an existing hypothesis did not already cover it, random intercept models were conducted to examine how each of these factors impacted: situation assessment variables (detection time, challenge appraisals, threat appraisals, and situation assessment effectiveness); planning and strategy selection variables

(time to plan, contingency planning, reactive strategic planning, all five behavioral strategy types, and planning and strategy selection effectiveness); execution and evaluation variables

(behavioral strategy effectiveness (impact), coping strategy effectiveness, execution and evaluation effectiveness); perceptions of how much adaptation was required (adjustment of thinking, adjustment of KSAs, adjustment of feelings/emotions, and adjustment of behaviors); cognitive and affective states (maintain focus, anxiety, and frustration); and overall adaptation effectiveness. The F-statistics are summarized briefly in Table 31, and additional details about the variance accounted for in each dependent variable for each analysis are provided in Table 32.

The gray-shaded boxes represent those relationships that were already discussed in the write-up of the hypotheses above. The takeaways from these results are summarized in the event-level summary section below.

Event-Level Summary

Overall, the results of the event-based hypotheses and supplementary analyses clearly revealed that situational characteristics (e.g., the type of complexity change) impact individuals’ strategies, reactions, and effectiveness when adapting to events. Specifically, these factors impact individuals’ cognitive, affective, and behavioral responses during all three phases of the

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adaptation process (situation assessment; planning and strategy selection; execution and

evaluation). Table 33 summarizes the results for easy comparison. As can be seen, events that are

primarily reactive in nature and those that are characterized as “handling work stress,” tend to

result in greater challenges during adaptation. These events require doing more in the same or

less amount of time, so there is not a luxury of exploring options. Instead, immediate action is

needed, yielding higher levels of anxiety and frustration and quicker behavioral adjustments.

Conversely, those events that are anticipated ahead of time or that require thinking through creative or unpredictable situations (as opposed to just more work stress), tend to be associated with fewer challenges and more effective responses. When adapting to these events, individuals have time to explore options and feel less threatened by the changes. Additionally, it appears that the type of adaptive event most strongly impacts the planning and strategy selection phase, at least when it comes to the effectiveness with which one engages in that phase of the adaptation process. Overall adaptation effectiveness does not vary based solely on the nature of the adaptive event. This suggests that overall adaptation effectiveness is likely indirectly impacted by the nature of the event, through the event’s impact on the processes and responses one engages in as they adapt. Overall, these results solidify the importance of looking beyond “adaptation performance” to what individuals are actually doing in response to an event, as these patterns would be missed otherwise.

Person-Level Summary

While the event-level hypotheses were of primary importance in this study, there were a few noteworthy takeaways from the person-level analyses. First, it became evident that individual difference characteristics are less potent predictors of some of the key adaptation variables than the event-related factors. Neither openness to experience nor perceived job

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autonomy yielded significant findings for the hypothesized relationships. However, goal

orientation and trait adaptability did yield some significant effects in the expected direction.

While goal orientation has proven to be a consistent predictor of adaptive performance in past work, this study provided needed support for trait adaptability, as measured by the I-ADAPT, given its limited use in the existing research. However, while it was expected that self-reported trait adaptability on a particular dimension would result in the highest correlations with adaptation performance within events that were categorized as that same dimension, this was not the case. This suggests that the trait measure and adaptive performance categorization may not

be completely aligned, or it is possible that the trait measure is more useful as an overall

assessment, rather than focusing on specific dimensions.

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DISCUSSION

The current study focused on extending our conceptual and empirical understanding of

the adaptation process. Over the last few decades, research has overwhelmingly demonstrated

that adaptation is required for success across all types of organizations – corporate, government,

military, and at all levels – individual, team, organizational (e.g., Bell & Kozlowski, 2008;

Ployhart & Bliese, 2006; Pulakos et al., 2000; 2002). However, the focus of that research and the

conceptualization and operationalization of adaptation has varied widely in the literature (Baard

et al., 2014). The majority of the adaptation literature has come from one of two perspectives: 1)

identifying individual differences that predict adaptive performance or that comprise a trait of

“adaptability” (e.g., Ployhart & Bliese, 2006; Pulakos et al., 2000; 2002), or 2) identifying,

developing, and evaluating training interventions that enhance the adaptive capabilities (e.g.,

knowledge and skill) of individuals within a specific domain, which should in turn positively

impact adaptive performance (e.g., Bell & Kozlowski, 2002; 2008; Keith & Frese, 2005; Smith,

Ford, & Kozlowski, 1997). A third perspective that has received less attention is focused on understanding the actual dynamic adaptation process that individuals and teams engage in as they are reacting to new or changing situations (e.g., Burke et al., 2006). Together, these research efforts have resulted in several noteworthy conceptual and empirical advances in our understanding of adaptation.

While these advances have pushed the field forward significantly over the last few decades, substantial gaps still exist in our understanding of the adaptation phenomenon. The goal of the current study was to address three of these critical limitations, which include the gaps in our understanding about: (1) the actual behaviors and dynamics of the adaptation process due to the over-reliance on static conceptualizations and empirical examinations of adaptation; (2) the

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nature of the changes or adaptive events that individuals face at work due to a lack of field

studies and the haphazard manipulation and definition of the “changes” being studied; and (3)

the role of cognitive, motivational, and affective processes and reactions in the adaptation

process (Baard et al., 2014). The sections below describe each of these gaps and highlight how

the current study addressed these gaps and contributed to our understanding of the adaptation

process.

The first limitation of the extant literature is that adaptation primarily has been

conceptualized and operationalized as a relatively static construct or performance outcome that is examined after a change takes place. While this simplistic representation of adaptation allows researchers to determine whether or not someone performed well after a change, it leaves us in

the dark when it comes to understanding what people actually did to respond to the change and

how and why some people had better outcomes than others. Individual differences may predict

some of the variance in adaptation outcomes, but as the current study shows, relying solely on

distal predictors such as openness to experience and goal orientation only accounts for a small,

and sometimes insignificant, portion of the variance in people’s performance after changes.

There is some existing conceptual work (e.g., Burke et al., 2006) that begins to shed light into this black box by breaking apart and defining some of the phases of adaptation that individuals and teams are likely to go through as they face an adaptive event. However, the current study extended this work by digging into the adaptation process to better understand some of the observable behaviors that represent critical aspects of these phases and that may be tractable in an empirical study. For example, the planning and strategy selection phase (Burke’s plan formulation phase) of the adaptation process consists of multiple activities. For the current study, the extent to which different types of planning were used, along with the extent to which

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different coping and behavioral strategies were selected to respond to the event were assessed.

Additionally, self-reported effectiveness for that phase of the adaptation process was collected.

Together, this information allowed for an exploration of what individuals were actually doing as

they adapted to the event and how effective those actions were at helping them adapt. By taking

this approach, it is possible to gather information about an individual’s responses in the face of

change that is more actionable and valuable than information coming from static explorations of

adaptation outcomes. For example, by tracking individuals’ coping and behavioral strategies in

certain types of situations, it is possible to understand why they are or are not adapting

effectively and under what conditions their behavioral choices are ineffective. This information

can inform targeted interventions focused on providing the individual with the skills and tools they need to make more effective behavioral strategy choices in the future. Overall, the results from this study provide support for examining the adaptation process, and the behaviors one engages in, rather than just relying on static outcomes.

The second limitation that was addressed in the current study is the lack of understanding about the types of changes and adaptive events that individuals actually encounter in the workplace. There are two separate factors that contribute to this gap. The first factor is that the majority of the adaptation work has been conducted using laboratory-based experiments with undergraduate populations. While this is a critical methodology for understanding adaptation, it is unclear whether or not the contrived changes and settings generalize to the types of changes and adaptive events individuals are encountering in the real world. To address this limitation, the current study collected data from organizational employees, across a variety of disciplines, using a descriptive, event-based sampling methodology. This allowed for an organic set of events and changes to emerge based on what individuals were experiencing in their day-to-day work

112 experiences. The second factor contributing to this gap is that the changes studied in the extant research are typically not carefully designed and are often poorly described (Baard et al., 2014).

As a result, it is unclear what is actually changing and which aspects of the change are driving differences in responses. Furthermore, it makes it difficult to integrate research findings when it is unclear how two research studies defined change. The current study begins to address this gap in the research by leveraging three existing, theoretically-relevant frameworks from the literature that were used to categorize the types of changes to which individuals are responding. First,

Pulakos and colleagues’ (2000) adaptive performance taxonomy was used to categorize adaptive events based on what type of adaptation was required. For example, did the event require an individual to adapt to increased workload (i.e., handling work stress) or to uncertain and unpredictable conditions (i.e., dealing with uncertain and unpredictable situations)? By categorizing the events using these dimensions, it was possible to examine if the use and effectiveness of different strategies varied depending on the adaptive performance dimension.

Wood’s (1986) taxonomy of task complexity was the second categorization scheme used to help shed light on how different types of changes may impact the adaptation process. It was assumed that changes that required individuals to just do more (i.e., component complexity) would place different demands on them than if the change required individuals to navigate a chaotic situation

(i.e., dynamic complexity). Finally, Ployhart and Bliese’s (2006) breakdown of reactive and proactive adaptation was used to differentiate between situations where individuals were forced to react to a situation that had already changed and situations where individuals proactively adapted to an anticipated, but not yet present, change. Together, these three categorizations were used in the current study to help clearly define the events that were being reported. Several hypotheses were proposed to explore the extent to which the type of change impacted the

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adaptation process, across phases, and overall adaptation effectiveness. In general, the results

from the study provided overwhelming support for the importance of carefully defining the

changes that are being studied, as they are associated with significantly different processes.

Finally, the third limitation addressed in the current study is the lack of understanding of

how the cognitive, affective, motivational, and behavioral pathways may play a role in the

adaptation process. While there has been a significant amount of research understanding how

these pathways, and the processes and states representing them, impact learning, there has been a

paucity of research focused on the role of these pathways during the adaptation process itself.

While additional work is needed, the current study identified a few of the relevant states (e.g.,

anxiety, attentional focus) and processes (e.g., planning) that occur during the adaptation phases and explored how these states and processes may be differentially impacted by the type of change. The results from this study reveal that these states and processes do vary based on the type of change to which an individual is responding, suggesting they are important to track and assess, as they can give insight into where individuals struggle or excel in the adaptation process.

Summary of Findings

Although this study was exploratory in nature, several hypotheses and research questions

were proposed to examine the extent to which individual differences, situational factors (i.e., the

different types of events and changes), and behavioral choices impacted different aspects of the

adaptation process. The study hypotheses can be grouped into two types – event-level hypotheses

and individual or person-level hypotheses. The event-level hypotheses primarily focused on how

the type of event impacted the different phases of the adaptation process. In general, these

hypotheses were supported, even when accounting for the person variance, with clear evidence

that the nature of the event or change being adapted to influences individuals’ perceptions,

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reactions, and responses throughout the phases of the adaptation process. The person-level hypotheses focused on exploring how conceptually relevant individual differences may influence

how people respond to the adaptive events they face, on average. The support for these hypotheses was mixed.

Event-Level Hypotheses

Use of coping strategies. Three of the hypotheses examined how individuals’ choice of coping strategy when adapting to an event impacted their cognitive and affective reactions

(hypothesis 2), appraisals of an event (hypothesis 3) and the effectiveness of their behavioral and coping strategies (hypothesis 4). Coping strategies can greatly impact how one experiences and behaves during events, especially those that are stressful (Carver et al., 1989; Lazarus &

Folkman, 1984). Specifically, research has shown that when individuals choose problem-focused coping strategies, they overcome the stress of an adaptive event by focusing on the problem and actively finding ways to make effective progress. However, the reliance on emotion-focused coping strategies puts the emphasis on one’s emotions rather than the problem at hand, often times resulting in less productive outcomes. The results of hypothesis 2 supported previous research, as those using problem-focused coping strategies reported more on-task attention, less anxiety and less frustration than those using emotion-focused coping strategies. Additionally, the results of hypothesis 4 also provided support for past research. Specifically, the use of problem- focused coping strategies tended to be associated with more problem-focused behavioral strategies. That is, when faced with an adaptive event, individuals engaged in behaviors focused on increasing effort and/or finding a new solution to the problem. However, those using emotion- focused coping strategies engaged in significantly more avoidant behaviors, such as withdrawing effort from the situation. Additionally, individuals reported feeling that their choice of coping

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and behavioral strategies was more effective when they engaged in problem-focused coping

strategies than when they relied on emotion-focused coping strategies. This suggests that not

only are the behaviors of these two groups different, but that problem-focused coping leads to

more effective behaviors as well.

However, the expected pattern for coping strategy use and appraisals was not supported.

Specifically, hypothesis 3 proposed that problem-focused coping would yield more challenged, but less threatened appraisals. Challenge appraisals are made when an individual feels that the change is relevant, but they have the resources to handle it (Lazarus & Folkman, 1984). Those engaging in problem-focused coping strategies therefore would be expected to feel more challenged by the event. However, the opposite pattern was found, such that emotion-focused coping were associated with more challenged appraisals. The most likely explanation for this finding is that the term “challenging” was interpreted differently by participants than it was intended. Specifically, if challenging was interpreted as demanding or difficult as opposed to relevant, but doable, then it makes sense that the result was reversed. Future research should more carefully define what is meant by challenging to determine whether or not this relationship holds. Additionally, threat appraisals (i.e., relevant, and not enough resources to handle) did not vary significantly based on coping strategy (problem-focused versus emotion-focused), which was contrary to expectations. This finding may be due to how this term was interpreted as well.

Given that individuals reported more frustration and anxiety when they used emotion-focused strategies, it is likely that they actually did feel more threatened than when using problem-

focused strategies, even if the results did not reveal that relationship.

Together, these results suggest one avenue by which adaptation may suffer. Monitoring

and assessing the coping strategies being used in adaptive situations can shed light on why an

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individual may not be adapting well. To help improve adaptation, it may be important for

organizational leaders to provide training and coping support tools for employees who are in highly dynamic jobs to help them engage in more effective coping strategies, thereby reducing the negative cognitive, affective, and behavioral consequences that they may experience.

However, while these results are mostly consistent with past research, some caution is needed when interpreting these results, as individuals only reported using emotion-focused coping strategies for a small number of events (n = 18 versus n = 190 for problem-focused coping). It is unknown whether the heavy skew in this breakdown is unique to this particular sample and the types of events being reported, or if problem-focused coping is much more common across samples. However, the sample used in this study consisted of highly educated, experienced professionals who operate in a very volatile, high demand environment on a daily basis.

Therefore, these individuals are more likely to use problem-focused coping strategies out of

necessity to be able to perform effectively in their jobs.

Reactive versus proactive events. The remaining event-level hypotheses examined the

extent to which the different event categorizations impacted the adaptation process. Two

hypotheses looked at how the reactive versus proactive nature of a change impacted planning

processes (hypothesis 1), appraisals, behavioral strategy use and effectiveness (hypothesis 13).

However, supplementary analyses were conducted to provide a more complete understanding of how the reactive versus proactive nature of events impacts each phase of the adaptation process.

The results of both the supplementary analyses and the hypotheses are summarized below by adaptation phase.

A mix of supplementary and hypothesized analyses was conducted to understand how the reactive versus proactive nature of an event impacted the situation assessment phase of the

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adaptation process. As a part of hypothesis 13, this distinction was expected to impact the

appraisals one made about the situation. Specifically, it was expected that reactive events would be associated with more threatened appraisals than proactive events. As expected, reactive events did result in more threatened appraisals than proactive events, providing support for the assumption that individuals will feel like they are less equipped (time, resources, etc.) to handle reactive adaptive events than proactive adaptive events. While not hypothesized, reactive events

also were reported to result in longer detection times. That is, the changes being adapted to took

significantly longer for the individual to detect than when an event was coded as proactive. This

is a common sense finding, as reactive events are reactive in part because they were not detected

sooner. The late detection of the event could be one contributing factor for the more threatened

appraisals people report, as it may feel that there is not enough time to effectively respond to the

situation if the change was detected late in the game. However, while detection time is slower

and threat appraisals are higher, individuals do not report less effective situation assessment

overall. This suggests that even with these barriers to effectiveness, individuals still felt they

were able to adequately assess the situation when responding to adaptive events.

When it comes to the planning and strategy selection phase, it was expected that reactive

events would be just that – reactive – which means, by their very nature, they allow for less time

to plan and develop contingency plans than those events that are anticipated ahead of time

(proactive). Hypothesis 1 proposed that reactive events would result in less contingency planning

and more reactive strategic planning than proactive events. These relationships were supported.

However, unexpectedly, the actual perceived time to plan was not significantly different

depending on if the event was reactive or proactive. While this seems counterintuitive, it is

possible that a similar perceived amount of time was spent on planning, but when the planning

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happened and the nature of that planning (contingency versus reactive strategic) differed. When looking at the actual open-ended event descriptions, the theme of planning emerges quite frequently across events. In fact, one of the common coping strategies for dealing with an adaptive event that was used was to engage in planning or re-planning. Therefore, even when an event was reactive, individuals, at least for the most part, still took the time to engage in planning to understand how to move forward. Future research is needed to explore whether or not a similar pattern happens in other samples. It is expected that in some environments, the types of reactive adaptive events individuals face would not allow the luxury of planning (e.g., if troops are on patrol and an improvised explosive device (IED) goes off, serving as an adaptive event,

the reactions would need to be immediate, thus planning time would be virtually non-existent).

Hypothesis 13 was supported as well, with different behavioral strategies emerging based on the

extent to which an event was reactive or proactive. Specifically, it was expected that reactive

events would be associated with the use of behavioral strategies that required less time and

exploration, and possibly more withdraw behaviors, than proactive events. Reactive events were

associated with significantly more exploitation behaviors (i.e., quickly find a strategy and exploit

it) and withdraw behaviors than proactive events. Reactive events also showed a trend toward

fewer exploration behaviors than proactive events. Together, these findings suggest that when

facing reactive events, individuals are put in a position to have to very quickly find a strategy and

run with it, while proactive events allow individuals the time to explore what the best option may

be. This has implications for effectiveness as well, given that individuals reported that their

planning and strategy selection effectiveness was significantly lower when responding to

reactive events than proactive events.

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This less-than-adaptive pattern continues when examining the execution and evaluation

phase of the adaptation process. Specifically, individuals reported that their choice of behavioral

and coping strategies were significantly less effective when responding to reactive events than when responding to proactive events. This is consistent with hypothesis 13. However, their perceived level of overall execution and evaluation effectiveness was not significantly different based on the nature of the event.

In addition to the impact on the different adaptation phases, there were also differences in individuals’ cognitive and affective states as a result of the type of event. Specifically, reactive events were associated with less attentional focus and more anxiety and frustration than proactive events. Overall, these findings bring to light the challenges associated with adapting to reactive situations, especially for the planning and strategy selection phase. Both planning and behavioral strategy selection were negatively impacted when individuals were responding to adaptive events, as evidenced by their choices and their perceived planning and strategy effectiveness. To circumvent these negative consequences, individuals, teams, and organizations

would benefit from engaging in more opportunity assessments to identify potential areas of

future change to which one could proactively respond. Additionally, equipping individuals with

detection aids and encouraging them to look at the long-term picture could help improve their

chances of detecting a change earlier. While becoming a more proactive employee is the ideal

solution, it is not always the case that events can be anticipated. In those cases, it is clear that there will likely be greater challenges to those adapting. Future research should explore factors that may improve one’s chance of success in reactive situations.

Adaptive performance dimensions. One hypothesis, and several supplementary analyses, was examined to explore the extent to which the adaptive performance dimension that best

120 categorized an event impacted the adaptation process. Similar to the summary above, the results of the hypothesis and the supplementary analyses are summarized below by adaptation phase.

For the situation assessment phase, hypothesis 14 predicted that the adaptive performance dimension within which an event was categorized would influence an individual’s challenge and threat appraisals. However, the exact nature of this influence was not hypothesized. While there was no difference in perceived challenge appraisals based on the dimension, there was a significant difference in threat appraisals. Specifically, when individuals encountered an adaptive event characterized by increased work stress, they reported more threatened appraisals than when responding to an event that was characterized by the need for new or more complex thinking.

This suggests that “the what” being adapted to has implications for how individuals’ appraise the event. In this case, having to do more is more threatening than having to think differently.

Similar to reactive events, work stress events also result in longer detection times, suggesting that individuals seem to take longer to notice that their workload will be or already is increasing than to notice that they need to think about a situation differently or learn something new. The combination of a slower detection time and more threatened appraisals suggests that situation assessment effectiveness may be less for stress events than for thinking events. However, there was not a significant difference in perceived effectiveness during the situation assessment phase based on this split. However, there was a trend that suggested that events requiring individuals to solve problems creatively may be associated with higher levels of situation assessment effectiveness than other types of events. Together, these findings suggest that considering what an individual is adapting to is critical to understanding their response – at least their ability to detect the change and their appraisal of the event – in the situation assessment phase. Additional

121 research is needed to better understand how threat appraisals and detection time influence the adaptation process, if not through their impact on situation assessment effectiveness.

For the planning and strategy selection phase, supplementary analyses explored the extent to which the type of adaptive event impacted planning processes. The results found that when facing work stress events, individuals engage in significantly less contingency planning than when they encounter thinking events. This could be due to the slower detection time identified in the situation assessment phase, such that stress events are detected later, thus allowing for less contingency planning. Additionally, this could be due to the nature of the event.

Contingency planning often allows people to work smarter – that is, to identify different courses of action based on what may happen – but for events that are represented by an increase in workload, contingency planning may be less helpful. Often times, the solution may be to just put more time in or work faster, neither of which really benefit from contingency planning. When looking at the relationship with behavioral strategy choices, as expected in hypothesis 14, different types of events were associated with different strategies. Specifically, work stress events were associated with significantly more exploitation behaviors and significantly less exploration and stay the course behaviors than thinking events. Events requiring individuals to solve a problem creatively were associated with the highest levels of exploration behavior, while events characterized by high levels of uncertainty were associated with the most stay the course behaviors. These patterns are consistent with what one would expect given the nature of these events. Again, work stress events require individuals to do more with less time. It makes sense that individuals adapting to these events would be more likely to quickly identify and exploit a new course of action, as they do not have the luxury of time to explore. However, when the event requires coming up with a creative solution to a problem, it makes sense that the most common

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strategy would be for individuals to explore several different options before finding the best path.

Finally, individuals rely more strongly on stay the course behaviors when facing an uncertain

situation. In these situations, individuals may choose to stay the course to see if the uncertainty diminishes before deciding what to do. The nature of the adaptive event also has implications for how effective individuals are at planning and strategy selection is in general. Specifically, work stress events were associated with less effective planning and strategy selection perceptions than thinking events, while creative events were associated with the highest perceived planning and

strategy selection effectiveness.

The findings above may suggest that the behavioral choices made when reacting to high

stress events (i.e., exploitation) are not as effective as those made when reacting to creative

events (i.e., exploration). This is similar to the pattern found for reactive versus proactive events,

suggesting that in general, exploitation may be a less favorable strategy when adapting than other

behaviors, like exploration. However, when looking at the execution and evaluation phase of the adaptation process, individuals did not report different levels of behavioral strategy effectiveness based on event type. However, when looking at the coders’ rating of the impact of one’s behaviors on the event, behaviors in response to work stress event were rated as having a less positive impact on the event than those in response to thinking events. This provides support for the fact that work stress events are more difficult to adapt to than other types of events. This finding is further bolstered by the finding that individuals’ perceptions of coping strategy effectiveness and overall execution and evaluation effectiveness are also lower when facing

stress events. The decreased effectiveness may in part be to the increased anxiety and frustration

that is reported during work stress events, and the perception that there is a greater need to adjust

one’s behavior during these events.

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Overall, the results provide a strong case for the need to account for the type of adaptive event when understanding the challenges to the adaptation process. Using Pulakos’ (2000) adaptive performance dimensions, it was possible to categorize all of the events based on their primary dimension. However, these results should be interpreted with some caution, as many events did not fit cleanly into a single dimension. Instead, most of the events could be represented by more than one dimension. The coders’ inability to cleanly map an event into one of the eight dimensions is not entirely surprising. Previous work focused on validating the adaptive performance taxonomy supported only a one factor solution, suggesting that raters were not able to distinguish between the eight dimensions (Pulakos et al., 2002). For the current study, the primary dimension (i.e., the one that best represented that event) was used. However, future work should look at the different combination of dimensions characterizing each event to better understand the nuances of how these dimensions influence the adaptation process. Additionally, while there were eight possible dimensions, the events reported in this study only represented five of those dimensions. Handling crises, demonstrating cultural adaptability and demonstrating physical adaptability dimensions were not represented. The nature of the work that is conducted by this sample tends to be best aligned with the handling work stress, solving problems creatively, and dealing with uncertainty dimensions. Future research should explore other samples that may allow for the capturing of events that represent the other dimensions as well.

Task complexity. The final event type break down explored the extent to which the type of task complexity change required by the event impacted the adaptation process. Hypothesis 15 focused specifically on understanding how task complexity influenced individuals’ appraisals of the event, as well as their behavioral strategy choices and the effectiveness of those behavioral strategies. Additional analyses were conducted to explore the relationship between task

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complexity and the remaining adaptation process variables. The results are summarized by

adaptation phase below.

For the situation assessment phase, detection time, challenge, and threat appraisals were

all significantly impacted by the type of complexity change. In all three cases, dynamic

complexity changes were associated with different processes than either component or coordinative changes. Specifically, hypothesis 15 predicted that there would be differences in challenge and threat appraisals based on the type of complexity change representing the event.

This hypothesis was supported, with dynamic events being associated with significantly higher challenge and threat appraisals than coordinative events (but not with component events). This suggests that dynamic changes, those characterized by changing relationships and, at times, chaotic conditions, are appraised as more difficult, demanding, and overwhelming by individuals. As appraisals focus on an individual’s perception of their ability to handle a situation, it is possible that the unpredictability of dynamic events makes it difficult for individuals to know whether or not they do have the resources to cope with the event, thus resulting in higher levels of perceived challenge and threat. Coordinative events on the other hand require individuals to find a new strategy based on a change or new information that has come into the situation. While this can be challenging, individuals feel better equipped to adjust to this shift than when lots of things are changing. Additionally, the lack of a difference in appraisals between dynamic and component complexity events suggest that an increase in workload (that is, the “do more” situation) may be just as threatening and challenging to individuals as the dynamic conditions, even though the conditions are more predictable. In this case, the appraisal may be driven by knowing that they lack the time or resources to cope with the increased demand, rather than being driven by being unsure of what the demand will be, as it

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is for dynamic events. Additionally, for dynamic events, detecting a change early on does not

seem to alleviate the perceived threat and challenge of the event. Even though these changes are detected earlier than component changes, they still result in threatened and challenged appraisals, likely due to the fact that knowing that a change is happening without knowing what to do about it or how to react is still a threat. Although the situation assessment processes vary by complexity type, perceptions of overall situation assessment effectiveness do not. Once again, this gap between processes and effectiveness is interesting. Correlations between detection time, threat appraisals, challenge appraisals and situation assessment effectiveness revealed small to insignificant negative relationships. Future research is needed to explore additional situation assessment processes that may help explain differences in situation assessment effectiveness.

For the planning and strategy selection phase, unlike the other event type breakdowns reported above, the type of task complexity change did not impact any of the planning processes.

This finding is somewhat surprising, as it would be expected that component complexity changes would result in less contingency planning or less time to plan than coordinative changes given the time demand. Given that these categories are in some ways conceptually similar to the work stress versus thinking adaptive performance dimension breakdowns explored above, it was expected that a similar relationship would emerge. The fact that it did not suggests that there may be other factors at play. Future research should explore these relationships further to understand how task complexity changes may impact planning processes in other samples or domains.

While planning processes were not affected by task complexity type, complexity did impact the types of behaviors that were selected as predicted in hypothesis 15. There were significant differences in the reliance on exploitation, increase effort and stay the course behaviors based on the type of complexity change. Specifically, when facing dynamic events,

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individuals relied more on stay the course behaviors and less on exploitation behaviors than when facing other types of changes. This is consistent with what would be expected, as dynamic events have a high degree of uncertainty associated with them. As a result, individuals may just stay the course trying to wait for more information that reduces the uncertainty before selecting a new course. Conversely, coordinative change events were associated with significantly less stay the course and increase effort behaviors than other types of changes. Again, this finding makes

sense, as coordinative changes result from new or different information coming about that

requires a change in strategy. As a result, neither staying the course nor increasing effort using

the existing strategy are likely to be effective. Finally, component changes were associated with the highest degree of increasing effort behavior. Given that component changes require

individuals to work harder and do more, increasing effort seems to be the most logical and

effective strategy one could choose. Although it appears that individuals selected the most

appropriate strategies for the type of complexity they were facing, planning and strategy

selection effectiveness did vary by task complexity type with component complexity events

leading to lower effectiveness than dynamic complexity events, and coordinative events leading

to more effective behaviors than dynamic events. This suggests that even when using what

appears to be an optimal strategy, there may be some event types for which it is just perceived as

harder to effectively plan and find effective strategies. Alternatively, it may be that individuals’

perceptions of their planning and strategy effectiveness were biased by how well they were able

to execute those strategies, as discussed below.

When looking at the execution and evaluation phase, the pattern of findings for

behavioral strategy effectiveness mirror those of the planning and strategy selection phase.

Specifically, the results of hypothesis 15 reveal that the behavioral strategies used to respond to

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coordinative events were viewed as the most effective, while those used to respond to component

events were the least effective. Additionally, when looking at the coder-rated behavioral impact

of those strategies, the same relationship emerged. This suggests that whether self-report or

coder-rated, the effectiveness of these behavioral strategies did vary based on task complexity

type. However, it is unclear whether individuals just did not execute the strategies effectively for component events or if there is simply no effective strategy for component events. Future research needs to explore this further. It may be that in the case of this sample, component events

pushed an already demanding situation beyond their control, and although they strived to put

more effort in, they did not have the time or resources to do so effectively. It was also found that

component events were associated with less effective coping strategies, and less effective overall

execution and evaluation processes, suggesting that in this sample, component events may have

tipped the scales to a point that individuals were just not able to adjust as effectively as they

would have liked. While this may be the case, component changes were not significantly

associated with higher levels of perceived adjustment (i.e., thinking, behavior, feelings) or higher

levels of anxiety or frustration than other events. Instead, dynamic events were associated with

the greatest perceived need to adjust thinking and KSAs, and the highest levels of frustration and

anxiety. However, despite this, component events still associated with the greatest challenges in

both the planning and strategy selection and the execution and evaluation phases of the

adaptation process.

Summary. Looking back at Table 32, the pattern of relationships described above is easily

seen. Together, these findings provide overwhelming support for the need to carefully consider

the types of changes one is adapting to when designing studies or understanding adaptation

challenges in the workplace. While adaptation processes are driven in part by the individual,

128 these event type factors demonstrated a widespread effect on processes and effectiveness across the adaptation phases. More importantly, these findings also provide support for the fact that just looking at overall adaptation effectiveness masks important patterns going on under the surface.

None of the event type breakdowns significantly impacted ratings of overall adaptation effectiveness; however, as evidenced above, there are several relationships underneath the surface that were impacted and that could shed light onto where individuals may struggle as they are facing adaptive events.

Person-Level Hypotheses

Seven of the study hypotheses examined how individual difference characteristics impacted individuals’ responses to adaptive events, on average. For these hypotheses, rather than looking at each event separately, an individual’s responses to all of the events he/she reported were averaged. Three hypotheses (6, 7, and 8) focused on the role of goal orientation on individuals’ appraisals, reactions, and behavioral strategies. Hypothesis 9 looked at how openness to experience impacted these same outcomes. Hypothesis 10 looked at how trait adaptability, as measured by the I-ADAPT, influenced adaptation effectiveness. And finally hypotheses 11 and 12 looked at how perceived job autonomy influenced reactions, strategy choice and effectiveness. The results of these hypotheses were less promising than those of the event-level hypotheses, with several hypotheses not being supported. The results are summarized below, and additional commentary is provided on why some of these relationships may have not held up.

Goal orientation. The three types of goal orientation – learning, performance prove, and performance avoid – were expected to impact individuals’ reactions and responses to adaptive events differently. In the research, learning orientation is typically related to more effective and

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adaptive outcomes, as it allows individuals to achieve mastery over a task and maintain higher

levels of motivation in the face of challenges (e.g., Bell & Kozlowski, 2008; Kozlowski et al.,

2001). Performance prove and performance avoid orientations are typically less adaptive when

situations are challenging, as individuals want to avoid making mistakes, and therefore may lose

motivation or become more anxious when faced with an adaptive situation.

Hypothesis 6 explored how learning orientation impacted appraisals of, reactions to, and

behavioral responses to adaptive events. The results were mixed. While learning goal orientation was associated with more adaptive affective reactions (i.e., less anxiety and less frustration), it did not significantly predict how challenged an individual felt in their appraisal of the event, nor did it predict the use of more exploratory behavioral strategies. However, there was a trend when looking at the use of qualitative strategies (i.e., both exploitation and exploration) more generally, with higher learning goal orientation leading individuals to rely more heavily on qualitative strategies. This pattern of findings suggests that while learning goal orientation may buffer individuals against negative affective reactions, regardless of event type, other processes including appraisals and behavioral choices may be more heavily swayed by the type of event

the individual is reporting. When looking across event types, it does appear that individuals

higher in learning goal orientation rely more heavily on one of the two qualitative strategies,

suggesting that while there may be some variability across event types, on average they are

relying more on qualitative strategies than other, less productive, behavioral strategies such as

withdraw.

It was also expected that performance prove goal orientation would impact exploitation

behavior use; however, this relationship was not supported. Again, it may be that other factors,

such as the type of event, are stronger drivers of strategy use than individual differences.

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However, when looking at performance avoid goal orientation, those individuals with higher

levels not only reported higher levels of anxiety as expected, but also reported using more

emotion-focused coping strategies and withdraw behavioral strategies than those lower in

avoidance. These results suggest that while the situation may be a stronger driver of behavior

when considering performance prove orientation, this may not be the case with performance

avoid orientation. In fact, these results suggest that avoidance orientation may actually be

stronger than the situation, overriding the type of event and determining how one responds.

While avoidance orientation did not predict threat responses and frustration as expected, the

other findings highlight the need to consider avoidance orientation when putting individuals in

positions where they will need to be able to adapt, as high avoidance orientation is likely to be

associated with ineffective behaviors, regardless of the situation.

Openness to experience. Similar to learning goal orientation, openness to experience has

been shown in past research to benefit performance when adaptation is required (e.g., LePine et al., 2000). Conceptually, higher openness should be associated with more exploration and more resilience in the face of challenges than those lower in openness. Hypothesis 9 predicted that openness would be related to better situation assessment, more exploration, lower anxiety and frustration, and more challenged appraisals. However, none of the expected relationships were supported. The lack of support for these relationships once again suggests that personality traits, such as openness, may be too distally related to behaviors and outcomes to account for much variance. Additionally, it is likely that the situational factors trumped the effects of openness, making it less likely for any significant relationships to emerge. Researchers have suggested that one way to improve the predictive capacity of personality variables, such as openness, is to contextualize the items such that they are less broad-band and more specific to the types of

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situations one is performing in. Future research should explore under what conditions, if any,

openness is the most predictive of adaptation processes and effectiveness.

Trait adaptability. Ployhart and Bliese’s (2006) trait adaptability measure was developed

based on Pulakos and colleagues (2000) eight adaptive performance dimensions. Therefore, individuals rate their trait adaptability on each of the eight dimensions, and those scores can be

used to predict adaptation effectiveness across events. For the current study, learning and cultural

adaptability scores did not significantly correlate with adaptation effectiveness by phase, or

overall. As a result, only the remaining six dimensions were used in the analyses for hypothesis

10. Together, these six trait adaptability scores significantly predicted planning and strategy

selection effectiveness, execution and evaluation effectiveness, and overall adaptation

effectiveness. Trait adaptability predicted the greatest amount of variability in planning and

strategy selection effectiveness (36%) and the least in situation assessment effectiveness (19%;

ns). These findings suggest that trait adaptability is an important contributor to effectiveness, and

should be considered when placing individuals in positions requiring adaptability. However,

different dimensions emerged as the strongest predictors across equations, with work stress

adaptability consistently sitting in the top predictors. The different patterns suggest that different

skill sets may be critical at different phases of the adaptation process, providing further support

to look at adaptation as a process instead of just evaluating overall adaptation.

Unexpectedly the pattern of correlations did not support the hypothesis that trait

adaptability on one dimension would be the strongest predictor of performance in an event that is

characterized by that dimension. For example, if an individual is adapting to a work stress event,

his or her work stress trait adaptability score should be the strongest predictor of effectiveness

for that event. Instead, uncertainty and crisis trait adaptability scores tended to show the strongest

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predictors of effectiveness across event types. Several explanations are possible for this

unexpected finding. It is possible that although the I-ADAPT was developed based off of

Pulakos and colleagues’ (2000) adaptive performance dimensions, that individuals’ self-reported

adaptability on those dimensions does not completely align with how they actually are able to

perform in those dimensions. For example, individuals may believe that they are highly capable

at handling work stress events, but when they actually are put in that situation, their performance

falls short of their expectation. This is a common problem with self-reports in general and may provide some rationale for the unexpected findings. Additionally, as noted previously, categorizing the events by adaptive performance dimension was not a black and white task.

Many of the events were perceived to fit more than one category, but for the current analyses, only the primary category was used. It is possible that the blurred lines between dimensions contributed to the unexpected findings. Future research should examine whether accounting for the additional dimension categorizations yields different relationships. Finally, while the study allowed for events of any type to be reported, the majority of the adaptive events fell into only

three of the eight adaptive performance dimensions. Therefore, the patterns of results may not

generalize to samples that report events across the eight dimensions. Future work is also needed

to explore these relationships in other domains using other samples to see if similar findings

emerge.

Perceived job autonomy. Job autonomy was explored in hypothesis 11. The expectation

was that higher levels of autonomy will be associated with more control and flexibility when it

comes to making decisions and adjustments when responding to events. As a result, it was

expected that this built in flexibility would reduce anxiety and frustration when reacting to a

change, and more effective responses overall. However, none of the expected relationships were

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supported. Job autonomy did not significantly predict reactions, coping style, strategy

effectiveness, or adaptation effectiveness, by phase or overall. Similar to openness, it is possible that job autonomy is too distally related to behaviors and outcomes to account for much variance.

Additionally, it is possible that while job autonomy could benefit adaptation for some people, if the individual is not experienced enough or confident enough to make decisions and adjustments autonomously, it can actually be harmful. It is likely that other factors interact with job autonomy to explain differences in reactions and performance. Future research should explore these relationships to see what factors converge to explain differences in adaptation processes and performance. However, these findings should be interpreted cautiously, as the scale reliability for autonomy was very poor. While the scale came from the existing literature, it is possible that the type of environment in which this sample performs makes it more difficult to adequately self-report the autonomy of their role.

Summary. Overall, the lack of support across many of the person-level hypotheses supports the arguments in the literature that trait level variables are too distal to predict significant variance in behaviors and performance. Furthermore, the fact that trait adaptability showed the strongest, most consistent relationships with effectiveness supports the belief that

more contextualized predictors – those that closely mirror the type of event being performed –

have a better chance of accounting for substantial variance in the outcomes. Moving forward, the

focus should be on identifying these contextualized predictors and exploring how they predict

adaptation processes and performance. However, the lack of significant relationships also leaves

the door open to exploring more complex patterns of factors in future research. For example, how do individual differences interact with situational factors to predict processes and

performance? Future research should explore these complexities to understand how different

134 combinations of factors may predict reactions, responses, and effectiveness across the adaptation process.

General Research Questions

Finally, a few thoughts are provided below in response to the three general research question proposed in the introduction. The answers provided below are based on the basic patterns found in the data and from a high-level understanding of the study itself. Additional work is needed to more clearly address these questions.

The first research question asked if it was feasible to empirically tease apart the scanning, detecting, diagnosing, and appraising cognitive processes through the use of targeted self-report questions. Based on the current study, the answer is yes, to some degree. Detection, diagnosis and appraisal processes were all targeted via separate self- report questions and/or coding categories used by raters. Specifically, individuals were asked to describe when it came to their attention that conditions had changed (detection), what seemed to be the cause of the change and whether it was a current threat or potential opportunity (diagnosis), and how challenged and threatened they felt by the event

(appraisal). The only process not explicitly captured was scanning, although in future studies it may be feasible to ask individuals to rate the extent to which they were scanning or monitoring their environment for changes. While not all of these variables were central to the analyses in the current study, the methodology used here provides initial support for the ability to capture these processes separately, even in a field study.

The second research question focused on the utility of collecting unique data about the situation assessment processes (as discussed above). That is, does collecting data on the different situation assessment processes provide additional value above and beyond collecting a more

135 general measure of situation assessment? Again, the answer is yes, to some degree. For the relationships that were explored, the pattern of results suggest that each of these processes may be differentially impacted by individual and situational factors, thus providing further support for examining them separately. Additionally, by collecting data on the specific situation assessment processes (or any of the processes for that matter), it may provide valuable insight as to where individuals are breaking down in the phase. For example, it is possible that some individuals are just slow to detect a change, but they still make accurate and adaptive appraisals of the event.

This provides more actionable feedback than a simple evaluation of how effectively they assessed the situation. However, additional work is needed to better understand exactly how much added value these targeted measures provide.

The third research question focused on better understanding the within-event/between- person and within-person/between-event differences that may play a role in the adaptation process. Said in another way, are strategy use, reactions and effectiveness driven more by individual factors (that is, the person) or event factors (that is, type of event)? For each of the event-level hypotheses, both event factors (e.g., type of complexity change) and subject factors

(random intercept estimates) were examined to determine the extent to which the event type and subject (person) accounted for significant variance in the DV. In many cases, both event and person factors accounted for significant variance in the DVs, suggesting that both factors play a role. However, in some cases, this was not found. As one example, the reliance on four out of the five behavioral strategies varied significantly across the type of event and the person. However, for exploitation strategies, only the event type accounted for significant variance. It is unclear why some DVs varied by person and why others did not. Future research should continue to explore this question to gain a better understanding of these relationships.

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Theoretical Contributions and Implications

The current study extended existing work on adaptation by proposing a new process

model of adaptation. The model, which built upon the work by Burke and colleagues (2006),

specified quantifiable, theoretically-relevant variables that were measured to assess the

adaptation process. However, there are likely other states and processes that represent these

phases that are important to assess. For example, goal setting is an important aspect of planning and has implications for motivation during learning and performance. The current model provides the starting point for future work to build upon – adding and perhaps replacing the core variables to develop a more comprehensive theoretical model of the adaptation process.

However, the phased adaptation process model, along with the examination of self-regulatory process variables, contributes to our understanding of the adaptation process and the role of cognitive, motivational, and affective mechanisms in that process. Together, these contributions help address two of the gaps targeted in this study.

Additionally, the exploration of critical individual and situational (i.e., types of change) factors on the adaptation processes and effectiveness begins to shed light on some of the variables that may influence the adaptation process. While the relationships examined in the current study are a good starting point, the model highlights additional relationships and pathways that need to be explored. For example, even though situation assessment variables,

such as challenge and threat appraisals, did not influence situation assessment effectiveness, perhaps they impact the processes in the subsequent phase (i.e., planning and strategy selection).

The conceptual work completed in this study sets the stage for future work to empirically

examine the other relationships that will help provide a complete picture of the dynamics within

the adaptation process.

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Another theoretical contribution is the finding that even when ratings of overall

adaptation do not vary, event factors do differentially impact processes across the different

adaptation phases. This evidence provides support for two critical questions in the research. First,

this study shows that it is possible to empirically assess these phases separately, even in a field

study using self-report data. And second, the results reveal that by assessing these phases separately, you do gain additional, and more importantly, relevant information about what individuals are doing while they adapt and where the breakdowns may be. By opening up the

black box of the adaptation process, it allows researchers and organizations to see more clearly

what reactions and responses are likely given different conditions. As a result, these problem

areas can be more directly targeted for training or selection purposes, as described in the practical implications section below.

Perhaps the biggest theoretical contribution to this study is the use of conceptually relevant frameworks to meaningfully categorize adaptive events. By strategically categorizing events, it allows for hypotheses and explorations of relationships that are driven by the conceptual understanding of what is changing, rather than the vague understanding that there is a

change. Additionally, by leveraging existing frameworks, it allowed for individuals to describe

any type of adaptive event that they may experience at work which opened up the door to a wide

variety of event types. The events collected in the current study provide rich stories that varied

across the key dimensions of interest in this study (e.g., reactive versus proactive), but which

also highlighted other event features (e.g., time scale of the event; ability to assess effectiveness)

that are important to consider in future research. For example, while the conceptual model

presents different phases of the adaptation cycle, it is likely that the time scale on which an event

occurs largely dictates the extent to which individuals are able to engage in each of these

138 processes. The event stories collected during the current study lend credence to this assumption.

Some of the reported events happened very quickly, requiring nearly immediate adaptation and allowing little to no time for careful situation assessment and planning, while others were longer- term changes or anticipated well in advance, thus allowing for more deliberate engagement in each of the adaptation phases. These findings have implications for our understanding of the adaptation process, as they suggest that the adaptation process may be navigated differently depending on the nature of the event (e.g., the time scale of the event). Table 34 provides example snippets from the event stories collected to provide additional insight into what types of stories were collected and how they were (and could be) categorized along some of these dimensions. Together, this methodology provided new insight into the types of changes that need to be considered when studying adaptation and may help guide future laboratory or field-based adaptation studies. If future studies use these frameworks to help guide manipulations or coding of events, then it will be easier to compare and integrate findings across studies to arrive at a more generalizable understanding of adaptation.

Practical Contributions and Implications

Together the findings of the current study also have practical implications for the field.

Specifically, the results indicated that certain types of change events may be more difficult for individuals to respond to effectively, thus allowing organizations to be more aware of the conditions under which adaptation may suffer. For example, reactive, work stress, and component complexity events gave individuals the biggest headaches in this study, across all phases of the process. To the extent that this information can be used to better equip employees to handle these events, or to help reduce the number of times these types of events creep up, organizations may be able to increase adaptation effectiveness in their organizations and reduce

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the negative consequences (e.g., anxiety, frustration, withdraw) of these types of events at the

individual level. For example, organizations can put practices into place that help employees be

more proactive in their jobs. This may include encouraging and rewarding forward thinking or

opportunity assessment that allows individuals to foresee changes on the horizon that they can

begin planning for before they hit. Additionally, organizations may provide information to

employees sooner if they know of changes that are going to take place in the future, and provide

time and resources needed to help individuals prepare for those events. By doing so, the reliance

on reactive adaptation may be minimized. Researchers and organizations should use the results

of the current study to identify practices and tools that could be put into place to help overcome

the challenges associated with certain types of changes.

Additionally, the person-level results may have implications for selection and training in

organizations. While the findings were not consistent across variables, there were some

individual differences (i.e. avoidance goal orientation and trait adaptability) that predicted key adaptation processes and effectiveness, regardless of event type. In the case of performance

avoid goal orientation, it appears that individuals high in performance avoid goal orientation may

struggle in adaptive situations regardless of event type. Their affective and behavioral responses

could contribute to failure, which could have implications for the individual, team, and

organization. If future research continues to support this finding, it may suggest the need to

carefully select individuals for jobs and roles that are likely to require adaptation. However, as

adaptation becomes more ubiquitous across industries, it is likely that most jobs will require at

least some level of adaptability. In that case, individuals that have high avoidance orientation or

score low on the trait adaptability dimensions may need to be flagged and monitored more

closely, potentially even given additional training opportunities.

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Limitations and Future Research

While the current study made significant conceptual and empirical contributions to the field, there are a few limitations that are worth noting that future research can help address.

Many of these limitations were hinted at throughout the discussion above, but they are summarized more clearly below. Additional recommendations for future research are also included, where applicable.

Sample

The sample used in this study serves as both a contribution and limitation. The study participants came from a research and development organization that perfectly fit the requirements of the study. The types of work and experiences at the organization allowed for plenty of adaptive event opportunities. Additionally, by using an organizational sample, as opposed to a sample of undergraduates, the events provided were more relevant to the types of events individuals actually experience at work. However, given that the sample was from a single organization, it is possible that the results will not generalize to the broader population.

This is not uncommon in either laboratory or field based studies, as most study samples are relatively homogenous (Colquitt, 2008). While this is a concern, the specific content of the events was of less interest here than the types of events that were experienced – that is, were they reactive or proactive? Work stress or creative? By focusing on this level of categorization, it is less likely that the event relationships were unique to this particular sample. However, it is possible that within this organization a work stress event may have different implications than it would in another sample. This particular sample operates in a highly demanding, time pressured field in their normal day-to-day job. Events that constitute increased work stress may push the normal demand to a level that is unachievable, whereas an increase in work stress in a less

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demanding field may not have the same impact. Additionally, the organizational level variables

including leadership, support, and climate may also influence these relationships and the

experiences of adaptive events. Without having data from multiple samples across multiple

organizations, it is not possible to tease apart these effects.

Another factor related to the sample that may limit the generalizability of these findings

is the type of individuals employed at the organization. Specifically, the sample was highly-

educated, with a large portion having masters or doctoral degrees in their field. With this type of

sample, it is possible that their responses to and reactions to adaptive events may be different

than those who are less educated or in less advanced fields. It would be expected that more

highly educated individuals, who are likely to have higher cognitive ability and more cognitive

capacity, would be more effective in adaptive situations than those less highly educated. This

assumption stems from the research on cognitive ability and adaptation in the past (e.g., LePine

et al., 2000), which shows a consistently positive relationship between cognitive ability and

adaptive performance. If anything, using a highly educated sample is likely to reduce the

variability in responses and effectiveness, with more individuals using effective strategies.

Additionally, given that this sample consists of employees that are performing their jobs, this sample is also more likely to be highly motivated to perform well. This is likely more generalizable to other work populations than undergraduate samples who are participating as a requirement for course credit.

Future research should attempt to replicate this study in other organizational samples that span different domains and that are composed of employees at different levels of education and experience. Only then would it be possible to understand the extent to which the relationships are

142 generalizable across, or perhaps limited by, different samples. This is a critical next step to understanding the dynamics of the adaptation process.

Study Methodology and Timing

Similar to the sample, the study methodology provided unique contributions to the literature, while also having aspects that serve as limitations. The current study used an event- based sampling methodology to collect both open-ended descriptions of adaptive events as well as quantitative ratings of key study variables from study participants. Targeted questions were used as prompts for the open-ended event descriptions to ensure that enough information was provided about the critical aspects of the event for coders to use the coding framework and reliably code the responses for the variables of interest. This methodology allowed for a wide range of real world events to be described and collected. However, some limitations exist that need to be addressed.

The first potential limitation is the quality of the adaptive event data that was provided.

While careful attention was paid when setting up the study materials and providing guidance on what an “adaptive event” may look like, several of the descriptions provided lacked clear detail, which made it difficult to code reliably for key study variables, such as what type of adaptive performance dimension it best represented. The coders worked closely together to resolve any discrepancies that may have resulted due to the vague descriptions, and where necessary, left variables uncoded to avoid making assumptions that were not reasonable. While this limitation is likely to emerge for any field study that relies on open-ended survey responses, future research may help mitigate this limitation by providing a detailed example that highlights the level of detail that is desired for each question. The current study did not provide an example, as it was thought that it may prevent people from thinking beyond that example to other types of adaptive

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events. However, it may be helpful to provide additional guidance or examples that could

improve the quality of the open-ended data that is collected. As another avenue toward collecting

higher quality data in the future, the raw data gathered during the current study will be re-

examined to determine if a self-report survey can be constructed that focuses on the most relevant adaptive performance dimensions. This survey could then be used in future studies to

gather quantitative data on these dimensions that could improve our understanding of the types

of adaptive events that individuals are experiencing in the workplace.

Second, all of the raw data was gathered via self-report, whether it be open-ended

descriptions or quantitative ratings. While it would have been ideal to gather objective data or

ratings from observers or leaders to supplement the self-reports, the requirements for anonymity

of responses made it impossible to link individuals’ responses to other forms of data. However,

to help reduce the method bias, two independent coders interpreted the raw open-ended data to

generate the values from many of the event-level predictors (e.g., the type of complexity change). As a result, the data actually being used in the analyses came from two different sources

– coders and self-reports – thus reducing the chances that method bias drove the relationships.

However, future research should attempt to gather data from other sources, beyond the individual, to allow for a more robust examination of these relationships.

A third limitation of the current study methodology is that it limited the ability to explore the adaptation process over time or to infer causality. While the theoretical model emphasizes that this process unfolds over time, the methodology only allowed for individuals to provide a retrospective account of the event and their responses to that event. As a result, it was not

possible to truly examine the dynamic cycles of situation assessment, planning and strategy

selection and execution and evaluation that individuals may have went through as they

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responded to the event. Additionally, the current data does not allow for causal inference among

the adaptation process dynamics. While the events unfolded over time, individuals reported a

single retrospective summary of the event and their responses to that event. As a result, it was

not possible to determine that an individual’s behaviors at one point in the process led to (or

caused) behaviors at another point in the process. The contribution of the current study was to

confirm that it is possible to assess these separate processes in a field study, and to provide a

conceptual model to follow to extend this work to a more dynamic exploration. Due to the practical constraints associated with field studies, it may be necessary to use the findings from

this field study to inform future, more controlled laboratory studies that are able to carefully

manipulate and track these processes over time (see the Future Research: Next Steps section

below for an example).

Additionally, the current data did not allow for an exploration of adaptation across events

over time. While multiple events were collected from some individuals, the nature of the study

did not require these events to be related in anyway. As qualitatively different events, it was not

assumed that an individual would show growth over time across events. However, future

research should consider under what conditions growth should occur and how similar adaptive

events need to be to allow for growth and learning to occur between them. This is an important

future step to understanding how individuals may improve in their navigation of the adaptation

process over time as they are exposed to different adaptive situations.

Finally, there are also potential concerns due to the timing of the study. The study period fell during the holidays, which may have influenced the study response as well as the types of events and/or responses to those events. Several individuals took leave time during portions of the study period, and even those who were working may not have been working under their

145 typical circumstances. As a result, the number of events reported, and the type of these events, may be different than they would have been if the study period fell during a different time of year. To help reduce the impact of this limitation, the study period was extended for additional days to try to boost participation rates and collect additional events. It is possible that individuals’ motivation levels wane during the holidays, which may impact some of the reactions and responses to the reported events, although it is not expected that this impact would be significant. However, future research needs to consider the timing of the study period and be considerate of the external events that may influence the data provided and participant rates.

Analysis Approach

As mentioned in an earlier footnote, the event-level analyses were conducted using uncentered predictors. The literature discusses several centering options, and the pros and cons of each, when working with multilevel data, including raw uncentered variables, grand mean centering, and group-mean centering (e.g. Algina & Swaminathan, 2011; Enders & Tofighi,

2007). There are a number of complexities when determining which centering option is most appropriate for a multilevel analysis, including the assumptions one wants to make and the nature of the data. In the current study, the predictors of interest in the hypotheses were all categorical coded event-level factors (e.g., reactive versus proactive). It could be argued that person-mean centering the predictor variables would provide a cleaner interpretation of the event-type (level-1) effect, as the person effects would be partialed out. However, person- centering categorical factors continues to be a statistical challenge. It is possible that different centering approaches would change, and possibly significantly change, some of the relationships found in the current study. Future research needs to consider the conceptual nature of these

146 relationships and explore the extent to which, and conditions under which, alternative centering approaches would be the most appropriate.

Coding Challenges

The last limitation that should be noted results from the challenges of coding the qualitative data. Best practices were followed when developing the coding scheme and engaging in the coding of the qualitative data. Specifically, the coding categories were driven by the conceptual model and focused on the theoretically-relevant factors that may be relevant to the current study. Additionally, the coding framework was developed using an iterative process, with improvements being made after discussions between the two coders and attempts at coding practice data. Finally, two independent coders coded the data and interrater reliability was monitored closely over the course of the coding process. However, even with all of these practices in place, some of the coding categories continued to be a struggle across all events.

Specifically, two of the event-level categories: task complexity and adaptive performance dimension, were associated with numerous discrepancies between coders even after several discussions and clarification of the framework. The likely reason for these challenges is that these categories are not always clearly mutually exclusive, and many events seemed to have aspects of multiple categories. This was further exasperated by the lack of detail provided in some of the events. To resolve some of the problems, the coders began coding for the primary category as well as additional categories. Even with this allowance, the codes were still not as clean as they were expected to be. While this does cause some concern, it is not exactly surprising. In fact, there is some evidence in the research that the adaptive performance space may actually be more one-dimensional than multidimensional (Baard et al., 2014; Pulakos et al.,

2002). This suggests that the dimensions may not be as independent as would be expected, thus

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making them hard to distinguish while coding. Even with these challenges, the relationships

found in the current study do generally mirror expectations based on the conceptual distinctions

among these dimensions, which suggests that while not perfect, the coding was accurate enough

to allow for meaningful explorations in the data. Future work should attempt to further explore

the dimensionality of the different frameworks used in the current study. It is possible that over

time, these future efforts may lead to a revision of the adaptive performance taxonomy (e.g.,

reducing the number of meaningful dimensions). As this taxonomy is used throughout the

adaptation literature, it is critical to better understand how robust it is under different

circumstances.

Future Research: Next Step

While several future research directions were mentioned in the limitations section above,

the current section outlines a possible next step in this line of research based on the findings from

this work. Specifically, the time scale of the event, which will likely overlap heavily with the reactive versus proactive categorization, needs to be carefully considered and manipulated to

understand how the adaptation process is navigated as the time scale changes. The issue of causality also needs to be explored to better understand and provide evidence for the conceptual model proposed in the current paper. It is possible that the time scale of the event may impact the ability to assess causality, with events requiring immediate adaptation making it more difficult to track behaviors and assess causality. The next paragraph outlines a potential next study that

leverages the results of this study, while extending our understanding of time scale and causality

in the adaptation process.

To begin to address these two issues, researchers should leverage a controlled laboratory

experiment where they can (1) identify behaviors or behavioral indicators of the adaptation

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phases (e.g., situation assessment) that can be tracked and time-stamped by a system (e.g.,

recorded by a computer-based simulation) or observed by a human (in real-time or

retrospectively via video recording), and (2) generate scenarios that vary in time scale, from

immediate changes (i.e., highly reactive, little to no time to react) to long-term, anticipated

changes (i.e., highly proactive, plenty of time to think and react). During the study, participants

would perform multiple scenarios that vary along the time scale. During each scenario, the

system and/or observers would capture behaviors that were previously identified to reflect one of the adaptation phases and time stamp when those behaviors occurred. The time-stamping of the behaviors allows for a temporal sequencing of behaviors to be recorded, which is one condition necessary for inferring causality. Overall effectiveness on the scenario could be assessed via the system (e.g., points scored) or through observer or self-ratings of effectiveness. At the very least, the design of this future study would allow for a better understanding of how individuals sequence through the adaptation process. That is, do individuals sequence through the phases in a linear fashion or do individuals move back and forth through the phases as they perform? Do individuals engage in multiple processes in parallel (i.e., updating situation assessment while planning?)? Under what time scale conditions do individuals engage in behaviors that reflect each of these phases of the adaptation process? Under what conditions do individuals skip some of these processes? Does an individual’s effectiveness change as a result of how much time and/or what order they engaged in each of the adaptation processes? The control allowed for in a laboratory experiment can help shed light onto some of the process dynamics and causality questions that were not able to be addressed in the current study.

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Conclusion

The current study addressed three major gaps in the adaptation literature, while also shining light on several paths forward. Specifically, the current work: (1) developed an updated conceptual model of the adaptation process and found empirical evidence supporting the need to examine adaptation as a process, as opposed to just an outcome; (2) collected real world event data that was categorized using existing frameworks to help make meaningful predictions about the expected relationships between the nature of the change and individuals’ reactions and responses to that change; and (3) examined the role of cognitive, motivational, and affective processes and reactions in the adaptation process. The results of this study have both theoretical and practical implications for the study of adaptation in the workplace. Several future research directions were suggested throughout the discussion that can serve as guideposts for future work seeking to extend our knowledge and understanding of the adaptation process.

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APPENDICES

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APPENDIX A

Participant Guidelines: Adaptive Event Description and Examples

Instructions: Over the next two weeks, you will be asked to describe how you handled adaptive events that came up during your work day. For the purposes of this study, adaptive events are defined as situations that you encounter during your work day that deviate from your normal routine or expectations due to changes in your work or task environment. These events will likely be the most significant, challenging, or unusual situations that you encounter during your day. Additionally, you may view these events as representing either positive, neutral, or negative changes in your work environment.

At the end of each day, you will be asked to reflect on the adaptive events you encountered that day and choose the one event that was the most significant to you that day. Only report events that you were directly involved in. If you were an observer of an event or heard about an event from someone else, but you had no direct involvement in the event, then please do not choose that event.

Once you have selected the event you would like to describe, please respond to the questions on the next page regarding that event. You will be completing this questionnaire at the end of each day for the next two weeks, describing the adaptive event that was most significant to you that day.

If you have questions about the instructions or run into any problems, please contact the primary researcher, Tara Rench, at [email protected] to receive further explanation.

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APPENDIX B

Email Including Link to Consent Form and Background Survey

Subject Line:

Adaptive Event Assessment - Consent and Background Survey Link

Body:

As promised, the Adaptive Event Assessment study is now underway!

As I mentioned at the brownbag and in the follow-up email, the original motivation behind this study came from my dissertation work, and the study results will contribute to the completion of my PhD. Additionally, this study serves as a timely assessment of adaptive events surrounding the upcoming reorg at [Company Name4], and the results will be presented at a company-wide brownbag early next year.

ALL [Company Name] employees are encouraged to participate! Every perspective is important – the goal is to gain a holistic understanding of adaptation at [Company Name].

Your customized link to the consent form and background survey is below. If necessary, you will be able to re-enter the survey via this same link.

Here is the link to the survey:

This link is uniquely tied to this survey and your email address. Please do not forward this message.

When you click on the link above, you will have the opportunity to read the full consent form and indicate whether or not you agree to participate. If you agree to participate, you will be automatically directed to the background survey. The background survey will take approximately 10 minutes to complete.

If at all possible, please complete the consent and background survey by midnight EST today (12/10/12).

If you have any questions or run into any problems, please contact Tara at [actual contact information deleted].

Thank you!

4 All references to the company name in emails or other documents have been replaced by [Company Name] to protect employee confidentiality.

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APPENDIX C

Email Reminder to Employees about Consent and Background Survey

Subject Line:

Adaptive Event Assessment - Consent and Background Survey - REMINDER

Body:

Don’t forget to complete the Adaptive Event Assessment consent and background survey today! It should only take 10 minutes of your time. For your convenience, I have provided your customized link to this survey below.

Here is the link to the survey:

This link is uniquely tied to this survey and your email address. Please do not forward this message.

If at all possible, please complete the consent and background survey by midnight EST today (12/10/12).

If you have any questions or run into any problems, please contact Tara at [actual contact information deleted].

Thank you!

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APPENDIX D

Background Questionnaire

These questions were completed by individuals upon providing consent to participate in the study.

Included Measures:

 Demographics/Experience  Trait adaptability  Job autonomy  Goal orientation  Big Five personality

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Demographic and Experience Survey

The next set of questions asks you to provide general information about your background. These responses are helpful in understanding how characteristics of an individual may influence their experiences at work. The responses will be confidential and will only be used in aggregated form (i.e., no person will be identified and singled out).

1. What is your gender? a. Female b. Male 2. What is your age? a. Under 25 b. 25 to 34 c. 35 to 44 d. 45 to 54 e. 55 and over 3. What is your ethnicity? a. Hispanic or Latino b. Not Hispanic or Latino 4. What is your race? a. White b. Asian c. Black or African American d. American Indian or Alaska Native e. Native Hawaiian or Other Pacific Islander 5. Which of the following best describes your primary job responsibilities at [Company Name]? a. Research (psychologist, mathematician, model developer, etc.) b. Software development (software engineer, architect, multi-media, etc.) c. Senior or executive management (division director, VP, BD, etc.) d. Administration or finance (HR, IS, finance, security, contracts, IP, etc.) 6. How long have you been with [Company Name]? a. Less than 1 year b. 1 to 2 years c. 3 to 4 years d. 5 to 6 years e. 7 or more years

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Trait Adaptability

This survey asks a number of questions about your preferences, styles, and habits at work. Read each statement carefully. Then, for each statement, choose the response that best represents your opinion. There are no right or wrong answers.

Adaptability Strongly Strongly Disagree Neutral Agree Items 1 2 3 4 5 1. I am able to maintain focus during emergencies. 2. I enjoy learning about cultures other than my own. 3. I usually over-react to stressful news. 4. I believe it is important to be flexible in dealing with others. 5. I take responsibility for acquiring new skills. 6. I work well with diverse others. 7. I tend to be able to read others and understand how they are feeling at any particular moment. 8. I am adept at using my body to complete relevant tasks. 9. In an emergency situation, I can put aside emotional feelings to handle important tasks. 10. I see connections between seemingly unrelated information. 11. I enjoy learning new approaches for conducting work. 12. I think clearly in times of urgency. 13. I utilize my muscular strength well. 14. It is important to me that I respect others’ culture. 15. I feel unequipped to deal with too much stress. 16. I am good at developing unique analyses for complex problems. 17. I am able to be objective during emergencies. 18. My insight helps me to work effectively with others. 19. I enjoy the variety and learning experiences that come from working with people of different backgrounds. 20. I can only work in an orderly environment. 21. I am easily rattled when my schedule is too full. 22. I usually step up and take action during a crisis. 23. I need for things to be “black and white.” 24. I am an innovative person. 25. I feel comfortable interacting with others who have different values and customs. 26. If my environment in not comfortable (e.g., cleanliness), I cannot perform well. 27. I make excellent decisions in times of crisis. 28. I become frustrated when things are unpredictable. 29. I am able to make effective decisions without all relevant information. 30. I am an open-minded person in dealing with others.

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31. I take action to improve work performance deficiencies. 32. I usually am stressed when I have a large workload. 33. I am perceptive of others and use that knowledge in interactions. 34. I often learn new information and skills to stay at the forefront of my profession. 35. I often cry or get angry when I am under a great deal of stress. 36. When resources are insufficient, I thrive on developing innovative solutions. 37. I am able to look at problems from a multitude of angles. 38. I quickly learn new methods to solve problems. 39. I tend to perform best in stable situations and environments. 40. When something unexpected happens, I readily change gears in response. 41. I would quit my job if it required me to be physically stronger. 42. I try to be flexible when dealing with others. 43. I can adapt to changing situations. 44. I train to keep my work skills and knowledge current. 45. I physically push myself to complete important tasks. 46. I am continually learning new skills for my job. 47. I perform well in uncertain situations. 48. I can work effectively even when I am tired. 49. I take responsibility for staying current in my profession. 50. I adapt my behavior to get along with others. 51. I cannot work well if it is too hot or too cold. 52. I easily respond to changing conditions. 53. I try to learn new skills for my job before they are needed. 54. I can adjust my plans to changing conditions. 55. I keep working even when I am physically exhausted.

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Job Autonomy

For the following items, please consider the job you are in currently and respond to each item based on how accurate or inaccurate the description is for your current job.

Job Autonomy Very Neither Accurate nor Very Inaccurate Inaccurate Accurate Item 1 2 3 4 5 6 7 1. The way my job is performed is influenced a great deal by what others (supervisors, peers, customers, etc.) expect of me. 2. There is a lot of autonomy in doing my job.

3. My job is quite simple and repetitive.

4. If someone else did my job, they could do the tasks in a very different manner than I do. 5. The way my job is performed is influenced a great deal by company rules, policies and procedures. 6. The work itself provides a lot of clues about what I should do to get the job done.

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Goal Orientation

Please use the rating scale provided to indicate how much you agree or disagree that each of the statements represents your general orientation toward work. There are no right or wrong answers.

Goal Orientation Strongly Strongly Disagree Neutral Agree Item 1 2 3 4 5 1. I am willing to select a challenging assignment that I can learn a lot from. 2. I often look for opportunities to develop new skills and knowledge. 3. I enjoy challenging and difficult tasks where I’ll learn new skills. 4. For me, development of my ability is important enough to take risks. 5. I prefer situations that require a high level of ability and talent. 6. I’m concerned with showing that I can perform better than others. 7. I try to figure out what it takes to prove my ability to others. 8. I enjoy it when others are aware of how well I am doing. 9. I prefer projects where I can prove my ability to others. 10. I would avoid taking on a new task if there were a chance that I would appear rather incompetent to others. 11. I’m concerned about taking on a task at work if my performance would reveal that I had low ability. 12. Avoiding a show of low ability is more important to me than learning a new skill. 13. I prefer to avoid situations where I might perform poorly.

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Big Five Personality (Mini-IPIP; Donnellan, Oswald, Baird, & Lucas, 2006)

Please use the rating scale provided to indicate how much you agree or disagree that each of the statements represents how you are in general. There are no right or wrong answers.

Personality Strongly Strongly Disagree Neutral Agree Generally I… 1 2 3 4 5 1. Am the life of the party. 2. Sympathize with others’ feelings. 3. Get chores done right away. 4. Have frequent mood swings. 5. Have a vivid imagination. 6. Don’t talk a lot. 7. Am not interested in other people’s problems. 8. Often forget to put things back in their proper place. 9. Am relaxed most of the time. 10. Am not interested in abstract ideas. 11. Talk to a lot of different people at parties. 12. Feel others’ emotions. 13. Like order. 14. Get upset easily. 15. Have difficulty understanding abstract ideas. 16. Keep in the background. 17. Am not really interested in others. 18. Make a mess of things. 19. Seldom feel blue. 20. Do not have a good imagination.

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APPENDIX E

Daily Journal Surveys

If you have questions about the instructions or run into any problems, please contact the primary researcher, Tara Rench, at [email protected] to receive further explanation.

Included Measures:

 Describe the event (qualitative)  Describe your response to the event (qualitative)  Describe the results of the event (qualitative)  Additional event ratings (scales)

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Describe the Event

Please review the respondent guidelines before completing this questionnaire. Please do NOT include any identifying information (names, etc.) in your responses.

1. Today’s Date (mm/dd/yyyy)

2. What circumstances led up to the event that you are describing?

3. Who was involved in the event? Please DO NOT include names, but DO include position titles, ranks, etc.

4. How did conditions change relative to your routine or the expected conditions?

5. When and how did it come to your attention that conditions had changed? (i.e., what clued you into the need for change?)

6. What seemed to be the cause of the change? What situational, environmental, or organizational factors may have played a role in this event? Was it expected or unexpected?

7. Would you classify this event as: proactive (i.e., an opportunity to “stay ahead of the game” by anticipating and responding to a potential future problem or change) OR reactive (i.e., an effort to respond to a current problem or change)? Please explain.

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Describe Your Response to the Event

Please review the respondent guidelines before completing this questionnaire. Please do NOT include any identifying information (names, etc.) in your responses.

1. What specific actions/behaviors did you take in response to this event?

2. How did you feel emotionally and/or mentally as a result of this event? (e.g., stressed, anxious, engaged)

3. What coping strategies did you use (if any) when responding to the event? E.g., problem- focused coping strategies: actively coping with the task, engaging in planning, minimizing distractions, OR other coping strategies: venting emotions, disengaging from the task, seeking out social support.

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Describe the Results of the Event (Outcome)

Please review the respondent guidelines before completing this questionnaire. Please do NOT include any identifying information (names, etc.) in your responses.

1. What was the result or outcome of the event?

2. How did your behaviors impact the outcome of the event or others involved in the event? Were your behaviors effective or ineffective?

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Additional Event Ratings

Please respond to the following questions regarding the event you just described.

Nature of the Change (Valence) Negative Neutral Positive Item 1 2 3 4 5 1. How did you feel about the change/event that you reported?

Required Adaptation Not at all Somewhat A lot Item 1 2 3 4 5 1. To what extent did you have to adjust your thinking in response to this event? 2. To what extent did you have to adjust your feelings and emotions in response to this event? 3. To what extent did you have to adjust your behavioral strategy/approach in response to this event? 4. To what extent did this event stretch the knowledge, skills, and abilities you relied on to perform before this change occurred?

Diagnosis and Appraisal Not at all Somewhat A lot Item 1 2 3 4 5

1. To what extent did you find this event to be challenging?

2. To what extent did you find this event to be threatening?

3. To what extent did you find this event to be a potential opportunity to make adjustments or improvements to how things were being done before an actual problem occurred?

Planning Not at all Somewhat A lot Item 1 2 3 4 5 1. To what extent did you engage in contingency planning (considering alternative plans that may be effective)? 2. To what extent did you engage in reactive strategy planning (immediately adjusting plans in response to the change)? 3. How much time did you feel you had to plan (or adjust plans) after determining that you needed to do something different?

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Attention and Negative Emotions Strongly Strongly Disagree Neutral Agree Item 1 2 3 4 5 1. I was able to maintain focus on the important details of the adaptive event.

2. I found the adaptive event to be anxiety-producing.

3. I was frustrated during the adaptive event.

Behavioral Strategy Strongly Strongly Disagree Neutral Agree Item – When responding to the adaptive event, I... 1 2 3 4 5 1. Immediately modified the behavioral strategy I was using and replaced it with the first alternative strategy that came to mind (i.e., a quick, “good enough” adjustment). 2. Explored several alternative strategies that may work better and chose the one that was best (i.e., carefully considering the “best” response). 3. Engaged in the same behaviors/strategy I was already using but worked harder and/or faster (i.e., increasing effort).

4. Withdrew from the situation (i.e., decreased effort).

5. Did not make any changes to my behavioral strategy (i.e., continued doing what I was doing).

Effectiveness Not at all Somewhat Extremely effective effective effective Item 1 2 3 4 5 1. How effective was your choice of behavioral strategy you used to respond to the adaptive event? 2. How effective were the coping strategies you used when responding to the adaptive event? 3. How effective were you at assessing the situation (e.g., detecting, appraising and diagnosing the change). 4. How effective were you at planning and selecting strategies to respond to the adaptive event? 5. How effective were you at executing and evaluating your strategies while responding to the adaptive event? 6. Overall, how effective were you at adapting to the conditions of the adaptive event?

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APPENDIX F

Adaptive Events Coding Rules

1. Do NOT show or share the data you are coding with anyone except for Tara. You are not to download it on any computer other than your own personal one. Do not email the actual raw data. You will only be emailing Tara your coding sheet. 2. Only code one event at a time – do not try to look across multiple categories. 3. When coding, please remove yourself from any other distractions in your environment (TV, people talking, etc.). 4. It is recommended that you print off the coding framework so you can easily reference it as you are coding. 5. Take breaks between every 8 (or so) events so you don’t get overly tired – your reliability will decrease substantially if you don’t take some breaks. 6. Before coding an event, please read over the entire event first so you have a good understanding of the full event and all of the details provided. 7. There is not a 1 to 1 matching between a question asked on the survey and the coding categories – while you may find that some of the coding categories align well with a particular question, for others, you may have to look across responses to multiple questions to determine how to code it. 8. Use the descriptions in the coding framework to better understand how to interpret the data – for some, you may need to use your best judgment to determine which code to give based on their answer. If you do not feel like any categories apply or you feel like it is too unclear, then use N/A. 9. For ALL categories, only provide ONE code. a. Some coding categories provide an extra box “Other” that will allow you to put in additional information b. If you have comments or feel like more than one answer applies at other points in the coding, please use the comment feature in the excel coding document to describe your concern/question. 10. For the “who is involved”  only reference people who are involved in the interactions the person is reporting about. They may reference other people (e.g., the team was working on a product that would be delivered to a SME or a Prime – see Day 3, EventID99 for an example), but those people are not actually a part of the adaptive event and should not be included in your coding. 11. For the “circumstances of event” category: a. Only use 1 = people (staffing issue) if the event is about a project manager or someone trying to identify who to bring on to a particular project, proposal and/or if a project member is permanently removed (e.g., switched to another project, leaves the company) or added to the team.

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i. If a team member is out sick temporarily – this should be coded as “8” ii. If a person is talking about an empty labor plan or trying to fill their labor plan – this should be coded as “4” b. If a staffing issue, illness, or LP issue is related to other factors (e.g., causes a time crunch; results from the org restructure), then these secondary factors can be coded in the “other” column. 12. For the “Complexity” category: a. For Component Complexity, think “Work Harder” b. For Coordinative Complexity, think “Work Smarter” i. Finding new strategies, solutions that allow them to address the situation without simply just working harder by putting in more hours, etc. ii. Changing how you are doing something, not just how much time and effort you are putting into it c. For “Dynamic Complexity, think “Chaos” i. This could be things that happen that make it unclear how they may impact other things (e.g., the org change, having an empty labor plan). They may be taking behaviors to get through the initial chaos, but the nature of the conditions may cause other problems in the future (e.g., continued labor problems, etc.) – lots of uncertainty about what is going on. 13. For the Type_Cue category: a. If an employee reports a labor plan shortage, code this as a “1” (new/atypical situation) i. This doesn’t reflect a performance or timeline discrepancy that is focused on a specific project or due to an employee’s performance necessarily 14. For the SR_Change category: a. For Primarily Cognitive – use this category if the person primarily talks about paying attention to something new/different than they were, thinking through ideas or plans, seeking out additional information to learn more, etc.) b. For Primarily Affective – use this category if the person primarily talks about managing their emotions, trying to get motivated to work harder or more hours, adjusting their attitude about a situation, etc.) c. For Primarily Behavioral – use this category if the person primarily talks about taking action (e.g., started working on something), scheduling meetings, engaging in new behaviors  less about thinking/planning, more about doing) d. For Mixed – use this category if at least two of the three categories above seem to be about equally focused on in the participants’ response 15. For the Affect category:

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a. If the participant uses words like “a bit...”, “mildly,” “slightly” etc. when describing how they feel, code these as “Neutral” – gets at a mild reaction, as opposed to a strong reaction. 16. For the Primary Behavior category: a. For “1”: use this if the person reports taking action without mentioning really thinking through alternatives or exploring multiple options b. For “2”: use this if the person talks about thinking through multiple possible courses of action, seeking advice on the right course of action, etc. before they actually go down a path c. For “3”: use this is the person talks about simply putting more effort in or speeding up their effort (not doing different behaviors, just doing the behaviors faster or longer) 17. For Coping category: a. Choose #2 (Mental Simulation/Planning) if the primary coping response is engaging in brainstorming or thinking through plans without seeking out advice/help from others b. Choose #5 (Seeking social support) if participant reports seeking out others to get advice or help or help think through solutions 18. For Adaptive Dimension: a. If the event talks a lot about increased workload, more work, stress, etc.  primary code should be “2” b. If the event talks less about workload and “doing more” and more about trying to figure out how to tackle a problem or respond to a solution  primary code should be “4” c. If the event talks mostly about solving a new problem (creatively) or figuring out ways to address issues when there are available resources  primary code should be “3” 19. If a participant’s response does not make sense given the rest of their entry (e.g., marked an event as proactive, when it seems to be reactive), flag your code with a comment and describe why you think it should be something other than what the participant said. 20. Double check your work to make sure you have entered the codes in the correct row (the EventID in the coding sheet should match the EventID in the raw data you are coding). 21. If you have questions/concerns that need resolved before you move forward, contact Tara as soon as possible at [contact information deleted].

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APPENDIX G

Tables

Table 1. Overview of the Theoretical Perspectives of Adaptation

THEORETICAL APPROACH Domain General Domain Specific My Approach Conceptualization Ind. Diff. Construct Performance Construct Performance Change Process Blend Operationalization Relatively stable Relatively stable Performance in a A process unfolding A set of cognitive, (Key Assumptions) individual difference; performance construct; changed, novel during a novel, affective, motivational, Unique from existing multi-dimensional; task/event; Specific to the changing task/event; and behavioral IDs; Generalizable Generalizable across a context so cannot be Specific to the context processes unfolding in a across a variety of variety of jobs/tasks generalized across so cannot be novel, changing, and jobs/tasks jobs/tasks generalized across more demanding jobs/tasks context (adaptation) Base Literature Individual Performance Management Expertise/ Skill Expertise/ Skill Self-Regulation Differences Acquisition Acquisition? Target Application Selection Performance Assessment Training and/or Training and/or Selection & Training Development Development and/or Development Research Design Correlational Correlational Experimental (often with (Mostly Conceptual) - Field Study manipulation) Experimental (Descriptive over time) Measurement Self-Report/ SJT Self- or Other (SME)- Objective Task Primarily Conceptual Primarily subjective Ratings Performance (via ESM methodology) Level of Analysis Primarily Individual Primarily Individual Individual or Team Primarily Team Individual (within/between) Type of Change Varies Varies Increased Complexity Varies Varies (real events) Nature of Change Varies Varies Abrupt or Gradual; Single Abrupt or Gradual; Varies Change Varies (naturally occurring) Strengths Widespread Widespread Applicability Predictive Precision Predictive Precision/ Real World Support/ Applicability Examines Mechanisms Examines Mechanisms Weaknesses Limited Prediction Limited Prediction Limited Application Limited Application/ Limited Application/ Difficult to Test Difficult to Test Exemplar Ployhart & Bliese Pulakos, Arad, Donovan, Bell & Kozlowski (2008) Burke, Stagl, Salas, Current Study (2006) & Plamondon (2000) Pierce, & Kendall (2006)

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Table 2. Categorization Framework for Adaptive Events

Pulakos' Example adaptive Example effective behaviors Wood’s Reactive/ Cognitive/ Dimension* event characteristics Complexity* Proactive Affective Handling High risk (life Making split-second decisions; maintaining component/ mostly Affective emergencies or threatening, dangerous, emotional control/objectivity; keeping focused on dynamic reactive crisis situations or emergency) situation; quickly analyzing options complexity situations

Handling work Demanding workload Maintaining emotional control (remaining cool/calm; component mostly Affective stress or schedule; receiving keeping frustration in check); directing effort toward complexity reactive unexpected solutions; demonstrating effective leadership to news/situations; others (act as a calming /settling influence; stressful circumstances demonstrate professionalism)

Solving problems Insufficient resources to Using novel analyses to generate new coordinative/ mostly Cognitive creatively do job; atypical, ill- ideas/solutions; integrating different streams of dynamic reactive defined, and complex information; thinking outside of the box (being open complexity problems; novel to exploring possibilities); developing innovative problems methods

Dealing with Unpredictable or Taking action without the full picture; handling coordinative/ mostly Cognitive/ uncertain and unexpected ambiguity/uncertainty (not allowing it to paralyze dynamic reactive Affective unpredictable events/circumstances; you); imposing structure on a dynamic situation to complexity work situations dynamic situations help improve focus; changing gears/adjusting plans, goals, actions, or priorities

Learning work New work processes Being learning oriented (seeking out learning coordinative/ mostly Cognitive tasks, and procedures; opportunities to develop new skills, learn new dynamic proactive technologies, and changes in work approaches/technologies/methods); anticipating complexity procedures demands; performance changes and searching for ways to prepare one's self deficiencies

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Table 2 (cont’d)

Pulakos' Example adaptive Example effective behaviors Wood’s Reactive/ Cognitive/ Dimension* event characteristics Complexity* Proactive Affective

Demonstrating Dealing with others Being flexible/open-minded; considering/listening to N/A mostly Affective interpersonal (and specifically those others' viewpoints (when appropriate); having insight reactive adaptability with diverse into others’ behaviors and tailoring one's behaviors personalities); receiving to persuade, influence, or work more effectively with developmental them; developing/working well with highly diverse feedback individuals

Demonstrating Entering into a different Seeking out opportunities to learn about/understand N/A both Cognitive/ cultural culture/organization/gro the climate, orientation, needs, and values of other Affective adaptability up AND/OR having a groups, organizations, or cultures; adjusting new person from a behavior/appearance to show respect for others' different values and customs; being aware of the implications culture/organization/gro of one's actions up into your culture

Demonstrating Change in Adjusting to challenging physical or environmental N/A mostly Affective physically oriented environmental state states; adjusting weight and muscular strength to reactive adaptability (e.g., extreme heat, physically handle strenuous task humidity, cold, or dirtiness); Increase in physical demand of a task *Note. Pulakos’ (2000) taxonomy was used to generate the first three columns of information; Wood’s (1986) task complexity taxonomy was used to generate the fourth column of information.

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Table 3. Summary of Gaps and Contributions

Targeted Gaps Intended Study Contribution 1. A lack of conceptual and empirical The current study proposes a new individual level work focused on understanding the adaptation process model based on the existing adaptation process (especially at literature and will examine hypotheses empirically the individual level). to begin to evaluate the tenets of the model empirically. 2. A lack of knowledge about the The current study will employ a descriptive field types of adaptive events study approach and collect qualitative and individuals actually experience at quantitative data about a number of adaptive events work – existing work often fails to encountered during the work day across several use a theoretical framework to days and different jobs. These events will then be drive choices made to manipulate categorized using two existing, theoretically driven adaptive conditions and majority frameworks to explore the generalizability of the of work is lab-based. adaptation process and inform the changes manipulated in future research. 3. A lack of understanding about how The current study will collect data on a set of cognitive, affective, and individual states as well as on the choice of specific motivational states, as well as self- self-regulatory strategies to begin to explore how regulatory mechanisms, impact or these states and strategies relate to the adaptation are impacted by the adaptation process. process.

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Table 4. Event Coding Framework

Variable Categories Description Number of 1 = individual The only person involved was the one reporting the incident. Individuals Involved in Event 2 = dyad (person reporting + one Only the person reporting and one other person were involved in the (Num_Ind) person) incident. 3 = group/team (at least three total More than two people (plus the participant) were reported to be people) involved in the event. 4 = whole organization Whole organization was involved.

N/A = unknown Not enough information to determine Type of People 1 = Internal personnel only Participant reported that all people involved were internal to the Involved organization. (Type_People) 2 = External personnel only Participant reported that all people involved in the event (aside from person reporting) were external to the organization (e.g., clients/customers, sub-contractors, primes, etc.) 3 = Mixed personnel Participant reported that both internal and external personnel were involved in the event 4 = Non-work individuals only Participant reported that the event included family members or friends only (not work-related) N/A = unknown Not enough information to determine

Level of Internal Staff 1 = interaction with subordinate(s) only Participant reported that the event dealt with his/her subordinates only Involved (e.g., “my” subordinates, assistant, etc.). (Level_Internal) 2 = interaction with peer(s) only Participant reported that the event dealt with his/her peers only (e.g., “my”...team, peers, etc.). 3 = interaction with supervisor(s) only Participant reported that the event dealt with his/ her supervisors only (e.g., “my...” project manager (PM), project investigator (PI), Division Direction (DD), CEO, CFO, supervisor). 4 = interaction with mixed level group Participant reported that the event dealt with people across the organizational hierarchy. N/A = unknown/ not applicable Not enough information to determine or no internal staff included.

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Table 4 (cont’d)

Variable Categories Description Type of External 1 = interaction with client/ customer Participant reported that the event dealt with his clients/ customers only. Personnel Involved only (Type_External) 2 = interaction with partner Participant reported that the event dealt with partner(s) (e.g., prime, sub, consultant, SME, academic partner). 3 = interaction with other external Participant reported that the event dealt with external personnel other and/or mixed external group than the types mentioned above or a combination of different types of external personnel. N/A = unknown Not enough information to determine or no external staff mentioned. Circumstances of the 1 = people (staffing issue) The adaptive event involved a staffing issue (e.g., new team member; Event losing team member; deciding how to staff a project). (Event_Circ) 2 = people (conflict) The adaptive event involved a personnel conflict (e.g., disagreement;

personality conflict; work style conflict).

3 = technology (new, changed, not The adaptive event involved a technology issue (e.g., new or adapted working) technology; technology not working properly). 4 = workload/ labor plan The adaptive event involved a shift in workload or tasking (lighter or greater workload; new or removed tasking). 5 = error/ mistake/oversight/problem The adaptive event involved correcting an error or mistake that had been made. 6 = direction/ focus of work The adaptive event involved a disagreement, misunderstanding, or discussion about the direction a specific proposal, project, or other work-related task should go. 7 = timing issue The adaptive event involved a timing issue, such as a shift in timelines, conflict in timing (two things needing attention at once), or running out of time. 8 = personal/ non-work related The adaptive event involved a non-work related issue that impacted work (e.g., weather, illness, appointments, oversleeping) 9 = organizational level event/change The adaptive event involved a company-wide event or change (e.g., reorganization; restructuring of staff; company events; company policies). N/A = other The adaptive event involved circumstances not covered here (please describe).

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Table 4 (cont’d)

Variable Categories Description Type of Change in 1 = component complexity The adaptive event required that more be done (e.g., more information Complexity “work harder” coming in that one must attend to; more behaviors are required) to (Complexity) perform a project or task than was required previously, and within the same amount of time. Events that reflect this type of change likely lead to a strategy of “more of the same” – e.g., work harder, work faster, or put more effort in. 2 = coordinative complexity The adaptive event required that the behaviors being performed change “work smarter” in terms of timing, frequency, sequencing, intensity, or location in order to successfully complete a task. This is due to a change in the nature of the relationship between task/ project inputs and task/ project outputs. Events that reflect this type of change likely lead to a novel strategy or course of action. 3 = dynamic complexity The adaptive event required that completely different behaviors be “chaos” performed than usual for the type of task/project being worked on and/or new information to be considered to figure out what will be effective after changes in the state of the world which have an effect on the relationships between task or project inputs and products. N/A The adaptive event cannot be categorized into any of these categories. What was the 1 = new, atypical situation The participant realized adaptation was needed when they encountered situational cue that a new or atypical situation (e.g., office shut down; no project tasking). helped it come to your 2 = unexpected performance/timeline The participant realized adaptation was needed when they saw a attention? discrepancy discrepancy between expectations and reality in terms of performance (Type_Cue) or timelines on a project/task/proposal on which they are working (e.g., more negative feedback than expected; poor review; behind schedule (running out of time)). 3 = automated warning/indicator The participant realized adaptation was needed when they received feedback from an automated system that alerted them to a problem (e.g., warning/error; failed QA check; technology fails to work right). 4 = someone told them directly The participant realized adaptation was needed when someone told them directly through a phone call, face to face conversation, email, or another form of communication. N/A = unclear Not clear what the cue was or it does not fit into one of these categories.

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Table 4 (cont’d)

Variable Categories Description Detection Time 1 = Before conditions occurred that The participant detected that they would need to change/adapt before (Detect_Time) would require an adjustment the event required it (that is, anticipated that the situation was going to (anticipatory) require an adjustment). 2 = Immediately, or a short time, after The participant detected that there was a need to change/adapt conditions occurred that would require immediately after conditions occurred that would require that change an adjustment (that is, little delay between the situation changing and their recognition of and reaction to it). 3 = A significant amount of time after The participant did not detect that there was a need to change/adapt conditions occurred that would require until a significant amount of time passed. an adjustment

Underlying factors 1 = situational factors The adaptive event was a result of situational factors. that led to event (Under_Factors) 2 = environmental factors The adaptive event was a result of environmental factors (e.g., weather, economic environment, etc.) 3 = organizational factors The adaptive event was a result of organizational factors (e.g., organizational policies or practices or decisions of one’s own or other organization). 4 = political factors The adaptive event was a result of political factors.

5 = personal factors The adaptive event was a result of personal factors (e.g., illness, home life, personality style). N/A = unclear The adaptive event was a result of something not covered in the above categories or was unclear Reactive vs. Proactive 1 = Primarily Reactive Events that unfold as a result of an existing change (i.e., an effort to (React_Pro) respond to a current problem or change) 2 = Both Events that had both reactive and proactive associations.

3 = Primarily Proactive Events that arise due to a potential future change (i.e., an opportunity to "stay ahead of the game" by anticipating and responding to a potential future problem or change). N/A = unclear Not enough information to make a rating.

178

Table 4 (cont’d)

Variable Categories Description Expectation Level 1 = Unexpected Event was unexpected. (Expectation) 2 = Neutral or Mixed Knew that an event like this could possibly occur at some point, but did not necessarily expect it to happen right now. 3 = Expected Event was expected. N/A = Unclear Not enough information to make a rating. Type of SR Change 1 = Primarily Cognitive The adaptive event required primarily a cognitive change (e.g., (SR_Change) cognitive focus, shifting/reprioritizing where attention is focused, learning). 2 = Primarily Affective/ Motivational The adaptive event required primarily an affective change (e.g., managing emotions, changing attitude, increasing motivation or effort level). 3 = Primarily Behavioral The adaptive event required primarily a behavioral change (e.g., shifting course of action, changing the behaviors one is engaging in). 4 = Mixed The adaptive event required substantial changes in at least two of the three areas listed above (cognitive; affective/motivational; behavioral). N/A = unclear Not enough information to make a rating.

Primary Cognitive 1 = reprioritized/shifted focus of The participant reported that they shifted their attention to the highest Response attention priority/most important aspects of the task/project/situation. (Cognition) 2 = sought out information and ideas for The participant reported that they put effort into learning more about plans from other resources the situation or how to approach it (i.e., gathered additional information by asking others, looking up information, or looking to other resources). 3 = mentally evaluated/thought through The participant reported that they engaged in a mental exercise to think what to do (without using other through the situation and identify/visualize what they may be able to do resources) to adapt (self). 4 = mentally withdrew The participant reported that they withdrew attention from (stopped thinking about) the task/project/situation by shifting their attention off- task. N/A = unclear The participant reported actions that were not covered in the above categories or is unclear.

179

Table 4 (cont’d)

Variable Categories Description Affective Reactions 1 = Negative The participant reported reacting negatively to the event (e.g., (Affect) frustration, disappointment, anger, fear). 2 = Neutral The participant reported feeling neutral about the event (e.g., no strong reaction either way – calm, relaxed, only mildly stressed, frustrated, or worried). 3 = Positive The participant reported reacting positively to the event (e.g., engaged, excited, hopeful, etc.). 4 = Mixed The participant reported experiencing mixed feelings about the event (e.g., positive then negative; negative then positive, etc.) N/A = unclear Not enough information to make a rating.

Primary Behavior 1 = changed strategy/ course of action The participant reported that they quickly identified an alternative Response/ Action quickly (exploitation) course of action that they adopted to adapt to the event. Taken 2 = explored multiple alternative The participant reported that they explored multiple strategies or (Behavior) strategies/ courses of action courses of action to see what worked best. (exploration) 3 = same behavior, but increased effort The participant reported that they continued efforts as before, but increased the amount and/or pace of their efforts to adapt to the event. 4 = withdrew behaviorally The participant reported that they withdrew effort from the task/ project/ situation completely. 5 = stayed the course (no change) The participant reported that they did not make any behavioral change in response to the event (no action taken). N/A = unclear The participant reported actions that were not covered in the above categories or is unclear. Primary Coping 1 = Active Coping Taking direct action to address a problem Strategy 2 = Mental Simulation/Planning Thinking about and devising action steps for tackling a situation (Coping) 3 = Minimizing Distractions Ignoring or suppressing activities that may distract from the task at hand 4 = Restraint Coping Waiting for the appropriate time to act rather than acting impulsively

180

Table 4 (cont’d)

Variable Categories Description Primary Coping 5 = Seeking Social Support for Seeking people out for help or advice related to the problem Strategy Instrumental Reasons (Coping) 6 = Focusing on and venting emotions Focusing on the negative emotions or stress one is feeling and/or sharing those feelings with others (cont’d) 7 = Behavioral Disengagement Reducing effort or completely giving up on goals 8 = Mental Disengagement Daydreaming, focusing on other off-task activities to take mind off of current situation, sleeping N/A = None No coping strategy mentioned or explicitly stating that coping wasn’t necessary Adaptive Performance 1 = Handling emergencies or crisis Reacting with appropriate and proper urgency in life threatening, Dimension situations dangerous, or emergency situations; quickly analyzing options for (Adapt_Dim) dealing with danger or crisis and their implications; making split- second decisions based on clear and focused thinking; maintaining emotional control and objectivity while keeping focused on the situation at hand; stepping up to take action and handle danger or emergencies as necessary and appropriate. 2 = Handling work stress Remaining composed and cool when faced with difficult circumstances or a highly demanding workload or schedule; not overreacting to unexpected news or situations; managing frustrating well by directing effort to constructive solutions rather than blaming others; demonstrating resilience and the highest levels of professionalism in stressful circumstances; acting as a calming and settling influence to whom others look for guidance. 3 = Solving problems creatively Enjoying unique types of analyses and generating new, innovative ideas in complex areas; turning problems upside-down and inside-out to find fresh, new approaches; integrating seemingly unrelated information and developing creative solutions; entertaining wide-ranging possibilities others may miss, thinking outside the given parameters to see if there is a more effective approach; developing innovative methods of obtaining or using resources when insufficient resources are available to do the job.

181

Table 4 (cont’d)

Variable Categories Description Adaptive Performance 4 = Dealing with uncertain and Taking effective action when necessary without having to know the Dimension unpredictable work situations total picture or have all the facts at hand; readily and easily changing (Adapt_Dim) gears in response to unpredictable or unexpected events and circumstances; effectively adjusting plans, goals, actions, or priorities to (cont’d) deal with changing situations; imposing structure for self and others that provide as much focus as possible in dynamic situations; not needing things to be black and white; refusing to be paralyzed by uncertainty or ambiguity. 5 = Learning work tasks, technologies, Demonstrating enthusiasm for learning new approaches and and procedures technologies for conducting work; doing what is necessary to keep knowledge and skills current; quickly and proficiently learning new methods or how to perform previously unlearned tasks; adjusting to new work processes and procedures; anticipating changes in the work demands and searching for and participating in assignments or training that will prepare self for these changes; taking action to improve work performance deficiencies. 6 = Demonstrating interpersonal Being flexible and open-minded when dealing with others; listening to adaptability and considering others' viewpoints and opinions and altering own opinion when it is appropriate to do so; being open and accepting of negative or developmental feedback regarding work; working well and developing effective relationships with highly diverse personalities; demonstrating keep insight of others' behavior and tailoring own behavior to persuade, influence, or work more effectively with them. 7 = Demonstrating cultural adaptability Taking action to learn about and understand the climate, orientation, needs, and values of other groups, organizations, or cultures; integrating well into and being comfortable with different values, customs, and cultures; willingly adjusting behavior or appearance as necessary to comply with or show respect for others' values and customs; understanding the implications of one's actions and adjusting approach to maintain positive relationships with other groups, organizations, or cultures.

182

Table 4 (cont’d)

Variable Categories Description Adaptive Performance 8 = Demonstrating physically oriented Adjusting to challenging environmental states such as extreme heat, Dimension adaptability humidity, cold, or dirtiness; frequently pushing self physically to (Adapt_Dim) complete strenuous or demanding tasks; adjusting weight and muscular strength or becoming proficient in performing physical tasks as (cont’d) necessary for the job. Result/Outcome of 1 = Negative (Bad Outcome) Participant reported that the outcome of the event being discussed was Event negative (bad outcome). (Event_Outcome) 2 = Neutral Participant reported that the outcome of the event being discussed was neutral. 3 = Positive (Good Outcome) Participant reported that the outcome of the event being discussed was positive (good outcome). N/A = Unknown Outcome is unknown (by participant or not reported on at all). Behavioral Impact on 1 = Negative impact Participant reported that his/her behaviors contributed negatively to the Outcome outcome. (Behavior_Impact) 2 = No/Neutral/Mixed impact Participant reported that his/her behaviors contributed positively to the

outcome in some ways, but less positive (or negative), in others OR just neutral overall. 3 = Positive impact Participant reported that his/her behaviors contributed positively to the outcome. N/A = Unknown Impact is unknown (by participant or not reported on at all).

183

Table 5. Background Survey Correlations

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 I-ADAPT 1. Crisis 3.88 0.60 0.87 2. Cultural 4.26 0.56 0.19 0.82 3. Learning 4.10 0.50 0.43 0.34 0.87 4. Uncertainty 3.62 0.59 0.63 0.25 0.45 0.88 5. Physical 3.47 0.53 0.48 0.01 0.28 0.55 0.68 6. Interpersonal 4.19 0.38 0.37 0.68 0.35 0.49 0.24 0.64 7. Work Stress 3.48 0.66 0.58 -0.04 0.18 0.66 0.47 0.24 0.72 8. Creativity 3.86 0.67 0.50 0.33 0.45 0.47 0.43 0.38 0.23 0.85 IPIP 9. Conscientious 3.69 0.79 0.09 0.05 0.05 0.11 0.01 0.24 0.21 0.00 0.75 10. Extraversion 2.95 0.92 0.38 0.36 0.34 0.44 0.1 0.57 0.28 0.39 0.16 0.83 11. Neuroticism 2.66 0.72 -0.20 0.08 -0.35 -0.19 -0.12 -0.12 -0.44 -0.11 -0.20 -0.16 0.70 12. Openness 4.06 0.63 0.26 0.17 0.17 0.34 0.32 0.21 0.25 0.55 -0.29 0.14 0.01 0.71 13. Agreeable 4.01 0.60 0.20 0.51 0.12 0.31 0.12 0.71 0.12 0.30 0.22 0.41 -0.02 0.09 0.60 Goal Orientation 14. LGO 4.26 0.46 0.53 0.37 0.78 0.61 0.41 0.49 0.37 0.55 0.02 0.46 -0.30 0.43 0.26 15. PPGO 3.51 0.65 0.16 0.10 0.26 -0.14 0.01 0.11 -0.07 0.22 0.02 0.21 -0.11 0.04 0.22 16. APGO 2.29 0.80 -0.38 -0.16 -0.33 -0.60 -0.37 -0.20 -0.40 -0.38 0.06 -0.33 0.21 -0.23 -0.13 17. Autonomy 4.65 0.72 -0.10 0.18 0.07 0.27 -0.05 0.02 -0.05 0.15 -0.24 -0.04 0.25 0.27 0.01 Note: n = 62; Bolded numbers represent correlations that are significant at p < .05. Italicized numbers on the diagonal are the alpha reliabilities for each scale.

184

Table 5 (cont’d)

Mean SD 14 15 16 17 Goal Orientation 14. LGO 4.26 0.46 0.72 15. PPGO 3.51 0.65 0.28 0.67 16. APGO 2.29 0.80 -0.55 0.19 0.85 17. Autonomy 4.65 0.72 0.13 -0.25 -0.14 0.48 Note: n = 62; Bolded numbers represent correlations that are significant at p < .05. Italicized numbers on the diagonal are the alpha reliabilities for each scale.

185

Table 6. Average Event Ratings and Outcome Variables (Quantitative Variables Only)

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Nature of Change 1. Valence 3.32 1.03 1 Required Adaptation 2. Cognitive 3.16 .91 .41 1 3. Emotion 2.54 .88 -.20 .37 1 4. Behavior 2.96 .97 .10 .48 .37 1 5. KSAs 2.60 .93 .33 .70 .13 .49 1 Diagnosis & Appraisal 6. Challenge 3.03 .94 .00 .67 .39 .27 .63 1 7. Threat 2.07 .88 -.45 .18 .36 .38 .29 .45 1 8. Opportunity 2.82 1.03 .29 .58 .22 .49 .62 .37 .29 1 Planning 9. Contingency 2.89 .89 .06 .36 .17 .17 .32 .42 .14 .26 1 10. Reactive 3.40 1.07 .21 .36 .01 .33 .36 .22 .02 .33 .16 1 11. Time 2.35 .74 -.05 .28 .32 .41 .39 .30 .29 .52 .19 .31 1 Affective Reactions 12. Focused 3.98 .85 .60 .58 -.01 .39 .48 .35 -.07 .54 .17 .59 .38 1 13. Anxious 2.83 1.09 -.65 .10 .50 .17 .12 .43 .58 .00 .22 -.01 .18 -.26 1 14. Frustrated 2.65 1.17 -.72 .01 .29 .02 .08 .38 .54 -.09 .25 .06 .20 -.28 .82 1 Note: n = 51; Bolded numbers represent correlations that are significant at p < .05.

186

Table 6 (cont’d)

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Behavior Strategy 15. Exploit 3.03 .86 -.06 .17 .15 .37 .12 .08 .13 .20 -.16 .51 .17 .45 .11 .11 16. Explore 3.00 .97 .24 .27 .07 .20 .28 .27 .01 .37 .44 .25 .35 .39 .01 .06 17. Inc. Effort 2.70 .94 .04 -.08 -.04 .14 -.04 -.13 .07 .17 .15 .24 .21 .12 .09 -.03 18. Withdrew 1.54 .71 -.54 -.23 .19 -.17 -.23 .17 .46 -.25 .00 -.39 -.02 -.49 .61 .54 19. No Change 2.30 1.03 .24 .07 .01 -.31 -.01 .05 -.03 -.12 .14 -.35 -.35 -.13 .14 -.04 Effectiveness 20. Behavior 3.82 .88 .59 .54 .05 .38 .39 .16 -.25 .48 .26 .55 .28 .80 -.25 -.27 Strategy 21. Coping 3.65 .90 .70 .55 .07 .21 .25 .10 -.33 .40 .18 .43 .09 .74 -.36 -.45 Strategy 22. Sit. Assess 3.89 .93 .62 .41 -.16 .30 .35 .06 -.23 .32 .01 .56 .22 .78 -.35 -.37 23. Planning 3.87 .89 .49 .53 .17 .24 .29 .24 -.13 .38 .24 .44 .24 .77 -.18 -.16 24. Execution 3.78 .84 .53 .56 .10 .31 .34 .24 -.02 .46 .21 .52 .23 .81 -.20 -.22 25. Overall 3.87 .95 .66 .56 -.01 .44 .43 .12 -.18 .52 .12 .60 .24 .86 -.36 -.35 Note: n = 51; Bolded numbers represent correlations that are significant at p < .05.

187

Table 6 (cont’d)

Mean SD 15 16 17 18 19 20 21 22 23 24 25 Beh. Strategy 15. Exploit 3.03 .86 1 16. Explore 3.00 .97 .04 1 17. Inc. Effort 2.70 .94 .16 .45 1 18. Withdrew 1.54 .71 -.13 -.06 .07 1 19. No Change 2.30 1.03 -.27 -.05 .04 .26 1 Effectiveness 20. Behavior 3.82 .88 .35 .37 .16 -.52 -.03 1 Strategy 21. Coping 3.65 .90 .28 .31 .09 -.54 .12 .85 1 Strategy 22. Sit. Assess 3.89 .93 .37 .26 .10 -.48 -.07 .75 .75 1 23. Planning 3.87 .89 .34 .46 .10 -.44 .00 .84 .83 .66 1 24. Execution 3.78 .84 .38 .36 .08 -.41 -.06 .86 .84 .77 .92 1 25. Overall 3.87 .95 .42 .29 .07 -.64 -.07 .89 .86 .83 .80 .85 1 Note: n = 51; Bolded numbers represent correlations that are significant at p < .05.

188

Table 7. Breakdown of Hypotheses by Type

Event Person Level Brief Description Level Brief Description H1 Reactive vs. proactive event type H5 Not tested due to lack of data predicting contingency planning, reactive strategic planning, and time H2 Coping strategy type predicting H6 LGO predicting behavioral cognitive and affective reactions strategies, affective reactions, and appraisals H3 Coping strategy type predicting H7 PPGO predicting behavioral appraisals strategies H4 Coping strategy type predicting H8 APGO predicting behavioral behavioral strategy choice and strategies, coping strategies, effectiveness appraisals, and reactions H13 Reactive vs. proactive event type H9 Openness predicting SA predicting appraisals, behavioral effectiveness, behavioral strategies, strategy choice and effectiveness affective reactions, and appraisals H14 Adaptive performance dimension H10 Trait adaptability predicting predicting behavioral strategy and adaptation effectiveness behavioral strategy effectiveness H15 Type of complexity change H11 Autonomy predicting coping predicting behavioral strategy and strategies and affective reactions behavioral strategy effectiveness Supp. Multiple analyses H12 Autonomy predicting behavioral strategies and effectiveness

189

Table 8. Hypothesis 1 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) 2 Model 1) R2 Variance* 1a Contingency Planning Opportunity to Improve 1.21 1.20 0.9917355 0.008 0.83 Reactive vs. Mixed vs. Proactive 1.21 1.17 0.9669421 0.033 3.31 Reactive vs. Other 1.21 1.19 0.9834711 0.017 1.65 1b Strategic Planning Opportunity to Improve 1.08 1.08 1.0000000 0.000 0.00 Reactive vs. Mixed vs. Proactive 1.08 1.03 0.9537037 0.046 4.63 Reactive vs. Proactive 1.08 1.05 0.9722222 0.028 2.78 Reactive vs. Other 1.08 1.05 0.9722222 0.028 2.78 Proactive vs. Other 1.08 1.02 0.9444444 0.056 5.56 1c Time to Plan Opportunity to Improve 0.96 0.94 0.9791667 0.021 2.08 Reactive vs. Mixed vs. Proactive 0.96 0.87 0.9062500 0.094 9.38 Reactive vs. Other 0.96 0.89 0.9270833 0.073 7.29 Reactive vs. Proactive 0.96 0.85 0.8854167 0.115 11.46 *Variance explained by IV relative to variance remaining after removing variance accounted for by the subject (not relative to total variance).

Table 9. Hypothesis 2 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) Model 1) R2 Variance* 2 2a Maintain Focus Problem vs. Emotion Coping 0.6 0.56 0.9333333 0.067 6.67 2b Anxiety Problem vs. Emotion Coping 1.14 1.16 1.0175439 -0.018 -1.75 2c Frustration Problem vs. Emotion Coping 1.49 1.43 0.9597315 0.040 4.03 *Variance explained by IV relative to variance remaining after removing variance accounted for by the subject (not relative to total variance).

190

Table 10. Hypothesis 3 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) Model 1) R2 Variance* 2 3a Challenge Appraisals Problem vs. Emotion Coping 1.07 1.07 1.0000000 0.000 0.00 3b Threatened Appraisals Problem vs. Emotion Coping 0.97 0.95 0.9793814 0.021 2.06 *Variance explained by IV relative to variance remaining after removing variance accounted for by the subject (not relative to total variance). Table 11. Hypothesis 4 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) Model 1) R2 Variance* 2 4a Exploitation Problem vs. Emotion Coping 1.3 1.28 0.9846154 0.015 1.54 4a Exploration Problem vs. Emotion Coping 1.17 1.13 0.9658120 0.034 3.42 4a Increase Effort Problem vs. Emotion Coping 1 0.97 0.9700000 0.030 3.00 4a Withdraw Problem vs. Emotion Coping 0.5 0.38 0.7600000 0.240 24.00 4a Stay the Course Problem vs. Emotion Coping 0.95 0.91 0.9578947 0.042 4.21 4b Behavior Strategy Problem vs. Emotion Coping 0.53 0.49 0.9245283 0.075 7.55 Effectiveness (self-report) 4b Behavior Impact Problem vs. Emotion Coping 0.15 0.15 1.0000000 0.000 0.00 (coded) 4b Coping Strategy Problem vs. Emotion Coping 0.54 0.52 0.9629630 0.037 3.70 Effectiveness *Variance explained by IV relative to variance remaining after removing variance accounted for by the subject (not relative to total variance).

191

Table 12. Primary Behavioral Strategy Use (percentage) by Coping Strategy Type

Primary Behavioral Strategy Coping Exploitation Exploration Increase Withdraw Stay the Strategy Effort Course Problem 46% 44% 8% 0% 3% (n = 190) Emotion 22% 28% 11% 39% 0% (n = 18)

192

Table 13. Effects of Learning Goal Orientation (Hypothesis 6)

Dependent Variable Beta Sig. % Variance Accounted (R2) Proportion of events where .12 .42 1.3% primary strategy = exploration Proportion of events where .25 .08 6.1% primary strategy = qualitative Self-reported exploration .10 .49 1.0% Anxiety -.25 .08 6.2% Frustration -.29 .04 8.1% Challenge appraisals .22 .13 4.7%

Table 14. Effects of Performance Prove Goal Orientation (Hypothesis 7)

Dependent Variable Beta Sig. % Variance Accounted (R2) Proportion of events where -.13 .36 1.8% primary strategy = exploitation Self-reported exploitation .09 .55 0.7%

193

Table 15. Effects of Performance Avoid Goal Orientation (Hypothesis 8)

Dependent Variable Beta Sig. % Variance Accounted (R2) Proportion of events where .51 .00 25.6% primary strategy = withdraw Self-reported withdraw .38 .01 14.3% Proportion of events where .29 .04 8.6% coping type = emotion-focused Threat appraisals .18 .21 3.1% Anxiety .34 .02 11.4% Frustration .19 .17 3.7%

Table 16. Effects of Openness (Hypothesis 9)

Dependent Variable Beta Sig. % Variance Accounted (R2) Proportion of events where .03 .85 0.1% primary strategy = exploration Self-reported exploration -.07 .61 7.4% Situation assessment effectiveness .09 .54 0.8% Anxiety .02 .89 0.0% Frustration -.10 .48 1.0% Challenge appraisals .18 .22 3.1%

194

Table 17. Correlations between Individual Adaptability and Effectiveness by Phase and Overall

Individual Adaptability (by Dimension) Crisis Work Creativity Uncertainty Learning Interpersonal Cultural Physical Effectiveness Stress Situation .32 .31 .34 .34 .12 .29 .16 .26 Assessment Planning/ .52 .48 .39 .50 .24 .30 .10 .46 Strategy Execute/ .46 .40 .36 .43 .22 .31 .12 .40 Evaluate Overall .41 .46 .26 .50 .16 .35 .14 .34 Adaptation *Bold: p < .05; Italics: p < .10; Normal: p = ns.

195

Table 18. Effects of Trait Adaptability (Hypothesis 10)

Dependent Variable Beta Sig. % Variance Trait adaptability dimension Accounted (R2) Situation assessment effectiveness - .15 18.5% Crisis .03 .89 Work Stress .18 .39 Creativity .20 .28 Uncertainty .05 .83 Interpersonal .14 .37 Physical -.01 .94 Planning & strategy selection - .00 35.7% effectiveness Crisis .23 .23 Work Stress .15 .41 Creativity .07 .66 Uncertainty .08 .69 Interpersonal .07 .61 Physical .17 .31 Execution & evaluation effectiveness - .02 28.0% Crisis .20 .31 Work Stress .12 .53 Creativity .09 .62 Uncertainty .01 .97 Interpersonal .13 .37 Physical .16 .37 Overall adaptation effectiveness - .01 30.3% Crisis .05 .81 Work Stress .22 .25 Creativity -.04 .80 Uncertainty .27 .20 Interpersonal .16 .28 Physical .02 .92

196

Table 19. Correlations between Individual Adaptability and Situation Assessment Effectiveness by Event Type

Type of Adaptive Event Work Stress Solving Uncertain/ Learning Interpersonal Self-Rated (n = 58 events) Problems Unpredictable (n = 12 events) (n = 10 events) Adaptability Creatively Situations Score (IV) (n = 40 events) (n = 97 events) Work Stress .02 .07 .20 .11 .61 Creativity .32 .37 .14 .15 -.18 Uncertainty -.02 .36 .33 .09 .37 Learning .25 .18 .16 .05 -.63 Interpersonal .40 .23 .22 .18 .47 Crisis .17 .29 .29 .35 .21 Cultural .38 .31 .15 .27 .20 Physical .01 .02 .22 .06 .34 *Bold: Significant (p <= .05) *Gray shading = adaptability dimension matches event type

197

Table 20. Correlations between Individual Adaptability and Planning & Strategy Selection Effectiveness by Event Type

Type of Adaptive Event Work Stress Solving Uncertain/ Learning Interpersonal Self-Rated (n = 58 events) Problems Unpredictable (n = 12 events) (n = 10 events) Adaptability Creatively Situations Score (IV) (n = 40 events) (n = 97 events) Work Stress .44 .25 .27 .05 .83 Creativity .08 .33 .25 .38 -.26 Uncertainty .60 .40 .35 -.38 .79 Learning .15 .33 .26 -.48 -.10 Interpersonal .28 .34 .30 -.29 .00 Crisis .47 .31 .39 .40 .70 Cultural -.03 .43 .21 .00 -.19 Physical .36 .20 .29 -.07 .51 *Bold: Significant (p <= .05) *Gray shading = adaptability dimension matches event type

198

Table 21. Correlations between Individual Adaptability and Execution and Evaluation Effectiveness by Event Type

Type of Adaptive Event Work Stress Solving Uncertain/ Learning Interpersonal Self-Rated (n = 58 events) Problems Unpredictable (n = 12 events) (n = 10 events) Adaptability Creatively Situations Score (IV) (n = 40 events) (n = 97 events) Work Stress .24 .14 .20 -.05 .82 Creativity .19 .16 .14 .02 -.26 Uncertainty .44 .25 .25 -.39 .68 Learning .20 .14 .23 -.60 -.25 Interpersonal .28 .09 .26 -.24 .05 Crisis .36 .46 .31 .09 .43 Cultural .17 .19 .19 -.14 -.19 Physical .23 .11 .24 -.15 .51 *Bold: Significant (p <= .05) *Gray shading = adaptability dimension matches event type

199

Table 22. Correlations between Individual Adaptability and Overall Adaptation Effectiveness by Event Type

Type of Adaptive Event Work Stress Solving Uncertain/ Learning Interpersonal Self-Rated (n = 58 events) Problems Unpredictable (n = 12 events) (n = 10 events) Adaptability Creatively Situations Score (IV) (n = 40 events) (n = 97 events) Work Stress .63 .05 .29 .11 .82 Creativity -.06 .40 .02 .15 -.20 Uncertainty .71 .50 .35 .09 .66 Learning .04 .43 .18 .05 -.50 Interpersonal .42 .23 .27 .18 .38 Crisis .50 .26 .30 .35 .55 Cultural .01 .35 .13 .27 .15 Physical .36 .21 .19 .06 .46 *Bold: Significant (p <= .05) *Gray shading = adaptability dimension matches event type

200

Table 23. Effects of Autonomy (Hypothesis 11)

Dependent Variable Beta Sig. % Variance Accounted (R2) Proportion of events where coping -.03 .86 0.1% type = social support seeking Anxiety .01 .93 0.0% Frustration .01 .94 0.0%

Table 24. Effects of Autonomy (Hypothesis 12)

Dependent Variable Beta Sig. % Variance Accounted (R2) Behavioral strategy effectiveness -.11 .43 1.3% Situation assessment effectiveness -.11 .46 1.1% Planning & strategy selection -.11 .46 1.1% effectiveness Execution & evaluation effectiveness -.19 .18 3.6% Overall adaptation effectiveness -.07 .62 0.5%

201

Table 25. Hypothesis 13 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) 2 Model 1) R2 Variance* 13a Challenge Appraisals Reactive vs. Mixed vs. Proactive 1.07 1.09 1.0186916 -0.019 -1.87 13a Threatened Appraisals Reactive vs. Mixed vs. Proactive 0.97 0.94 0.9690722 0.031 3.09 13b Exploitation Reactive vs. Mixed vs. Proactive 1.3 1.27 0.9769231 0.023 2.31 13b Exploitation Reactive vs. Proactive 1.3 1.17 0.9000000 0.100 10.00 13b Exploration Reactive vs. Mixed vs. Proactive 1.17 1.15 0.9829060 0.017 1.71 13b Exploration Reactive vs. Other 1.17 1.15 0.9829060 0.017 1.71 13b Increase Effort Reactive vs. Mixed vs. Proactive 1 0.998 0.9980000 0.002 0.20 13b Withdraw Reactive vs. Mixed vs. Proactive 0.5 0.48 0.9600000 0.040 4.00 13b Withdraw Proactive vs. Other 0.5 0.48 0.9600000 0.040 4.00 13b Stay the Course Reactive vs. Mixed vs. Proactive 0.95 0.949 0.9989474 0.001 0.11 13b Stay the Course Reactive vs. Proactive 0.95 1.06 1.1157895 -0.116 -11.58 13c Behavior Strategy Reactive vs. Mixed vs. Proactive 0.53 0.52 0.9811321 0.019 1.89 Effectiveness (self-report) 13c Behavior Impact Reactive vs. Mixed vs. Proactive 0.15 0.15 1.0000000 0.000 0.00 (coded) *Variance explained by IV relative to variance remaining after removing variance accounted for by the subject (not relative to total variance).

202

Table 26. Primary Behavioral Strategy Use (percentage) by Reactive, Mixed, and Proactive Event Types

Primary Behavioral Strategy Nature of Exploitation Exploration Increase Withdraw Stay the Event Effort Course Reactive 42% 43% 7% 4% 4% (n = 137) Both 53% 35% 12% 0% 0% (n = 34) Proactive 40% 42% 9% 0% 9% (n = 45)

203

Table 27. Hypothesis 14 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) 2 Model 1) R2 Variance* 14a Challenge Appraisals Adaptive Dimension 1.07 1.08 1.0093458 -0.009 -0.93 14a Threatened Appraisals Adaptive Dimension 0.97 0.95 0.9793814 0.021 2.06 14a Threatened Appraisals Stress vs. Thinking 0.97 0.93 0.9587629 0.041 4.12 14b Exploitation Adaptive Dimension 1.3 1.3 1.0000000 0.000 0.00 14b Exploitation Stress vs. Thinking 1.3 1.32 1.0153846 -0.015 -1.54 14b Exploration Adaptive Dimension 1.17 1.12 0.9572650 0.043 4.27 14b Exploration Stress vs. Thinking 1.17 1.166 0.9965812 0.003 0.34 14b Exploration Creative vs. Other 1.17 1.13 0.9658120 0.034 3.42 14b Increase Effort Adaptive Dimension 1 1.01 1.0100000 -0.010 -1.00 14b Withdraw Adaptive Dimension 0.5 0.496 0.9920000 0.008 0.80 14b Stay the Course Adaptive Dimension 0.95 0.92 0.9684211 0.032 3.16 14b Stay the Course Stress vs. Thinking 0.95 0.89 0.9368421 0.063 6.32 14b Stay the Course Uncertainty vs. Other 0.95 0.93 0.9789474 0.021 2.11 14c Behavior Strategy Adaptive Dimension 0.53 0.55 1.0377358 -0.038 -3.77 Effectiveness (self-report) 14c Behavior Impact Adaptive Dimension 0.15 0.16 1.0666667 -0.067 -6.67 (coded) 14c Behavior Impact Stress vs. Thinking 0.15 0.16 1.0666667 -0.067 -6.67 (coded) *Variance explained by IV relative to variance remaining after removing variance accounted for by the subject (not relative to total variance).

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Table 28. Primary Behavioral Strategy Use (percentage) by Adaptive Performance Dimension

Primary Behavioral Strategy Type of Exploitation Exploration Increase Withdraw Stay the Adaptive Effort Course Event Work Stress 47% 26% 17% 7% 3% (n = 58) Solving 28% 65% 5% 0% 3% Problems Creatively (n = 40) Uncertainty/ 51% 39% 4% 1% 5% Unpredictable (n = 96) Learning 25% 67% 8% 0% 0% (n = 12) Interpersonal 30% 40% 0% 20% 10% (n = 10)

205

Table 29. Hypothesis 15 Variance Accounted For

Hyp DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) 2 Model 1) R2 Variance* 15a Challenge Appraisals Type of Complexity 1.07 0.99 0.9252336 0.075 7.48 15a Threatened Appraisals Type of Complexity 0.97 0.96 0.9896907 0.010 1.03 15b Exploitation Type of Complexity 1.3 1.29 0.9923077 0.008 0.77 15b Exploitation Dynamic vs. Other 1.3 1.29 0.9923077 0.008 0.77 15b Exploration Type of Complexity 1.17 1.18 1.0085470 -0.009 -0.85 15b Increase Effort Type of Complexity 1 0.95 0.9500000 0.050 5.00 15b Increase Effort Component vs. Other 1 0.95 0.9500000 0.050 5.00 15b Increase Effort Coordinative vs. Other 1 0.996 0.9960000 0.004 0.40 15b Withdraw Type of Complexity 0.5 0.5 1.0000000 0.000 0.00 15b Stay the Course Type of Complexity 0.95 0.93 0.9789474 0.021 2.11 15b Stay the Course Dynamic vs. Other 0.95 0.94 0.9894737 0.011 1.05 15b Stay the Course Coordinative vs. Other 0.95 0.93 0.9789474 0.021 2.11 15c Behavior Strategy Type of Complexity 0.53 0.51 0.9622642 0.038 3.77 Effectiveness (self-report) 15c Behavior Strategy Component vs. Other 0.53 0.52 0.9811321 0.019 1.89 Effectiveness (self-report) 15c Behavior Strategy Coordinative vs. Other 0.53 0.51 0.9622642 0.038 3.77 Effectiveness (self-report) 15c Behavior Impact Type of Complexity 0.15 0.15 1.0000000 0.000 0.00 (coded) 15c Behavior Impact Component vs. Other 0.15 0.15 1.0000000 0.000 0.00 (coded) 15c Behavior Impact Coordinative vs. Other 0.15 0.15 1.0000000 0.000 0.00 (coded)

206

Table 30. Primary Behavioral Strategy Use (percentage) by Type of Complexity Change

Primary Behavioral Strategy Type of Exploitation Exploration Increase Withdraw Stay the Complexity Effort Course Component 45% 25% 25% 3% 3% (n = 40) Coordinative 56% 38% 4% 1% 1% (n = 90) Dynamic 30% 53% 3% 6% 8% (n = 87)

207

Table 31. Supplementary Event-Level Analyses Results

Situational Factors DV Reactive vs. Proactive Type of Complexity Adapt Performance Dimension SA Detect Time Overall Significant Overall Significant Overall Significant F(2,209.24) = 27.34, p = .00 F(2,211.70) = 4.62, p = .01 F(4,208.87) = 2.71, p = .03 Reactive > Proactive (p = .00) Dynamic < Component (p = .08) Stress > Thinking (p = .01) Mixed > Proactive (p = .00) Challenge App. H13a: Not significant H15a: Overall Significant H14a: Overall NS F(2,208.04) = .43, p = .65 F(2,210.46) = 9.89, p = .00 F(4,206.56) = .23, p = .92 Dynamic > Coordinative (p = .00) Threat App. H13a: Reactive > Proactive H15a: Overall Significant H14a: Overall Trend F(1,171.50) = 5.94, p = .02 F(2,207.22) = 3.35, p = .04 F(4,201.07) = 2.20, p = .07 Dynamic > Coordinative (p = .03) Stress > Thinking F(1,193.67) = 7.04, p = .01

Effectiveness Overall NS Overall NS Overall NS F(1,172.17) = .62, p = .43 F(2,207.37) = 1.68, p = .19 F(4,200.05) = 1.78, p = .13 Creative > Not Creative F(1,206.25) = 2.55, p = .11 P&SS Planning Time to Plan H1c: Not significant Overall NS Overall NS F(1,180.99) = .02, p = .88 F(2,205.73) = .87, p = .42 F(4,211.14) = 1.28, p = .28 Contingency H1a: Reactive < Proactive Overall NS Overall Significant F(1,179.46) = 2.65, p = .11 F(2,211.33) = .58, p = .56 F(4,210.16) = 2.78, p = .03 Stress < Thinking F(1,204.29) = 4.14, p = .04 Reactive H1b: Reactive > Proactive Overall NS Overall NS F(1,163.91) = 12.80, p = .00 F(2,199.52) = 1.11, p = .33 F(4,196.32) = 1.44, p = .22

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Table 31 (cont’d)

DV Reactive vs. Proactive Type of Complexity Adapt Performance Dimension Strategy Select Exploitation H13b: Reactive > Proactive H15b: Significant H14b: Significant F(1,180.47) = 8.24, p = .01 F(2,191.07) = 1.90, p = .15 F(4,209.15) = 1.96, p = .10 Dynamic < Other Stress > Not Stress F(1,198.97) = 3.73, p = .06 F(1,211.15) = 5.90, p = .02 Stress > Thinking F(1,201.22) = 5.26, p = .02 Exploration H13b: Reactive < Other H15b: Not significant H14b: Significant F(1,202.56) = 3.15, p = .08 F(2,210.95) = 1.31, p = .27 F(4,206.39) = 3.32, p = .01 Stress < Not Stress F(1,206.90) = 6.94, p = .01 Creative > Non F(1,211.01) = 9.05, p = .00 Stress < Thinking F(1,200.46) = 7.33, p = .01 Increase Effort H13b: Not significant H15b: Significant H14b: Not significant F(2,201.82) = .21, p = .81 F(2,203.91) = 5.73, p = .00 F(4,199.52) = .58, p = .68 Component > Not F(1,205.33) = 11.41, p = .00 Coordinative < Not F(1,202.14) = 3.02, p = .08 Withdraw H13b: Reactive > Other H15b: Not significant H14b: Not significant F(1,194.85) = 5.85, p = .02 F(2,201.14) = .46, p = .46 F(4,195.03) = 1.03, p = .39 Stay Course H13b: Reactive < Proactive H15b: Significant H14b: Significant F(1,170.78) = 3.58, p = .06 F(2,200.37) = 3.28, p = .04 F(4,193.08) = 1.98, p = .10 Coordinative < Not Stress < Not stress F(1,196.65) = 6.23, p = .01 F(1,191.95) = 7.01, p = .01 Dynamic > Not Uncertain > Non F(1,207.47) = 4.71, p = .03 F(1,192.32) = 2.64, p = .11 Stress < Thinking F(1,185.54) = 6.28, p = .01

209

Table 31 (cont’d)

DV Reactive vs. Proactive Type of Complexity Adapt Performance Dimension Effectiveness Reactive < Proactive Overall Significant Overall Significant F(1,166.96) = 6.84, p = .01 F(2,199.80) = 5.11, p = .01 F(4,196.41) = 3.36, p = .01 Dynamic < Coordinative (p = .11) Stress < Thinking Dynamic > Component (p = .08) F(1,190.80) = 8.78, p = .00 Creative > Not Creative F(1,200.90) = 7.89, p = .01

E&E Behavior H13c: Reactive < Proactive H15c: Significant H14c: Not significant Strategy Mixed < Proactive F(2,192.90) = 5.13, p = .01 F(4,190.41) = .53, p = .72 F(2,191.70) = 3.47, p = .03 Component < Not F(1,194.69) = 6.58, p = .01 Coordinative > Not F(1,188.77) = 8.09, p = .01 Coping Strategy Significant Overall Significant Stress < Not Stress Reactive < Other F(2,194.39) = 3.43, p = .03 F(1,188.24) = 4.84, p = .03 F(1, 186.54) = 2.92, p = .09 Component < Not Uncertain > Non F(1,193.85) = 6.83, p = .01 F(1,188.19) = 2.75, p = .10 Stress < Thinking F(1,181.72) = 6.24, p = .01 Behavior Impact H13c: Not significant H15c: Overall Trend H14c: Stress < Thinking F(2,176.59) = .66, p = .52 F(2,179.75) = 2.50, p = .09 F(1,163.34) = 2.76, p = .10 Component < Not F(1,179.52) = 3.79, p = .05 Coordinative > Not F(1,177.44) = 3.45, p = .07 Effectiveness Overall NS Overall Significant Overall Trend F(1,174.52) = .36, p = .55 F(2,207.75) = 4.65, p = .01 F(4,201.94) = 2.15, p = .08 Dynamic > Component (p = .01) Stress < Thinking F(1,196.30) = 5.92, p = .02 Uncertain > Not F(1,199.93) = 2.61, p = .11

210

Table 31 (cont’d)

DV Reactive vs. Proactive Type of Complexity Adapt Performance Dimension PERCEPTIONS Adjust Thinking Reactive < Proactive Overall Significant Overall Trend F(1,176.41) = 3.51, p = .06 F(2,207.67) = 3.77, p = .03 F(4,204.03) = 2.20, p = .07 Dynamic > Coordinative (p = .01) Stress < Thinking F(1,196.72) = 3.55, p = .06 Adjust KSAs Reactive < Proactive Overall Significant Overall NS F(1,178.94) = 3.14, p = .08 F(2,210.95) = 3.05, p = .05 F(4,208.08) = 1.61, p = .17 Dynamic > Coordinative (p = .02) Stress < Thinking F(1,202.27) = 2.94, p = .09 Adjust Feelings Overall NS Overall NS Overall NS F(1,180.97) = .39, p = .54 F(2,210.57) = 1.11, p = .33 F(4,210.69) = 1.81, p = .13 Stress/Thinking NS F(1,204.93) = 1.55, p = .22 Adjust Behavior Overall NS Overall NS Overall NS F(1,171.08) = .07, p = .79 F(2,199.36) = .069, p = .93 F(4,193.42) = 1.80, p = .13 Stress > Thinking F(1,181.06) = 3.25, p = .07 STATES Maintain Focus Reactive < Proactive Overall NS Overall NS F(1,172.27) = 13.86, p = .00 F(2,207.82) = .22, p = .81 F(4,203.00) = .61, p = .66 Anxiety Reactive > Proactive Overall Significant Overall Significant F(1,171.28) = 8.74, p = .00 F(2,204.11) = 3.85, p = .02 F(4,197.16) = 5.14, p = .00 Dynamic > Coordinative (p = .02) Work Stress > Thinking F(1,190.14) = 18.58, p = .00 Frustration Reactive > Proactive Overall Significant Overall Significant F(1,173.42) = 22.30, p = .00 F(2,208.78) = 3.99, p = .02 F(4,201.46) = 3.68, p = .01 Dynamic > Coordinative Work Stress > Thinking OVERALL Adaptation Overall NS Overall NS Overall NS F(1,152.74) = 1.53, p =.22 F(2,182.63) = 1.33, p = .27 F(4,179.67) = 1.21, p = .31

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Table 32. Supplementary Analyses Variance Accounted For

DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) 2 Model 1) R2 Variance* Detection Time Reactive vs. Mixed vs. 0.19 0.16 0.8421053 0.158 15.79 Proactive Type of Complexity 0.19 0.18 0.9473684 0.053 5.26 Adaptive Dimension 0.19 0.18 0.9473684 0.053 5.26 Situation Assessment Reactive vs. Mixed vs. 0.68 0.67 0.9852941 0.015 1.47 Effectiveness Proactive Type of Complexity 0.68 0.68 1.0000000 0.000 0.00 Adaptive Dimension 0.68 0.67 0.9852941 0.015 1.47 Time to Plan Type of Complexity 0.96 0.97 1.0104167 -0.010 -1.04 Adaptive Dimension 0.96 0.96 1.0000000 0.000 0.00 Contingency Planning Type of Complexity 1.21 1.21 1.0000000 0.000 0.00 Adaptive Dimension 1.21 1.20 0.9917355 0.008 0.83 Reactive Strategic Planning Type of Complexity 1.08 1.07 0.9907407 0.009 0.93 Adaptive Dimension 1.08 1.09 1.0092593 -0.009 -0.93 Planning & Strategy Selection Reactive vs. Proactive 0.64 0.66 1.0312500 -0.031 -3.13 Effectiveness Type of Complexity 0.64 0.62 0.9687500 0.031 3.13 Adaptive Dimension 0.64 0.63 0.9765625 0.023 2.34 Stress vs. Thinking 0.64 0.66 1.0234375 -0.023 -2.34 Creative vs. Other 0.64 0.62 0.9687500 0.031 3.13 Coping Strategy Effectiveness Reactive vs. Other 0.54 0.54 1.0000000 0.000 0.00 Type of Complexity 0.54 0.54 0.9925926 0.007 0.74 Component vs. Other 0.54 0.53 0.9888889 0.011 1.11 Stress vs. Thinking 0.54 0.54 0.9925926 0.007 0.74 Execution & Evaluation Reactive vs. Proactive 0.69 0.73 1.0579710 -0.058 -5.80 Effectiveness Type of Complexity 0.69 0.68 0.9797101 0.020 2.03 Adaptive Dimension 0.69 0.69 0.9942029 0.006 0.58 Stress vs. Thinking 0.69 0.70 1.0144928 -0.014 -1.45

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Table 32 (cont’d)

DV IV Model 1 Model 2 Ratio (Model 2/ Pseudo % (null) 2 (constrained) 2 Model 1) R2 Variance* Adjust Thinking Reactive vs. Proactive 0.99 1.00 1.0101010 -0.010 -1.01 Type of Complexity 0.99 0.95 0.9595960 0.040 4.04 Adaptive Dimension 0.99 0.98 0.9858586 0.014 1.41 Stress vs. Thinking 0.99 0.97 0.9838384 0.016 1.62 Adjust KSAs Reactive vs. Proactive 1.22 1.24 1.0163934 -0.016 -1.64 Type of Complexity 1.22 1.19 0.9754098 0.025 2.46 Adaptive Dimension 1.22 1.22 1.0000000 0.000 0.00 Stress vs. Thinking 1.22 1.26 1.0327869 -0.033 -3.28 Adjust Feelings Reactive vs. Proactive 1.22 1.30 1.0655738 -0.066 -6.56 Type of Complexity 1.22 1.22 1.0000000 0.000 0.00 Adaptive Dimension 1.22 1.22 1.0000000 0.000 0.00 Adjust Behavior Reactive vs. Proactive 0.80 0.89 1.1125000 -0.113 -11.25 Type of Complexity 0.80 0.81 1.0125000 -0.013 -1.25 Adaptive Dimension 0.80 0.79 0.9875000 0.013 1.25 Stress vs. Thinking 0.80 0.75 0.9375000 0.063 6.25 Maintain Focus Reactive vs. Proactive 0.60 0.52 0.8666667 0.133 13.33 Type of Complexity 0.60 0.61 1.0166667 -0.017 -1.67 Adaptive Dimension 0.60 0.62 1.0333333 -0.033 -3.33 Anxiety Reactive vs. Proactive 1.14 1.17 1.0263158 -0.026 -2.63 Type of Complexity 1.14 1.12 0.9824561 0.018 1.75 Adaptive Dimension 1.14 1.06 0.9298246 0.070 7.02 Stress vs. Thinking 1.14 1.07 0.9385965 0.061 6.14 Frustration Reactive vs. Proactive 1.49 1.42 0.9530201 0.047 4.70 Type of Complexity 1.49 1.46 0.9798658 0.020 2.01 Adaptive Dimension 1.49 1.42 0.9530201 0.047 4.70 Overall Adaptation Reactive vs. Proactive 0.42 0.44 1.0357143 -0.036 -3.57 Effectiveness Type of Complexity 0.42 0.42 1.0000000 0.000 0.00 Adaptive Dimension 0.42 0.43 1.0142857 -0.014 -1.43

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Table 33. Summary of Event-Level Relationships

Situational Factors Reactive versus Proactive Type of Complexity Change Adapt Performance Dimension Reactive events [compared Dynamic events [compared to Work stress events to proactive events] result component or coordinative [compared to “thinking” Phase in... events] result in... events] result in... SA  Longer detection  Shorter detection times  Longer detection times times [component]  More threatened  More threatened  More threatened appraisals appraisals appraisals [coordinative]  More challenged appraisals [coordinative] Degree of  Less adjustment of  More adjustment of KSAs  Less adjustment of Adaptation KSAs [coordinative] KSAs Required  Less adjustment of  More adjustment of  Less adjustment of thinking thinking [coordinative] thinking  More adjustment of behaviors P&SS  Less contingency  No differences  Less contingency (Plan) planning planning  More reactive strategic planning P&SS  More exploitation  Less exploitation [both]  More exploitation (Strategy)  Less exploration  Less increase effort  Less exploration  More withdraw [component]  Less stay the course  Less stay the course  More increase effort [coordinative]  More stay the course [both]  Reactions  Less attentional focus  More anxiety  More anxiety  More anxiety [coordinative]  More frustration  More frustration  More frustration [coordinative]

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Table 33 (cont’d)

Reactive versus Proactive Type of Complexity Change Adapt Performance Dimension Reactive events [compared Dynamic events [compared to Work stress events to proactive events] result component or coordinative [compared to “thinking” Phase in... events] result in... events] result in... E&E  Less effective  [Component] Least  Less effective coping behavioral strategies effective behavioral and strategies  Less effective coping coping strategies  Less positive behavior strategies  [Coordinative] Most impact effective behavioral and coping strategies Phase &  Less effective P&SS  Less effective P&SS  Less effective P&SS Overall [coordinative]  Less effective E&E Effective  More effective P&SS [component]

215

Table 34. Example Snippets from Collected Stories Reflecting Different Event Types

Example Snippet Reactive vs. Type of Adaptive Time Scale Able to Proactive Complexity Performance Determine Dimension Effectiveness? “Data analyses conducted by other team member on Reactive Coordinative Solving Medium No, started general data were not as successful as anticipated...thought Problems (days) follow up about potential follow up analyses.” Creatively analyses, but results are TBD “....final software features we need to add to a project...I Reactive Component Handling Medium Mixed, prepared was resigned to the fact that I needed to work some extra Work Stress (same day) good list, but no hours to complete this task as well as the proposal work.” customer feedback yet “...the content of the meeting surprised us; rather than Reactive Dynamic Handling Short Yes, elicited starting with the typical round of updates, the prime Work Stress/ (immediate), good contractor person introduced the SME and asked our Dealing with information internal team to do a knowledge elicitation session...we Uncertainty even with the had no clue this was coming!” short time scale “...this was a case where I could work ahead to make sure I Proactive Coordinative Dealing with Long Yes, effectively got enough tasking, to avoid me realizing I had nothing to Uncertainty (weeks) planned tasking do while I was [working from home] and many other proactively for people are on holiday leave...I thought about next the rest of steps...brainstormed additional tasking...” month “a planned maintenance event...the need to change our Mixed Coordinative Dealing with Medium Yes, effective strategy became apparent because we were approaching a Uncertainty (days) progress was time constraint. It’s actually not unusual with this type of made that maintenance to encounter issues, but the time constraint averted along with unexpected issues forced a change in strategy.” disruption of business “...I realized that I was not as far along with developing a Proactive Component Handling Long No, it was not technical approach as I thought, and that there was still a Work Stress (weeks) yet clear how lot more work to be done this week...I need to work out the effective their current kinks in a way that will prevent disaster later on behaviors would and leave us with a strong proposal.” be

216

APPENDIX H

Figures

ADAPTIVE CYCLE

Situation Assessment OUTPUTS INPUTS Individual Factors Performance  Cognitive Ability  Self‐rated  Goal Orientation  Objective  Adaptability Cognitive, Learning  Perceived Autonomy Motivational  What have I & Affective Situational Factors States learned?  Adaptive Performance  What strategies are Planning & Execution & most effective? Dimension Strategy Evaluation  Type of Complexity Selection  Reactive vs. Proactive

Figure 1. Full dynamic process heuristic of the individual adaptation process

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Situation Planning & Execution & Assessment Strategy Selection Evaluation

Cue Proactive Detection • Contingency Strategy Potential Planning Execution Opportunity • Strategy Selection Scanning Diagnose

Existing Threat Reactive • Reactive Strategic Strategy Appraisal Planning No Evaluation (Relevant?) • Strategy Selection

Cognitive, Affective, and Motivational States

Figure 2. Heuristic of the adaptive cycle phases

218

Figure 3. Multiple adaptive event cycle heuristic

219

N = # of participants opening the survey (# of participants providing an event for that day)

Figure 4. Participant flow chart

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Figure 5. Representational model of key study variables

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