Table 12. ANOVA Regression Analysis Between Total PCQ and Active Job. 80. Search Behaviors .... The Psychological Capital Questionnaire, or PCQ,. (Luthans ...... the Cronbach alphas for each of the four six-item adapted measures and the.
A QUANTITATIVE STUDY OF THE CORRELATIONAL IMPACT OF PSYCHOLOGICAL CAPITAL ON JOB SEARCH INTENSITY AS MEASURED BY JOB SEARCH BEHAVIORS by Michael I. Oglensky PHILLIP RANDALL, PhD, Faculty Mentor and Chair RICHARD LIVINGOOD, PhD, Committee Member RAJ SINGH, PhD, Committee Member Barbara Butts Williams, PhD, Dean, School of Business and Technology
A Dissertation Presented in Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy
Capella University July 2013
UMI Number: 3588173
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© Michael Oglensky, 2013
Abstract High levels of unemployment have resulted from the recent economic downturn. Job search related research has been limited with respect to the impact of psychological capacities in relation to job search intensity. The purpose of this non-experimental quantitative study was to test the theory of Psychological Capital (PsyCap) that relates PsyCap to preparatory and active job search behaviors. This study focused on active job searchers receiving outplacement services at an international organization for outplacement services. Two research hypotheses were tested: the four subscales of the psychological capital assessment do predict preparatory job search behaviors and the four subscales of the psychological capital assessment do predict active job search behaviors. The findings from an analysis of the results from this study did not provide results that were statistically significant in support of the first hypothesis. The results from the second hypothesis, however, reflected statistical significance in support of the second hypothesis. This study expands the research on positivity as it relates to job search intensity and contributes to the field of Organization and Management by analyzing the impact of PsyCap on job search intensity. The identification of job searchers’ PsyCap provides an opportunity to expand on the theoretical framework of positive psychology as it relates to the reemployment process. PsyCap is also “developable” (Luthans & Youssef, 2007), which provides a basis for unemployment consultants in strengthening job searchers’ overall PsyCap and increasing their intensity levels. This strengthening of job searcher PsyCap should facilitate faster return to employment.
Dedication This dissertation is dedicated to my wife, Stacy for her support, encouragement, and loving devotion. Thank you for being my life’s partner and primary supporter in the accomplishment of this enlightening journey.
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Acknowledgments I would like to acknowledge and thank the following people who provided the support, guidance, and partnership required for a successful completion of this dissertation. Dr. Phillip Randall, my mentor and committee chair, provided the highest level of support and encouragement throughout out this journey. His ongoing challenges provided me with phenomenal opportunities to stretch my cognitive boundaries with respect to this dissertation study. Not only did Dr. Randall provide the necessary challenges but also allowed me the freedom to think and respond creatively to these challenges. My committee members, Dr. Livingood and Dr. Singh, thank you so much for the constructive and candid feedback provided in response to this dissertation. Your questions and comments caused me to reflect critically throughout the development of this dissertation. I would like to thank Joe Dougherty, a lifelong friend, and Anita Gardner. Thank you both for providing me with access to the active job searchers receiving support from your organization, which was instrumental to the success of this research project. Finally, I would like to thank my sister, Dr. Bonnie Oglensky, for all of her support, guidance, and encouragement throughout this scholarly endeavor.
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Table of Contents Acknowledgments
iv
List of Tables
viii
List of Figures
ix
CHAPTER 1. INTRODUCTION
1
Introduction to the Problem
1
Background of the Study
3
Statement of the Problem
5
Purpose of the Study
5
Rationale
6
Research Questions and Hypotheses
6
Significance of the Study
7
Definition of Terms
8
Assumptions and Limitations
10
Theoretical Framework
12
Organization of the Remainder of the Study
14
CHAPTER 2. LITERATURE REVIEW
15
Introduction
15
Psychological Capital
16
Introduction to Positive Organizational Scholarship and Positive Organizational Behavior
20
Psychological Capital
23
Theoretical Basis of PsyCap Subconstructs
26
v
Psychological Capital Research
30
Summary
34
The Job Search Process
35
Summary and Conclusion
42
CHAPTER 3. METHODOLOGY
44
Introduction
44
Research Questions and Hypotheses
45
Research Design
45
Population/Sample
48
Instrumentation/Measures
51
Data Collection
53
Data Analysis
54
Validity and Reliability
56
Ethical Considerations
57
Summary
58
CHAPTER 4. RESULTS
59
Introduction
59
Description of the Population and Sample
60
Summary of Results
61
Details of Analysis and Results
62
Descriptive Statistics
63
Pearson Correlation Results
72
Regression Analysis: PCQ Subscales and Job Search Behaviors
74
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Regression Analysis: Total PCQ and Job Search Behaviors
78
Summary
80
CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS
82
Introduction
82
Discussion of Results
83
Implications of the Study Results
86
Limitations
89
Recommendations for Future Study
90
Conclusion
92
REFERENCES
95
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List of Tables Table 1. Age and Gender of Participants
61
Table 2. Cronbach’s Alpha for PCQ and Job Search Behaviors
63
Table 3. Descriptive Statistics: Total PCQ and Subscale Scores
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Table 4. Descriptive Statistics: Job Search Behaviors, Preparatory Job Search Behaviors, and Active Job Search Behaviors
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Table 5. Correlation Results: PCQ Subscales, Preparatory Job Search Behaviors, And Active Job Search Behaviors
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Table 6 . ANOVA Regression Analysis Between PCQ Subscales and Preparatory Job Search Behaviors
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Table 7. Coefficients Regression Analysis Between PCQ Subscales and Preparatory Job Search Behaviors
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Table 8. ANOVA Regression Analysis Between PCQ Subscales and Active Job Search Behaviors
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Table 9. Coefficients Regression Analysis Between PCQ Subscales and Active Job Search Behaviors
78
Table 10. ANOVA Regression Analysis Between Total PCQ and Preparatory Job Search Behaviors
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Table 11. Coefficients Regression Analysis Between Total PCQ and Preparatory Job Search Behaviors
79
Table 12. ANOVA Regression Analysis Between Total PCQ and Active Job Search Behaviors
80
Table 13. Coefficients Regression Analysis Between Total PCQ and Active Job Search Behaviors
80
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List of Figures Figure 1. Conceptual Framework
13
Figure 2. Total PCQ Scores
65
Figure 3. Self-efficacy Scores
66
Figure 4. Hope Scores
67
Figure 5. Resiliency Scores
68
Figure 6 Optimism Scores
69
Figure 7. Total Job Search Behaviors Scores
70
Figure 8. Preparatory Job Search Behaviors Scores
71
Figure 9. Active Job Search Behaviors Scores
72
Figure 10. PCQ Subscales
75
Figure 11. Scatter Plot for Total PCQ and Active Job Search Behaviors
76
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CHAPTER 1. INTRODUCTION Introduction to the Problem Over the past five years the United States has experienced one of the most dramatic economic downturns since the depression of the 1930s, resulting in high levels of unemployment along with nearly nonexistent job growth. Businesses are being challenged with continuous change and competitive pressure to provide products and services faster, cheaper, and better. Corporate downsizing has become a common method for cutting costs and improving profitability (Maertz, Wiley, LeRogue, & Campion, 2010): “more than 6.5 million jobs have been downsized” (p. 282) in the United States since 2007 (Datta, Guthrie, Basuil, & Pandey, 2010). The path to reemployment involves an active job search process—which can be lengthy, unpredictable, and unsuccessful— and job search intensity is a key predictor in one’s ultimate reemployment success (Wanberg, Kanfer, & Rotundo, 1999). This research study explores the impact of psychological capital on job search intensity, as measured by job search behaviors. Prior research related to the job search process has been largely based on the impact of cognitive job-seeking skills and networking competencies as they relate to job search behaviors (McArdle, Waters, Briscoe, & Hall, 2007). These studies focused primarily on the impact of human capital (know-how) and social capital (social network connections). More recent research examines the psychological impact of job search intensity as measured by job search behaviors (Kanfer, Wanberg, & Kantrowitz, 2001). According to Vinokur and Schul (2002), most of the earlier psychologically oriented research concerned the negative impact of unemployment on psychological and physical health. However, with the advent of the positive psychology movement (Seligman & 1
Csikszentmihalyi, 2000), research regarding the job search process began to focus on positive psychological capacities rather than negative dysfunction. Luthans, Luthans, and Luthans (2004) explored the impact of positive psychology on the job search, focusing primarily on self-efficacy and optimism. They argued that there was a positive relationship between the constructs of self-efficacy and optimism and one’s return to employment, a suggestion that was supported by significant prior research (Shamir, 1986; Winefield, Tiggemann, & Winefield, 1992; Eden & Aviram, 1993; Wanberg, 1997; Kanfer, Wanberg, & Kantrowitz, 2001; Waters & Moore, 2002; Crossley & Stanton, 2005; McArdle, Waters, Briscoe, & Hall, 2007). Taking this argument further, Fleig-Palmer, Luthans, and Mandernach (2009) proposed that a positive state of resiliency could successfully guide job searchers toward reemployment. They suggested their proposed theoretical framework would serve as a starting point for future empirical research focusing on positive approaches leading to reemployment. The following study expands the research on positivity as it relates to job search intensity and contributes to the field of Organization and Management by analyzing the impact of psychological capital (PsyCap) on job search intensity. PsyCap is defined as a state-like, higher level core construct consisting of four primary sub constructs: selfefficacy, optimism, hope, and resilience (Luthans, Youssef, & Avolio, 2007). PsyCap is considered state-like due to its ability to be developed, unlike trait-like capacities, which are less open to development. It has been empirically demonstrated that the resultant psychological capacity generated by the interaction of the four sub constructs is greater than each construct alone (Luthans & Youssef, 2007).
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As described earlier, prior research related to positive psychology and its impact on the job search process focused primarily on the individual constructs of self-efficacy and optimism. PsyCap’s addition of hope and resilience, along with the synergistic effects of all four sub constructs, provides an opportunity to expand the theoretical framework regarding positivity as it relates to the reemployment process. Furthermore, as PsyCap is considered developable, it can be strengthened through short-term micro training initiatives (Luthans & Youssef, 2007), and prior research supports positive performance results as an outcome of such initiatives (Luthans, Avey, & Patera, 2008; Luthans, Avey, Avolio, & Peterson, 2010). The results of the following study will provide unemployment consultants with a theoretical and practical basis that will allow them to focus on the identification, development, and strengthening of job searchers’ PsyCap, resulting in increased job search intensity and a faster return to employment. Background of the Study Positivity is not a new field of exploration, and many organizational scholars have studied the relationship between positivity and work performance (Seligman, 1998; Seligman & Csikszentmihalyi, 2000; Diener, 2000; Peterson, 2000; Snyder, 2000; Cowen & Kilmer, 2002; Dutton, 2003; Cameron & Caza, 2004; Spreitzer & Sonenshien, 2004; Luthans, 2002; Luthans & Youssef, 2007; Youssef & Luthans, 2007; Luthans, Avolio, Avey, & Norman, 2007; Bakker & Schaufeli, 2008). Luthans (2002) believed that “positively oriented human strengths and psychological capacities” (p. 59) could yield an improved level of individual performance. The discipline of Positive Organizational Behavior (POB) focuses at the micro level on positive human strengths and psychological capacities, which can be further developed and improved through brief training initiatives 3
(Luthans, 2002). While POB is a new discipline, its theoretical basis is rooted in prior organizational studies related to positive psychological capacities, which include the four PsyCap sub constructs, as well as confidence, subjective well-being, and emotional intelligence. PsyCap has been found to determine one’s psychological state of development (Luthans & Youssef, 2007) and is viewed as a “measurable second-order, core construct” (Luthans & Avolio, 2009, p. 300). The Psychological Capital Questionnaire, or PCQ, (Luthans, Youssef, & Avolio, 2007) consists of 24 questions—6 questions per sub construct—and is a measure of PsyCap used to identify employee PsyCap levels and ultimately predict level of performance. According to Luthans, Norman, Avolio and Avey (2008), PsyCap is concerned with “who you are and what you can become” (p. 223). With the recent theoretical shift regarding positive psychology, the job search, and ultimate reemployment, limited research has explored the impact of PsyCap on the job search process. A recent study by Chen and Lim (2012) is one of the first to explore PsyCap and the job search. The authors examine PsyCap as a predictor of coping resources and strategies and their impact on job search behaviors. Even following recent published works (Fleig-Palmer et al., 2009; Chen & Lim, 2012) pertaining to PsyCap and the job search process, there is still a gap in the research as it relates to the impact of psychological capital on job search intensity as measured by job search behaviors. This study extends the present state of existing research as it relates to PsyCap and job search intensity.
4
Statement of the Problem The recent economic downturn along with increased corporate focus on globalization, outsourcing, and downsizing has resulted in a significant level of unemployment in the United States. “More than 6.5 million jobs have been downsized” (p. 282) in the United States since 2007 (Datta et al., 2010). Even though the rate of unemployment has recently declined, these statistics can be misleading Published unemployment rates include only individuals in transition and actively engaged in a job search (Coy, 2012). Rates do not include students; prisoners; discouraged inactive job searchers; the self-employed; the disabled; and the underemployed, those working fewer hours or in a role beneath their capabilities (Krueger & Katz, 1999). Of critical importance to a successful return to work is a job searcher’s level of job search intensity. Blau (1994) referred to job search intensity as the frequency and scope of both preparatory and active job search behaviors. Purpose of the Study The purpose of this non-experimental, quantitative study is to test the theory of psychological capital (PsyCap) by exploring the impact of PsyCap on preparatory and active job search behaviors. This study will focus on active job searchers receiving outplacement services at an international organization for outplacement services. PsyCap will be generally defined as: an individual’s positive psychological state of development and is characterized by: (1) having confidence (self efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (2) making a positive attribution (optimism) about succeeding now and in the future; (3) persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and (4) when beset by problems and adversity, sustaining and bouncing back and even beyond (resilience) to attain success. (Luthans et al., 2007, p. 3) 5
Preparatory job search behaviors will generally be defined as those dealing with the gathering of job related information, while active job search behaviors will be defined as those focusing on job search activation related activities (Blau, 1994). Rationale The design for this study is a correlational non-experimental, one-shot quantitative model. This approach is suitable for the types of interval-level data that will be collected to both measure (Creswell, 2009) and assess the association between the independent and dependent variables and at the same time determine whether statistically significant relationships are evident (Cozby, 2009; Creswell, 2009; Vogt, 2007). Linear relationships involving two variables can be successfully measured though the use of a correlational design (Creswell, 2009). A correlation signifies numerically what type of relationship exists between variables (Trochim & Donnelly, 2007). Furthermore, by measuring the correlative relationship between psychological capital and preparatory and active job search behaviors, this design is appropriate for focusing on the research problem and research questions pertaining to this study. Research Questions and Hypotheses This study will focus on the following research questions: RQ1: Do the four subscales of the psychological capital assessment predict preparatory job search behaviors? RQ2: Do the four subscales of the psychological capital assessment predict active job search behaviors? The associated hypotheses are as follows:
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Ho1: The four subscales of the psychological capital assessment do not predict preparatory job search behaviors. Ha1: The four subscales of the psychological capital assessment do predict preparatory job search behaviors. Ho2: The four subscales of the psychological capital assessment do not predict active job search behaviors. Ha2: The four subscales of the psychological capital assessment do predict active job search behaviors. Significance of the Study Prior research regarding the job search emphasized the relationship between human and social capital and reemployment, while studies regarding positivity and the job search process focused primarily on self-efficacy and optimism. This dissertation will contribute to the field of Organization and Management by expanding the body of knowledge regarding psychological capital and its relationship to the job search. The introduction of hope and resilience—and the resulting efficacy of the interaction between all four PsyCap sub constructs—will be studied in relation to job search intensity, as measured by job search behaviors. Furthermore, PsyCap’s synergistic focus on all four sub constructs provides an opportunity to expand on the theoretical framework of positive psychology as it relates to the reemployment process. Job searchers will appreciate the significance of PsyCap as it relates to their reemployment process. Since PsyCap can be measured, developed, and assessed relative to its impact on performance, this study will provide a basis for unemployment
7
consultants to focus on strengthening job searchers’ PsyCap, leading to increased levels of job search behaviors and faster reemployment (Luthans & Youssef, 2004). Definition of Terms Hope: “A positive motivational state that is based on an interactively derived sense of successful (a) agency (goal directed energy) and (b) pathways (planning to meet goals)” (Snyder, Irving, & Anderson, 1991, p. 287). Human Capital: “Education, experience, and implicit knowledge of human resources” (Luthans et al., 2010, p. 42). Job Search Behaviors: “The specific activities through which effort and time are spent on job search” (Blau, 1993, p. 315). Job Search Intensity: “The effort which individuals make during their job search” (Blau, 1994, p. 288). Optimism: “A mood or attitude associated with an interpretation about the social or material future—one that the evaluator regards as socially desirable to his [or her] advantage, or for his [or her] pleasure” (Tiger, 1979, p. 18). Positive Organizational Behavior: “The study and application of positively oriented HR strengths and psychological capacities that can be measured, developed, and effectively managed for performance improvement in today’s workplace” (Luthans, 2002, p. 59). Psychological Capital (PsyCap): “An individual’s positive psychological state of development that is characterized by: (1) having confidence (self efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (2) making a positive attribution (optimism) about succeeding now and in the future; (3) 8
persevering towards goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and (4) when beset by problems and adversity, sustaining and bouncing back and even beyond (resiliency) to attain success” (Luthans et al., 2007, p. 3). Positive Psychology: “A science of positive subjective experience, positive individual traits, and positive institutions promises to improve quality of life and prevent the pathologies that arise when life is barren and meaningless” (Seligman & Csikszentmihalyi, 2000, p. 5). Resilience: “The positive psychological capacity to rebound or bounce back from adversity, conflict, failure, or even positive vents, progress, and increased responsibility” (Luthans, 2002, p. 702). Self-Efficacy: “The individual’s convictions or confidence about his or her abilities to mobilize the motivation, cognitive resources, and courses or action needed to successfully execute a specific task within a given context” (Stajkovic & Luthans, 1998, p. 66). Social Capital: “A variety of entities with two elements in common: they all consist of some aspect of social structure, and they facilitate certain actions of actors...within the structure” (Portes, 1998, p. 2). State-like Capacities: “ Relatively malleable and open to development; the constructs could include not only efficacy, hope, resilience, and optimism, but also a case has been made for positive constructs such as wisdom, well-being, gratitude, forgiveness, and courage as having state-like properties as well” (Luthans, Avolio, Avey, and Norman, 2007, p. 544). 9
Trait-like Capacities: “Relatively stable and difficult to change; represents personality factors and strengths. Examples could include the Big Five personality dimensions, core self-evaluations, and character strengths and virtues (CSV)” (Luthans et al., 2007, p. 544). Assumptions and Limitations The following assumptions are based on prior works related to positive psychological capital and job search intensity, as measured by job search behaviors and will be taken for granted with this study. 1. As proposed by Luthans et al. (2004), there is a relationship between the positive psychological constructs of self-efficacy and optimism and one’s return to employment. This assumption is supported by significant prior research (Shamir, 1986; Winefield, Tiggemann, & Winefield, 1992; Eden & Aviram, 1993; Wanberg, 1997; Kanfer, Wanberg, & Kantrowitz, 2001; Waters & Moore, 2002; Crossley & Stanton, 2005; McArdle, Waters, Briscoe, & Hall, 2007). 2. As argued by Fleig-Palmer et al. (2009), a positive state of resiliency can successfully guide job searchers toward reemployment. 3. PsyCap, serving as a “higher-level” core construct, will provide the synergistic impact of all four sub constructs: self-efficacy, optimism, resilience, and hope. 4. Unemployed active job searchers have a goal to become employed. 5. Based on this study’s positive psychology theoretical framework, PsyCap is assumed present within unemployed active job searchers. 10
6. The use of the proposed web-based questionnaire instrument is reliable and valid. 7. Survey questions are clear and understandable. 8. Question responses are honest and accurate.
According to Creswell (2009), a research study’s limitations reveal potential design weakness that might be related to its methodological approach for data collection and analysis. Delimitations narrow the study’s scope to include participants, sites, and variables (Creswell, 2009). 1. Data collection measures were not specific enough for unemployed individuals and current job search methods. Minor modifications to question language were made; such modifications were also made in a prior study (Chen & Lim, 2012). 2. Survey is being conducted with a single, one-shot sample. Furthermore, the use of only a single outplacement organization’s participants presents generalization issues related to bias, with survey participant responses being representative of the study’s population. The outplacement organization will consider including other participants from partner organizations. 3. Lack of participation incentive of perceived value. Introducing an incentive of value was considered. 4. Low survey response rate is a concern. Potential participants may be re -invited with a follow-up email or link. 5. A delimitation of this study is the use of a single outplacement organization with candidates from its metropolitan area. Other outplacement partner organizations were considered for inclusion to broaden the geographic scope of this study. 11
Theoretical Framework This study focuses on the constructs of psychological capital (PsyCap) and job search intensity, as measured by preparatory and active job search behaviors. Early theorists such as Maslow felt that psychology had a negative, and thus imbalanced, emphasis, and as a result called for the development of a more positive perspective (Avey, Luthans, Smith, & Palmer, 2010), but the Positive Psychology movement—in which PsyCap has its theoretical basis—did not emerge until the late 20th century. The Positive Psychology movement, which was primarily influenced by Seligman and Csikszentmihalyi (2000), changed the focus of psychology from looking at what was wrong with people to a positive emphasis on human flourishing and growth development (Luthans et al., 2010). Beginning with the works of Seligman and Csikszentmihalyi (2000), which explored the positive constructs of hope, optimism, subjective well-being, and individual happiness and development, two organizational branches emerged: Positive Organizational Scholarship (POS) and Positive Organizational Behavior (POB). POS engages on a macro level with positivity as it relates to optimizing human performance within an organization. In contrast, POB functions at the micro level, exploring positive human strengths and capacities that can be measured and developed (Luthans, 2002). The construct of PsyCap resides within POB as a state-like higher-level core construct that can be developed and changed and can ultimately affect an individual’s performance (Luthans, 2002). Job search intensity is rooted in the behaviorist theoretical framework that focuses on the level of job search intensity as it relates to preparatory and active job search 12
behaviors (Blau, 1994). Those behaviors emphasize how and what activities job searchers focus their efforts on. Preparatory job search behaviors deal with the gathering of job related information, while active job search behaviors focus on job search activation related activities (Blau, 1994). Figure 1 provides a graphical model of this study’s conceptual framework that is consistent with the research questions and related hypotheses attempting to demonstrate a correlative relationship between PsyCap (independent variable) and job search behaviors (dependent variable). Conceptual Framework
Change in PsyCap
Change in Job Search Intensity
Subscales: Selfefficacy Optimism Hope Resiliency
Job Search Behaviors: Preparatory Active
Change in PsyCap
Change in Job Search Intensity
Figure 1: Conceptual framework suggesting a correlative relationship between PsyCap and job search behaviors.
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Organization of the Remainder of the Study The content of this study will be covered in five chapters. Chapter 1 provides an introduction framing the research context as it relates to the topic of this study. Following this will be the background of the study, statement of the problem, purpose of the study, rationale, research questions and hypotheses, significance of the study, definition of terms, limitations and assumptions, and theoretical framework. The above establishes the context and significance of this study. Chapter 2 (literature review) provides a synopsis and analysis of existing research related to this study’s topic. This review begins with an introduction to psychological capital (PsyCap) and a review of its theoretical foundation, as a means of establishing the basis for this study. Chapter 2 also includes an analysis and evaluation of PsyCap as a higher order construct; its theoretical foundation originating from positive psychology; its connection with positive organizational scholarship (POS) and positive organizational behavior (POB); the PsyCap sub constructs of hope, optimism, self-efficacy, and resilience; and other related research. An analysis and evaluation of the job search follows, comparing and contrasting traditional versus current theoretical methodologies. Chapter 3 covers this study’s research methodology, focusing on research design, data collection and analysis, and ethical considerations. Chapter 4 is a presentation of the findings of this study. Chapter 5 contains a summary and discussion of this study’s results, conclusion, and future recommendations.
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CHAPTER 2. LITERATURE REVIEW Introduction This literature review will explore the theoretical and research-related works pertaining to psychological capital (PsyCap) and the job search process. An overview of PsyCap’s theoretical foundations will establish the basis for this study and will detail its relationship to positive psychology; its connection with positive organizational scholarship (POS) and positive organizational behavior (POB); the interaction between the PsyCap subconstructs of hope, optimism, self-efficacy, and resilience; and other related research. An analysis and evaluation of the job search follows, comparing and contrasting traditional versus current theoretical methodologies. This literature review concludes with a summary and final comments. Foundational research regarding the job search process has evolved in relation to three models: sequential (sequential stages), learning (learned behaviors), and emotional response (modified activity as a stress-related emotional response) (Saks & Ashworth, 2000). Most research studies explored the effects of social capital (knowing whom) and human capital (knowing how) as they related to job search intensity and related job search behaviors (Kanfer & Hulin, 1985; Wanberg, Kanfer, & Banas, 2000; Prussia, Fugate, & Kinicki, 2001; Boudreau, Boswell, Judge, & Bretz, 2001; Fugate, Kinicki, & Ashworth, 2004; McKee-Ryan, Song, Wanberg, & Kinicki, 2005; Thompson, 2005; McArdle, Waters, Briscoe, & Hall, 2007). As defined by Blau (1994), job search intensity is “the effort which individuals make during their job search” (p. 288). Successful job search outcomes have been associated with high levels of job search intensity and its related behaviors (Sacks & Ashford, 1999; Kanfer et al., 2001). 15
More recent research analyzed PsyCap capacities and their impact on job search intensity. These studies explored the relationship of self-efficacy (Kanfer & Hulin, 1985; Eden & Aviram, 1993; Wanberg, Kanfer, & Rotundo, 1999; Saks & Ashforth, 1999; Saks & Ashworth, 2000; Vinokur & Schul, 2002; Crossley & Stanton, 2004; Hall & Chandler, 2005; Saks, 2005; Brown, Cober, Kane, Levy, & Shalhoop, 2006; McArdle, Waters, Briscoe, & Hall, 2007), optimism (Scheier & Carver, 1985; Wanberg, 1997; Kanfer, Wanberg, & Kantrowitz, 2001), and resiliency (Wanberg, 1997; Fleig-Palmer, Luthans, & Mandernach, 2009) to the job search. One of the first studies published regarding PsyCap and the job search examined PsyCap’s moderating effect on coping resources and strategies and their ultimate impact on job search behaviors (Chen & Lim, 2012). Numerous studies have empirically validated the impact of PsyCap on performancerelated outcomes (Luthans, Avolio, Avey, & Norman, 2007; Luthans, Norman, Avolio, & Avey, 2007; Luthans, Avey, Smith, & Li, 2008; Peterson, Luthans, Avolio, Walumbwa, & Zeng, 2011; Avey, Reichard, Luthans, & Mhatre, 2011). This dissertation study suggests that the identification, measurement, and development of job searchers PsyCap will enhance job search intensity and behaviors, resulting in faster reemployment. Psychological Capital Theoretical Foundation Before the end of World War II, the discipline of psychology had the goals of healing the mentally ill, improving the health and happiness of already healthy individuals, and assisting in the advancement of actualizing one’s human potential (Luthans, Youssef, & Avolio, 2007). With the traumatic aftereffects of the war, psychology’s main objective became healing the mentally ill. The remaining two 16
regenerative goals of the psychological movement were sacrificed for an emphasis on societal illnesses. Beginning with Maslow (1970), the more positive emphasis of psychology reemerged through a focus on human potential and personal growth (Maslow, 1971). Almost six decades later, the positive psychology movement has experienced significant growth, emphasizing a focus on “subjective experiences or states, positive individual traits, and civic virtues and the institutions which move individuals toward better citizenship” (Seligman & Csikszentmihalyi, 2000, p. 5). With the advent of the positive psychology movement, two new disciplines, Positive Organizational Scholarship (POS) and Positive Organizational Behavior (POB), emerged and are now at the center of most current organizational research related to positive psychology relative to the business domain. PsyCap resides within the POB movement as a “high-order” construct consisting of the subconstructs of self-efficacy, hope, optimism, and resilience (Luthans, Youssef, & Avolio, 2007). Positive Psychology As mentioned earlier, the initial mission of the field of psychology emphasized healing the mentally ill, improving health and happiness, and assisting in the achievement of one’s actualized self (Luthans et al., 2007). Following the end of the World War II, two key initiatives helped redirect the thrust of psychology. The establishment of the Veterans Administration and the National Institute of Mental Health made it financially advantageous for physicians and scholars to treat and research psychology though a disease-based paradigm (Seligman & Csikszentmihalyi, 2000). This resulted in the field adopting a methodology based on human pathology and the disease model in order to treat the ills of society thought to be related to mental health (Seligman & 17
Csikszentmihalyi, 2000). This bottom-up perspective had a preoccupation with what was wrong rather than the development of inherent human psychological capacities. Unfortunately, the discipline of psychology regressed until Abraham Maslow’s (1954) research regarding what was known as humanistic psychology. Maslow considered the father of humanistic psychology, viewed psychology’s single focus on human pathology “a failure of personal growth” (Maslow, 1971, p. 25). He argued that human existence has a biological predisposition toward growth development and the achievement of all that was humanly possible. He considered the quest to be “fully human” or “self-actualized” the ultimate phase in one’s growth and development (Maslow, 1971). Maslow’s behavioral approach to psychology did not wholly avoid the existing perspective; he did, however, argue that the diseased-based model would be appropriate for physical ailments but not sufficient for psychological purposes. Other behavioral psychologists, including Hertzberg (1959) and McGregor (1960), followed Maslow’s seminal explorations in furthering research regarding humanistic psychology in the workplace. Martin Seligman, as president of the American Psychological Association, delivered a speech in 1998 challenging the psychological community to develop a more positive approach to psychology, one focused on human strengths and virtues. This event signified the birth of the positive psychology movement. As defined by Seligman (2000), positive psychology is “a science of positive subjective experience, positive individual traits, and positive institutions promises to improve quality of life and prevent the pathologies that arise when life is barren and meaningless” (Seligman & Csikszentmihalyi, 2000, p. 5). Although positive psychology emphasizes positive 18
strengths, weaknesses should not be ignored but leveraged to optimum capacity (Clifton & Harter, 2003). Critical Opinions The new movement was not without its critics. Bohart and Greening (2001), in a counter-point perspective to Seligman and Csikszentmihalyi (2000), argued that “humanistic psychology did not attract much of a cumulative empirical base, and it spawned myriad therapeutic self-help movements” (p. 81). Furthermore, they argued that the works of Maslow might have been flawed for the same reasons. They also were critical of the authors’ limited consideration of the seminal works of other behavioral psychologists. In a response, Seligman and Csikszentmihalyi (2001) emphasized that their intent was not to treat positive psychology as an “exclusive movement” (p. 89) and provided recognition to other behavioral theorists who preceded them. They also agreed to “bow out with grace” (p. 90), if their positions were not shown to be empirically supported (Seligman & Csikszentmihalyi, 2001). Cowen and Kilmer (2002) argued that the positive psychology movement suffered from the following limitations: isolation from prior works, lack of a cohesive theoretical framework, and a cross-sectional research orientation. Fineman (2006) portrayed the shortcomings of positive psychology as limited in is research methodologies, “separative” in focusing solely on the positive while excluding the negative, insensitive to cultural norms, and designing the positive without boundaries and limitations. Cameron (2008) responded to some of these criticisms by clarifying the connotative meaning of “positive” and the paradoxical characteristics of positive change, which required investigative attention. 19
The next section will provide an overview of two more-recent positive psychology disciplines: Positive Organizational Scholarship (POS) and Positive Organizational Behavior (POB). Introduction to Positive Organizational Scholarship and Positive Organizational Behavior Positive Organizational Scholarship (POS) and Positive Organizational Behavior (POB) branched off from the positive psychology movement in the academic setting. At the University of Michigan, research in POS focused on identifying which positive human characteristics resulted in truly exceptional organizational thriving (Luthans, Youssef, & Avolio, 2007). Conversely, at the University of Nebraska, academic scholars went in a different direction with POB (Luthans et al., 2007). With an emphasis on individual strengths, POB was concerned with “being the best you could be” at an individual level. Positive Organizational Scholarship Dutton (2003) made a call to the psychological community for “breathing life into organizational studies” (p. 5). What she articulated was a need for organizational studies to leave psychology’s negative past behind and advance the exploration of generative human processes as they relate to organizations. POS was the answer to Dutton’s call. With an emphasis on positive deviance, POS focuses on extraordinarily positive human behaviors and conditions that lead to the highest level of organizational performance (Cameron, Dutton, & Quinn, 2003). Positive deviance is defined by Spreitzer and Sonenshein (2004) “as intentional behaviors that improve the human condition” (p. 209). Positive deviance runs counter to the illness-related negative deviance, which was 20
concerned with fixing what was wrong, harmful, and terrible. At a foundational level, the theoretical basis of POS is not new, but it draws from a variety of interdisciplinary scholarly works from the fields of psychology, sociology, and anthropology. More specifically, POS has a “top down,” macro-level focus on organizational thriving resulting from positive human strengths, including dignity, forgiveness, compassion, integrity, virtuousness, and vitality. As defined by Cameron and Caza (2004), Positive organizational scholarship is the study of that which is positive, flourishing, and life giving in organizations. Positive refers to the elevating processes and outcomes in organizations. Organizational refers to the interpersonal and structural dynamics activated in and through organizations, specifically taking into account the context which positive phenomena occur. Scholarship refers to the scientific, theoretically derived, and rigorous investigation of that which is positive in organizational settings. (p. 731) POS as a balanced theoretical framework considers the negative characteristics of organizations as tipping points for bringing out organizational strength and flourishing. Positive Organizational Behavior Emerging from the Gallup Leadership Institute at the University Of Nebraska, Positive Organizational Behavior (POB) developed as another branch of positive psychology (Luthans et al., 2007). Unlike POS’s ultimate focus on organizational thriving, POB emphasizes the identification, measurement, and development of human strengths and capacities in the workplace (Luthans & Youssef, 2007). Similar to POS, the theoretical basis of POB is grounded in prior research and emphasizes continued research and theory development regarding organizations and individual behaviors (Luthans & Avolio, 2009). As defined by Luthans (2002), POB is “the study and application of positively oriented human resource strengths and psychological capacities that can be measured, developed, and effectively managed for performance improvement in today’s 21
workplace” (p. 59). For a psychological capacity to be considered within the POB framework, it must be considered state-like. State-like refers to the ability to develop and change a psychological capacity (Luthans et al., 2007). Conversely, trait-like capacities (unlike state-like capacities) are stable, less open to development, and tend to be used in particular situations. The development of a state-trait continuum reflects on the degrees of capacity stability with the two extremes being either a state or trait (Youssef & Luthans, 2007). This state-like characteristic further theoretically separates POB from POS. POS psychological strengths tend to be viewed as more trait-like, emphasizing individual value without a connection to specific work performance. Operating as “high level” core constructs, several psychological capacities have met the above criteria to be considered POB subconstructs and are thus referred to as comprising Psychological Capital (PsyCap): self-efficacy (confidence), optimism (making positive attributions regarding success), hope (goal-oriented perseverance and intuitiveness), and resilience (capacity to adapt to life events) (Luthans, Avey, Avolio, & Norman, 2007). Critical Opinions Similar to the positive psychology movement, POS and POB were not without their critics. Hackman (2009) articulated six concerns regarding POS and POB: (1) the emphasis of positive organizational scholarship on individual-level phenomena, (2) the ahistorical character of POB research and writing, (3) the construct validity of key concepts, (4) over–reliance on a particular research strategy, (5) implicit acceptance of fundamental flaws in how work and organizations are designed, and (6) the seductiveness of new research paradigms. (p. 309)
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Bakker and Schaufeli (2008) argued that “POB will need to show the added value of positive above negative” (p. 147). Their argument challenged POB to take a more balanced and comprehensive view as far as considering and adding to the research pertaining to the negative as well as “the positive.” This argument furthered those by Fineman (2006) discussed earlier. Luthans and Youssef (2007) responded to arguments made by Fineman (2006) by agreeing that too much emphasis on the positives may provide inaccurate messages, leading to undesirable behaviors—as is the case with the ethical improprieties associated with unprincipled corporate leaders. They also raised concerns related to cultural interpretations of what truly defines negative and positive, respectively. They argued that there is value related to an integrated perspective, considering both the negative with the positive greatly outweighs the alternative of each one alone. Luthans and Avolio (2009) responded to the critics by further clarifying the following: both what POB is and is not, the role of positive psychology, the difference between POS and organizational behavior, what differentiates POB from other positive approaches, the role of the negative, and the importance further research plays in the development of POB. Psychological Capital From the traditional perspective, the competitive capital characteristics of an organization consisted of financial, human, and social capital. Financial capital deals with the financial resources of an organization. Human capital (know what) relates to an individual’s knowledge, skills, and abilities. Social capital (know who) is an individual’s relationships, networks, and culture dynamics (Luthans & Youssef, 2004). These forms of capital were viewed as an organization’s source of competitive advantage; they 23
operated as barriers to entry, due to their unique nature and inability to be copied or imitated. With the increased speed of technology and global growth of design and manufacturing capabilities, maintaining a competitive advantage is much more challenging. Financial and human capital resources are much easier to imitate and have become less of a barrier to entry. PsyCap was seen as being paramount to an organization’s competitive advantage though a realization of the maximum advantage of their human resources (Avolio, 2005). According to Luthans and Youssef (2004), psychological capital (who am I) adds to an organization’s unique sources of competitive advantage and continued performance by emphasizing an individual’s “positively oriented human resource strengths and psychological capacities” (p. 152). Avolio and Luthans (2006) elaborate further with their view of PsyCap as being not only “who I am” but also “what I can become,” thus emphasizing the developmental nature of PsyCap. Empirical research supports the discriminate validity (Bryant & Cvengros 2004; Carifio & Rhodes, 2002; Luthans, Avolio, Avey & Norman, 2006; Magaletta & Oliver, 1999) and convergent validity (Luthans, Avolio, & Walumbwa, 2005; Luthans, Avey, Avolio, & Norman, 2006; Luthans, Avolio, Avey, & Norman, 2006; Youssef, 2004) of each of PsyCap’s subconstructs. Furthermore, the developmental characteristics of PsyCap’s capacities has been demonstrated through brief training initiatives (Luthans, Avey, Avolio, Norman & Coombs, 2006; Luthans, Avey, & Patera, 2008). Hobfoll’s (2002) psychological resources theory indicates that some psychological constructs are best conceptualized as underlying core constructs. Such multidimensional models have been used by other behavioral theorists (Spreitzer, 1995; 24
Judge & Bono, 2001). A conceptual framework linking multiple constructs to a higher level core construct was designed by Law, Wong, and Mobley (1998). This framework was adopted for the creation of the PsyCap model. PsyCap, as defined by Luthans, Youssef, and Avolio (2007), is an individual’s positive psychological state of development and is characterized by: (1) having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (2) making a positive attribution (optimism) about succeeding now and in the future; (3) persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; (4) when beset by problems and adversity, sustaining and bouncing back and even beyond (resilience) to attain success. (p. 3) PsyCap’s synergistic integration of all four capacities together, from an organizational performance perspective, has been proven to have a much greater impact than each capacity individually. The concept of developing a higher-order construct to address positive psychological capacities was also explored by Stajkovic (2006). Similar to the PsyCap model, Stajkovic developed the high-order construct of core confidence, which coincidently consisted of the subconstructs of hope, self-efficacy, optimism, and resilience and was related to elevated levels of performance. He argued that the connections between these subconstructs have not been “discussed as part of a common core” (p. 1209). Furthermore, he argued that he “believe[s] that the conceptual evidence points out that hope, self-efficacy, optimism, and resiliency may share a common confidence core” (p. 1209). To further this theoretical development, Stajkovic suggested that construct validation and empirical testing, along with the development of a measurement scale, would be required. Stajkovic recommended the adoption of measures created by former theorists with regard to hope (Snyder, Sympson, Ybasco, Borders, 25
Babyak, & Higgins, 1996), self-efficacy (Bandura, 1986, 1997), optimism (Seligman, 1998), and resilience (Dyer & McGinness, 1996; Klonhlen, 1996; Rutter, 1986). Stajkovic’s research, although conceptual in nature, provided foundational support for the theoretical basis related to PsyCap. As mentioned earlier, the emergence of PsyCap and its theoretical basis is grounded in the research of prior psychological theorists. A review of foundational research covering each PsyCap sub construct will be covered in the next section. Theoretical Basis of PsyCap Subconstructs Hope Snyder and Anderson (1991) defined hope as a “positive motivational state that is based on an interactively derived sense of successful (a) agency (goal directed energy) and (b) pathways (planning to meet goals)” (p. 287). Luthans and Youssef (2007) referred to agency as “willpower” and pathways as “waypower” (p. 330). As a PsyCap subconstruct, hope incorporates both the agency and pathways leading toward the successful accomplishment of predefined goals. Agency is referred to as motivational energy, with pathways providing various alternatives toward the successful accomplishment of goals (Snyder, 2000). High-capacity levels of hope support faster goal accomplishment though proactively dealing with obstacles and leveraging contingency planning (Snyder, 2000). The capacity of hope has been proven to predict a significant impact on life processes and performance outcomes (Snyder, 2000). To date, there has been limited research related to hope in work settings (Adams, Snyder, Rand, King, Sigmon, & Pulvers, 2002; Jensen & Luthans, 2002; Peterson & Luthans, 2003; Luthans, Avolio, 26
Walumbwa, & Li, 2005). Most research validating the construct of hope focuses on such areas as academics, athletics, and health-related scenarios. In these studies the relationship between hope and more favorable outcomes has been demonstrated (Snyder, 2002). Prior research findings do support hope as meeting the PsyCap criteria of being state-like, measurable, and developable (Snyder, 2000; Snyder, Sympson, Ybasco, Borders, Babyak, & Higgins, 1996). Optimism Tiger (1979) defined optimism as “a mood or attitude associated with an expectation about the social or material future—one which the evaluator regards as socially desirable, to his [or her] pleasure” (p. 18). Scheier and Carver (1985) viewed optimists as being optimistic in general, regardless of the setting, whereas pessimists were generally always negative. Furthermore, they argued that dispositional optimism indicated an expectation that there would be a surplus of positive things and a scarcity of negative things. Their “self-regulatory” model was based on one’s perception of the level of difficulty associated with a goal and whether there was a belief that it could be accomplished. This would result in either an optimistic or pessimistic outcome. Scheier and Carver (1985) also demonstrated that optimism and pessimism operated as two independent behaviors. According to Segerstrom, Taylor, Kemeny, and Fahey (1998), optimists tend to mitigate the impact of performance-related stress and see obstacles to goal accomplishment as being resolvable. Seligman (1998) proposed the defining characteristics of an optimist as seeing misfortune as a challenge and non-controllable, temporary setback. Furthermore, he viewed optimism as being impacted by one’s explanatory style: those who externalized negative events were seen as optimists, 27
whereas those who internalized these events were viewed as pessimistic. Optimists tend to be motivated by goals that appear to have value. Positive events are viewed internally as being more controllable. Optimists’ positive expectancies can come either from within or through external avenues, and the sources of negative occurrences are almost always externalized. Snyder (2002) saw similarities with optimism and hope related to cognations of perceived outcomes. A study by Luthans et al. (2005) demonstrated the relationship between optimism and work performance. Optimism meets all of the PsyCap criteria of being state-like, measurable, and open to development (Luthans & Youssef, 2007). In prior studies, optimism has predicted higher levels of individual performance in a work setting (Chemers, Watson, & May, 2000; Shulman, 1999: Wunderley, Reddy, & Dember, 1998). Self-efficacy Seminal researcher Albert Bandura (1997) argued that a person’s agentic selfregulated beliefs regarding the desired effects of their actions would serve as a major factor with respect to their aspirations, choices, and level of performance-related effort. Such beliefs expand the performance impact of individual agency with the enabling beliefs regarding one’s positive self-efficacy. Bandura (1998) viewed self-efficacy as “the foundation of action” (p. 52). Self-efficacy, as defined by Stajkovic and Luthans (1998b), is “an individual’s conviction (or confidence) about his or her abilities to mobilize the motivation, cognitive resources, and courses or action needed to successfully execute a specific task within a given context” (p. 66). Judge, Erez, and Bono (1998) explored the relationship between positivity and job performance though a trait-based construct consisting of the self-concepts of self-esteem, generalized self28
efficacy, locus of control, and emotional stability. Their findings supported a strong correlation between these four subconstructs as a common construct and job performance. A positive relationship between self-efficacy and work performance has also been supported by other prior research (Sadri & Robertson, 1993; Stajkovic & Luthans, 1998; Judge, Jackson, Shaw, & Rich, 2007), and other research findings reinforce self-efficacy’s satisfaction of PsyCap’s inclusion criteria. Resilience Resiliency, as defined by Luthans (2002), is “the capacity of individuals to cope successfully in the face of significant change, adversity, or risk” (p. 702). This ability to adapt or cope also becomes evident in light of positive occurrences such as winning a significant reward. Luthans (2002) viewed this adaptation as rebounding or bouncing back. Masten (2001) regarded resilience as being a common capacity triggered from human adaptational mechanisms, with these adaptations resulting in positive outcomes from situations that were viewed as threatening. Resilience is considered to be a developable capacity, but in order to develop the capacity of resilience one must experience a threat to one’s adaptation or development (Masten, 2001). In other words, there is a causal relationship between risk and the development of resilience. As Coutu (2002) argued, “resilience is something you realize you have after the fact” (p. 47). Furthermore, she characterized resilient people as having “a staunch acceptance of reality; a deep belief, often buttressed by strongly held values, that life is meaningful; and an uncanny ability to improvise” (p. 48). Research studies related to resilience in organizational settings are limited (Doe, 1994; Horne & Orr, 1998; Mallak, 1998; Luthans, Avolio, Walumbwa, & Li, 2005), with most works pertaining to child 29
development. Prior research has demonstrated the measurement and state-like capacities of resilience (Luthans, Avolio, Avey, & Norman, 2007; Wagnild & Young, 1993) and developmental characteristics (Masten, 2001; Masten & Reed, 2002). Resilience therefore meets PsyCap’s inclusion criteria. Psychological Capital Research Since the development of PsyCap in 2007, significant research has been conducted in relation to PsyCap mostly as an independent variable in the prediction of a variety of outcomes. The Psychological Capital Questionnaire (PCQ), a 24-question survey tool used to measure PsyCap’s four subconstructs (self-efficacy, hope, optimism, and resilience) (Luthans, Youssef, & Avolio, 2007). A meta-analysis conducted by Avey, Reichard, Luthans, and Mhatre (2011) included a total of 51 independent samples from a cross-section of 12,567 employee participants drawn from global organizations. The study adhered to the following inclusion criteria: “(1) PsyCap was quantitatively measured as a composite, core construct, and (2) PsyCap was quantitatively related to one or more outcome variables” (Avey et al., 2011, p. 135). Excluded studies were those that contained only PsyCap theory or calls for additional PsyCap research and studies focusing individually on one or more PsyCap subconstructs. Outcome variables included employee behaviors (desirable/undesirable), attitudes (desirable/undesirable), and employee performance. Included studies were experimental and quasi-experimental (correlational) (Avey et al., 2011). Effect size statistic (r) was used, given the correlational nature of this meta-analysis and the “cutoff value for effect sizes was 0.73. Cronbach’s alpha coefficients ranged from a low 0f .68 to a high of .99” (p. 138-139).
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Results of this meta-analysis supported prior findings in concluding that there were significant relationships between PsyCap levels and the outcomes of desirable employee behaviors, attitudes, and performance (Avey et al., 2011). Negative relationships were also identified between PsyCap and undesirable employee behaviors and attitudes (Avey et al., 2011). Areas for future research were suggested, including studies pertaining to the formation (antecedents) of PsyCap and alternative research methods such as qualitative and mixed methods (Avey et al., 2011). The results of this study provided a strong response to the criticisms mentioned earlier (Bohart & Greening, 2001; Cowen and Kilmer, 2002; Bakker and Schaufeli, 2008; Hackman, 2009) in demonstrating empirically the effectiveness of PsyCap as a predictor of desirable behavioral, attitudinal, and performance-related employee outcomes. The following section provides a high-level overview regarding some of the PsyCap-related research works covered in this meta-analysis. In each of the studies, PsyCap was used as an outcome predictor. Soon after the concept of PsyCap was established, a PsyCap intervention (PCI) was conducted focusing on developing this capacity (Luthans, Avey, Avolio, Norman, & Combs, 2006). Micro training initiatives were implemented with the specific goal of PsyCap development. PCI results supported the synergistic nature of PsyCap’s subconstructs and the developmental effectiveness of the PCI short-term micro training initiatives. Luthans, Avolio, Avey and Norman (2007a) conducted two studies analyzing PsyCap’s subconstructs—individually and together, respectively—with PsyCap serving as a predictor of work-related performance and satisfaction. Results for the first study provided foundational psychometric validation for the PsyCap measure, the PCQ. Cronbach alphas for four samples were as follows: hope 31
(.72, .75, .80, .76); resilience (.71, .71, .66, .72); self-efficacy (.75, .84, .85, .75); optimism (.74, .69, .76, .79); and PsyCap (.88, .89, .89, .89) (Luthans et al., 2007). The second study reinforced the significance of PsyCap as a higher-order core construct in relationship to the prediction of employee performance and satisfaction-related outcomes. The meditating role of PsyCap in relationship to a supportive organizational climate and employee performance was explored by Luthans, Norman, Avolio, and Avey (2007b). Results of this study supported PsyCap’s positive relationship with employee satisfaction, performance, and commitment. Avey, Wernsing, and Luthans (2008) investigated the relationship PsyCap and emotions on work-related attitudes and behaviors, and the results of this study suggested that both PsyCap and positive employee emotions together could counteract negative attitudes and behaviors. In a study testing the cultural implications of PsyCap, Luthans, Avey, Smith, and Li (2008a), a sample of 456 Chinese factory employees were given a survey consisting of the PCQ. Results were analyzed in relation to employee performance ratings and found significant support for PsyCap as a predictor of employee performance. As a follow-up to the PsyCap intervention (PCI) mentioned earlier (Luthans et al., 2006), another micro training initiative was implemented by Luthans, Avey, & Patera (2008b) that utilized web-based technology for PsyCap development. A sample of 187 individuals participated in a 2-hour online training program. Results of this study reinforced positive PsyCap development through micro training initiatives. Furthermore, it validated the effective use of internet-based training tools for PsyCap development. Employees’ stress levels and intentions to voluntarily terminate their employment were explored by Avey, Luthans, and Jensen (2009). Findings of this two-survey-based 32
study indicated a positive relationship between PsyCap and work-related stress; it also found a negative relationship between PsyCap and an employee’s intention to terminate employment. Furthermore, this negative effect influenced job search behaviors in the same manner. Avey, Luthans, and Youssef (2010) conducted a study to explore the relationship between PsyCap and organizational behaviors. Overall, the findings of their study supported the predictive nature of PsyCap as a determinant of employee behaviors. In the first longitudinal study to explore employee PsyCap change over time, Peterson, Luthans, Avolio, Walumbwa, and Zeng (2011) suggested a statically significant, positive causal relationship between PsyCap change and work performance outcomes. Study findings did not determine whether a change in individual PsyCap was predictive of a performance change. This study, however, provided a response to the critics (Bohart & Greening, 2001; Cowen & Kilmer, 2002; Bakker & Schaufeli, 2008; & Hackman, 2009) regarding the emphasis on cross-sectional research designs related to PsyCap studies. One of the first studies investigating the impact of PsyCap on the job search was conducted by Chen and Lim (2012). This study explored the meditational impact of perceived employability and problem-focused coping with regard to PsyCap’s effect on job search behaviors. Using a sample of 179 unemployed individuals, they found PsyCap to be a positive factor in relation to perceived employability, a coping resource further resulting in problem- and symptom-focused coping strategies. Problem-focused coping strategies had a positive relationship with preparatory and active job search behaviors, while symptom-focused coping strategies did not demonstrate this same relationship.
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Summary This literature review pertaining to PsyCap supports the selection of PsyCap and its measure—the PCQ—for this dissertation research study. Changing a paradigm is difficult, as has been demonstrated by the critics throughout the positive psychology movement. These criticisms have been counteracted both in published responses and empirically based research. Both the meta-analysis (Avey et al., 2011) and contributing works (Luthans et al., 2006; Luthans et al., 2007; Avey et al., 2008; Luthans et al., 2008a; Luthans et al., 2008b; Avey et al., 2009; Avey et al., 2010; Peterson et al., 2011; Chen et al., 2012) strongly support a significant predictive relationship between PsyCap and positive outcomes. More specifically, this relationship was supported in some of the above referenced studies relative to work-related performance. The job search can be construed to be work-related performance. The main difference is that unemployed individuals are performing in order to get their next job. Expanding the job search focus from human and social capital presents an opportunity for PsyCap research to look beyond employees in the work setting to the unemployed who desire to return to the work setting. The selection of PsyCap as a predictor was partially due to its criteria for inclusion: measurable, developable, state-like in nature, and connected to individual work performance (Luthans & Youssef, 2004). Furthermore, prior research related to PsyCap as a high-order construct, its subconstructs, and the PCQ have been empirically supported by numerous research studies (Luthans et al., 2006; Luthans et al., 2007; Avey et al., 2008; Luthans et al., 2008a; Luthans et al., 2008b; Avey et al., 2009; Avey et al., 2010; Peterson et al., 2011), all arriving with the same general
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conclusion: significant relationships between PsyCap levels and the outcomes of desirable employee behaviors, attitudes, and performance. The Job Search Process The following section provides an overview of the job search process and its theoretical foundation though the application of three job search models: emotional, learning, and sequential. This section also highlights which capital focus (human or social) applies within each model. The next section will cover job search research focusing on PsyCap subconstructs, job search behaviors, and reemployment. This section will conclude with a summary. Job Search Process The job search is a process “by which individuals identify, investigate, and decide among alternative job opportunities” (Barber, Daly, Giannantonio, & Philips, 1994, p. 739). Three job search process models—emotional, learning, and sequential—will be used to highlight the job search from a psychological and capital-focused perspective. The emotional model is concerned with a job searcher’s stress-related emotional responses to their search. The job search can be like an emotional roller coaster, and the emotional highs and lows experienced by a job searcher may result in avoidance or withdrawal (Barber et al., 1994). From a psychological perspective, theoretical research aligned with the emotional model fell within a negative illness-centered paradigm of postwar psychology. The attention was on “fixing what was not right” instead of leveraging “being the best you can be” with the goal of successful reemployment. Foundational research related to the job search generally emphasized the psychologically based mental health effects related to a job loss and the job search 35
process (Kanfer & Hulin, 1985). A meta-analytic study related to psychological and physical wellbeing during unemployment was conducted by Mckee-Ryan, Song, Wanberg, and Kinicki (2005). A variety of psychological predictors and outcomes were analyzed. Predictors included coping resources and strategies, social support, financial strain, and reemployment expectation. Outcomes included psychological, life and domain satisfaction, and physical health. Findings from this study provided evidence that the unemployed had lower levels of psychological and physical wellbeing compared to those who were employed. Furthermore, predictors including coping resources and strategies and cogitative appraisals had stronger relationships with mental health than human capital (Mckee-Ryan et al., 2005). An overview of selected research included in this study will be covered in the following section. A theoretical job loss-and-search model developed by Amundson and Borgen (1982) focused on the psychological impact of a job loss without consideration given to factors associated with a successful return to work. Individual factors related to a successful job search received far less interest. An experimental intervention conducted by Caplan, Vinokur, Price, and Page (1989) explored the impact of preventative programs—from a mental health perspective—on job searcher motivation and reemployment. Intervention programs included job searcher training and problem solving. Job searcher motivation was measured using a scale developed by Vinokur and Caplan (1987). Compared to a control group, participants who received intervention reported higher job quality and satisfaction in newly acquired jobs; even those who continued to be unemployed reported a higher level of job search motivation. Of
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particular mention was the motivational impact of job searcher self-efficacy and its influence on accomplishing desired outcomes (Caplan et al, 1989). Wanberg (1997) continued to explore the predictive relationship of individual behaviors on the job search from a mental health perspective. More specifically, in a longitudinal study Wanberg explored the association of the composite of three predictors of job searcher coping behavior: optimism, self-esteem, and perceived control and their impact on short- and long-term mental health and reemployment outcomes. This study found that high levels of optimism, self-esteem, and perceived control were related to greater mental health coping strategies and faster reemployment (Wanberg, 1997). The University of Michigan’s JOBS Program (1984) provided training programs to support to the unemployed during their period in transition and develop job searcher resilience as referenced by Wanberg (Caplan, Vinokur, Price, & van Ryn, 1989). Two years later, in a follow up study regarding the longer term impact of this program, the experimental group continued to demonstrate higher levels of reemployment and mental health (Vinokur, Schul, Vuori, & Price, 2000). A longitudinal study by Vinokur and Schul (2002) examined the relationships of job search self-efficacy and motivation as personal coping resources to job search intensity and reemployment quality. They found, however, that increased motivation, as a function of improved coping resources, resulted in increased reemployment and did not have an impact on the quality of reemployment. Prussia, Fugate, and Kinicki (2001) developed a coping-goal construct and applied it to the job loss context. Results of this study found that “human capital, employment commitment, internal coping resources, and anticipation of job loss positively predicted a reemployment coping goal” (p. 1179). 37
The learning job search model suggests that a job searcher’s effectiveness improves throughout a job search as a result of ongoing learning, which occurs as they progress through their job search (Barber et al., 1994). This learning—though the acquisition of knowledge and experience during the job search process—emphasizes a human and social capital perspective toward the job search. As defined by Luthans, Avey, Avolio, and Peterson (2010), human capital is “education, experience, and implicit knowledge of human resources” (p. 42). Social capital is defined as “a variety of entities with two elements in common: they all consist of some aspect of social structure, and they facilitate certain actions of actors...within the structure” (Portes, 1998, p. 2). As mentioned earlier, a review conducted by Schwab, Rynes and Aldag (1987) evaluated prior research related to the job search process. A process model of job search and evaluation was developed to provide a structure for the evaluation of these research works. This model contained five categories: individual, labor market, search, evaluation, and outcomes. Findings from this review supported the importance of job search activities related to the acquisition of labor market information and job search intensity (Schwab et al., 1987). Wanberg, Kanfer, and Banas (2000), examined the predictive impact of networking—a form of social capital—in relationship to reemployment. Findings of this study did not support networking intensity as a predictor of either reemployment or reemployment speed, especially in relation to other job search measures. Fugate, Kinicki, and Ashworth (2004) examined the interrelationships of three dimensions of employability: career identity, social and human capital, and personal adaptability as a multidimensional psycho-social construct. They defined employability 38
“as a form of work specific active adaptability that enables workers to identify and realize career opportunities” (p. 16). To be considered employable does not require one to be either employed or unemployed. It is person-specific, without a relationship to one’s employment situation. The researchers’ perspective was that the ability to identify and gain employment was highly influenced by human and social capital. McArdle, Waters, Briscoe, and Hall (2007) empirically tested this model in a longitudinal study using a sample of 416 unemployed individuals. Results of this study supported this psycho-social construct and its use with the unemployed. Findings from this study provided overall support for Fugate et al.’s (2004) model; however, it provided limited support for networking or human capital as significant factors in relation to employability. The sequential job search model suggested that the job search process occurred in logical sequential stages over the lifecycle of a search (Barber et al., 1994). Early job search theorists (Solberg, 1967; Bowen, 1982) viewed the job search as a two-step process of preparatory and active phases. Schwab et al. (1987) hypothesized that the generation of job alternatives “was a function of (1) the sources used to acquire information about job vacancies…; and (2) the intensity with which such information is pursued. Then job offers are generated, they must somehow be evaluated by the job seeker to arrive at a decision” (p. 133). What Schwab et al. (1987) hypothesized was an early conceptual view of the sequential job search model. Blau (1994) also suggested that the search process occurred in the preparatory and active sequential phases. During the preparatory phase, a job searcher executes certain job search behaviors to acquire information from a variety of sources regarding job search alternatives. During the active phase, through the execution of other job search behaviors, the job searcher selectively 39
filters information acquired in the preparatory phase, then initiating an active job search. The emphasis of this model was on leveraging job search behaviors in relation to the job searcher’s preparation for action in an active job search. In a study regarding the job search and voluntary turnover, Blau (1993) found a stronger relationship between an employed individual’s intention to quit a job and active job search behaviors than those considered preparatory. Blau’s (1994) two-dimensional measure of job search behaviors received empirical support as a valid sequential job search model. Research related to the job search (Barber et al., 1994; Sacks & Ashforth, 2000; Wanberg, Glomb, Song, & Sorenson, 2005), provided the strongest support with regard to the sequential model in relationship to effective job search intensity through the leveraging of related job search behaviors. Support for the emotional and learning job search models were not consistently as favorable (Barber et al, 1994). Job Search Research Focusing on PsyCap Subconstructs As mentioned earlier, prior research related to PsyCap and the job search specifically focused on the sub constructs of self-efficacy, optimism, and resilience. An overview related to this research will be provided in this section. Several studies explored the sub construct of self-efficacy as it related to the job search. In a foundational study, Kanfer and Hulin (1985) researched self-efficacy for both unemployed and reemployed job searchers. They found that reemployed job searchers had higher levels of selfefficacy than their unemployed counterparts. Furthermore, they concluded that reemployment success was greatly influenced by job searchers confident expectations of a successful search. In a study extending these findings, Eden and Aviram (1993) introduced the dimension of training to develop job searchers general self-efficacy (GSE) 40
and reemployment success. They found a positive relationship between increased GSE, job search behaviors, and job search intensity resulting in increased reemployment outcomes. This study provided empirical validation regarding the impact of job searcher GSE development initiatives and reemployment. Wanberg et al. (1999) found a significant relationship between job searcher self-efficacy, motivational control, job search intensity, and reemployment success. Blau’s (1994) job search behaviors scale, focusing on preparatory and active job search behaviors, was used to measure job search intensity. This scale was modified to reflect the current internet-based job search context. Saks and Ashworth (1999) found that job search self-efficacy predicted job search behaviors and job search intensity, which positively influenced reemployment success. Study findings also provided support regarding the development of self-efficacy though training interventions and job search behaviors. This study also used a modified version of Blau’s job search behavior scale as a measure of job search behaviors. Findings of this study provided greater support of earlier studies regarding self-efficacy, job search behaviors, and reemployment outcomes (Kanfer & Hulin, 1985; Eden & Aviram, 1993). Saks and Ashworth (2000), found a strong relationship between job searcher self-efficacy and job search behaviors. Saks (2005) found job searcher self-efficacy to be a strong predictor of job search behaviors, job offers, and employment status. An adapted version of Blau’s job search behavior scale was used in this study. Kanfer et al. (2001), in a metaanalytic review regarding job search and employment, found that the psychological construct of positive affectivity (which includes self-efficacy) had a significant impact on the job search. Optimism, another tested positive-affectivity construct, was not shown to
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be significantly related to job search behaviors. Furthermore, findings from this study reinforced the relationship between job search behaviors and reemployment. Research related to the positive psychological constructs of optimism and resilience in relationship to the job search was not as robust as studies related to selfefficacy. As addressed in greater detail earlier, Wanberg (1997) examined the relationship between optimism, self-esteem, and perceived control as coping behaviors and reemployment. Findings from this study reflected a significant relationship between high levels of optimism, self-esteem, and perceived control in relation to greater mentalhealth coping strategies and faster reemployment. Kanfer et al. (2001) found optimism, as a positive-affectivity construct, was not significantly related to job search behaviors. Fleig-Palmer, Luthans, and Mandernach (2009) proposed that one’s positive state of resiliency could successfully guide job seekers toward reemployment. They suggested that their proposed framework serves as a starting point for future empirical research focusing on positive approaches leading to reemployment. Summary and Conclusion Three job search models—emotional, learning, and sequential—were covered in this review through a multi-dimensional focus emphasizing psychological, capital, and performance-related perspectives relative to the job search. The sequential job search model received the strongest empirical support regarding effective job search intensity (Barber et al., 1994; Sacks & Ashforth, 2000; Wanberg, Glomb, Song, & Sorenson, 2005). The emotional and learning job search models were not as consistently favorable in relation to job search intensity (Barber et al., 1994). Strong empirical support has been demonstrated regarding the effects of the PsyCap subconstructs of self-efficacy and 42
optimism as they relate to the job search and the performance-related outcome of reemployment. A recommendation was made by Fleig-Palmer et al. (2009) that resiliency could guide job searchers toward reemployment. Chen and Lim (2012) found empirical support for PsyCap as a predictor of coping resources and strategies and their impact on job search behaviors. Most research to date regarding the job search has dealt with the impact of human and social capital on the job search, while research related to PsyCap and the job search is more limited. A gap in the research (as evidenced by this literature review) relates to the exploration of PsyCap and its impact on job search performance. This study expands the research related to PsyCap and the job search by investigating the correlational impact of psychological capital on job search intensity as measured by job search behaviors. Chapter 3 will cover this dissertation study’s research methodology, focusing specifically on its design, data collection and analysis, and required steps taken to ensure this study is conducted in an ethical manner.
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CHAPTER 3. METHODOLOGY Introduction The purpose of this study is to test the theory of psychological Capital (PsyCap) that links PsyCap to both the preparatory and active job search behaviors of active job searchers receiving outplacement services through an international organization for outplacement services. Two measurement instruments, the psychological capital questionnaire (PCQ; Luthans, Youssef, & Avolio, 2007) and Job Search Behaviors Scale (Blau, 1994) were used to examine, respectively, psychological capital and job search intensity. PsyCap is defined as an individual’s positive psychological state of development and is characterized by: (1) having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (2) making a positive attribution (optimism) about succeeding now and in the future; (3) persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; (4) when beset by problems and adversity, sustaining and bouncing back and even beyond (resilience) to attain success. (Luthans, Youssef, & Avolio, 2007, p. 3) As defined by Blau (1994), job search intensity is “the effort which individuals make during their job search” (Blau, 1994, p. 288). Job search behaviors are defined as “the specific activities through which effort and time are spent on job search” (Blau, 1993, p. 315). This dissertation furthers existing research by focusing on the core construct of PsyCap as it relates to job search intensity. PsyCap’s addition of the subconstructs of hope and resilience, along with the synergistic effects of all four subconstructs, provides an opportunity to expand on positivity in relation to the reemployment process.
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Research Questions and Hypotheses The purpose of this study is to test the theory of Psychological Capital (PsyCap) that relates PsyCap to preparatory and active job search behaviors of active job searchers receiving outplacement services at an international organization for outplacement services. The study focused on the following research questions: RQ1: Do the four subscales of the psychological capital assessment predict preparatory job search behaviors? RQ2: Do the four subscales of the psychological capital assessment predict active job search behaviors? PsyCap, as a “higher-level” core construct, is made operational through a consolidated scoring of the four psychological capacity subconstructs of self-efficacy, optimism, hope, and resilience (Luthans, Avey, Avolio, & Norman, 2007). Job search intensity is measured by job search behaviors through the scoring of two types of job search behaviors: preparatory and active. To respond to the research questions, the following research hypotheses are suggested: Hypothesis 1: The four subscales of the psychological capital assessment do predict preparatory job search behaviors. Hypothesis 2: The four subscales of the psychological capital assessment do predict active job search behaviors. Research Design Much research has been conducted regarding the relationship of the Psychological Capital (PsyCap) subconstructs of self-efficacy and optimism and an unemployed 45
individual’s return to work (Luthans, Luthans, & Luthans, 2004; Shamir, 1986; Winefield, Tiggemann, & Winefield, 1992; Eden & Aviram, 1993; Wanberg, 1997; Kanfer, Wanberg, & Kantrowitz, 2001; Waters & Moore, 2002; Crossley & Stanton, 2005; McArdle, Waters, Briscoe, & Hall, 2007). In contrast, this study examined the reemployment process through the lens of the relationship between all four PsyCap subconstructs—self-efficacy, optimism, hope, and resilience—and job search intensity, as measured by preparatory and active job search behaviors. This study explored the following questions: whether the four subscales of the psychological capital assessment predict preparatory job search behaviors, and whether those same subscales predict active job search behaviors. The design for this study is a correlational non-experimental, oneshot quantitative model. This approach is suitable for the types of interval-level data that will be collected to both measure (Creswell, 2009) and assess the association between the independent and dependent variables and at the same time determine whether statistically significant relationships are evident (Cozby, 2009; Creswell, 2009; Vogt, 2007). Linear relationships involving two variables can be successfully measured though the use of a correlational design (Creswell, 2009). Furthermore, by measuring the correlative relationship between psychological capital and preparatory and active job search behaviors, this design is appropriate for focusing on the research problem and research questions pertaining to this study. Many quantitative research studies adopted a similar correlational design to explore the constructs of PsyCap (Luthans, Avolio, Avey, & Norman, 2007; Youssef & Luthans, 2007; Luthans, Norman, Avolio, & Avey, 2008; Avey, Wernsing, & Luthans, 2008; Luthans, Avey, Clapp-Smith, & Li, 2008; Avey, Reichard, Luthans, & Mhatre, 2011) and job search intensity (Saks & Ashworth, 1999; 46
Kanfer, Wanberg, & Kantrowitz, 2001; Saks, 2006; Chen & Lim, 2012). This section provided an overview of the methodological approach and specific research design adopted for this study. The next section provides a high level overview of this study’s survey based methodology. An internet survey was given to a sample of unemployed individuals receiving job search assistance at an outplacement services organization. A web-based version of the PCQ (Luthans, Youssef, & Avolio, 2007) and Job Search Behaviors Scale (Blau, 1994) instruments were used for this study. According to Cooper and Schneider (2008), the use of a web-based survey provides greater candidate reach at significantly lower cost to the researcher. Furthermore, use of a web-based survey has the advantage of a potentially faster survey response (Fowler, 2002). The PCQ consisted of four PsyCap subconstruct categories consisting of six questions each, with six-point Likert response scales. The Job Search Behaviors Scale, a five-point Likert response instrument, measured search intensity looking at two job search behaviors: preparatory and active, with six questions each. Quantitative statistical analysis was conducted in order to examine the relationship between preparatory and active job search behaviors and PsyCap. To assess the research questions and determine whether the four subscales of the psychological capital assessment predict the two behaviors of job search intensity, two multiple linear regressions were conducted. When the researcher seeks to determine the relationship between multiple continuous independent variables and a single continuous dependent variable, the multiple regression is the correct statistical analysis (Tabachnick & Fidell, 2012). 47
All predictor variables were entered into the model at the same time. The individual predictors were independently examined to determine the contribution each made to the outcome variable (Tabachnick & Fidell, 2012). Two regression analyses were conducted, one for each criterion variable. The multiple regression utilizes the F test to ascertain if the model containing hope, resiliency, self-efficacy, and optimism predicting the outcome variable. The R2 value presented the percentage of the variance in the outcome that was attributable to the model containing the four predictor variables. Statistical Package for the Social Sciences (SPSS) version 18.0 (IBM, 2009) for Windows, a computer-based statistical application, was used for statistical analysis. Population/Sample The population studied was unemployed individuals receiving outplacement assistance through an international organization for outplacement services. The sample frame consisted of active job searchers with a goal to return to traditional employment. According to Blau (1994), an active job searcher is one who is behaviorally committed to the job search. As such, transition candidates seeking alternative outcomes such as entrepreneurship, consulting, or volunteer opportunities will be excluded from this study. Candidate selection was random with each participant’s selection having equal probability and representative of the study’s population (Creswell, 2009). The outplacement services organization used for this study was founded in 1988 and is one of the largest women-owned, independent human capital consulting organizations. Furthermore, it operates in 25 countries, with 180 offices globally. Services provided include a wide range of outplacement services focused on the career transition goals of senior executives, managers and directors, professionals, and 48
administrators. Services and programs vary based on the candidate level; these range from virtual programs in which the candidate can work from home while also receiving personal coaching support, to a full-service program in which the candidate works directly with a Senior Career Coach developing and executing job search strategies and tactics until successful career transition takes place. It is important to note that “successful career transition” might result in a new career in a traditional employment setting or in entrepreneurship, consulting, or volunteer endeavors focused on charities and community. At any given time, in excess of 200 career transition candidates in various phases of the process are supported under one of the above stated programs. This study’s sample consists of career transition candidates in an active career search and receiving outplacement support, with the goal of gaining traditional employment. As such, transition candidates seeking alternative outcomes such as entrepreneurship, consulting, or volunteer opportunities were excluded from this study. Candidate selection was random, with each participant’s selection having equal probability and representative of the study’s population (Creswell, 2009). Randomization was accomplished with the use of “The Hat” (Harmony Hollow Software, 2012), a software utility that determines sample randomization from a list of potential participant’s email addresses. A recent study by Chen and Lim (2012) used similar sampling methods in conjunction with a webbased survey. This study’s research questions required a sample size adequate enough to generalize study findings to unemployed, active job seekers at large. The data analysis involved the use of two multiple linear regression models. G*Power version 3.1 (Faul, 49
Erdfelder, Lang, & Buchner, 2012) was utilized to determine an appropriate sample size for a multiple linear regression using four predictors, a medium effect size (f2 = .15) (Cohen, 1988), an alpha of .05, and a power of .80 (Howell, 2010). To achieve empirical validity, the recommended sample size was determined to be 85 participants. As stated earlier, the sample for this study consisted of career transition candidates in an active career search receiving outplacement support with the goal of gaining traditional employment. Permission was provided by the EVP/owner of this outplacement organization for research activities conducted in conjunction with this study. More specifically, permission was granted for the recruitment of career transition candidates between the ages of 18 and 65 and conducting an active job search to participate in a voluntary online survey. Participant recruitment and selection process for this study commenced with the EVP/owner of the outplacement organization providing the researcher with a list of email addresses of candidates receiving outplacement consulting services through this firm. This list was randomized, with the subsequent sort provided back to the outplacement organization. Sort-selected candidates received an email, authored by the outplacement organization’s EVP/owner, introducing them to this study. This email was sent to all potential survey candidates one week in advance of the survey invitational email. One week later, an invitational email was sent to prospective participants by this study’s researcher. This email included an Informed Consent Statement and a web link. Clicking on the survey web link indicated conformance with inclusion criteria, informed consent acceptance, and agreement to voluntarily participate in the survey.
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Instrumentation/Measures Two measurement instruments were used to identify a relationship between the four PsyCap subscales and prediction of preparatory and active job search behaviors: the Psychological Capital Questionnaire (PCQ) ( Luthans, Avolio, Avey, & Norman, 2007) and Job Search Behaviors Scale (Blau, 1994). The PCQ is a 24-question, Likert-based survey tool utilizing a 6-point response scale: (1) strongly disagree, (2) disagree, (3) somewhat disagree, (4) somewhat agree, (5) agree, and (6) strongly agree (Luthans et al., 2007). The development of the PCQ took into consideration measurement tools focusing on self-efficacy, hope, resilience, and optimism, as developed by Parker (1998), Snyder et al. (1996), Wagnild and Young (1993), and Scheier and Carver (1985). The PCQ consists of six questions per PsyCap subconstructs (self-efficacy, hope, optimism, and resilience) that measure the state-like strength of each survey participant’s psychological capital. The averages of each subconstruct scales are reported. Overall participant PCQ score consists of the mean score of the four PsyCap subconstruct averages. Published psychometric information related to the PCQ appears relative to two prior studies by Luthans et al. (2007) that measured the PsyCap subconstructs both individually and collectively from a wide range of participants, including college students, engineers, and administrative employees. Study results supported PsyCap’s psychometric relevance as well as its higher-level value in measuring the collective strength of the PsyCap four subconstructs in relation to predicting job satisfaction and performance. Selected PCQ questions originated from prior published works focused on PsyCap’s subconstructs (self-efficacy: Parker, 1998; hope: Snyder et al., 1996; resilience: Wagnild & Young, 1993; optimism: Scheier & Carver, 1985). These prior works have 51
supported PCQ’s subconstruct reliability and validity as they relate to employment settings. In two studies consisting of four samples, Luthans et al. (2007) stated that the Cronbach alphas for each of the four six-item adapted measures and the overall PsyCap measure for the four samples were as follows: hope (.72, .75, .80, .76); resilience (.71, .71, .66, .72); self-efficacy (.75, .84, .85, .75); optimism (.74, .69, .76, .79); and overall psychological capital (.88, .89, .89, .89). Although the optimism scale in the second sample (.69) and the resilience scale in the third sample (.66) did not reach the generally accepted levels of internal consistency, the reliability of the overall PsyCap measure in all four samples was consistently above conventional standards. (p. 555) The construct of job search intensity as measured by job search behaviors was addressed through the use of a survey tool developed by Blau (1994). This instrument consists of 12 questions: 6 associated with preparatory job search behaviors and 6 focusing on active job search behaviors. These 12 questions were based on measures in previous studies related to job search behaviors (Dyer, 1972; Kanfer & Hulin, 1985; Sheppard & Belitsky, 1966; Vinokur & Caplan, 1987). A 5-point Likert scale using the following metric was used to record responses (Blau, 1994): 1= never (0 times), 2 = rarely (1 or 2 times), 3 = occasionally (3-5 times), 4 = frequently (6-9 times), and 5 = very frequently (at least 10 times). The psychometric relevance, construct validity, and reliability of this tool is evidenced based on alphas associated with a study of 114 hospital employees (.80 and .79, respectively) for preparatory and active job search behaviors. In a separate study of 103 managers in a pharmaceutical company, Cronbach alphas were .79 for preparatory and .76 for active job search behaviors. Instrument validation was established for both preparatory and active job search scales in prior studies consisting of three samples through Confirmatory Factor Analysis (Blau, 1994).
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Data Collection This web-based study was conducted during a period of almost three weeks, in conjunction with SurveyMonkey, a provider of web-based survey tools and solutions. Potential study participants received an initial introductory email from the EVP/owner of the outplacement organization. One week later, potential participants received an invitational email that included an Informed Consent Statement. Consent was indicated by clicking on the survey’s web link, which certified that participants were between the ages of 18 and 65, actively engaged in a job search, had read and understood the informed consent statement, and agreed to voluntarily participate in this survey. Participants who clicked on the link were taken to a SurveyMonkey-hosted web page representing the start of the survey. The survey's first two questions focused on the two inclusion factors:
Are you currently conducting an active search with a goal to return to traditional employment?: Yes_________ No_________
Are you between the ages of 18 and 65?: Yes________ No________
A “no” response to either of these questions prevented potential candidates from participating in this survey. They exited from the survey and taken to another web page that included a communication thanking them for their support. The following two survey questions addressed participant demographics:
Age: _________
Gender:
Male________
Female__________
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Following these questions, participants responded to the PCQ's 24 questions and the job search behaviors scale’s 12 questions. Once these scales were completed, participants were brought to another web page thanking them for their participation. Two follow-up reminder emails were sent to prospective candidates. Both communications subtly urged those who had not responded to complete the survey. The first email was sent after the first week of the survey, with the second email sent after the second week. Data Analysis In order to assess this study’s research questions and determine if the four subscales of the psychological capital assessment predict the two behaviors of job search intensity, two multiple linear regressions analyses were conducted. When the researcher's goal is to comprehend the influence between a several continuous predictor variables on a continuous outcome variable, the multiple linear regression is the correct statistical analysis. The following regression equation was used: y = b0 +b1*x1 + b2*x2 + b3*x3 + b4*x4 + e; where y = the response variable, b0 = constant, b1 = first regression coefficient, b2 = second regression coefficient, b3 = third regression coefficient, b4 = forth regression coefficient, x1 = hope, x2 = resilience, x3 = self-efficacy, x4 = optimism, and e = the residual error (Tabachnick & Fidell, 2012). The predictor variables in the model are hope, resilience, self-efficacy, and optimism. All predictor variables are treated as interval data. The dependent variables in the analysis are the two subscales of the job search behavior scale (preparatory job search behaviors and active job search behaviors). Both criterion variables were also treated as interval data. One regression was conducted for each criterion variable. 54
Backward stepwise regression was used. All predictor variables were entered into the model at the same time. Each predictor variable was removed from the model to determine if the deletion of the variable improved the predictability of the model. Variables were removed and re-entered in order to build the strongest possible model with the available predictors. The process was repeated until the strongest possible model was created. Predictor variables were evaluated based on what each added to the prediction of the criterion variable that was different from the predictability provided by the other predictors (Tabachnick & Fidell, 2012). To determine if the model correctly predicted the outcome variable, an F test was conducted. R2 was presented to demonstrate how much variability in the dependent variable was attributable to the model consisting of four predictors. The t test assessed the significance of the individual predictors. For those significant predictors, beta coefficients were examined. For each positively, significant beta coefficient, a one-unit increase in the predictor will result is an increase in the outcome variable by the unstandardized beta value. For each negatively, significant beta coefficient, a one-unit increase in the predictor will result in a decrease in the outcome variable by the unstandardized beta value (Tabachnick & Fidell, 2012). The assumptions of multiple regression, linearity, homogeneity, and absence of multicollinearity were assessed prior to analysis (Stevens, 2009). Residual scatter plots were used to examine homogeneity and linearity. If a linear relationship was found between the independent and dependent variables, linearity was met. If residual scores were rectangularly distributed around the regression line, homogeneity was met (Stevens,
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2009). The presence of multicollinearity was assessed to be certain independent variables were not too related and was assessed with Variance Inflation Factors (Stevens, 2009). Resultant data related to this study was electronically transferred into SPSS version 18.0 (IBM, 2009) for Windows. Sample characteristics were described with the presentation of descriptive statistics. For categorical variables of interest, such as gender and age, frequencies and percentage were conducted. Means and standard deviations were calculated for the subscales of psychological capital and the subscales of the Job Search Behaviors Scale. Validity and Reliability Any type of bias was limited due to the use of preexisting measurement instruments for this study. Both the PCQ and Job Search Behaviors Scale have been used in many prior empirical research studies and as a result have been psychometrically validated. As covered earlier, published psychometric information related to the PCQ appears relative to two prior studies by Luthans et al. (2007) that measured the PsyCap subconstructs both individually and collectively from a wide range of participants. Study results supported PsyCap’s psychometric relevance as well as its higher-level value in measuring the collective strength of the four PsyCap subconstructs in relation to predicting job satisfaction and performance. The psychometric relevance, construct validity, and reliability of the Job Search Behaviors Scale was evidenced based on alphas associated with a study of 114 hospital employees (.80 and .79, respectively) for preparatory and active job search behaviors. Furthermore, in a separate study of 103 managers in a pharmaceutical company, Cronbach alphas were .79 for preparatory and .76 for active job search behaviors. 56
Ethical Considerations The Belmont Report (1979) summarizes recommended basic ethical principles and guidelines for research involving human subjects. U.S. Federal Law (45 CFR 46) adopted these recommendations and developed the “Common Rule,” which focuses on three ethical aspects: principles of justice (equity), beneficence (risk and benefit analysis), and respect (confidentiality and privacy) as they relate to research involving human subjects. There was minimal participant risk associated with this study. Prospective participants were presented with an informed consent statement concerning the study’s purpose, risks, benefits, individual rights, and confidentiality. The web-based survey invited the entire population sub-group to participate anonymously. Even though this survey was introduced to prospective participants by the owner of the outplacement organization, participant responses remained anonymous and confidential. Only combined data from all survey responses were reported; individual personal information was not provided. No one has access to participant identity, providing complete confidentiality and protection from potential coercion. The only participation inclusion criteria for this study were that prospective participants be unemployed and engaged in an active job search and be between the ages of 18 and 65 at the time of the study. As such, invited study participants had an equal chance of being selected to participate. Furthermore, there were no ethical risks related to this study’s sampling methodology, and no psychological, financial, legal, or physical risks were involved with this research study. Therefore, the potential benefits exceed all risks associated with this study. All enrolled participants equally shared in the burdens and benefits of this study. The indirect benefits to study participants will be reflected in 57
advancements in the outplacement industry with respect to improvements in one’s return to gainful employment. Summary This section provided justification for the researcher’s choice of research design, data collection and analysis, and required step insuring that this research is conducted in an ethical manner. Furthermore, this research design will effectively answer the hypotheses related to this study. The research design for this study was a correlational non-experimental, one-shot quantitative model. Quantitative statistical analysis was conducted, utilizing multiple linear regressions, in order to signify the relationship between preparatory and active job search behaviors and PsyCap. The following chapter will provide an analysis of the collected data based on the research methodology detailed in this chapter.
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CHAPTER 4. RESULTS Introduction The purpose of this non-experimental quantitative study was to test the theory of Psychological Capital (PsyCap) that relates PsyCap to preparatory and active job search behaviors. More specifically, this study investigated whether a correlational relationship existed between active job searchers’ PsyCap as articulated by Luthans, Youssef, and Avolio (2007) and job search behaviors as articulated by Blau (1994). This chapter presents an analysis of data addressing the following research questions and subsequent hypotheses: RQ1: Do the four subscales of the psychological capital assessment predict preparatory job search behaviors? Ho1: The four subscales of the psychological capital assessment do not predict preparatory job search behaviors. Ha1: The four subscales of the psychological capital assessment do predict preparatory job search behaviors. RQ2: Do the four subscales of the psychological capital assessment predict active job search behaviors? Ho2: The four subscales of the psychological capital assessment do not predict active job search behaviors. Ha2: The four subscales of the psychological capital assessment do predict active job search behaviors.
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This chapter is presented in four sections: a description of the population and sample, a summary of results, a detailed analysis of results, and conclusive commentary focusing on the major findings of this research study. Description of the Population and Sample This section provides a description of the population, sampling procedures and size, demographic characteristics of participants, and other relevant information pertaining to the analysis and results of this study. The population studied was unemployed individuals receiving outplacement assistance through an international organization for outplacement services. The sample frame consisted of active job searchers with the goal of returning to traditional employment. At the time of this study, a total population of 453 career transition candidates was included in the sample frame. A 90% randomization of the 453 candidates resulted in a survey sample of 407 potential participants. A total of 105, of the 407 potential participants, started the survey; 16 were prevented from participating in the study due to exclusion criteria, 5 exited the survey without completion, and 84 participants completed the survey. G*Power version 3.1 (Faul, Erdfelder, Lang, & Buchner, 2012) was used to calculate the recommended sample size for a multiple regression using four predictors, a medium effect size (f2 = .15), an alpha of .05, and a power of .80. The recommended sample size to achieve empirical validity was calculated to be 85 participants. A G*Power post hoc calculation resulted in power of .797 compared to the a priori power of .803. This section provides a demographic summary of survey participants that addresses the categorical variables of age and gender. Table 1 provides an overview of this demographic information. The results of this summary reflect that the most common 60
age range was between 55 to 64 years (n=84) with an age minimum of 26, mean of 50.24 and maximum of 63.
Table 1. Age and Gender of Participants Demographic
Frequency
Percent
Age category 18 to 24
0
0.0
25 to 34
6
7.1
35 to 44
14
16.7
45 to 54
30
35.7
55 to 64
34
40.5
Female
42
50
Male
42
50
Gender
Summary of Results 1. Ho1: The four subscales of the psychological capital assessment do not predict preparatory job search behaviors. Ha1: The four subscales of the psychological capital assessment do predict preparatory job search behaviors. Results: Insufficient evidence to reject the null hypothesis. 2. Ho2: The four subscales of the psychological capital assessment do not predict active job search behaviors. Ha2: The four subscales of the psychological capital assessment do predict active job 61
search behaviors. Results: Reject the null hypothesis (p = .002). Details of Analysis and Results This section provides an analysis of data collected to test each research hypothesis, beginning with an overview of descriptive statistics calculated and focusing on measurements of central tendency and dispersement. A published dissertation focusing on psychological capital was reviewed and considered for the format and layout of this section (Abdullah, 2009). Following are the results of the correlation and regression analyses. Resultant data from this study’s survey was transferred into a Microsoft Excel file and imported into SPSS version 18.0 (IBM, 2009) for Windows for analysis. The Psychological Capital Questionnaire’s four subscales (self-efficacy, hope, resilience, optimism) and two job search behaviors subscales (preparatory and active) were scored by averaging the responses for each subscale. Measurement reliability and internal consistency was assessed by Cronbach’s alpha calculations, which measure reliability and internal consistency of survey responses with reliability coefficients ranging from 0.0 (no internal reliability) to 1.0 (perfect internal reliability). The subscales of the PCQ and job search behaviors scales were measured individually. The results of these Cronbach’s alpha calculations (Table 2) reflect a high degree of reliability and internal consistency for these two measurement instruments.
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Table 2. Cronbach’s Alpha for PCQ and Job Search Behaviors Instrument
α
PsyCap
.943
Self-efficacy
.907
Hope
.891
Resilience
.839
Optimism
.896
Job Search Behaviors
.792
Preparatory
.613
Active
.808
Descriptive statistics measuring values including means, medians, minimums, maximums, and standard deviations were then calculated. These statistics are depicted graphically in histograms reflecting normality and distribution shape. Descriptive Statistics Presented in Table 3 are the descriptive statistics for the survey participants’ total PCQ and subscale scores. These scales range from a minimum of 1 to a maximum of 6. Closeness in the mean scores was observed with high participant scoring for both total PCQ and related subscales. The highest participant scoring was 5.44 (self-efficacy) and the lowest was 4.38 (hope). The remaining mean scores were 4.70 (total PCQ), 4.54 (resilience), and 4.45 (optimism).
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Table 3. Descriptive Statistics: Total PCQ and Subscale Scores Source
N
Minimum
Maximum
Mean
SD
Total PCQ
84
2.08
6.00
4.70
.67
Self-efficacy
84
2.50
6.00
5.44
.64
Hope
84
2.17
6.00
4.38
.88
Resilience
84
2.17
6.00
4.54
.79
Optimism
84
1.50
6.00
4.45
.90
The following provides a series of histograms in order to provide a perspective regarding distribution shape and dispersion. Histograms were created for total PCQ scores as well as subscale scores. Figure 2 provides a distribution for total PCQ scores and indicates a normal distribution with a minor negative skew. All scores are in the upper end of the scale, with some variability; a small number of scores fall in the center, while the majority of scores are at the higher end. One score was an outlier, representing the only score falling between the middle and lower half.
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Total PCQ Scores Figure 2. Distribution of total PCQ scores. A histogram portraying the distribution of scores for self-efficacy is reflected in Figure 3. Scores reflected in this figure are slightly negatively skewed with the highest peaks occurring at the top of the scale. All scores are in the highest end of the scale with the exception of one outlier falling between the middle and lower end of the range.
65
Self-efficacy Scores Figure 3. Distribution of self-efficacy scores. Figure 4 provides a histogram reflecting the distribution of the scores related to the subconstruct of hope. Negatively skewed, the majority of scores fall with the upper end of the range, above the value of 4. One outlier fell in the lower end of the range.
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Hope Scores Figure 4. Distribution of hope scores. Figure 5 provides a histogram reflecting the distribution for the subconstruct of resiliency. The scores for resiliency, with a minor negative skew, have a narrow distribution, with the majority of scores in the upper end of the range between 4 and 6. One outlier fell in the lower end of the range. There is a score mode is visible between the values of 4 and 5.
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Resiliency Scores Figure 5. Distribution of resiliency scores. Figure 6 portrays a histogram reflecting the distribution of the scores for optimism, the final PCQ subconstruct. Negatively skewed, the majority of scores fall in the upper end of the distribution, with a central tendency between the values of 4 and 5.
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Optimism Scores Figure 6. Distribution of optimism scores. This next section provides descriptive statistics and histograms pertaining to job search behavior scores. Presented in Table 4 are the descriptive statistics for the survey participants’ total job search behavior scores as well as preparatory and active job search behaviors scores. The scales range from a minimum of 1 to a maximum of 5. Closeness in the mean scores was observed, with total and preparatory job search behaviors scoring higher.
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Table 4. Descriptive Statistics: Job Search Behaviors, Preparatory Job Search Behaviors, and Active Job Search Behaviors Scores Source
N
Minimum
Job search behaviors
84
1.92
Preparatory
84
Active
84
Maximum
Mean
SD
4.83
3.47
.66
2.17
5.00
3.68
.67
1.17
5.00
3.27
.89
Histograms portraying score distributions and shape for total job search behaviors, preparatory job search behaviors, and active job search behaviors were created. Figure 7 is a histogram portraying the score distribution for total job search behaviors.
Total Job Search Behaviors Scores Figure 7. Total job search behaviors scores.
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Figure 8 reflects the score distribution for preparatory job search behaviors. The majority of scores fall with the upper end of the range, with a modal peak occurring around the value of 4.
Preparatory Job Search Behaviors Scores Figure 8. Distribution of preparatory job search behaviors scores. Figure 9 is a histogram representing the score distribution for active job search behaviors. The scores for active job search behaviors are normally distributed, with a minor negative skew. The majority of scores are positioned in the upper end of the range; a peak mode is visible between the values of 3 and 4.
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Active Job Search Behaviors Scores Figure 9. Distribution of active job search behaviors scores. Pearson Correlation Results Pearson correlations were calculated for PCQ and job search scale variables. The calculation to determine the Pearson correlation coefficient (r) was conducted between all scale and subscale variables. Table 5 (below) provides an analysis of the Pearson correlation results between total PCQ, PCQ subscales, preparatory job search behaviors, and active job search behaviors. Results of this correlation analysis reflect high correlations between PCQ subscale variables, ranging from an r of 0.39 to 0.76; moreover, these relationships reflected a high level of significance (p = 0.00). With these high correlations occurring between two variables, they appear to measure the identical 72
capacity; thus, conclusions cannot be made with respect to the impact of any of the four PCQ subscale predictor variables and preparatory and active job search behaviors. This will impact subsequent regression analysis focusing on these individual subscales. Correlations between total PCQ and preparatory job search behaviors was positive, though close to zero and not significant (r = 0.18, p = 0.10). However, correlations between total PCQ and active job search behaviors was higher, and possessed a high degree of significance (r = 0.28, p = 0.01). Table 5. Correlation Results: PCQ Subscales, Preparatory Job Search Behaviors, and Active Job Search Behaviors
Self-efficacy r p Hope r p Resilience r p Optimism r p Preparatory r p
Self-efficacy
Hope
Resilience
Optimism
Preparatory
Active
1
.386 0
.521** 0
.539** 0
0.139 0.104
.189* 0.043
.386** 0
1
.628** 0
.657** 0
0.098 0.187
.309** 0.002
.521** 0
.628** 0
1
.759** 0
0.125 0.128
.318** 0.002
.539** 0
.657** 0
.759** 0
1
.231* 0.017 84
0.123 0.132
0.139 0.104
0.098 0.187
0.125 0.128
.231* 0.017
1
.420** 0
84 Active r p
.189* 0.043
.309** 0.002
.318** 0.002
0.123 0.132
** Correlation is significant at the 0.01 level (1-tailed). * Correlation is significant at the 0.05 level (1-tailed).
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.420** 0
1
Regression Analysis: PCQ Subscales and Job Search Behaviors In order to respond to this study’s research questions, multiple linear regressions were conducted for each of the job search behaviors scales (preparatory and active) and PCQ subscales. More specifically, two regression analyses were conducted; one for each dependent variable. To determine if the model correctly predicted the outcome variable, an F test was conducted. R2 was presented to demonstrate how much variability in the dependent variable was attributable to the model consisting of four predictors. The t test assessed the significance of the individual predictors. For those significant predictors, beta coefficients were examined. For each positively, significant beta coefficient, a one-unit increase in the predictor will result is an increase in the outcome variable by the unstandardized beta value. For each negatively, significant beta coefficient, a one-unit increase in the predictor will result in a decrease in the outcome variable by the unstandardized beta value (Tabachnick & Fidell, 2012). The presence of multicollinearity was assessed to be certain independent variables were not too related and was assessed with Variance Inflation Factors (Stevens, 2009). The assumptions of multiple regression, linearity, homogeneity, and absence of multicollinearity were assessed prior to analysis (Stevens, 2009). Residual scatter plots were used to examine homogeneity and linearity. Figures 10 and 11 display scatter plots for PCQ subscales and preparatory job search behaviors and for PCQ and active job search behaviors, respectively. Slight linearity is evident in both scatter plots; however, there is a lack of homogeneity exhibited by high degree of variability. Homogeneity is required for regression analysis to be valid. 74
PCQ Subscales and Preparatory Job Search Behaviors Figure 10. Scatter plot for PCQ subscales and preparatory job search behaviors.
75
PCQ Subscales and Active Job Search Behaviors PCQ Subscales Figure 11. Scatter plot for PCQ subscales and active job search behaviors. Results of the regression analysis between individual PCQ subscales and preparatory job search behaviors can be view in Tables 6 and 7. These results reflected no significant relationship between the PCQ subscales’ independent variables and the dependent variable of preparatory job search behaviors (R2 = .063, F = 1.33, p = .267). Based on the results of this regression analysis, the alternative hypothesis that the four subscales of the psychological capital assessment do predict preparatory job search behaviors must be rejected, and the null hypothesis that the four subscales of the psychological capital assessment do not predict preparatory job search behaviors must be retained. 76
The results of the regression analysis between individual PCQ subscales and active job search behaviors can be viewed in Tables 8 and 9. These results were more favorable, reflecting a highly significant relationship between the PCQ subscale independent variables and dependent variable of active job search behaviors (R2 = .188, F = 4.59, p = .002). Based on the results of this regression analysis, the alternative hypothesis that the four subscales of the psychological capital assessment do predict active job search behaviors must be accepted, and the null hypothesis that the four subscales of the psychological capital assessment do not predict active job search behaviors must be rejected. Table 6. ANOVAb Regression Analysis Between PCQ Subscales and Preparatory Job Search Behaviors Model 1
SS
df
MS
F
p
Regression
2.330
4
.582
1.328
.267a
Residual
34.634
79
.438
Total
36.964
83
a. Predictors: (constant), optimism, self-efficacy, hope, and resilience. b. Dependent variable: preparatory job search behaviors.
Table 7. Coefficientsa Regression Analysis Between PCQ Subscales and Preparatory Job Search Behaviors Model 1 (Constant)
B* 2.977
SE* .650
β
T 4.577
p .000
Tolerance
VIF
Self-efficacy
.042
.139
.040
.301
.764
.680
1.470
Hope
-.055
.114
-.072
-.482
.631
.528
1.893
Resilience
-.090
.150
-.106
-.598
.552
.379
2.636
Optimism
.251
.137
.337
1.828
.071
.349
2.869
a. Dependent variable: preparatory job search behaviors. * Unstandardized coefficients.
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Table 8. ANOVAb Regression Analysis Between PCQ Subscales and Active Job Search Behaviors Model 1
SS
df
MS
F
p
Regression
12.521
4
3.130
4.587
.002a
Residual
53.911
79
.682
Total
66.432
83
a. Predictors: (constant), optimism, self-efficacy, hope, and resilience. b. Dependent variable: active job search behaviors.
Table 9. Coefficientsa Regression Analysis Between PCQ Subscales and Active Job Search Behaviors Model 1 (Constant)
B* 1.031
SE* .811
β
T 1.271
p .207
Tolerance
VIF
Self-efficacy
.133
.174
.094
.766
.446
.680
1.470
Hope
.307
.142
.302
2.164
.033
.528
1.893
Resilience
.469
.187
.413
2.510
.014
.379
2.636
Optimism
-.439
.171
-.439
-2.559
.012
.349
2.869
a. Dependent variable: active job search behaviors. * Unstandardized coefficients.
Regression Analysis: Total PCQ and Job Search Behaviors In response to identified high correlations between PCQ subscales, two additional regression analyses were conducted that focused on total PCQ as an integrated construct as well as the constructs of both preparatory and active job search behaviors. These high correlations make it difficult to identify the effect of any of the four predictor variables on the criterion variables. The results of the regression analysis between total PCQ and preparatory job search behaviors are not significant and show no relationship between
78
total PCQ and preparatory job search behaviors (R2 = .032, F = 2.73, p = .102). Additional detail related to this regression analysis can be viewed in Tables 10 and 11. The results of the regression analysis between total PCQ and active job search behaviors, however, reflected a highly significant relationship between total PCQ and active job search behaviors (R2 = .282, F = 7.07, p = .009). Tables 12 and 13 provide additional detail related to this regression analysis.
Table 10. ANOVAb Regression Analysis Between Total PCQ and Preparatory Job Search Behaviors Model 1
SS
df
MS
F
p
2.733
.102a
Regression
1.192
1
1.192
Residual
35.772
82
.436
Total
36.964
83
a. Predictors: (constant), total PCQ. b. Dependent variable: preparatory job search behaviors.
Table 11. Coefficientsa Regression Analysis Between Total PCQ and Preparatory Job Search Behaviors Model 1
B*
SE*
(Constant)
2.833
.515
Total PCQ
.179
.109
a. Dependent variable: preparatory job search behaviors. * Unstandardized coefficients.
79
B .180
t
p
5.499
.000
1.653
.102
Table 12. ANOVAb Regression Analysis Between Total PCQ and Active Job Search Behaviors Model 1
SS
df
MS
F
p
7.070
.009a
Regression
5.273
1
5.273
Residual
61.159
82
.746
Total
66.432
83
a. Predictors: (constant), total PCQ. b. Dependent variable: active job search behaviors.
Table 13. Coefficientsa Regression Analysis Between Total PCQ and Active Job Search Behaviors Model 1
B*
SE*
(Constant)
1.498
.674
Total PCQ
.377
.142
B .282
t
p
2.224
.029
2.659
.009
a. Dependent variable: active job search behaviors. * Unstandardized coefficients.
Summary The results of this research study do not support the hypotheses that the four subscales of the psychological capital assessment predict preparatory job search behaviors. Thus, the first research question must be rejected with the null hypotheses, that the four subscales of the psychological capital assessment do not predict preparatory job search behaviors being retained. The results of this study, however, do support the second research question and hypothesis that the four subscales of the psychological capital assessment do predict active job search behaviors. Therefore, the research hypothesis, the
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four subscales of the psychological capital assessment do predict active job search behaviors, is accepted, with the null hypothesis being rejected. Additional regression analyses were performed between total PCQ and preparatory and active job search behaviors. The results of these analyses reflected no significant relationship between total PCQ and preparatory job search behaviors. A significant relationship was identified between total PCQ and active job search behaviors. An overview of the data analysis related to this study was presented in this dissertation chapter. These results facilitated the testing of this study’s research questions and respective hypotheses. Interpretations of these findings, study limitations and implications, and recommendations will be discussed in Chapter 5.
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CHAPTER 5. DISCUSSIONS, IMPLICATIONS, RECOMMENDATIONS Introduction The purpose of this research study was to test the theory of Psychological Capital (PsyCap) that related PsyCap to preparatory and active job search behaviors. The design of this study focused on active job searchers receiving outplacement services at an international organization for outplacement services. This survey was based on two measures: the Psychological Capital Questionnaire and Job Search Behaviors Scale, thus measuring PsyCap and job search behaviors, respectively. Chapter 5 provides an interpretive discussion regarding the results of this study, a synopsis of this study’s implications and limitations, recommendations for future research, and concluding comments. Summary of Results Business challenges, competitive pressures, and an economic slowdown have resulted in high levels of unemployment. This study’s exploration of the synergistic impact of all four subconstructs of PsyCap provided an opportunity to expand on the theoretical framework of positive psychology as it relates to the reemployment process. PsyCap was investigated in relation to job search intensity as measured by preparatory and active job search behaviors. The literature reviewed in association with this study focused on the theoretical foundations and research relating to positive psychological capital and the job search process. Luthans, Luthans, and Luthans (2004) found that there was a relationship between the positive psychological capacities of self-efficacy and optimism and a job searcher’s return to employment. A significant amount of prior research (Shamir, 1986; Winefield, Tiggemann, & Winefield, 1992; Eden & Aviram, 82
1993; Wanberg, 1997; Kanfer, Wanberg, & Kantrowitz, 2001; Waters & Moore, 2002; Crossley & Stanton, 2005; McArdle, Waters, Briscoe, & Hall, 2007) supports these findings. Fleig-Palmer, Luthans, and Mandernach (2009) proposed that a positive state of resiliency could successfully guide job seekers toward reemployment and recommended a theoretical framework for empirical research. The research methodology selected for this study was a correlational, nonexperimental, one-shot quantitative model. The findings from this study reflect nonsignificant, minimal statistical support for a relationship between the four subscales of the PCQ assessment and preparatory job search behaviors. However, there was high statistical significance regarding a positive relationship between the four subscales of the PCQ assessment and active job search behaviors. These findings further the existing research regarding positive psychology and the job search by focusing on the core construct of PsyCap and its relationship preparatory and active job search behaviors. Discussion of Results Positive Psychological Capital (PsyCap), as a higher level core construct, yields a synergistic effect resulting from the integration of the individual capacities of selfefficacy, optimism, hope, and resilience, with this effect leading to optimal performance (Luthans, Youssef, & Avolio, 2007). Job search intensity has been identified as a critical factor regarding a job searcher’s return to work and has been operationalized through preparatory and active job search behaviors (Blau, 1994). It has been argued that positive psychological capacities contribute to increased job search intensity (Sacks, 2005; Luthans & Mandernach, 2009; Chen & Lim, 2012). Furthermore, it has been suggested that increased job search intensity impacts the speed at which a job searcher becomes 83
reemployed (Schwab et al., 1987; Blau, 1994; Saks, 2005). Therefore, this research study explored the relationship between PsyCap’s four subscales as a predictor of job search intensity, as measured by preparatory and active job search behaviors. Strong reliability and internal consistency of PCQ and Job Search Behaviors measures was demonstrated by high Cronbach’s alpha scores. These scores are consistent with the psychometric results related to prior studies, as referenced in Chapter 3. Both instruments have been proven to be highly reliable with regard to measuring PsyCap (Luthans et al., 2007) and job search behaviors (Blau, 1994). In analyzing the descriptive statistics related to this study, it appears that the participants perceived their PsyCap as relatively high, which is represented by total PCQ and subscale value means in the upper third of the PCQ scale. A similar observation was made when reviewing self-scored responses related to job search behaviors, with participant value-score means appearing well above the upper half of the respective scales. However, inflated results related to self-scored surveys has been identified as a limitation in prior studies (Avey et al., 2011; Peterson et al., 2011) and research methods-related works (Cooper & Schindler, 2008; Creswell, 2003: Fowler, 2002; Swanson & Holton, 2005). Next, a review of the results of the Pearson correlation analysis indicates high correlations between all PCQ subscale values. These correlations give the impression that each subscale is measuring almost the same psychological capacity, making it difficult to measure their independent effect on both preparatory and active job search behaviors. This limitation of the PCQ instrument limits the ability to distinguish the effect of each subscale on job search behaviors. PsyCap as a second order, higher-level construct recognizes that interdependencies exist between its four subconstructs. These correlations 84
appear to reflect these interdependencies and a shared commonality, thus providing justification for the PCQ subscale correlations. Two regression analyses were performed to compare the PCQ subscales to preparatory and active job search behaviors. The assumptions of multiple regression, linearity, homogeneity, and the absence of multicollinearity were assessed prior to analysis (Stevens, 2009). Scatter plots were examined to determine if homogeneity and linearity were present. These scatter plots reflected slight linearity and a lack of homogeneity. PCQ subscale values for preparatory and active job search behavior value scores had a high degree of variation and were not normally distributed around the regression line. Multicollinearity was not present with VIF, all VIF values being under 10. Overall, the assumptions of multiple regression analysis were not satisfied due to a lack of linearity and homogeneity, thus limiting the validity of these regression analyses. The results of the first regression reflected a minimal relationship of no significance between PCQ subscale values and preparatory job search behaviors. Based on this result, the null hypotheses that the four subscales of the psychological capital assessment do not predict preparatory job search behaviors was not rejected. The results of the second regression, which evidenced a higher level of significance, did reflect a positive relationship between the PCQ subscales and active job search behaviors. Therefore, the null hypothesis that the four subscales of the psychological capital assessment do not predict active job search behaviors was rejected. The results of these regression analyses are reflective and consistent with the sequential phases and respective behaviors associated with the job search behaviors scale. The preparatory phase leverages a job searcher’s social and human capital in preparation 85
for the active phase. During the active phase, the impact of PsyCap is more evident with respect to actionable active job search behaviors. It is also important to note that the cross-sectional nature of this study limited the ability to infer causality between the PCQ subscales and preparatory and active job search behavior variables. Two additional regression analyses were conducted in response to identified high correlations between PCQ subscales. These regressions were between the total PCQ values and preparatory and active job search scale values. These analyses, although not responding specifically to this study’s research questions, provide further empirical findings regarding PsyCap as a higher-level core construct and its relationship to job search behaviors (Luthans et al., 2007). The regression analysis result for total PCQ and preparatory job search behaviors scale values reflected borderline significance, while the regression analysis result for the total PCQ values and active job search behaviors scales reflected a highly significant relationship. These results provide support for PsyCap as a second-order, higher-level construct in relation to job search behaviors (Luthans et al., 2007). Implications of the Study Results As mentioned earlier, the PsyCap construct was designed based on a conceptual framework created by Law, Wong, and Mobley (1998) and Hobfoll’s (2002) psychological resources theory. These theorists argued that some psychological constructs are best conceptualized as underlying core constructs. “This is especially evident when the constructs are highly related yet integrated with each other,” claimed Luthans et al. (2008, p. 212). Furthermore, PsyCap as a second-order construct is “comprised of the shared variance between the four recognized positive psychological 86
resources of hope, optimism, self-efficacy, and resilience” (Avey et al., 2011, p. 130). This is evident even though all four PsyCap subconstructs and reflect independence, convergent, and discriminate validity (Bandura, 1997; Luthans & Jensen, 2002; Luthans et al., 2007; Snyder, 2000). Even though PCQ subscale independence and validity has been demonstrated in prior research, the results of these two regression analyses support the development and application of PsyCap as a higher-level core construct (Luthans et al., 2007). The four PsyCap capacities together have been validated as a second-order higher-level construct (Luthans et al., 2007; Luthans et al, 2010). The reported high correlations between the PCQ subscales give the impression that these subscales appear to measure the same thing. Stajkovic (2006) argued that “conceptual evidence points out that hope, self-efficacy, optimism, and resiliency may share a common confidence core” (p. 1209). Furthermore, Snyder (2002) identified similarities between optimism and hope in relation to perceived outcomes. Therefore, it is not possible to conclude that any of the four subscales predict preparatory or active job search behaviors. As mentioned in the literature review, there are three models representing the job search process: sequential, learning, and emotional. The job search behaviors scale, being sequential, portrays the job search process occurring in two phases: preparatory and active. Schwab et al. (1987) suggested that a job searcher’s success was based on the sources used to gain information and the subsequent use of this information in conjunction with a successful active execution. Chen and Lim (2012) argued that preparatory and active search behaviors have different antecedents; in the preparatory phase, a job searcher’s activities are focused more on the social and human capital 87
aspects of the job search. These activities include reading job advertisements, preparing a résumé, reading about the job search, talking to others about job leads, and locating open jobs on the Internet. The learning job search model suggests that a job searcher’s effectiveness improves throughout a job search as a result of the ongoing learning that occurs as they progress through their job search (Barber et al., 1994). This learning, through the acquisition of knowledge and experience during the job search process, emphasizes a human and social capital perspective toward the job search. The inability of this dissertation study to demonstrate a significant relationship between both the PCQ subscales and total PCQ values and preparatory job search behaviors is based on the nature of these activities and a greater reliance on a job searcher’s social and human capital as compared to their psychological capital. With this in mind, it comes as no surprise that the findings of this study did not support the first hypothesis that the four subscales of the psychological capital assessment do predict preparatory job search behaviors. During the active job search phase, the job searcher actually commits to the job search (Blau, 1994). Job search behaviors related to this phase include posting résumés on job websites, sending résumés to potential employers, completing job applications, interviewing, and contacting search firms and prospective employers. These active job search activities leverage the results of the preparatory phase in positioning the job searcher for engagement in an active job search. Blau (1993) argued that “individuals who felt more positive about their job-seeking skills were more likely to move from the preparatory job search stage to the active job search stage” (p. 307). During this phase, 88
the PsyCap capacities of self-efficacy, hope, optimism, and resilience are critical. Saks (2005) found the subconstruct of self-efficacy to be a highly significant predictor of active job search behaviors. When dealing with rejection and defeat, the job searcher’s PsyCap provides the necessary positive motivational capacities for a successful active job search phase. Therefore, the acceptance of this study’s second hypothesis—that the four subscales of the psychological capital assessment do predict preparatory job search behaviors—is supported by the reviewed research literature. Limitations The limitations related to this study were identified in the discussion of results but were not discussed with any level of detail. In this section, these limitations will be fully discussed, including suggestions for reasonable improvements that will result improvements in research and stronger results from future similar studies. The limitations associated with this study were, personal bias associated with a self-report measure, a high correlation of PCQ subscales, the multiple regressions’ lack of validity, and a crosssectional design, produce an inability to infer causality. A delimitation associated with this study involves the lack of external validity resulting from the use of a single site or narrow geography. The first limitation is personal bias associated with a self-report measure. This survey characteristic has been reported as a limitation in prior research (Avey et al., 2011; Peterson et al., 2011) and research-related texts (Cooper & Schindler, 2008; Creswell, 2003: Fowler, 2002; Swanson & Holton, 2005). The second limitation, high correlation of PCQ subscales, is associated with the design of PsyCap as a second-order core construct. Theoretical research discussed earlier 89
(Long, Wong & Mobley, 1998; Hobfoll, 2002; Luthans et al., 2008; Avey et al., 2011) indicated there is a common interrelatedness of subconstructs associated with a higherorder core construct. The use of a modified measure that includes alternative subconstructs could mitigate this issue. Luthans (2002) identified the capacities of subjective well-being and emotional intelligence as meeting the subconstruct criteria associated with PsyCap. Youssef and Luthans (2011) also identified the testing of other psychological capacities as a recommendation for future research. The third limitation focuses on not satisfying the assumptions of multiple regression; linearity, homogeneity, and absence of multicollinearity (Stevens, 2009). The forth limitation, the cross-sectional design of this study, prevents any inferences of causality. A longitudinal design would provide insight into “if and how PsyCap fluctuates over time and the corresponding effects on important organizational outcomes such as employee development and performance” (Avey et al. 2008, p. 705). A delimitation of this study deals with a lack of validity and inability to generalize the findings due to the single site and limited geography of this study. Budget and time constraints hindered the execution of identified alternatives. Recommendations for further study will be covered in the next section, with the limitations and delimitations identified in this section providing a basis for recommendations for further study. Recommendations for Further Study The recommendations for further study fall into four categories: 1) recommendations developed directly from the data; 2) recommendations derived from methodological, research design or other limitations of the study; 3) recommendations 90
based on delimitations; and 4) recommendations to investigate issues not supported by the data but relevant to the research problem. Recommendations Developed Directly From the Data The data collected and analyzed reflected high correlations between PCQ subscale variables. As mentioned earlier, this presents a challenge in relation to the identification of the impact of each PsyCap subconstruct on job search behaviors. Other researchers could replicate this study using a modified PsyCap measure and substituting some of the subconstructs with others, as is recommended in the literature (Luthans, 2002; Youssef & Luthans, 2011). This substitution might result in less intercorrelation. Recommendations Derived From Methodological, Research Design, or Other Limitations of the Study As discussed earlier, the self-reporting nature of this study provided for potential personal bias in relation to survey question responses. According to Peterson et al., (2011), “Like any self-report measure with potential biases in terms of social desirability, the PCQ should be paired with other types of assessments to ensure organizations are getting a valid picture of employees psychological capital (p. 446). In response to this personal bias limitation, the adoption of a mixed-methods approach would add a qualitative dimension. Though a triage of both quantitative and qualitative results, participant bias would be mitigated. The cross-sectional nature of this study limits the researcher’s ability to infer causality. A recommendation for further research would be conduct two surveys with the same sample, with the first occurring at the start of the job search and second survey being distributed four months into the search. Given the sequential nature of preparatory
91
and active job search behaviors, this longitudinal approach could provide more meaningful data regarding the impact of PsyCap on job search behaviors. Recommendations Based on Delimitations The single site, or limited geographic nature, of this study limits the validity and ability to generalize the results. Other researchers might want to consider a larger geography or multisite design. Recommendations to Investigate Issues Not Supported by the Data but Relevant to the Research Problem The emphasis of this study was related to the impact of PsyCap on job searcher behavior. The literature on PsyCap (Luthans, Avolio, Avey, & Norman, 2007a; Luthans, Norman, Avolio, & Avey, 2007b; Luthans, Avey, Smith, & Li, 2008a; Peterson, Luthans, Avolio, Walumbwa, & Zeng, 2011; Avey, Reichard, Luthans, & Mhatre, 2011). emphasizes its impact on performance outcomes. Furthermore, the literature on job search intensity and job search behaviors (Barber et al., 1994; Sacks & Ashforth, 2000; Wanberg, Glomb, Song, & Sorenson, 2005) reinforces the importance of these behaviors on reemployment. A recommendation for further research would be to conduct a longitudinal study measuring the additional dimension of reemployment outcome. Conclusion The purpose of this dissertation study was to test the theory of Psychological Capital (PsyCap) by exploring the impact of PsyCap on preparatory and active job search behaviors. The results and findings of this study were not statistically significant (p = .102) with respect to the relationship between PsyCap and preparatory job search behaviors. The relation between PsyCap and active job search behaviors was highly significant (p = .009). These results are reflective of the sequential nature of the job 92
search and a reliance on PsyCap being more critical during the active phase. The highly correlated relationships between the PCQ subscales provide cause for the exploration of other measurement options in association with positive psychological capacities and job search behaviors. Furthermore, the literature indicates that continued consideration of alternative PsyCap subconstructs is recommended for further research. The results of this study, particularly the acceptance of the null hypothesis with respect to PsyCap and preparatory job search behaviors and the rejection of the null hypothesis regarding the relationship between PsyCap and active job search behaviors, comes as no surprise. The psychological capacities supported by PsyCap provide the job searcher with the necessary personal strength to successfully execute the job search. A more holistic view of the job search would embrace a multidisciplinary, integrative perspective. All three forms of capital—human, social, and psychological—play critical roles in a successful job search and a quick return to employment. Some of the critical comments made regarding positivity, positive organizational behavior, and positive psychological capital discourage the adoption of a “silver bullet” as a solution to a societal phenomenon. The result of this dissertation reinforced this perspective. To conclude, the results of this study extend the theory and research related to psychological capital and its relationship to reemployment through the introduction of hope, resilience, and the synergistic power of all four subconstructs together as they relate to job search intensity as measured by job search behaviors. PsyCap extends the research regarding positivity and job search intensity by the addition of the two subconstructs of hope and resilience a part of a second-order core construct. With the addition of PsyCap as an enabler for the active job searcher, a latent competency gap has 93
been identified, but it can be strengthened and developed. The job searcher will now have the opportunity to successfully engage with the job search through an integrated approach leveraging cognitive, networking, and psychological competencies related to the job search.
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