1 person-organization and person-job fit perceptions of new employees

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PERSON-ORGANIZATION AND PERSON-JOB FIT PERCEPTIONS OF NEW EMPLOYEES: WORK OUTCOMES AND GENDER DIFFERENCES

Viswanath Venkatesh Distinguished Professor and George and Boyce Billingsley Chair in Information Systems

Walton College of Business University of Arkansas Fayetteville, AR 72701

Tel: 479-575-3869; Fax: 479-575-3689 Email: [email protected]

Jaime B. Windeler Assistant Professor of Information Systems

Department of Operations, Business Analytics and Information Systems Carl H. Lindner College of Business

University of Cincinnati Cincinnati, OH 45221

Tel: 513-556-7120; Fax: 513-556-5499 E-mail: [email protected]

Kathryn M. Bartol Robert H. Smith Professor of Leadership and Innovation

Co-Director, Center for Leadership, Innovation and Change 4538 Van Munching Hall

Robert H. Smith School of Business University of Maryland

College Park, MD 20742 Tel: 301-405-2249

Email: [email protected]

Ian O. Williamson Associate Dean of International Relations

Melbourne Business School 200 Leicester St Carlton VIC 3053

Tel: 61 3 9349 8157; Fax: 61 3 9349 8414 Email: [email protected]

mailto:[email protected]



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Author Biographies Viswanath Venkatesh is a Distinguished Professor and Billingsley Chair in Information Systems at the University of Arkansas, where he has been since June 2004. Prior to joining Arkansas, he was on the faculty at the University of Maryland and received his Ph.D. at the University of Minnesota. His research focuses on understanding the diffusion of technologies in organizations and society. His work has appeared or is forthcoming in leading journals in information systems, organizational behavior, psychology, marketing, and operations management. Recently, he was recognized as one of the most influential scholars in business and economics (highlycited.com). His articles have been cited about 50,000 times and about 12,000 times per Google Scholar and Web of Science, respectively. He developed and maintains a web site that tracks researcher and university research productivity (http://www.myvisionsresearch.com/ISRankings). He has published a book titled Road to Success: A Guide for Doctoral Students and Junior Faculty Members in the Behavioral and Social Sciences (http://vvenkatesh.com/book). Jaime B. Windeler is an Assistant Professor of Operations, Business Analytics and Information Systems in the Carl H. Lindner College of Business at the University of Cincinnati. She earned her Ph.D. in Information Systems from the Sam M. Walton College of Business at the University of Arkansas. Jaime’s research focuses on the management of distributed software development teams and the attraction, selection and retention of IT professionals. She is currently working on a number of projects that examine how leadership functions in the distributed team context and in support of IT professionals. Jaime’s research has been published or is forthcoming in premier outlets such as Information Systems Research, MIS Quarterly, the Journal of the Association for Information Systems, and Information Systems Journal, among others. She is a member of the Association for Information Systems and INFORMS. Kathryn M. Bartol is the Robert H. Smith Professor of Leadership and Innovation and Co-Director of the Center for Leadership, Innovation and Change at the Robert H. Smith School of Business, University of Maryland, College Park. She completed her Ph.D. at the Michigan State University. She is a Past President of the Academy of Management and a past Dean of the Fellows of the Academy of Management. Her research interests include leadership, teams, knowledge sharing, creativity, and gender and work. Her many articles have appeared in such leading journals as the Academy of Management Journal, the Journal of Applied Psychology, the Academy of Management Review, Personnel Psychology, the Journal of Personality and Social Psychology, and MIS Quarterly. She is a Fellow of the Academy of Management, the American Psychological Association, the Society for Industrial/Organizational Psychology, and the American Psychological Society. Ian O. Williamson is the associate dean of international relations in the Melbourne Business School (Australia). He also serves as director of the Asia Pacific Social Impact Leadership Centre, where his focus is on developing effective partnerships between business schools, not-for-profit, for-profit, philanthropic, and government entities to address intractable social issues. His research examines how the development of effective “talent pipelines” influences organizational and community outcomes. Specifically, he examines how the recruitment, selection, and retention of employees influence firm performance, talent management in small businesses, the management of diverse workforces, and the role of human resource practices in driving firm innovation.

http://www.myvisionsresearch.com/ISRankings

http://vvenkatesh.com/book

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Acknowledgements

We appreciate the feedback and guidance of the Senior Editor, Dr. Paulo Goes, the AE, and reviewers. This manuscript is based partly upon work supported by the National Science Foundation under Grant No. 0089941. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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PERSON-ORGANIZATION AND PERSON-JOB FIT PERCEPTIONS OF NEW EMPLOYEES: WORK OUTCOMES AND GENDER DIFFERENCES

ABSTRACT

Drawing from a total rewards perspective, we introduce three work outcomes—namely, extrinsic, social and intrinsic—as determinants of person-organization (PO) and person-job (PJ) fit perceptions of new IT employees. Gender is proposed as a moderator of the relationships between valuations of different work outcomes and fit perceptions. We found support for our model in three separate studies. In each of the studies, we gathered data about the work outcomes and fit perceptions of IT workers. The studies were designed to complement each other in terms of cross-temporal validity (studies were conducted at difference points in time over ten years, in periods of differing economic stability) and in terms of prior work experience (entry-level workers in studies 1 and 2, and those with prior work experience starting new jobs in study 3). All three studies also included data both pre- and post-organizational entry in order to further validate the robustness of the model. The studies largely supported our hypotheses that: (a) the effect of extrinsic outcomes on PO fit was moderated by gender, such that it was more important to men in determining their PO fit perceptions; (b) the effects of social outcomes on both PO fit and PJ fit was moderated by gender, such that it was more important to women in determining their fit perceptions; and (c) intrinsic outcomes influenced perceptions of PJ fit for both men and women. We discuss implications for research and practice.

INTRODUCTION

Given the dynamic nature of the global marketplace and the pace at which it changes, the attraction,

motivation and retention of workers is critical for the continued success of organizations (see Dineen et al.

2002). In times of economic downturn, voluntary turnover of an employee can result in the business unit not

being allowed to replace the lost worker or face high replacement costs (Timpany 2013). Attracting,

motivating and retaining workers hinges on fulfilling their needs at work (Prasad et al. 2007). Understanding

the work outcomes that are important to individuals across various phases of the professional pipeline is

important for several reasons. Entry-level workers possess the advantage of having been formally trained

and being skilled in cutting-edge techniques and approaches. For example, changes in the information

technology (IT) industry have always been rapid, prompting the attractiveness of entry-level workers who are

trained in the latest concepts, techniques and tools. Likewise, experienced workers have value for

organizations seeking employees with diverse experiences, developed leadership skills and knowledge from

competitors. An investigation of work outcome valuation is necessary in light of the changes in work

environments after recent economic challenges and in light of efforts to fuel innovation in critical sectors

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related to technology, healthcare and security (Anderson 2009; Gates 2013). Moreover, the study of the work

values of entry-level IT workers is particularly important. The U.S. President Obama’s recent Educate to

Innovate initiative has launched an effort to improve the training of the next generation of IT workers,

underscoring the need for a continuous supply of high-quality professionals (White House 2013). Attracting,

motivating and retaining IT workers have been formidable challenges for more than a decade. Despite the

recent slowdown in the market, being able to hire and retain high-quality IT talent is vital (Ferratt et al. 2005;

Moore 2000) and the issue can be expected to regain prominence as the market for IT workers is expected

to accelerate again (e.g., Bureau of Labor Statistics 2012). Despite recent attention given to the need to

recruit and retain IT workers (e.g., Ferratt et al. 2012), IS research has primarily focused on existing

employees, with little research focused on new employees (Jiang and Klein 1999; Jiang et al. 2001). In order

to maintain a continuous supply of IT professionals, it is important to understand their work outcome

valuation across various phases of the professional pipeline.

An additional focal point of interest to organizations is to create and maintain a workplace that is

equitable to both women and men. There is a long-standing view that what women and men want from a

workplace is often different (Brief and Aldag 1975; Chow and Ngo 2011; Kilmartin 2000). Recent IS research

indicated that work-related values and preferences are not always homogenous across gender (Trauth et al.

2009). In a review paper, Smith (2002) argued that various exclusionary and inclusionary policies at the

micro, macro and meso levels have tended to make workplaces less sensitive to the needs and preferences

of women. In fact, in a number of professions, and in IT in particular, women have tended to be

underrepresented in the workplace (e.g., Ahuja 2002; Baroudi and Igbaria 1995; Igbaria and Chidambaram

1997; Klawe et al. 2009). Although women today earn more undergraduate degrees than men do (e.g., Justis

2008), the proportion of women earning undergraduate degrees in technology-related fields has actually

been shrinking: from 37% in 1985, down to just 18% in 2008 (NCWIT 2010). As a result, many organizations

are pursuing active strategies to create a workplace that is more encouraging of women’s participation and

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retention—an example is Intel’s diversity initiative aimed at supporting women.1 Thus, investigating gender

differences in work outcome valuation is essential to create an equitable workplace.

Although there are many frameworks available to examine issues related to employee attraction,

motivation, and retention, one that has been recently related to several important outcomes is that of

employee fit perceptions. Fit perceptions are broadly defined as an individual’s perceptions of the

congruence between him (or her) and his (or her) job and/or organization (Edwards 1991; Kristof 1996;

Kristof-Brown 2000). Fit perceptions are critical in increasing applicant attraction to an organization (e.g.,

Judge and Cable 1997), job satisfaction (e.g., Verquer et al. 2003) as well as increasing organizational

commitment and reducing turnover intentions (e.g., van Vianen 2000; Verquer et al. 2003). The use of fit

perceptions to study attraction, motivation and retention of employees complements other prevalent

approaches to studying these issues. However, recent research, including a meta-analysis of fit perceptions,

indicated that although the consequences of fit have been well researched, exploring the mechanisms that

stimulate fit are long overdue (Barrick et al., 2013; Colbert et al. 2008; Kristof-Brown et al. 2005). The

contemporary view of motivation, compensation and incentives of employees emphasizes a total rewards

perspective (e.g., Jiang et al. 2009). We employ this perspective in considering not only tangible outcomes,

such as compensation and benefits, but also intangible outcomes, such as facilitating work-life balance,

offering development opportunities (Lawler and Finegold 2000) and intrinsic benefits (Hackman and Oldham

1980). For example, there has been recent interest in providing IT workers with skill development

opportunities through participation in open source projects (Mehra and Mookerjee 2012). The current work

employs a set of work outcomes, grounded in prior theory and reflective of contemporary thought, as

determinants of new IT workers’ fit perceptions. Specifically, we examine three types of work outcomes—i.e.,

extrinsic (e.g., pay, promotion), social (e.g., friendly co-workers, work-life balance) and intrinsic (e.g., creative

1 (http://www.intel.com/jobs/diversity/women.htm)

http://www.intel.com/intel/diversity/divexecpers.htm

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work, skill development). We theorize that expectations prior to organizational entry and experiences after

organizational entry about the extent to which various work outcomes will be present in the new job and

organization will be moderated by gender to determine fit perceptions. We are also interested in the

generalizability of our model. Lee and Baskerville (2003, 2012) highlighted four types of generalizability that

involve generalizing from and to theory as well as from and to empirical statement. They note that TE

generalizability (from theory to empirical statements) or cross-population and contextual generalization

(Tsang and Williams 2012) is “arguably the most important form of generalizability in business-school

research” (Lee and Baskerville 2003, p. 237).

Against this backdrop, our objectives are:

(1) to develop a model of person-organization and person-job fit that accounts for gender differences;

(2) to validate the model through empirical studies among entry-level IT workers; and

(3) to examine the generalizability of our model by studying different contexts, including entry-level to

experienced workers and from IT to other professional domains.

This work is expected to contribute to the literature in three important ways. First, by studying this

model in the context of entry-level IT workers, this work contributes to the IT personnel literature. An

understanding of the unique characteristics and needs of IT workers in today’s business environment is

somewhat limited (see Joseph et al. 2007). Although some prior research has found no differences between

IT and non-IT workers (e.g., Ferratt and Short 1986, 1988), other research has indicated some differences

between IT workers and those in other domains (Bartol and Martin 1982; Loh et al. 1995). In exploring work

outcome values and fit perceptions among entry-level workers, we add to the IT personnel literature that

seeks a deeper understanding of the factors that are important to IT workers. Second, we establish the

generalizability of the model by examining it in the context of new and experienced IT workers, and

contribute to the broader vocational and organizational behavior (OB) literature by examining its applicability

across different professional domains with data collected at difference points in time over ten years (see Lee

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and Baskerville 2003, 2012). Tsang and Williams (2012, p. 14) note that, “…social scientists have to

investigate whether their research findings collected in one space-time setting are generalizable to other

significantly different space-time settings; in other words, whether these findings are contextually and

temporally generalizable.” Third, we contribute to the broader research on human resources. In examining

differences in work outcomes, we shed light on the interplay between work outcomes, gender and fit

perceptions to provide a deeper understanding of gender differences in the workplace. Further, this

exploration provides practitioners with actionable guidance for how they can enhance the attraction,

motivation, and retention of qualified workers. By expanding the nomological network related to fit

perceptions, we also extend prior work related to employee fit (Kristof 1996; Kristof-Brown 2000).

THEORY

In this section, we first review the literature related to our dependent variables, namely PO fit and PJ

fit. We then provide the background for our independent variables, namely extrinsic, social and intrinsic work

outcomes. Following this, we provide the justification for our hypotheses related to the direct effects of work

outcomes on PO fit and PJ fit as well as moderation of these relationships by gender.

Dependent Variables: Person-organization Fit and Person-job Fit

There has been great interest in the various types of fit between individuals and their workplaces

(Kristof-Brown et al. 2002, Kristof-Brown et al. 2005; Kristof-Brown and Guay 2011; Yang and Yu 2014). The

concept of fit has its roots in interactional psychology (see Kristof 1996; Kristof-Brown 2000; Kristof-Brown

and Billsberry 2013), and focuses on the congruence in person-situation interaction (Edwards 1991). Fit

perceptions in general are defined as the congruence between an individual’s interests and what is offered

by the job and the organization (see Edwards 1991; Kristof 1996; Kristof-Brown 2000; Kristof-Brown and

Billsberry 2013; van Vianen 2000 for reviews), thus resulting in two fit constructs: person-organization (PO) fit

and person-job (PJ) fit. PO fit focuses on the congruence between an individual and the broad organizational

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environment, and PJ fit focuses on the congruence between an individual and the specific job environment

(Kristof-Brown 2000; Kristof-Brown and Guay 2011).

PJ fit ties to vocational interests, i.e., job interests, and PO fit relates to an individual’s general needs

and interests. PO fit is the answer to the question “Do I fit in this organization?” PO fit is defined as the

congruence between the value system of the individual and the culture and value system of the organization

(Bretz et al. 1989). PO fit occurs when an individual and an organization share similar values (Cable and

Judge 1996; Kristof 1996; Kristof-Brown and Billsberry 2013), can supply what each other needs (Kristof-

Brown and Billsberry 2013) and when there is congruence between the outcomes of importance to the

individual and the characteristics of the organization (Cable and Judge 1997). PJ fit focuses on the extent to

which there is congruence between what the individual brings to the table, what the job needs are and what

the job provides the individual (see Edwards 1991; Kristof 1996; Kristof-Brown 2000; Kristof-Brown and Guay

2011). Although related, PO fit and PJ fit are conceptually and empirically distinct (e.g., Lauver and Kristof-

Brown 2001; Kristof-Brown et al. 2005).

Understanding both types of fit is underscored by their key roles in the nomological network of

various job-related constructs. PO fit predicts job choice intentions, work attitudes (Cable and Judge 1996),

job satisfaction, intention to quit (Saks and Ashforth 1997) and organizational attraction (Yang and Tu 2014).

Similarly, PJ fit influences a variety of outcomes including coping, job satisfaction, intention to quit, turnover,

commitment (Edwards 1991; Kristof-Brown et al. 2005), organizational identification (Saks and Ashforth

1997) and even psychological well-being (Park et al. 2011). Although the two fit perceptions have effects on

similar constructs, they have independent effects on the various outcomes (see Kristof-Brown et al. 2002;

Saks and Ashforth 1997; Kristof-Brown et al. 2005). Overall, these two fit perceptions play a role in all stages

of attraction, motivation and retention of employees, thus tying into employee fit in broad research

frameworks, such as the attraction-selection-attrition (ASA) framework (see Schneider 1987).

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Broadly speaking, much prior research on fit in general, and both PO fit and PJ fit in particular, has

focused on employer and organizational actions, such as selection, recruitment, socialization tactics and

what an individual can bring to an organization/job (Kristof-Brown et al. 2002; Kristof-Brown and Guay 2011).

Although such an employer view is important, given the two-way nature (i.e., employee employer) of all

decisions related to employment, a focus on employees’ expectations and experiences is also critical.

Related to this, it has been noted that research on the determinants of fit from an employee perspective is

lacking (Colbert et al. 2008; Kristof-Brown et al. 2005).

Work Outcomes

Over the past three decades, IS, OB and vocational behavior research have suggested a variety of

work outcomes (Crepeau et al. 1992; Ferratt and Short 1986, 1988; Guimaraes and Igbaria 1992; Holtom et

al. 2006; Igbaria and Baroudi 1995; Lawler and Finegold 2000; Munyon et al. 2014). We identify a specific

set of work outcomes based on the total rewards perspective (Lawler 2011; Parus 1999). This approach

involves going beyond the traditional compensation practices rooted in pay and promotion opportunities. The

total rewards perspective considers all benefits afforded by employment in an organization, including

opportunities for learning, personal and professional development and quality of life and work environment,

thus representing the firm’s entire value proposition for prospective and current employees (Parus 1999).

The total rewards perspective allows organizations to emphasize appealing aspects of the work environment

and organization that are not just tied to financial compensation, making it particularly well-suited for leaner

economic periods, which has been of global significance in the past two decades. It also allows organizations

to customize their rewards packages for particular jobs and roles, providing greater flexibility when recruiting

new workers. Leading organizations, such as Microsoft, Johnson & Johnson, IBM and AstraZeneca, are

among those using this approach to help attract, motivate and retain employees (Rumpel and Medcof 2006).

Grounded in the total rewards perspective, we define work outcomes as being related to material,

social and psychological states (see Super 1980) that are a heuristic set of guiding principles important to

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individuals to evaluate work and/or job environments (see Ros et al. 1999). Early research on work

outcomes, such as the Minnesota Importance Questionnaire (MIQ; Gay et al. 1971), the Work Aspect

Preference Scale (Pryor 1983) and the Work Values Inventory (Super 1980), has emphasized the

importance of various extrinsic and intrinsic outcomes in the workplace. Extrinsic or instrumental outcomes

are defined as the results of work activity provided by another source other than the employee on the job

(Schuler 1975). These outcomes typically focus on direct, concrete external consequences, such as pay,

promotion, prestige and job security. The importance of extrinsic outcomes, such as pay, to employees is

both intuitive and well-documented. As our understanding of work outcomes progressed, more recent

thought has been dominated by a variety of social outcomes that are important to employees (Cable and

Parsons 2001). Social outcomes are defined as the result of work activities that are affected by interpersonal

relationships and include both work and non-work ties, such as work-life balance, friendliness of co-workers

and family proximity. Finally, the role of intrinsic outcomes has been studied via theoretical perspectives,

such as the job characteristics model (JCM; Hackman and Oldham 1980), with an emphasis on intrinsically

interesting work (Brief et al. 1988). Intrinsic outcomes are defined as the results of work activity that arise

from the relationship between the employee and his or her task activity (Schuler 1975). These outcomes

typically focus on intangible, internal benefits (i.e., feelings or cognitions), such as task variety, creativity and

skill development. We specifically focus on the extent to which individuals perceive each of these three types

of outcomes to be present in their work. We present hypotheses related to each type of outcome, followed by

those related to moderation by gender. Building on prior research that has related various outcomes to fit

perceptions, we present the model shown in Figure 1.

Although prior research has not typically used theory-driven categories to describe specific work

outcomes, research has typically identified a few constructs that fall within each of the three general work

outcomes shown in Figure 1. Within the content domain of each of the three constructs, we identify examples

of these work outcomes in order to develop our hypotheses. The specific work outcomes we discuss

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throughout are not meant to represent all possible dimensions within each construct (work outcome domain),

rather they are expected to serve as key examples of motivational forces and are expected to be important in

evaluating a job and a workplace (Gay et al. 1971; Pryor 1983; Super 1980).

Hypotheses Development

Extrinsic Outcomes

Traditional perspectives have demonstrated that extrinsic work outcomes are critical for employee

job satisfaction, organizational commitment and consequently, reducing turnover (Lawler and Finegold

2000). Of the extrinsic factors examined in past research, pay—i.e., monetary compensation—is a factor that

is intuitively appealing. A related extrinsic factor is promotion. In addition to typically being a driver of pay,

promotion is appealing given that it is typically a reflection of performance and an indicator of employee

advancement within the organization. Prestige, or the extent to which a position elicits respect from others, is

a related factor has been shown to be significantly related to an individual’s financial earnings (Judge et al.

1995). In addition, job security creates the potential for a stable source of income and provides an affective

gain in knowing that one’s job is stable. Each of these extrinsic work outcomes has been shown to be a

Extrinsic

Outcomes

Social Outcomes

Intrinsic

Outcomes

PO Fit

PJ Fit

Figure 1. Research Model

Gender

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predictor of key job outcomes, such as job satisfaction and turnover (e.g., Bartol and Durham 2000; Bartol

and Locke 2000).

Extant research supports the notion that PO fit is driven by valuations of extrinsic outcomes, given

that these outcomes are controlled by organizational-level factors, such as culture, organizational reputation,

HR practices and firm policies. PO fit is tied to extra-role behaviors (Lauver and Kristof-Brown 2001), thus

suggesting that aspects beyond the specific job play a role in determining PO fit. Pay has been tied to PO fit

(Cable and Judge 1994), rather than PJ fit. This stands to reason because individuals, especially in the job

search process, often see organizations as the pay masters and identify specific organizations or types of

organizations—e.g., consulting firms—as paying higher or lower salaries in general. Although the pay may

vary based on the specific job, the internal pay structure (pay hierarchy) is often fixed within an organization

(see Cable and Judge 1994; McLean et al. 1996). This argument also applies to promotion. Prestige is

largely based on the organization’s name and fame. For example, being a software engineer at Google

carries more prestige than being a software engineer at a smaller, lesser known firm. Organizations often

acquire a reputation regarding job security. For example, in the first 80 years since its founding, IBM never

had a layoff, whereas other organizations have been quick to lay off employees, particularly during difficult

economic times. Given that pay, promotion, prestige and job security are tied to the organization, an

individual’s perceptions of these factors determine PO fit. Thus, we hypothesize:

H1: Expectations about extrinsic outcomes will positively influence PO fit perceptions. Social Outcomes

There are two key social considerations that are integral to one’s work life. One relates to social

support provided by the organization and the other relates to respect for employees’ social needs outside

work. These have been captured in the prior literature by a variety of factors, such as proximity to family,

friendly co-workers and work-life balance. Proximity to family captures the extent to which a workplace or job

allows for interactions with family. The construct of friendly co-workers captures the extent to which social

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and work-related support will be provided in the workplace—this is shown to be valued for relational

purposes (Minton and Schneider 1980) and for informational input from others relative to job- and

organization-related questions and problems (Goldstein and Rockart 1984). Work-life balance reflects the

extent to which a work situation supports employee efforts to manage the interface between their paid work

and other important life activities, such as family (Lazarova et al. 2010). Work-life balance has received much

attention in recent years and has been viewed as a critical factor in retention (Ahuja et al. 2007; Lazarova et

al. 2010).

The role of social outcomes is two-fold. The first role of social outcomes is tied to satisfying relational

needs. The second role is more work-oriented in that employees often rely on co-workers for advice, problem

solving (Sykes 2015; Sykes et al. 2009; Sykes et al. 2014) and other types of support (e.g., flexible work

hours to accommodate personal situations) necessary to perform one’s job duties. The former role ties

valuation of social outcomes to PO fit because the organization and organizational culture are seen as the

entities that create and maintain the overall work environment. The latter role is related to PJ fit because it is

one’s immediate job environment that provides various types of work-related support. For example, in terms

of social outcomes, we suggest that family proximity and work-life balance relate to PO fit. Family proximity is

largely determined by an organization’s location. Expectations about how proximal an organization is to

family will tie to how well an individual’s relational needs will be met and this will determine PO fit. Work-life

balance is driven to some extent by policies and practices in place at an organization. Therefore, the balance

that an individual expects to receive from working in a particular organization will drive his or her perceptions

of PO fit.

Following a detailed review and analysis of the various conceptualizations of PJ fit, Kristof (1996)

noted that PJ fit incorporates the congruence between the individual and specific job requirements and what

the specific job environment, rather than the broader work environment, has to offer. From an employee’s

perspective, PJ fit ties to specific aspects related to the job (see Kristof-Brown 2000). Beyond organizational

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policies, work-life balance is afforded by a particular job. For example, some jobs may require significant

overtime and/or weekend work. Still other jobs may require frequent travel away from the office location

where one works. For example, such additional hours and travel are typical of consultant jobs in the IT

industry. Friendly co-workers are an integral part of the immediate job environment and determine the extent

of support available to the employee for social interactions within the workplace and also, support and help

for work-related questions (Sykes 2015; Sykes et al. 2009; Sykes et al. 2014). Together, this suggests that

valuations of social outcomes can be tied to the demands of a particular job and thus drive perceptions of PJ

fit.

The importance of the role of social outcomes in driving PO fit is evident in the context of IT workers.

For example, many organizations have adopted agile software development methodologies that advocate

40-hour work weeks as an importance practice (Lee and Xia 2010). Such organizations will create

perceptions about achieving greater work-life balance among workers. In contrast, enterprise-wide

dependency on real-time information means that many organizations will require IT workers to be available

24/7 for support services to clients (Guzman and Stanton 2009). Service level agreements guaranteeing

99.99% availability of IT resources (Greiner and Paul 2009) mean IT workers may have to be on-call around

the clock, eroding work-life balance.

The importance of social outcomes in driving PJ fit is underscored by the nature of IT jobs. Entry-

level IT workers typically perform programming/coding tasks (McMurtrey et al. 2008) that often call for the

assistance of co-workers due to the complex problems faced in the substantive area of the work and the

possible need for solutions from others (Cheng et al. 2004). Likewise, experienced IT workers, who are

tasked with more complex responsibilities, such as software design or project leadership, are likely to call

upon their co-workers for advice and rely on them for input into “big picture” solutions. IT workers in general

share solutions through personal interactions and bulletin boards. In fact, they represent one of the most

active online professional communities (Assimakopoulos and Yan 2009). Given this professional culture and

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socialization in the IT profession, the need for social support along these lines is expected to be important in

determining perceptions of fit. Thus, we hypothesize:

H2: Expectations about social outcomes will positively influence PO fit perceptions.

H3: Expectations about social outcomes will positively influence PJ fit perceptions.

Intrinsic Outcomes

Several theories, such as the job characteristics model (JCM), with roots in psychology and OB,

have emphasized the importance of intrinsic work outcomes (Hackman and Oldham 1980). IS researchers

have also drawn on these theories and studied them in the context of IT workers (e.g., Ferratt and Short

1986, 1988; Igbaria et al. 1994) and IT implementations (Morris and Venkatesh 2010; Venkatesh, Bala, and

Sykes 2010). For instance, one of the predictors from JCM, i.e., variety, has been employed across multiple

studies in IS (Crepeau et al. 1992; Ferratt and Short 1988; Jiang and Klein 1999). In addition, creativity is an

important motivator for IT workers (Sumner et al. 2005). Research has also identified skill development as an

intrinsic growth factor as it underscores employees’ desire for learning and staying at the cutting-edge of their

profession (Kraimer et al. 2011). JCM suggests that the greater the supply of the core job characteristics, the

higher individuals’ motivation and job satisfaction. Given that the predictors in the JCM—i.e., autonomy,

feedback, identity, significance and variety—focus on intrinsic outcomes, it further stands to reason that PJ fit

is determined by an individual’s valuations about how well a job will provide or provides intrinsic outcomes,

such as task variety, creativity and skill development.

An example of the importance of intrinsic outcomes can be seen in the context of IT workers. The IT

industry constantly changes and individuals socialized in the IT profession will feel a better fit with jobs that

will provide them the ongoing opportunity to learn and perform a variety of tasks, thus helping them to be

skilled and at the cutting-edge (see Morris and Venkatesh 2010). Continuing opportunities for learning are

particularly critical in today’s volatile IT job environment as it will allow IT workers to perceive a promising

career path (Turmel 2011). Further, technology workers are often innovators (see Rogers 1995) and like to

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“play” with the latest tools, languages, etc., which is likely driven by their need for variety, drive to indulge in

creative activities related to their profession and need to learn continuously (Agarwal et al. 2007). These are

the very habits and behaviors that have led to the formation of the stereotype of a “techie” or “geek.” Thus,

we hypothesize:

H4: Expectations about intrinsic outcomes will positively influence PJ fit perceptions.

Moderation by Gender

Building on prior research that has related various outcomes to fit perceptions, we present a

moderated model, shown earlier in Figure 1. New employees form fit perceptions in reaction to salient

attributes of their work environment. For decades now, it is accepted that a basic motivation for people to

enter the labor market is to gain access to valued outcomes (Simon 1951). It is likely that the various types of

outcomes available in the work environment will thus play an important role in shaping individuals’ fit

perceptions (Westerman and Cyr 2004). However, interactional psychology suggests that employees’ PO fit

or PJ fit perceptions may not be determined solely by the attributes of the work environment. Instead, fit

perceptions are theorized to be a function of both individual and work environment characteristics (Schneider

1987). Thus, from an interactionist perspective, a more complete understanding of how PO fit and PJ fit

perceptions are formed requires the consideration of information about relevant work outcomes and

employees’ preferences (Bretz et al. 1989).

The effect of outcomes on fit perceptions is influenced by how much value an employee places on

particular outcomes. Valuations of work outcomes shape fit perceptions because work outcomes represent

the personal standards used by employees when evaluating their work environment (e.g., Latham and Pinder

2005). If a work environment provides an employee with a high amount of a less-valued outcome, it is

unlikely that the person will perceive a high level of fit with their work setting. Conversely, if an employee

perceives that his or her work setting offers a high amount of a desirable outcome, he or she is likely to

18

perceive high levels of fit. Thus, we expect that employees will perceive the highest levels of PO fit or PJ fit

when they have access to outcomes they view as important.

Rather than assess the importance of various work outcomes and their valuation about the

outcomes supplied by their jobs and workplaces that leads to a nearly infinite possible combinations (cells),

we use gender to provide better scope and to gain practical significance tied to important contingencies. We

argue that a key mechanism shaping the importance placed by individuals on different work outcomes is

socialization. Socialization is the process through which an individual develops a set of values and beliefs

that guide his or her decision-making and behaviors (Cable and Parsons 2001). We argue that

developmental socialization underlies why women (vs. men) value different work outcomes that in turn will

cause them to form different fit perceptions.

Developmental socialization begins in an individual’s formative years and is tied to the interactions

an individual has with friends, family and society. These interactions teach an individual roles, norms and

acceptable behaviors. Over time, developmental socialization is a core driver of an individual’s value and

belief system. Given that entry-level job applicants are often young (typically, in their early 20s) and have

little work experience, developmental socialization can be expected to play a significant role in determining

what an individual wants from a workplace and/or job. Gender stereotypes, i.e., generalizations about the

characteristics of individuals based on biological sex (Bem 1993), drive developmental socialization that in

turn is the basis for understanding gender differences in work outcomes and their effects on fit perceptions.

Gender stereotypes take the form of norms that are prescriptive (what one should do) and proscriptive (what

one should not do) in the developmental stages (early years) of women and men (Kilmartin 2000). According

to socialization theories, these norms and behaviors are encouraged from the very formative years and

influence individuals’ perceptions of their later social roles (Antill 1987). These norms influence orientations

that women (girls) and men (boys) develop and influence the games, chores and other activities in which

they are involved (see Kilmartin 2000).

19

Empirical evidence, based on large data sets, suggests the possibility of some similarities between

women and men. Rowe and Snizek (1995) did not find consistent support for gender differences in work

attributes based on analyses of data for full-time employed workers in 12 national samples from the General

Social Survey (NORC 1985) collected between 1973 to 1990. Rather, the findings suggested that the

relationship between gender and the importance of work attributes is affected by age, education and

occupational prestige, with the latter two strongly favoring the potential role of occupational differences in the

importance of various work attributes, namely income, job security, working hours, chances for advancement

and work that gives a feeling of accomplishment. Based on British Household Panel Survey Data, Fagan

(2001) suggested that women’s priorities change with age, thus indicating that entry-level women job

applicants may be somewhat similar to their male counterparts in their thinking. After controlling for some

other variables—e.g., rank—no gender differences were found, thus suggesting that sometimes women

identify more with their work, rather than gender, role and behave more like men in similar work roles.

We expect developmental socialization to underlie how and why gender will moderate the

relationships between valuation of work outcomes and fit perceptions. Given that PO fit is an assessment of

the congruence between an individual’s needs and what the workplace provides, developmental socialization

is expected to be the primary mechanism that underlies the assessment of PO fit. Specifically, based on

developmental socialization, we expect gender to moderate the effect of the valuation of extrinsic outcomes

on PO fit, such that they will be more influential in men’s (compared to women’s) assessment of PO fit. A

wealth of research on gender differences provides the basis for the relative importance of extrinsic outcomes.

Gender predicts valences, instrumentalities and expectations that drive job choice (Sumner and Neiderman

2003). Gender ideology, defined as socially constructed scripts that prescribe different characteristics,

values, attitudes, behaviors and activities for women and men (e.g., Konrad et al. 2000a), influences the

importance of instrumentality and interpersonal relations to women and men. Social identity theory suggests

that membership in social groups affects the development of self-concepts (e.g., Konrad et al. 2000b).

20

Because women (girls) and men (boys) socialize primarily within gender in their early years, the importance

of instrumentality to men is further reinforced. Such stereotypes, formed through developmental socialization,

play a key role in the formation of PO fit perceptions (Cleveland 1991).

Men, more so than women, tend to be instrumental and identify with traits, such as “objective” or

“logical” (see Rosenkrantz et al. 1968). Cross and Madson (1997), based on a review, concluded that men

create and maintain independent self-construal. Such independence in self-construal is expected to cause

men to place greater emphasis on extrinsic outcomes, particularly in the workplace, as men seek ways to

demonstrate their uniqueness and separateness from others in concrete ways. Women, in contrast, create

and maintain interdependent or relational self-construal, leading women to seek more interconnectedness

with others, thus seeking and providing more social support (see Cross and Madson 1997).

There is evidence to suggest that extrinsic outcomes, such as financial security and prestige, are

very important to men (O’Neil 1981). Men’s self-image is strongly tied to instrumental and tangible

accomplishments (e.g., Cross and Madson 1997). Men tend to place greater emphasis on their work role

(Barnett and Marshall 1991) and career objectives (Bartol and Manhardt 1979), thus likely resulting in the

greater importance of tangible goal attainment, such as promotion (Gati et al. 1995). Men, compared to

women, are also more likely to seek promotions (see Savery 1990) and place greater importance on status

and prestige (Arenofsky 1998). The instrumental focus and the emphasis on the work role for men (Barnett

and Marshall 1991; O’Neil 1982) is also expected to make other extrinsic outcomes, such as job security,

important to men (Gati et al. 1995) because men have much of their self-worth tied to their work and

accomplishments. Thus, we hypothesize:

H5: The effect of extrinsic outcomes on PO fit perceptions will be moderated by gender such that it will be more important to men.

Much of the earlier discussion about schemas, stereotypes, ideologies and practices related to

gender differences is at the heart of why social outcomes, such as family proximity and work-life balance, will

21

be more influential in women’s assessments of PO fit and PJ fit than social outcomes would be in the case of

men. Work-life balance and family proximity will help fill lifestyle and interpersonal needs that are important to

women. Although, in the case of entry-level positions, job applicants are typically younger and have fewer

familial responsibilities, women tend to be closer to their families (Markham and Pleck 1986) and, therefore,

will consider family proximity to be a more important criterion than will men. For experienced workers, who

are older, especially in the case of women the family role becomes particularly important (Tilly and Scott

1989). Women, more than men, may also consider social aspects of their jobs to be more important (see

Brief and Aldag 1975). A variety of contemporary arguments support the greater importance of work-life

balance to women in their formation of PO fit and PJ fit perceptions. Compared to the valuations of men,

women tend to value affiliation and friendships (Gati et al. 1995) more, value and use social support more

(Savery 1990), are more perceptive of interpersonal problems (Gwartney-Gibbs and Lach 1994) and place

more emphasis on community (Estes 1992). Drawing on these justifications, we theorize that the effect of

social outcomes on PO fit will be strongest among women.

Social outcomes are also thought to influence PJ fit perceptions due to the social atmosphere

created by one’s co-workers. The relationship between social outcomes and PJ fit perceptions will be

moderated by gender such that it will be most important to women. The rationale for importance to women is

consistent with our justification for H5. Developmental socialization results in women valuing social support

more than men do (e.g., Brief and Aldag 1975; Estes 1992; Gwartney-Gibbs and Lach 1994; Savery 1990).

Research indicates that the underrepresentation of women in IT is due in part to social and network barriers

related to a male-centric occupational culture as well as a lack of role models and mentors for women in

organizations (Ahuja 2002). In conjunction with developmental socialization, such characteristics should lead

women in IT to place a high value on the social support they receive from co-workers. Drawing on these

justifications, we theorize that the effect of social outcomes on PJ fit will be strongest among women. Thus,

we hypothesize:

22

H6: The effect of social outcomes on PO fit perceptions will be moderated by gender, such that it will be more important to women. H7: The effect of social outcomes on PJ fit perceptions will be moderated by gender, such that it will be more important to women.

Although research has investigated gender differences in the importance of intrinsic motivations, the

evidence has been mixed, with some finding it to be more important to men and others finding it to be more

important to women (for a review see: Kilmartin 2000). For example, Herzberg et al. (1975) found that men,

compared to women, place more importance on intrinsic outcomes, such as overall enjoyment of their work.

In contrast, Brenner et al. (1988) found that women, compared to men, placed more importance on intrinsic

outcomes. They acknowledged the mixed results regarding gender differences and work values, and suggest

that other factors, such as race, may interact with gender to explain these mixed results. Still other research

found that, after controlling for demographics, such as age, marital status, education, experience,

organizational position and culture, men and women do not differ in the extent to which they value intrinsic

outcomes at work (Akhtar 2000; Kaufman and Fetters 1980). Given these mixed results, we do not theorize

moderation of the effect of intrinsic outcomes on fit perceptions, but rather suggest that intrinsic outcomes

will directly impact PJ fit.

METHOD

We conducted three studies, each with two waves of data collection, to test our model. The first

study was conducted among graduating seniors who had accepted jobs and was conducted in 2000-’01,

after the dotcom bubble burst and thus, represents a time of significant economic recession. The second

study was also conducted among graduating seniors but conducted about six years after the first study when

the economic conditions in the US were significantly better. The third study was conducted over two years,

overlapping with the start of the second study and continuing for a year after, among employees who had

three or more years of work experience and were beginning a new job. In all three studies, data were

collected prior to organizational entry and six months after organizational entry, which allowed us to validate

23

the model based on expectations and experiences, thus strengthening the applicability and scope of the

models. By collecting multiple waves of data, we establish cross-temporal validity.

We first present an overview of the three studies conducted, followed by a description of the

measurement instrument and scale development to ensure the appropriateness of the instrument. The

results are then presented in three parts: (1) model tests; (2) tests for the generalizability and boundary

conditions of the model; and (3) tests for robustness of the model. Each part of the analysis employed each

of the three studies outlined. We begin by presenting the results of the model tests. Using data from each of

the three studies, we tested the model for those in the IT field, prior to their organizational entry. Following

this, we tested the generalizability and boundary conditions of the model by comparing the results for IT

workers to those in other business domains, prior to their organizational entry. Finally, we examined the

robustness of model by examining the results across all business domains, after the participants have

entered the organization. This allowed us to determine whether the model is robust to changes between

expectations prior to organizational entry and actual experiences after organizational entry. Table 1

summarizes the methodology, including the purpose of each analysis performed, a synopsis of the samples

and the time periods in which they were collected.

In order to fully examine the model’s generalizability, boundary conditions and robustness, while at

the same time, maintaining parsimony in our model, we grouped the business domains into three categories:

quantitative domains, people-oriented domains and IT. Quantitative domains are accounting, economics and

finance, and people-oriented domains are management and marketing. IT is viewed as distinct from these

groups. Grouping professional domains in this way is grounded in the prior literature on education and

vocational behavior. Several prior studies have grouped accounting, economics and finance together, and

management and marketing together (e.g., Kidwell and Kidwell 2008; Lawrence et al. 2000; Pritchard et al.

2004; Schlee 2005; Worthington and Higgs 2003), based upon the primary foci of these fields of study. This

is not to say that people-oriented domains do not work with numbers and that quantitative domains do not

24

focus on qualitative issues. This also does not suggest that, as these fields evolve, they have not embraced

more balanced perspectives. Rather, quantitative domains are predominantly concerned with measurable,

quantifiable issues, whereas people-oriented domains are predominantly concerned with human factors

(Kidwell and Kidwell 2008). IT has been grouped with qualitative domains (Lawrence et al. 2000), with

people-oriented domains (Kidwell and Kidwell 2008), as well as not grouped with any other domain and

instead been considered a “technical” field (Pritchard et al. 2004; Schlee 2005). This suggests some element

of divergence compared to the consistent grouping of other business domains. Extant research in the IT

personnel literature supports the assertion that IT workers do represent a unique and distinct occupational

subculture (e.g., Guzman et al. 2008; Joseph et al. 2007; Wynekoop and Walz 1998). Based on prior

research and our interest in drawing contextual implications for IT recruitment and retention challenges, IT is

kept separate from the other business domains.

Table 1. Method Summary

Analysis and purpose Samples and data collection period Domain examined

in analysis Job status

1. Test the model Study 1: 2000-2001; graduating seniors Study 2: 2007-2008; graduating seniors Study 3: 2007-2009; workers with 3+ years of experience

IT Pre-

organizational entry

2. Test generalizability and boundary conditions of model

Study 1: 2000-2001; graduating seniors Study 2: 2007-2008; graduating seniors Study 3: 2007-2009; workers with 3+ years of experience

All business domains

Pre-organizational

entry

3. Test robustness of model using experiences rather than expectations

Study 1: 2000-2001; graduating seniors Study 2: 2007-2008; graduating seniors Study 3: 2007-2009; workers with 3+ years of experience

All business domains

Post-organizational

entry

Study 1

The population was graduating business school seniors who had accepted jobs. The sampling frame

was the list of graduating seniors in a business school at a large university in the eastern US. Participants

were solicited from a capstone course and other senior-level electives at the school. Each class section

typically comprised between 35 and 45 students. Data were collected in two waves: before organizational

entry with a focus on expectations and after organizational entry with a focus on experiences. In the first

25

wave of the data collection, which was conducted during the last month of the semester, one of the authors

or a research assistant visited each class and followed a script that described the objective of the survey to

be one that was aimed at gathering information about students’ feelings about jobs and their job search.

Participation was voluntary. The instructor of the class was not present during any part of the data collection.

The second wave of the data collection was conducted by contacting the same participants, who were now

holding jobs, via email about six months after they had started their jobs (based on the start date they

provided in the initial survey). The follow-up survey asked the same questions but about their experiences,

not expectations and about their fit perceptions. Questions measuring various constructs were intermixed.

Also, other filler questions, not discussed here, were included in the survey to minimize the threat of demand

characteristics.

A total of 656 graduating seniors participated in the pre-organizational entry survey (wave 1), with

592 providing usable responses (90.2%). The different professional domains (majors) that were studied and

the number of participants in each domain was: 124 in accounting, 119 in finance, 173 in IT, 107 in

management and 69 in marketing that resulted in a breakdown of 173 (29.2%) in IT, 243 (41.1%) in

quantitative domains and 176 (29.7%) in people-oriented domains. The sample comprised 224 women

(37.8%). The further breakdown was as follows: women in IT—74 (out of 173), women in quantitative

domains2—44 (out of 243) and women in people-oriented domains—106 (out of 176). The average age was

22.34, with a standard deviation of 2.61. Of the 592 respondents in the pre-organizational entry survey, 391

participated in the post-organizational survey (wave 2) for a response rate of approximately 66% relative to

the wave 1 survey. The demographic profile of respondents in both waves was highly similar—the profile in

wave 2 was: 126 (51 women) in IT, 151 (30 women) in quantitative domains and 114 (70 women) in people-

oriented domains.

2 Although economics is a quantitative domain, our study settings (study 1 and 2) did not include any economics majors.

26

Study 2

Study 2 was conducted following the same design as study 1. The only difference, as noted earlier,

was when the data were collected. Given that the economic climate often tends to vary, theory developed

and data collected at a particular point in time, based on prevailing wisdom, may not generalize to new

settings, especially if the underlying circumstances and assumptions of the theory and data have changed.

However, demonstrating invariance over time, (i.e., cross-temporal validity) is important to establish

generalizability and is considered to be a form of external validity (see Cook and Campbell 1979). In keeping

with this idea, study 2 was conducted approximately six years after study 1.

A total of 770 graduating seniors participated in the pre-organizational entry survey (wave 1), with

752 providing usable responses (97.7%). The different professional domains (majors) that were studied and

the number of participants in each domain was: 197 in accounting, 260 in finance, 89 in IT, 114 in

management, and 92 in marketing that resulted in a breakdown of 89 (11.8%) in IT, 457 (60.8%) in

quantitative domains and 206 (27.4%) in people-oriented domains. The sample comprised 310 women

(41.2%). The further breakdown was as follows: women in IT—35 (out of 89), women in quantitative

domains—135 (out of 457) and women in people-oriented domains—140 (out of 206). The average age was

23.40, with a standard deviation of 2.98. Of the 752 respondents in the pre-organizational entry survey, 526

participated in the post-organizational survey (wave 2) for a response rate of approximately 70% relative to

the wave 1 survey. The demographic profile of respondents in both waves was highly similar—the profile in

wave 2 was: 76 (30 women) in IT, 310 (89 women) in quantitative domains and 140 (95 women) in people-

oriented domains.

Study 3

The design of study 3 was similar to both studies 1 and 2. It was conducted immediately following

study 2. One of the scoping conditions of studies 1 and 2 was that the sample comprised graduating college

seniors. To expand the scope for study 3, we collected data from those who were starting new jobs in three

27

different organizations and already had three or more years of work experience. This allowed us to examine

the generalizability of our model to those who were older, had more work experience and were consequently,

starting at least their second job.

A total of 1,320 out of 2,401 new employees entering each of the three organizations over a two-year

period provided responses to our pre-organizational entry survey (55%). The different professional domains

of the jobs that the employees were starting and their majors (undergraduate and/or graduate) were

collected. Given the scope and focus of our work, we only included those in the sample that had stayed in

the same domain as the major of their most recent degree. In our sample, this comprised 770 new

employees. Although the remaining employees are an interesting group, i.e., those who have made career

changes, because our focus was not on such individuals, we excluded them from our sample. Further, it is

likely that the model explaining such individuals’ choices will have to consider other, different factors and this

was, as such, beyond the scope of this work. The number of participants in each domain was: 202 in

accounting, 237 in finance, 123 in IT, 120 in management, and 88 in marketing that resulted in a breakdown

of 123 (16%) in IT, 439 (57%) in quantitative domains and 208 (27%) in people-oriented domains. The

sample comprised 311 women (40.4%). The further breakdown was as follows: women in IT—41 (out of

123), women in quantitative domains—129 (out of 439) and women in people-oriented domains—141 (out of

208). The average age was 31.33, with a standard deviation of 6.45. Of the 770 respondents in the pre-

organizational entry survey, 502 participated in the post-organizational entry survey (wave 2) for a response

rate of approximately 65% relative to the wave 1 survey. The demographics of respondents in both waves

were highly similar—the profile in wave 2 was: 82 (28 women) in IT, 281 (80 women) in quantitative domains

and 139 (91 women) in people-oriented domains.

Measurement

Independent Variables: Scale Development

28

The items used to measure the importance of various extrinsic, intrinsic and social outcomes were

developed through a rigorous process that was in keeping with instrument development guidelines (see

DeVellis 2011). This process consisted of three stages: item creation, scale development and instrument

testing (DeVellis 2011).3 The item creation stage involved creating pools of items for each of the work

outcomes that map to their corresponding conceptual definitions. We examined existing scales and, where

necessary, created additional items to ensure content validity. Items were drawn primarily from the

Minnesota Importance Questionnaire (MIQ; Gay et al. 1971), the Work Aspect Preference Scale (Pryor 1983)

and the Work Values Inventory (Super 1980). Extrinsic outcomes were measured using items assessing the

importance of pay, promotion, prestige and job security. Social outcomes were measured using items

assessing the importance of family proximity, work-life balance and friendly co-workers. Intrinsic outcomes

were measured using items assessing the importance of variety, creativity and skill development. Due to the

number of work outcome dimensions and the desire to keep the final survey a manageable length for

participants, we aimed to retain three items per dimension for a total of 30 items measuring the importance of

the 10 different dimensions of work outcomes. DeVellis (2011) recommends generating 3-4 times the

number of items to be included in the final scale. Thus, we identified a pool of 10 items for each dimension,

for a total initial pool of 100 items. The importance of each type of outcome was measured on a 7-point

Likert-type agreement scale. The items are listed in Appendix A.

The second stage in the instrument development process is development of the scales (DeVellis

2011). The goal of this stage is to conduct an initial assessment of construct validity and to weed out

ambiguous or poorly worded items. To this end, we conducted a card-sorting procedure in which participants

(2 sections of an undergraduate business capstone course and 2 sections of a second-year MBA elective,

with approximately 200 students total) were asked to sort the various items into construct categories.

3 Our instrument development pre-dated the more recently described ten-step procedure (for an example, see Hoehle and Venkatesh 2015).

29

Different samples were used to evaluate items pertaining to the three constructs (i.e., 30 items)—this was

done to minimize participant fatigue. Each participant was given a stack of randomly ordered cards

corresponding to items and were asked to group cards together into categories. The number of categories

participants could create was not restricted. Participants were then asked to label each category with a word

or phrase that reflected the category. Construct validity is assessed by examining the convergence and

divergence of items within the categories and across participants (DeVellis 2011). Items that are consistently

placed in the same category evince convergent validity with that construct and discriminant validity with other

constructs. Moreover, the degree of overlap across participants in number of categories, category labels and

items grouped together is an indicator of convergent and discriminant validity. Item paring was conducted on

the basis of inter-rater agreement, using Cohen’s Kappa (Cohen 1960). Items with values lower than the

accepted threshold of .65 (DeVellis 2011) were dropped such that five items were retained for each work

outcome (three items to retain, plus two possible items for the instrument testing stage). These items are

shown in italics in Appendix A.

The scales were combined for the final stage: instrument testing. We conducted this stage in two

steps. The first step involved administering the questionnaire to a small sample to obtain a preliminary

indication of reliability and make any necessary adjustments. For this purpose, we used one section of an

undergraduate business capstone course and one section of a second-year MBA elective for a total of

approximately 80 students. We asked participants to complete the questionnaire and to provide comments

on its length, wording or instructions. Cronbach’s alpha values were in the .75 to .85 range, indicating a high

degree of reliability. In general, participant feedback revolved around the length of the questionnaire, which

we sought to reduce for the next step of instrument testing, while still retaining a high degree of reliability.

Thus, we reduced the length of each scale from five items to three items by eliminating items with the lowest

loadings. At the same time, we ensured that domain coverage was not affected by deleting a particular item.

Cronbach’s alpha for reduced scales were all above .70, indicating that they were reliable. The final items

30

related to expectations are shown in Table 2; the wording for the experiences survey was suitably adapted.

Finally, we administered the questionnaire to a larger sample that was somewhat more representative of the

population (2 sections of the undergraduate capstone business course, 1 section of a full-time MBA elective

and 1 section of a part-time MBA elective for a total sample of approximately 200). We again examined scale

reliabilities and performed a factor analysis using an oblimin rotation that resulted in a ten-factor solution. All

reliabilities were above .70. All cross-loadings were below .42, as shown in Table 3.4 Thus, we conclude that

the scales demonstrate reliability and validity.

Table 2. Survey Items Retained (Expectations) WORK OUTCOMES (Scale: 1 = Not at All to 7 = A Great Deal)

We would like you to tell us how much of each characteristic you expect to see present in your job. For example, there is a characteristic “Friendly co-workers.” You will rate on a 7-point scale how much the job will provide the opportunity for you to have “friendly co-workers.”

Pay

1. Salary level 2. The opportunity to become financially wealthy

3. The amount of pay

Promotion

4. Opportunities for advancement 5. Promotion opportunities 6. Chances for advancement

Prestige

7. Having others consider my work important 8. Obtaining status in the eyes of others

9. Being looked up to by others

Job Security

10. Being certain of keeping my job 11. Being sure I will always have a job 12. Being certain my job will last

Family Proximity

13. Being in the same geographic location as my immediate family (i.e., parents, brother, sister) 14. Living in the same area as my immediate family 15. Being in very close geographical proximity of my immediate family

Friendly Co-workers

16. Friendly co-workers 17. Collegial co-workers 18. Co-workers who are supportive

Work-Life Balance

19. Being able to balance my family and work life 20. Having time for my personal life 21. A work environment that supports work/family balance

Variety

22. Doing a variety of things 23. Doing something different every day 24. Doing many different things on the job

Creativity 25. Trying out new ideas and suggestions

4 Note that in the case of exploratory factor analysis, such as what is conducted here, only the independent variables are included. In later testing using PLS, both independent and dependent variables are included.

31

26. Creating something new 27. Contributing new ideas

Skill Development

28. Opportunities to develop new skills

29. Developing new knowledge through training 30. Acquiring new career-relevant skills

FIT PERCEPTIONS (Scale: 7-point Likert agreement scale)

PO Fit

31. The organization will be a total fit for me. 32. Taking everything into account, the organization will be a complete fit for me.

33. I would fit right into the organization.

PJ Fit

34. I would fit right in to the job.

35. Taking everything into account, the job is a complete fit for me. 36. The job provides a total fit for me.

Note: For the post-organizational entry items, the present tense is used throughout. For instance, the instructions were modified to: We would like you to tell us how much of each characteristic you experience in your job. For example, there is a characteristic “Friendly co-workers.” You will rate on a 7-point scale how much the job provides the opportunity for you to have “friendly co-workers.” Likewise, in the post-organizational entry survey, “will be” in the first two PO fit items is replaced with “is.” Also, “would” is dropped in the 3rd PO fit item and 1st PJ fit item.

Table 3. Factor Analysis with Oblimin Rotation 1 2 3 4 5 6 7 8 9 10

1

Pay1 .77 .03 .38 .06 .03 .08 .10 .12 .08 .23

Pay2 .75 .21 .40 .09 .24 .16 .08 .12 .11 .29

Pay3 .73 .17 .15 .38 .28 .11 .19 .28 .18 .22

2

Promotion1 .28 .77 .14 .02 .12 .06 .05 .20 .03 .06

Promotion2 .23 .80 .38 .20 .20 .25 .07 .25 .24 .26

Promotion3 .24 .82 .17 .16 .23 .19 .11 .15 .02 .15

3

Prestige1 .37 .18 .78 .28 .30 .20 .07 .05 .19 .20

Prestige2 .38 .37 .75 .17 .13 .11 .08 .17 .13 .14

Prestige3 .30 .38 .73 .22 .28 .21 .07 .14 .16 .15

4

Security1 .28 .01 .12 .84 .25 .10 .23 .02 .17 .25

Security2 .25 .02 .01 .83 .08 .13 .09 .08 .14 .16

Security3 .30 .15 .26 .80 .01 .03 .20 .21 .15 .09

5

Work-life balance1 .18 .01 .18 .06 .73 .12 .26 .14 .06 .23



6

Friendly co-workers1 .07 .28 .11 .04 .38 .77 .03 .19 .04 .13



7

Family proximity1 .01 .16 .22 .07 .10 .12 .80 .13 .28 .03



8

Variety1 .04 .07 .25 .04 .11 .22 .19 .73 .35 .18

Variety2 .08 .05 .27 .11 .03 .27 .20 .75 .38 .30

Variety3 .01 .30 .05 .12 .20 .17 .18 .76 .21 .02

9 Creativity1 .37 .11 .09 .04 .23 .17 .03 .39 .70 .16

Creativity2 .35 .20 .13 .27 .05 .15 .10 .41 .76 .16

32

Creativity3 .23 .13 .23 .18 .10 .24 .13 .40 .75 .26

10

Skill development1 .21 .25 .23 .06 .14 .29 .04 .04 .06 .82



Dependent Variables

There have been varying operationalizations of fit in different streams of research, both in IS and

OB. For instance, IS researchers have measured task-technology fit by measuring both components—task

and technology—and determining fit (Goodhue and Thompson 1995). Alternatively, job fit has been

measured directly by using items about fit (Thompson et al. 1991). More specifically, in the context of the

types of fit studied here, there have been different measurement approaches in the OB literature.

Rather than measure fit directly, some researchers have measured fit by using a profile comparison

process via a Q-sort technique—for an example of an application of this technique to the measurement of PJ

fit, see Caldwell and O’Reilly (1990). There have also been direct measures of PO fit and PJ fit (e.g., Cable

and Judge 1996, 1997; Cable and Parsons 2001; Dineen et al. 2002; Judge and Cable 1997; Kristof-Brown

2000; Saks and Ashforth 1997). These direct measures are not without detractors (see Edwards 1991).

However, direct measures of fit are beneficial when attempting to capture respondents’ perceived fit (Kristof

1996). Given that the participants’ perceptions are the focus of this work, we used direct measures of PO fit

and PJ fit by adapting measures from Cable and Judge (1997).

Control Variables

In study 3, age and number of jobs held prior to the new job that we were studying were included as

control variables. These variables were not included as controls in studies 1 and 2 because there was such

little variance among the samples in those studies (i.e., graduating students). In study 3, we also included

two categorical dummy variables to distinguish across organizations but it had no direct or moderating effect

in any of the model tests and was thus dropped from the analysis.

Pilot Study

33

Prior to conducting study 1, we conducted a pilot study to examine differences in the importance of

work outcomes across gender. The purpose of the pilot study was to examine the suitability of our procedure

and to determine whether the pattern of gender differences in the importance of work outcomes followed

what was predicted in our model. The population of the pilot study was graduating seniors—i.e., applicants

for entry-level jobs—pursuing degrees in IT and other functional areas in business. The sampling frame and

data collection procedure were similar to studies 1 and 2. We did not measure fit perceptions in the pilot

study because not all students had jobs. A total of 1,637 students participated in the pilot study, with 1,513

providing usable responses (92.4%). The results of the pilot study provided general support for our ideas

based on the mean differences: gender differences were in the directions predicted. Prior to conducting study

3, we conducted another pilot study among approximately 100 graduating full-time MBA students—a sample

that we deemed appropriate because of their work experience and the fact that they would be looking for

jobs soon. Feedback from the pilot study participants indicated no problems with the question wording or

procedure. Given the small sample size and the non-availability of dependent variables, we only assessed

reliability and validity of the scales, which were found to be satisfactory, and did not test the full model.

RESULTS

The data were analyzed using partial least squares (PLS) (SmartPLS 2.0), a components-based

structural equation modeling technique. PLS has the advantage of maximizing the explained variance of

endogenous variables, making it particularly well-suited when research objectives are prediction-oriented

(see Chin 1998), as is the case in our work. Moreover, PLS is flexible to the inclusion of both reflective and

formative measures (Diamantopoulos and Winklhofer 2001), as is the case with our model, and does not

produce problems with model identification that can occur with covariance-based SEM approaches (Chin

1998). PO and PJ fit were modeled with first-order reflective indicators. The first-order constructs of pay,

promotion, prestige, job security (extrinsic outcomes), family proximity, work-life balance, friendly co-workers

(social outcomes), variety, creativity and skill development (intrinsic outcomes) were modeled as reflective.

34

The second-order constructs of extrinsic, social and intrinsic outcomes were modeled as formative. We

applied the guidelines suggested by Petter et al. (2007) for determining that our second-order constructs

were formative: (1) the direction of causality is from the items to the construct; (2) the items for the construct

are not interchangeable (e.g., as in the case of items for family proximity and those for work-life balance); (3)

the covariance between measures is not necessary; and (4) formative measures need not share common

antecedents and consequences.

We assessed the measurement model for each of the three samples of IT workers to examine the

psychometric properties of the data. The details of this analysis are provided in Appendix B, which reports

the results of our measurement invariance analysis, and Appendix C, which reports our measurement model

results. Support was found for measurement invariance, as well as reliability and validity of our measurement

models, allowing us to proceed with examination of the structural models.5

Tables 4a, 4b and 4c present the descriptive statistics and correlations (as they relate to the

structural model variables) for the pre-organizational entry samples of IT workers. Means for extrinsic, social

and intrinsic outcomes in each of the three studies were between 4.36 and 5.08, with standard deviations

between 1.07 and 1.38. For PO fit and PJ fit, means were between 4.51 and 5.00, with standard deviations

between 1.06 and 1.23. The three outcomes were significantly correlated with both PO fit and PJ fit

perceptions in all three studies, with one exception being that in study 3 extrinsic outcomes were not

correlated with PJ fit perceptions. These correlations are in the direction of our hypotheses H1 through H4.

We next examined the results of the structural model tests. Table 5 shows the results of the model

tests for pre-organizational IT workers in each of the three studies. The general pattern of results across the

three studies was similar. The variance explained in fit perceptions across the groups ranged from

approximately 9% to 23%. We examined support for the direct effect hypotheses predicting PO fit and PJ fit.

5 To provide further confidence in the validity of our results, we assessed the potential for common method variance. This analysis is reported in Appendix D and demonstrates that common method variance is unlikely to have affected our results.

35

In all three studies, H1 through H4 were supported—i.e., extrinsic and social outcomes positively influenced

PO fit perceptions, and social and intrinsic outcomes positively influenced PJ fit perceptions. Next, we

examined support for the moderation hypotheses predicting PO fit and PJ fit. H5 predicted that extrinsic

outcomes would have a stronger effect on PO fit for men. This was supported. Support was also found for H6

that predicted social outcomes would have a stronger effect on PO fit for women. In examining the results

related to predictions of PJ fit, the results supported H7 that predicted that social outcomes would have a

stronger effect on PJ fit for women.

Table 4a. Study 1: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers M SD ICR 1 2 3 4 5 6

1 Extrinsic outcomes 4.72 1.18 - .87

2 Social outcomes 4.98 1.38 - .29*** .86

3 Intrinsic outcomes 5.08 1.21 - .25*** .23*** .89

4 PO fit 4.84 1.06 .75 .16** .31*** .13* .91

5 PJ fit 4.91 1.11 .77 .12* .30*** .33*** .55*** .92

6 Gender - - - .28*** -.21*** .04 .24*** .30*** -

Note: * p<.05; ** p<.01; *** p<.001; ICR = Internal consistency reliability; Diagonal elements represent the average variance extracted (AVE); Gender was dummy-coded as 0 for women and 1 for men.

Table 4b. Study 2: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers M SD ICR 1 2 3 4 5 6


2 Social outcomes 4.60 1.29 - .23*** .84


4 PO fit 4.69 1.06 .76 .20** .32*** .15* .89

5 PJ fit 5.00 1.21 .78 .16** .33*** .31*** .56*** .91

6 Gender - - - .31*** -.22*** .06 .29*** .24*** -


Table 4c. Study 3: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers M SD ICR 1 2 3 4 5 6


2 Social outcomes 4.71 1.33 - .22*** .93


4 PO fit 4.51 1.07 .80 .20** .34*** .14* .87

5 PJ fit 4.56 1.23 .79 .08 .30*** .30*** .53*** .94

6 Gender - - - .29*** -.19** .08 .25*** .23*** -


Table 5. Structural Model Results for Pre-organizational Entry IT Workers DV: PO fit Study 1: n=173 Study 2: n=89 Study 3: n=123

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

R2 .09 .23 .11 .21 .11 .23

36

Age - - - - .08 .08

Previous job count - - - - -.05 -.07

Extrinsic outcomes (H1) .13* .11* .16** .08 .16** .13*

Social outcomes (H2) .23*** .12* .26*** .14* .24*** .10

Gender - .03 - .06 - .03

Extrinsic*Gender (H5) - .24*** - .23*** - .22***

Social*Gender (H6) - -.28*** - -.25*** - -.25***

DV: PJ fit

R2 .13 .19 .12 .19 .18 .22

Age - - - - .01 .05

Previous job count - - - - -.01 .06

Social outcomes (H3) .21*** .14* .21*** .15* .27*** .24***

Intrinsic outcomes (H4) .26*** .28*** .25*** .28*** .27*** .26***

Gender - .05 - .01 - .06

Social*Gender (H7) - -.25*** - -.24*** - -.12*

Note: * p<.05; ** p<.01; *** p<.001; Gender was dummy-coded as 0 for women and 1 for men.

Generalizability and Boundary Conditions

To assess the generalizability of the model and determine its boundary conditions, we next

examined support for our model across professional domains for pre-organizational entry workers. As noted

earlier, this type of generalizability is related to empirical testing or deductive prediction (Lee and Baskerville

2003, 2012; Tsang and Williams 2012) and represents the only way a researcher can generalize across

contexts.

Assessment of the reliability and validity of the measurement model is shown in Appendix C. The

pattern of results was highly similar to the primary analysis reported above, with the measurement models

demonstrating reliability and validity. Tables 6a, 6b and 6c provide the descriptive statistics and correlations,

which are also highly similar to earlier results (see Tables 4a, 4b, and 4c). Structural model results across

gender and professional domain for all business domains prior to organizational entry are shown in Tables

7a, 7b and 7c. To examine gender and professional domain differences, we analyzed each subsample

separately, as recommended when examining moderation by categorical variables in PLS (Carte and Russell

2003). The general pattern of results across the three studies was again fairly similar. The variance

explained in fit perceptions in various groups ranged from approximately 5% to 21%. However, we observed

some interesting differences across professional domains, when compared to the results for IT workers. To

37

assess whether one effect was statistically stronger than another, we performed a series of Chow’s tests

(Chow 1960). The significance of these differences, based on the Chow’s tests, are reported in Table 8. In

terms of differences between professional domains, extrinsic outcomes were observed to have a stronger

effect on PO fit for those in quantitative domains, compared to people-oriented domains and IT. Social

outcomes had a stronger effect on both PO fit and PJ fit for those in people-oriented domains and IT,

compared to those in quantitative domains. The results also showed that, in two of the three studies,

perceptions of intrinsic outcomes had a stronger impact on PJ fit for those in IT, compared to those in

quantitative domains and people-oriented domains. We speculate about possible reasons for these

differences in the discussion section.

38

Table 6a. Study 1: Descriptive Statistics and Correlations for Pre-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9


2 Social outcomes 4.83 1.40 - .18** .84

3 Intrinsic outcomes 4.61 1.23 - .25*** .19** .87

4 PO fit 5.03 1.11 .73 .23*** .35*** .20** .91

5 PJ fit 5.21 1.11 .84 .07 .29*** .13* .57*** .92

6 Gender - - - .33*** -.29*** .25*** .30*** .29*** -

7 Domain: Quantitative - - - .20** -.18** .07 .07 .16* .25*** -

8 Domain: People-oriented - - - -.13* .17** .14* .19** .03 -.07 .15* -

9 Domain: IT - - - .13* .14* .18** .15* .15* .03 .07 .07 -


Table 6b. Study 2: Descriptive Statistics and Correlations for Pre-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9


2 Social outcomes 4.87 1.38 - .21*** .86

3 Intrinsic outcomes 4.68 1.28 - .26*** .22** .88

4 PO fit 5.12 1.09 .78 .26*** .38*** .22*** .91

5 PJ fit 5.25 1.06 .73 .05 .31*** .14* .55*** .92

6 Gender - - - .35*** -.30*** .28*** .30*** .24*** -

7 Domain: Quantitative - - - .21*** -.22*** .07 .04 .16** .26*** -

8 Domain: People-oriented - - - -.17** .17** .15* .19** .04 -.07 .17** -

9 Domain: IT - - - .13* .13* .20** .17** .14* .10 .04 .08 -


Table 6c. Study 3: Descriptive Statistics and Correlations for Pre-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9


2 Social outcomes 4.75 1.39 - .20*** .92


4 PO fit 4.60 1.12 .74 .28*** .35*** .22*** .88

5 PJ fit 4.63 1.13 .79 .17** .30*** .14* .50*** .93

6 Gender - - - .33*** -.31*** .28*** .26*** .29*** -

7 Domain: Quantitative - - - .27*** -.22*** .07 .07 .17** .25*** -

8 Domain: People-oriented - - - -.24*** .22*** .16** .21*** .15* -.08 .17** -

9 Domain: IT - - - .15* .19** .20** .17** .19** .10 .01 .03 -

39


Table 7a. Study 1: Structural Model Results for Pre-organizational Entry Workers Overall Gender Professional domain Gender and professional domain

DV: PO fit n=592

Men n=368

Women n=224

IT n=173

People n=176

Quant n=243

IT men n=99

IT women

n=74

People men n=70

People women n=106

Quant men

n=199

Quant women

n=44

R2 .07 .07 .07 .09 .09 .11 .09 .10 .08 .14 .14 .10

Extrinsic outcomes (H1) .10 .23*** .08 .13* .05 .30*** .20** .03 .14* .08 .35*** .23**

Social outcomes (H2) .22*** .05 .23*** .23*** .26*** .07 .20** .30*** .17** .35*** .08 .20**

DV: PJ fit

R2 .13 .07 .15 .13 .10 .07 .13 .18 .06 .15 .08 .10

Social outcomes (H3) .23*** .14* .31*** .21*** .26*** .06 .19** .30*** .20** .31*** .08 .19**

Intrinsic outcomes (H4) .19** .16** .17** .26*** .08 .20** .28*** .26*** .05 .16** .25*** .21***

Note: * p<.05; ** p<.01; *** p<.001; Quant = Quantitative domains, People = People-oriented domains.

Table 7b. Study 2: Structural Model Results for Pre-organizational Entry Workers Overall Gender Professional domain Gender and professional domain

DV: PO Fit n=752

Men n=442

Women n=310

IT n=89

People n=206

Quant n=457

IT men n=54

IT women

n=35

People men n=66

People women n=140

Quant men

n=322

Quant women n=135

R2 .10 .07 .08 .11 .10 .11 .11 .10 .10 .16 .13 .11

Extrinsic outcomes (H1) .08 .24*** .07 .16** .05 .30*** .22*** .05 .18** .02 .35*** .22***

Social outcomes (H2) .28*** .04 .25*** .26*** .30*** .09 .22*** .31*** .20** .40*** .02 .22***

DV: PJ Fit

R2 .11 .06 .13 .12 .07 .05 .11 .17 .05 .13 .07 .09

Social outcomes (H3) .24*** .14* .31*** .21*** .26*** .07 .19** .29*** .22*** .33*** .04 .19**

Intrinsic outcomes (H4) .21*** .16* .15* .25*** .07 .20** .26*** .29*** .07 .15* .26*** .21***


40

Table 7c. Study 3: Structural Model Results for Pre-organizational Entry Workers Overall Gender Professional domain Gender and professional domain

DV: PO Fit n=770

Men n=459

Women n=311

IT n=123

People n=208

Quant n=439

IT men n=82

IT women

n=41

People men n=67

People women n=141

Quant men

n=310

Quant women n=129

R2 .06 .17 .09 .11 .10 .19 .11 .14 .10 .19 .21 .14

Age .12* .02 .08 .08 .10 .13* .05 .10 .03 .05 .16** .12*

Previous job count -.03 -.04 .08 -.05 .07 .06 -.08 -.05 .05 .03 .08 .06

Extrinsic outcomes (H1) .12* .29*** .01 .16** .10 .37*** .22*** .07 .16** .03 .41*** .22***

Social outcomes (H2) .10** .13* .26*** .24*** .25*** .06 .21*** .33*** .16** .40*** .04 .22***

DV: PJ Fit

R2 .16 .10 .20 .18 .11 .10 .15 .19 .13 .21 .12 .13

Age .12* .02 .02 .01 .02 .15* .07 .08 .04 .04 .17** .11*


Social outcomes (H3) .30*** .14* .38*** .27*** .33*** .08 .24*** .31*** .28*** .39*** .05 .17**

Intrinsic outcomes (H4) .20** .23*** .18** .27*** .05 .25*** .26*** .25*** .05 .18** .26*** .23***


Table 8. Chow’s Test for Statistical Differences

Coefficients Compared Study 1 Signif. of

differences Study 2

Signif. of differences

Study 3 Signif. of differences

Extrinsic Outcomes PO Fit (H1)

Quant vs. People .30*** vs..05 *** .30*** vs..05 *** .37*** vs..10 ***

Quant vs. IT .30*** vs..13* *** .30*** vs..16** ** .30*** vs..13* **

Social Outcomes PO Fit (H2)

People vs. Quant .26*** vs..07 *** .30*** vs..09 *** .25*** vs..06 ***

IT vs. Quant .23*** vs..07 *** .26*** vs..09 *** .24*** vs..06 ***

Social Outcomes PJ Fit (H3)

People vs. Quant .26*** vs..06 *** .26*** vs..07 *** .33*** vs..08 ***

IT vs. Quant .21*** vs..06 *** .21*** vs..07 *** .27*** vs..08 ***

Intrinsic Outcomes PJ Fit (H4)

IT vs. People .26*** vs..08 *** .25*** vs..07 *** .27*** vs..05 ***

IT vs. Quant .26*** vs..20** * .25*** vs..20*** * .27*** vs..25*** ns


41

Robustness Checks

We assessed the robustness of the model between expectations and experiences by examining

the model using data collected from respondents after organizational entry. Tables 9a, 9b and 9c provide

the descriptive statistics and correlations, which are also highly similar to our primary model tests.

Structural model results across gender and professional domain for all business domains after

organizational entry are shown in Tables 10a, 10b and 10c. The general pattern of results across the three

studies is similar to those for the pre-organizational entry data.

Table 11 shows a comparison of the results across the three studies with both pre- and post-

organizational entry data for all business domains. This table pulls together results from Tables 7a, 7b, 7c,

10a, 10b and 10c. The results show some interesting effects in terms of the impact of valuations of

outcomes on PO fit and PJ fit perceptions for all people and all professional domains. In the first two

studies, which included entry-level workers, social outcomes predicted PO fit perceptions, whereas

extrinsic outcomes were not significant in predicting PO fit. Both social and intrinsic outcomes predicted PJ

fit perceptions. In study 3, which included experienced workers, the effect of social outcomes in predicting

PO fit was significantly stronger post-organizational entry, compared to pre-organizational entry. For PJ fit

perceptions, social and intrinsic outcomes were significant predictors, but the effect of social outcomes was

stronger in study 3 compared to what it was in studies 1 and 2. The significance of these differences, based

on the Chow’s tests, are reported in Table 12.

These results highlight the important role played by valuations regarding intrinsic and social

outcomes, in particular, in predicting fit perceptions. Additionally, in examining the results from each of the

two waves of measurement in each of the three studies, it is interesting to note that the pattern of results

remains virtually unchanged between pre- and post-organizational entry, suggesting considerable stability

in terms of the factors influencing fit perceptions.

42

Table 9a. Study 1: Descriptive Statistics and Correlations for Post-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9


2 Social outcomes 4.75 1.39 - .19** .82


4 PO fit 4.67 1.07 .74 .24*** .37*** .21*** .89

5 PJ fit 4.85 1.04 .79 .13* .30*** .17** .56*** .90

6 Gender - - - .35*** -.30*** .26*** .30*** .28*** -

7 Domain: Quantitative - - - .19** -.20*** .12* .04 .17** .31*** -

8 Domain: People-oriented - - - .17** .17** .13* .20** .08 -.10 -.19** -

9 Domain: IT - - - .15* .16** .19** .14* .16** .08 .06 .05 -

Note: * p<.05; ** p<.01; *** p<.001; ICR = Internal consistency reliability; Diagonal elements represent the average variance extracted (AVE);

Table 9b. Study 2: Descriptive Statistics and Correlations for Post-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9


2 Social outcomes 4.76 1.35 - .22*** .85


4 PO fit 4.65 1.04 .73 .25*** .37*** .21** .86

5 PJ fit 4.77 1.05 .78 .08 .32*** .16** .53*** .91

6 Gender - - - .33*** -.31*** .25*** .32*** .29*** -

7 Domain: Quantitative - - - .22*** -.21*** .13* .08 .21*** .30*** -

8 Domain: People-oriented - - - -.14* .20** .14* .21*** .03 -.10 -.20** -

9 Domain: IT - - - .13* .15* .18** .14* .16** .03 .02 .03 -


Table 9c. Study 3: Descriptive Statistics and Correlations for Post-organizational Entry Workers

M SD ICR 1 2 3 4 5 6 7 8 9


2 Social outcomes 4.71 1.41 - .23*** .89


4 PO fit 4.31 1.07 .73 .23*** .32*** .24*** .87

5 PJ fit 4.25 1.08 .75 .14* .31*** .20** .46*** .92

6 Gender - - - .35*** -.31*** .28*** .31*** .29*** -

7 Domain: Quantitative - - - .25*** -.24*** .14* .03 .19** .31*** -

8 Domain: People-oriented - - - -.15* .20** .14* .18* .13* -.10 -.17** -

9 Domain: IT - - - .16* .14* .18** .18** .14* .05 .07 .06 -


43

Table 10a. Study 1: Structural Model Results for Post-organizational Entry Workers Overall Gender Professional domain Gender and professional domain

DV: PO fit n=391

Men n=240

Women n=151

IT n=126

People n=114

Quant n=151

IT men n=75

IT women

n=51

People men n=44

People women

n=70

Quant men

n=121

Quant women

n=30

R2 .08 .08 .08 .11 .08 .11 .10 .10 .08 .12 .12 .13

Extrinsic outcomes (H1) .08 .24*** .05 .15* .06 .31*** .21*** .05 .15* .05 .31*** .21***


DV: PJ fit

R2 .10 .06 .15 .12 .07 .06 .12 .13 .05 .14 .08 .08

Social outcomes (H3) .24*** .13* .31*** .22*** .25*** .08 .19** .25*** .21*** .31*** .08 .19**

Intrinsic outcomes (H4) .17** .17** .18** .24*** .04 .21*** .26*** .25*** .09 .16** .24*** .19**


Table 10b. Study 2: Structural Model Results for Post-organizational Entry Workers Overall Gender Professional domain Gender and professional domain

DV: PO fit n=526

Men n=312

Women n=214

IT n=76

People n=140

Quant n=310

IT men n=46

IT women

n=30

People men n=45

People women

n=95

Quant men

n=221

Quant women

n=89

R2 .08 .07 .10 .10 .08 .11 .09 .10 .07 .14 .12 .10

Extrinsic outcomes (H1) .10 .26*** .04 .15* .08 .30*** .20** .05 .16** .04 .34*** .22***


DV: PJ fit

R2 .11 .06 .13 .12 .07 .06 .13 .17 .05 .13 .07 .09

Social outcomes (H3) .25*** .16** .31*** .21*** .26*** .05 .21*** .28*** .22*** .30*** .02 .20**

Intrinsic outcomes (H4) .16** .16** .14* .27*** .03 .22*** .29*** .28*** .04 .18** .24*** .19**


Table 10c. Study 3: Structural Model Results for Post-organizational Entry Workers Overall Gender Professional domain Gender and professional domain

DV: PO fit n=502

Men n=303

Women n=199

IT n=82

People n=139

Quant n=281

IT men n=54

IT women

n=28

People men n=48

People women

n=91

Quant men

n=201

Quant women

n=80

R2 .10 .12 .08 .11 .09 .17 .15 .12 .10 .17 .20 .12

Age .12* .03 .14* .03 .08 .16** .04 .04 .03 .07 .17** .13*


Extrinsic outcomes (H1) .13* .31*** .04 .13* .08 .37*** .22*** .08 .16** .02 .43*** .24***

Social outcomes (H2) .20** .13* .25*** .23*** .25*** .05 .22*** .31*** .16** .40*** .03 .21***

44

DV: PJ Fit

R2 .15 .11 .20 .19 .15 .08 .15 .17 .08 .19 .10 .10

Age .14* .03 .17** .08 .08 .15* .03 .06 .03 .03 .14* .14*


Social outcomes (H3) .30*** .17** .40*** .29*** .33*** .08 .25*** .30*** .24*** .38*** .07 .21***

Intrinsic outcomes (H4) .19** .21*** .18** .25*** .08 .22*** .24*** .23*** .05 .15* .25*** .21***


Table 11. Comparison of Results Across Pre- and Post-organizational Pre-organizational entry Post-organizational entry

Entry-level workers: Study 1 / 2

Experienced workers: Study 3

Entry-level workers: Study 1 / 2

Experienced workers: Study 3

Extrinsic outcomes PO fit .10 / .08 .12* .08 / .10 .13*

Social outcomes PO fit .22*** / .28*** .10** .24*** / .23*** .20**

Social outcomes PJ fit .23*** / .24*** .30*** .24*** / .25*** .30***

Intrinsic outcomes PJ fit .19* / .21*** .20** .17* / .16* .19**

Note: * p<.05; ** p<.01; *** p<.001.

Table 12. Chow’s Test for Statistical Differences

Coefficients compared Signif. of

differences

Social Outcomes PO Fit

Study 3: Pre-org vs. Post-org entry .10** vs. .20** *

Social Outcomes PJ Fit

Pre-org entry: Study 3 vs. Study 1 .30*** vs. .23*** *

Pre-org entry: Study 3 vs. Study 2 .30*** vs. .24*** *

Post-org entry: Study 3 vs. Study 1 .30*** vs. .24*** *

Post-org entry: Study 3 vs. Study 2 .30*** vs. .25*** *

Note: * p<.05; ** p<.01; *** p<.001; Pre-org = Pre-organizational entry; Post-org = Post-organizational entry.

45

DISCUSSION

Drawing from prior research in IS, OB and vocational behavior, we hypothesized that three work

outcomes—namely, extrinsic, social and intrinsic—as predictors of PO fit and PJ fit perceptions of new

employees. Building on prior research that the valuations of these outcomes are important in assessing PO

fit and PJ fit perceptions, using developmental socialization as the underlying mechanism, we hypothesized

that the relationship between the valuations of these work outcomes and fit perceptions would be

moderated by gender. We tested the model in three studies among IT workers, with data collected pre-

organizational entry, across periods of differing levels of economic stability. The theorized direct and

gender moderation effects were empirically supported. Extrinsic and social outcomes directly affect PO fit,

whereas social and intrinsic outcomes directly affect PJ fit for IT workers. Gender moderated the effect of

extrinsic outcomes on PO fit such that this relationship was stronger for men in IT (compared to women).

The effects of social outcomes on PO fit and PJ fit were moderated by gender such that this relationship

was stronger for women in IT. Additionally, we examined the generalizability and boundary conditions of the

model by testing it among those in other business domains. We found that extrinsic outcomes had a

stronger effect on PO fit perceptions for those in quantitative domains and social outcomes had a stronger

effect on PO fit and PJ fit for those in people-oriented domains and IT. Also, intrinsic outcomes had a

stronger effect on PJ fit perceptions for those in IT. Finally, we tested the robustness of the findings by

comparing pre- and post-organizational entry data. We found that the results were highly similar across

pre- and post-organizational entry data. Thus, we conclude that the model is robust within the range of the

comparisons conducted. We offer contributions and implications, further discussion of professional domain

differences, future research ideas and practical implications.

Contributions and Implications

The current work makes several contributions. By studying our model in the context of IT workers,

46

we contribute to a deeper understanding of the attraction, retention and motivation of IT workers. Ferratt

and Short (1986, 1988) had concluded that there was no evidence to merit managing IT workers and non-

IT workers differently. However, based on data collected in different studies over a decade after their

studies, our findings provide evidence to the contrary. It appears that the work outcomes driving fit

perceptions of IT workers are different from what is important to those in quantitative and people-oriented

domains. It certainly appears that the total reward view of compensation is particularly appealing to IT

workers, given that their perceptions of PO and PJ fit are driven by social and intrinsic outcomes. With

recent interest in using skill development opportunities, such as participation in open source projects, as a

means to keep direct wage payments in check (Mehra and Mookerjee 2012), we provide insight into how

such initiatives can be successful through enhancing the intrinsic appeal of a job. Our findings provoke a

few interrelated and important issues/questions about IT workers that merit further study—for example: (a)

does such a pattern of results also hold in the case of IT workers with a lengthy history working in the field?

(b) how elastic is the IT workers’ focus on intrinsic outcomes? and (c) other measurement approaches,

such as the constant sum method (e.g., Agarwal and Venkatesh 2002) and different approaches to the

measurement of social outcomes in terms of social networks (see Sykes 2015; Sykes, Venkatesh, and

Gosain 2009; Sykes, Venkatesh, and Johnson 2014). Such studies may shed light on the unique aspects of

IT workers’ value systems and will serve as a way of further validating our findings. Even as it stands, our

work provides useful information to counsel students, particularly in their job search (see Saks and Ashforth

1997). Similarly, our findings can help improve the communication between career centers and

organizations.

Our paper also highlights differences between men and women in IT in terms of how their

valuations of work outcomes influence fit perceptions. We observed professional domain differences as

well. The pattern of interactions suggests that understanding differences based on gender and professional

domain are important to effectively manage a workforce. Our model contributes to the literature on

47

employee fit perceptions by identifying specific outcomes that impact perceptions. Such an exploration of

the drivers of fit are long overdue (Barrick et al. 2013; Colbert et al. 2008; Kristof-Brown et al. 2005). In

terms of future research, there is an important stream of work on met expectations and various alternative

models—e.g., assimilation model, contrast model, generalized negativity model, assimilation contrast (see

Brown, Venkatesh, and Goyal 2012, 2014)—of how individuals react when expectations are met, not met or

exceeded. Longitudinal research will help understand which of the models of met expectations are at play

in the case of new employees. As we have noted, there are other competing models/perspectives, mostly

from an employer’s viewpoint, that should be compared to our model. Beyond a comparison, an integration

of employer- and employee-centric views could provide a holistic view of fit perceptions. Specifically, future

work might integrate employees’ valuations of work outcomes used in the current study with employer’s

selection or socialization tactics to determine which combination of tactics produce optimal fit.

Our work also has implications for the human resource management literature that examines

equality and gender issues in the workplace, especially because the gender imbalance in the IT industry

has long been a concern (Trauth 2011). We add to the growing body of work that aims to identify ways in

which the IT industry can be made more attractive to women by emphasizing those aspects that are more

appealing to them. The wage differential between women and men has been studied extensively and is

often a topic of discussion in the trade press. There have been a few explanations offered for this

phenomenon, including the glass ceiling. Some of the other explanations include attributing the wage

differential to the selection of lower-paying professions by women (e.g., Gupta 1993). Although additional

research is necessary to validate our position, we offer an employee-supervisor interactive explanation. In

particular, if women place less emphasis on extrinsic outcomes than men do, it is possible that supervisors

take that into account, be it consciously or unconsciously (see Bartol and Martin 1988). Igbaria and Baroudi

(1995) found that, despite similar job performance ratings, women were perceived to be less likely to be

promoted than men. In addition, as women are less likely than their male counterparts to perceive lowered

48

fit in such a situation given the weaker extrinsic outcomes-PO fit relationship among women, organizations

may not suffer negative consequences by perpetrating unfair practices of wage differentials and less

advancement for women.

We have endeavored to extend the work of Kristof-Brown and her colleagues (1996, 2000, 2005)

by expanding the nomological network of fit perceptions. Future research should expand on the

nomological network presented here to include other types of fit, such as person-group fit, person-

supervisor fit and person-job cognitive style fit, as there is evidence these other fit perceptions also relate to

employee well-being (Chilton et al. 2005), organizational commitment and turnover intention (e.g., van

Vianen 2000). Another fit that is pertinent to this research area is person-group fit (Guzzo and Salas 1995)

given the extensive use of teams in today’s workplaces, especially in the IT industry. Recent research in IS

has examined how IT might be leveraged to help determine team composition based on person-group fit

(Malinowski et al. 2008). Such research would benefit from a deeper understanding of the drivers of fit

perceptions and may help to further the study of virtual teams and specific IT workgroups, such as face-to-

face and geographically-dispersed IS development teams. Other important job outcomes—e.g., job

satisfaction—could also be examined using the work outcomes identified here. An important next step

relates to understanding the elasticity of the importance of different work outcomes. Although different

groups of individuals reported different work outcomes as being important, it is possible that when faced

with continuing difficulty in finding a job, the importance placed on some work outcomes may be more

elastic. Longitudinal research will help shed light on this issue as it will help understand how the importance

of work outcomes changes over time and how the importance changes in the face of adversity or when

faced with an actual situation of weighing specific job choices.

Professional Domain Differences

By examining our model across ten years of historical and economic change, across both new and

experienced workers and across multiple professional domains, we found that it was both temporally and

49

contextually generalizable. Our model did broadly generalize to other business domains, subject to some

boundary conditions. This is consistent with accumulated wisdom, for decades now, that there are

differences in employees’ reactions and beliefs across professions (Centers and Bugental 1966; Gruenberg

1980; Williams 1972). In fact, some prior work found differences in employee reactions based on

occupation rather than gender (Almquist 1974). Even as far back as the 1940s, Verniaud (1946) made the

observation that women in some occupational groups provided responses that were considered more

“masculine.” England and Stein (1961) found major differences across occupations and called for special

scales for different occupations. More recent research has also supported such occupational differences.

For example, Dierdorff and Moregeson (2007) found that consensus about work roles and requirements

differs across 98 different occupations including management, computer and math, legal, social service,

art, health care, construction, production and transportation.

A theoretical basis to expect professional domain differences is professional socialization or

professional commitment, both of which have received significant attention in OB (e.g., McGowen and Hart

1990; Schaubroeck et al. 2012). Professional socialization is the process by which individuals are

introduced to and familiarized with the objective and subjective aspects of a professional domain. The

objective element of professional socialization is learning relevant knowledge and skills to be successful in

the profession including learning the common language for communication with others in the profession

(McGowen and Hart 1990). The subjective element of professional socialization includes becoming familiar

with expectations, understanding norms and acquiring values that are prevalent in the profession

(McGowen and Hart 1990). For entry-level workers, the relevant knowledge and influence will originate

from people and work in their professional domain: professors, peers, friends and short-term work (e.g.,

internships). For experienced workers, this relevant knowledge and influence will be fostered through

sustained contact, working with others in the industry, such as co-workers and professional societies.

Professional domains tend to have specific value systems (see Barley 1996), thus resulting in different

50

predictors of key outcomes across different professions (see Lee et al. 2000). In contrast to research on

developmental socialization that has spanned several decades, in-depth psychological studies of

professional socialization that go beyond vocational interests are far more recent (e.g., Schaubroeck et al.

2012).

The subjective element of professional socialization can certainly be expected to play a key role

here as students get socialized to the value system of their profession and as experienced professionals

are socialized to the value system of their field through their work experience. Past research suggests that

professional commitment also develops to a great extent through educational processes that occur prior to

individuals entering the job market and that this phenomenon is especially relevant in the IT field

(Vandenberg and Scarpello 1994). Such commitment only continues to grow as one gets entrenched in

one’s profession. Further, as noted by Bennett and Whittaker (1994), individuals already in a particular

profession are socialized to a set of norms and acquire distinct sets of skills and language causing people

in a particular professional domain to be more similar than different. Also, women’s focus on their

profession, workplace and career has increased greatly in recent years (e.g., Capell 2004), potentially

creating greater levels of identification with their professional domain. With the further emergence of STEM,

future work will be essential to examine the cross-temporal patterns in our findings.

Limitations and Future Research Directions

We note five limitations. First, we conducted a cross-sectional survey, thus common method bias is

a potential concern. Although there were several filler questions from the perspective of this paper, the

cross-sectional analysis in this study poses a limitation. However, this was alleviated to some extent here

because the moderator variable—gender—is not a perceptual construct. Further, we conducted statistical

analyses that minimize concerns related to common method bias (Harman 1976; Lindell and Whitney 2001;

Malhotra et al. 2006) and found that it was not a concern. Still, additional research employing other data

collection and methodological approaches is necessary to rule out this bias. Another limitation due to the

51

cross-sectional design was that causality among the variables cannot be established and reverse causality

cannot be eliminated as a possibility. Moreover, the ability to assess change over time and to rule out

alternative hypotheses were limited by the cross-sectional design. Longitudinal data collection is needed to

rule out these possibilities.

Second, although we sought ways to establish the generalizability of our results (i.e., across

domains), generalizability was limited to some extent by our sample. Data in studies 1 and 2 were collected

from only one U.S. university, which could limit the results to the culture of the university or the

geographical region. Data were collected from only one college—a business college—that could have

implications for the results based on the types of jobs that were sought, the types of students who decide to

pursue a business education, or the training that business students receive. To rule out limitations due to

the sample, a larger-scale study needed.

Third, another potential concern was social desirability bias in the participants’ responses. Social

outcomes, in particular, may be generally seen as things one should value, and there was likely normative

pressure to report a good fit with the current employer and job. Thus, the reported valuation of outcomes

and perceptions of fit may be inflated to some degree. However, the variability in responses provides some

evidence that social desirability was less of a concern in our data.

Fourth, as our study was one of the first to examine PO and PJ fit predictors, it was not surprising

that the variance explained is modest. However, we feel this work represents an important starting point for

future research and opportunities to explore additional sources of variation. Although we are not aware of

any other studies that have examined the antecedents to PO and PJ fit, we found that other highly cited

studies with similar constructs report similar R2 values. For example, the work of Colbert et al. (2008) on

organizational goal congruence (similar to the concept of fit) report R2 values ranging from 2% to 11%.

Other studies examining similar constructs, such as organizational commitment and organizational

52

citizenship behaviors, report similar R2 values (e.g., Becker et al. 1996; Jaramillo et al. 2005; Li et al. 2010;

Schappe 1998).

Finally, we studied only two moderators, including the post-hoc examination across professional

domain. Age, culture and personality are potential additional moderators. Differences in the importance of

work outcomes with age have been documented (e.g., increasing emphasis on family and need for a stable

income source) but their interaction with gender and professional domain merits investigation. Parkes et al.

(2001) found professional domain differences tied to differences in individualism-collectivism, which is an

important dimension of culture—e.g., collectivists were more committed. Further research that incorporates

culture-related variables and/or race is important to move us closer to the ultimate goal of a diverse and

equitable workplace. Research on cultural differences has established that people from some eastern

cultures are more collectivist in their thinking relative to western cultures (Oyserman et al. 2002). This

examination of culture as a moderator is of further significance in today’s environment of extensive

offshoring of IT work and business process activities to China and India (e.g., Rai et al. 2009; Venkatesh et

al. 2010). Such an expansion of the potential list of moderators may also necessitate revisiting the work

outcomes identified here to see if research conducted in other cultures helps unearth work outcomes that

had not been previously considered in North American settings. Future work should focus on personality

variables to examine whether it is demographic variables or certain personality attributes, such as the

popular Big Five, that are sources of the moderation. There is evidence that personality influences an

employee’s preferred managerial style (Stevens and Ash 2001). Taken together, gender and professional

domain could be pitted against personality variables to examine the relative importance of the two classes

of variables as moderators of the various relationships.

Practical Implications

The fit of an individual for a job can be accomplished through the selection process. Similarly, a job

can be fit to an individual through work redesign (see Furnham 2001) and socialization (Cable and Parsons

53

2001). Our results have implications for organizational socialization tactics (Cable and Parsons 2001). As

organizations have an opportunity to influence employees’ expectations regarding various work outcomes,

knowing which outcomes are critical to whom is important. Our results not only supported that men and

women (and, to some extent, people in different professional domains) were driven by different work

outcomes, but also allowed us to identify specific elements that can be used to motivate and manage

specific constituencies. Pratt (1998) reviewed research on organizational identification that served as an

important ingredient in managing employees’ assimilation into organizations. Armed with the relative

importance of the work outcomes detailed here, a richer management process to facilitate smooth

organizational entry for new employees, particularly for women (who more strongly value social outcomes)

and IT workers (who more strongly value intrinsic outcomes), can be created. For example, we encourage

practitioners interested in attracting and retaining women in their workforce to offer programs and policies

focused around social outcomes. Flex-time, telework and childcare resources are examples of interventions

that could help women address concerns about work-life balance and proximity to family. Mentoring

programs, collaborative work and social outings or activities may be useful interventions in supporting the

cultivation of friendly relationships among co-workers. The results also have implications for managing the

needs of both men and women after organizational entry by designing interventions to enhance employees’

fit perceptions. By knowing that social outcomes, for example, tend to become more important in

determining PJ fit as workers gain experience, organizations can implement the above interventions that

aim to provide greater social support from the organization and more opportunities for social interaction

among co-workers.

By studying entry-level IT workers, our research has practical implications for organizations that will

hire the next generation of IT workers. Recent research has shown that the IT job market continues to

change. As programming and user support roles are offshored (Panko 2008) and lower-paying secondary

IT labor markets emerge (Joseph et al. 2012), the profile of the “typical” IT worker is bound to change and

54

perhaps change continuously. Entry-level IT workers will need to look at acquiring different skills sets and

pursuing different opportunities within the IT industry that align with their work values (Heinze and Hu 2009;

Joshi et al. 2010; Mourmant et al. 2009). Effective training, career and educational counseling requires an

understanding of what these IT workers value, and how various IT jobs may be able to fulfill those needs. In

response to an understanding that IT workers place more value on social outcomes, organizations may

want to place more emphasis on professional socialization for incoming IT workers. For example, recent

evidence suggests that “Gen Y” IT workers are highly attuned to socialization via Web 2.0 tools, such as

social media, (Leidner et al. 2010) and organizations may want to take advantage of this as both an

assimilation mechanism and a means to boost morale by facilitating interpersonal interactions between co-

workers.

Our findings can help managers optimize the hiring, selection, retention and management

processes by emphasizing a total rewards perspective that goes beyond financial compensation. As we

noted at the outset, such an approach is particularly important in leaner economic times when it is more

important to keep employee motivation and morale up, as failing to do so can negatively impact productivity

(Gadd 2008). At the same time, when budgets are tight, it may be more feasible for an organization to

provide non-financial incentives, such as consideration for work-life balance through flex-time or

telecommuting (Levere 2011). Thus, focusing on social and intrinsic outcomes, particularly for women and

those in marketing, management and IT, can serve as a way to both motivate employees and keep costs

low. In turn, organizations may be able to reduce turnover intentions, increase productivity and reduce the

risk that they may be unable to replace a lost worker.

CONCLUSIONS We presented three work outcomes—i.e., extrinsic, social and intrinsic—that play a role in

determining perceptions of PO fit and PJ fit. Based on three empirical studies of over 1,300 entry-level

workers and 700 experienced workers, we found significant gender and professional domain differences in

55

the effects of these outcomes on PO fit and PJ fit perceptions at the time of organizational entry and post-

organizational entry, thus supporting a total reward and employee-centric view of the formation of these fit

perceptions. Our findings have implications for future research on potential interventions, socialization

tactics and expanding the nomological network to other job outcomes. The findings also have practical

implications for organizations that seek to effectively manage the attraction, motivation and retention of new

workers in general and IT workers in particular.

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http://www.whitehouse.gov/issues/education/k-12/educate-innovate

http://www.whitehouse.gov/issues/education/k-12/educate-innovate

66

Appendix A: Survey Items ------------------------------------------------------ WORK OUTCOMES We would like you to tell us how much of each characteristic you expect to see present in your job. For example, there is a characteristic “Friendly co-workers.” You will rate on a 7-point scale how much the job will provide the opportunity for you to have “friendly co-workers.” Scale (1 = Not at All to 7 = A Great Deal) ------------------------------------------------------ Pay 1) Salary level 2) The opportunity to become financially

wealthy 3) The amount of pay 4) A good salary 5) Receiving enough pay to live well 6) Opportunities to receive more than my normal

pay for good work 7) An opportunity to earn a high income 8) Periodic wage raises 9) Have pay increases that keep up with the cost of

living 10) Opportunities to earn more than my regular

paycheck

Promotion 1) Opportunities for advancement 2) Promotion opportunities 3) Chances for advancement 4) Opportunities to attain higher rank in the

organization 5) Chances to climb the corporate ladder 6) Opportunities to move up in the organization 7) Support for helping me gain a higher position in

the organization 8) The possibility of promotions 9) Chances to move up the organizational hierarchy 10) Opportunities to gain a higher position in the

organization Prestige 1) Having others consider my work important 2) Obtaining status in the eyes of others 3) Being looked up to by others 4) Getting a good reputation for my good work 5) Being respected by others 6) Being influential 7) My job bringing me prestige

8) Being held in high esteem by others 9) Having the admiration of others 10) Having the respect of others

Job Security 1) Being certain of keeping my job 2) Being sure I will always have a job 3) Being certain my job will last 4) Having a secure future 5) Having another job in the organization if my

present job ends 6) Being sure my job will be around for a long time 7) Knowing that I can count on having my job in the

near future 8) Knowing that I can work for my organization as

long as I want 9) Knowing that my organization would find me

another job if my current job were to end 10) Being sure I can count on this job lasting

Family Proximity 1) Being in the same geographic location as my

immediate family (i.e., parents, brother, sister) 2) Living in the same area as my immediate

family 3) Being in very close geographical proximity of

my immediate family 4) Having my immediate family live close to me 5) Not living far away from my immediate family 6) Not having to travel far to see my immediate

family 7) Living close to my immediate family 8) Being only a short distance from my immediate

family 9) Having my immediate family close by 10) Having my immediate family in close proximity to

where I live

Friendly Co-workers 1) Friendly co-workers 2) Collegial co-workers 3) Co-workers who are supportive 4) Forming friendships with co-workers 5) Having good relationships with co-workers 6) Having good connections with co-workers 7) Supportive co-workers 8) Being able to turn to my co-workers if I have a

problem 9) Sharing personal information with co-workers

67

10) Having a good report with co-workers

Work-Life Balance 1) Being able to balance my family and work life 2) Having time for my personal life 3) A work environment that supports

work/family balance 4) Providing enough leisure time off the job 5) Flexibility in balancing my personal life with my

work life 6) Work policies that support me in handling

personal or family concerns 7) The ability to disconnect from work when I am

away from it. 8) Support for balance between my work and

personal life 9) Having enough time to enjoy my family and

personal life 10) Work policies that let me alter my schedule so I

can attend to personal or family events

Variety 1) Doing a variety of things 2) Doing something different every day 3) Doing many different things on the job 4) Having variety in duties and activities 5) Having changes in my job 6) Not doing the same thing all the time 7) Opportunities to do different types of tasks 8) Variation in the work I do 9) Having different things to do over time 10) Working on different projects or tasks

Creativity 1) Trying out new ideas and suggestions 2) Creating something new 3) Contributing new ideas 4) Originating new ideas and/or products 5) Experimenting with different ways of doing things 6) Designing new things 7) Developing original ideas 8) Using my creative talents to do things in a new

way 9) Experimenting with my own ideas

10) Innovating with new ideas

Skill Development 1) Opportunities to develop new skills 2) Developing new knowledge through training 3) Acquiring new career-relevant skills 4) Improving the skills I have 5) Opportunities to increase my knowledge 6) Opportunities to acquire specialized skills 7) Adding to the abilities I already have 8) Encouraging continued development of my

knowledge and skills 9) Enhancing my current skill set 10) Extending my knowledge and skills ------------------------------------------------------ FIT PERCEPTIONS Scale: 7-point Likert agreement scale ------------------------------------------------------ Person-organization Fit 1) The organization will be a total fit for me. 2) Taking everything into account, the

organization will be a complete fit for me. 3) I would fit right into the organization.

Person-job Fit 1) I would fit right in to the job. 2) Taking everything into account, the job is a

complete fit for me. 3) The job provides a total fit for me.

Notes: 1. For the post-organizational entry items, the present

tense is used throughout. For instance, the instructions were modified to: We would like you to tell us how much of each characteristic you experience in your job. For example, there is a characteristic “Friendly co-workers.” You will rate on a 7-point scale how much the job provides the opportunity for you to have “friendly co-workers.”

2. Likewise, in the post-organizational entry survey, “will be” in the first two PO fit items is replaced with “is.” Also, “would” is dropped in the 3rd PO fit item and 1st PJ fit item.

3. Items retained in the final survey are shown in bold italics.

68

Appendix B: Measurement Invariance Analysis

Prior to evaluating our structural model, we sought to establish multigroup measurement

invariance. Configural and metric invariance analyses were conducted using AMOS 5.0. First, we tested for

configural invariance, which checks whether groups have the same factor structure by examining the extent

to which the pattern of loadings are congeneric when allowing parameters to vary freely across groups (Doll

et al. 1998; Steenkamp and Baumgartner 1998). The results demonstrated that the pattern of item loadings

was congeneric across the groups. Each of the baseline models showed adequate fit—all CFIs and NNFIs

being between .92 and .95—thus supporting the generalizability of the factor pattern. Metric invariance

testing determines whether the groups have the same factor loadings. To perform this analysis, a series of

equality constraints were imposed on the item loadings. Changes in CFI between the nested models

(configural and metric) should be smaller than the suggested threshold of .01 to demonstrate metric

invariance (Cheung and Rensvold 2002). Changes in CFI for our model were all less than .01, providing

support for metric invariance. The results indicated that the models exhibited measurement equivalence

across groups, permitting meaningful comparison between the path coefficients (Cheung and Rensvold

2002).

69

Appendix C: Measurement Model Validity Assessment

The analysis reported here employed data from men and women IT workers prior to organizational

entry. We examined the results for the constructs with reflective indicators, followed by the constructs with

formative indicators. Convergent validity is established when the item loadings are high (>.70) and when

the average variance extracted (AVE) is at least .50 (Fornell and Larcker 1981). Tables C1a, C1b and C1c

show the item loadings and crossloadings. All items exhibited high loadings on their respective factor, with

the exception of prestige1 (.69) and skill development1 (.68) in Table C1a for study 1, family proximity2

(.69) and skill development2 (.69) in Table C1b for study 2, and creativity3 (.69) in Table C1c for study 3.

Because these values were close to the cut-off of .70, and their corresponding AVEs were all above the

threshold of .50, we retained them in the model. Moreover, Gefen and Straub (2005, pp. 93-94) note that,

“all the loadings of the measurement items on their assigned latent variables should be an order of

magnitude larger than any other loading. For example, if one of the measurement items loads with a .70

coefficient on its latent construct, then the loadings of all the measurement items on any latent construct but

their own should be below .60” (for illustrations, see Choudhury and Karahanna 2006; Siponen and Vance

2010). This was the case with our model. We compared the square roots of the AVEs to the correlations

among constructs. The results also supported discriminant validity as the square roots of the AVEs were all

greater than the inter-construct correlations (Fornell and Larcker 1981). Tables C2a, C2b and C2c show the

AVEs, inter-construct correlations and demonstrated that internal consistency reliability values were greater

than the threshold of .70 (Nunnally and Bernstein 1994), providing support for reliability of PO and PJ fit.

Thus, we concluded that the measurement model results provided evidence for reliability and validity.

Formative measures are not required to exhibit internal consistency or reliability (Petter et al.

2007). In fact, multicollinearity can be problematic for formative constructs as it can suggest that multiple

indicators are tapping into the same aspect of the construct and destabilize the model (Diamantopoulos

and Siguaw 2006). Thus, we used the variance inflation factor (VIF) to assess the degree of

70

multicollinearity, with values less than 3.3 indicating that multicollinearity was not a concern. For extrinsic,

social and intrinsic outcomes, we found that the VIFs were all below 3, indicating that multicollinearity was

not an issue (Diamantopoulos and Siguaw 2006). For formative measures, the validity of the measurement

model involves examination of the item weights (Petter et al. 2007). Non-significant weights may be

eliminated, whereas significant weights provide insight into the relative importance of each indicator. Table

C3 shows the weights of the formative indicators associated with extrinsic, social and intrinsic outcomes.

When formative indicators explain all of the variance in a construct, the average of their weights is

√(1/𝑛), where n is the number of indicators (Klein and Rai 2009). Thus, the maximum average weight is

.50 for extrinsic outcomes with 4 indicators, .58 for intrinsic with 3 indicators and .58 social outcomes with 3

indicators. The observed weights, shown in Table C3, for extrinsic, social and intrinsic outcomes indicate

the relative importance of each of the formative indicators. To assess discriminant validity, we examined

the item-to-item and item-to-construct correlations (Petter et al. 2007). Using PLS item weights for each

formative indicator, we computed composite construct scores that were then used to calculate these

correlations and evaluate discriminant validity (Ravichandaran and Rai 2000). We found that the item-to-

item correlations were greater than the item-to-construct correlations and that the items had higher

correlations with the composite scores of their proposed construct than they did with the scores of other

constructs. We thus found support for discriminant validity of the constructs with formative indicators.

Tables C4a, C4b, C4c and C5 present the loadings, crossloadings and formative indicator weights

for the sample of all pre-organizational entry workers. The pattern of results was highly similar to that of our

results for IT workers, with the results generally providing evidence for reliability and validity. All items

exhibited high loadings (>= .70) on their respective factor. Discriminant validity was supported as the

square root of the AVEs were all greater than the inter-construct correlations (Fornell and Larcker 1981).

71

Tables C6a, C6b and C6c present the AVES, descriptive statistics and correlations, which were highly

similar to our primary analysis.

Tables C7a, C7b, C7c and C8 present the loadings, crossloadings and formative indicator weights

for the sample of all post-organizational entry workers. The pattern of results was highly similar to the

previous results, with the results generally providing evidence for reliability and validity. All items exhibited

high loadings on their respective factor, with the exception of variety2 (.69) in Table C7a for study 1 and

promotion3 (.69), family proximity3 (.69) and person-org fit3 (.69) in Table C7b for study 2. Based on the

same reasoning given above, we elected to keep these items in the model. Discriminant validity was

supported as the square roots of the AVEs were all greater than the inter-construct correlations (Fornell

and Larcker 1981). Thus, we concluded that all measurement model results provided evidence for reliability

and validity.

72

Table C1a. Study 1: Loadings and Crossloadings for Model with Pre-organizational Entry IT Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .74 .28 .55 .57 .57 .51 .40 .52 .28 .46 .32 .56

Pay2 .76 .47 .51 .39 .32 .37 .37 .33 .55 .30 .38 .49

Pay3 .75 .28 .39 .35 .57 .45 .35 .48 .61 .35 .29 .55

2

Promotion1 .35 .73 .61 .32 .61 .61 .41 .28 .55 .56 .50 .54

Promotion2 .51 .77 .37 .33 .34 .30 .38 .54 .40 .51 .46 .44

Promotion3 .50 .78 .49 .48 .43 .28 .30 .42 .57 .37 .34 .56

3

Prestige1 .31 .51 .69 .52 .55 .46 .53 .36 .57 .56 .45 .41

Prestige2 .54 .58 .74 .41 .61 .54 .62 .56 .42 .30 .56 .33

Prestige3 .55 .55 .73 .31 .49 .33 .40 .46 .35 .28 .35 .41

4

Security1 .57 .56 .38 .71 .46 .58 .50 .57 .35 .49 .60 .38

Security2 .48 .53 .28 .74 .62 .56 .34 .37 .48 .41 .45 .58

Security3 .30 .59 .51 .70 .57 .56 .56 .38 .55 .38 .33 .59

5

Work-life balance1 .56 .40 .28 .44 .77 .49 .40 .61 .39 .39 .39 .47



6

Friendly co-workers1 .56 .34 .49 .56 .42 .71 .45 .53 .61 .36 .55 .38



7

Family proximity1 .31 .59 .38 .60 .50 .54 .75 .55 .50 .40 .62 .32



8

Variety1 .29 .34 .32 .34 .38 .54 .51 .71 .50 .53 .35 .44

Variety2 .44 .33 .41 .48 .54 .57 .43 .74 .42 .57 .39 .54

Variety3 .57 .60 .40 .55 .39 .50 .44 .73 .38 .41 .38 .59

9

Creativity1 .46 .35 .60 .38 .31 .59 .35 .29 .71 .34 .55 .31

Creativity2 .40 .40 .48 .55 .35 .28 .31 .39 .74 .59 .38 .59

Creativity3 .51 .51 .45 .57 .58 .34 .55 .60 .71 .58 .48 .61

10

Skill development1 .50 .41 .52 .52 .34 .46 .33 .35 .36 .68 .57 .42



11

Person-org fit1 .35 .42 .43 .34 .55 .55 .47 .47 .57 .53 .71 .28

Person-org fit2 .36 .39 .39 .37 .30 .52 .42 .29 .62 .43 .77 .61

Person-org fit3 .61 .60 .39 .52 .30 .46 .58 .45 .43 .41 .72 .59

12

Person-job fit1 .42 .61 .45 .56 .50 .33 .31 .54 .37 .40 .36 .74

Person-job fit2 .32 .35 .55 .31 .60 .30 .50 .60 .35 .39 .29 .75

Person-job fit3 .31 .34 .50 .58 .60 .43 .61 .60 .47 .56 .55 .71

73

Table C1b. Study 2: Loadings and Crossloadings for Model with Pre-organizational Entry IT Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .74 .61 .53 .57 .29 .43 .35 .48 .46 .60 .49 .61

Pay2 .75 .58 .52 .53 .58 .61 .38 .59 .37 .54 .31 .52

Pay3 .72 .31 .34 .48 .42 .61 .53 .62 .55 .41 .30 .50

2

Promotion1 .60 .75 .45 .34 .46 .59 .34 .41 .39 .45 .34 .45

Promotion2 .40 .71 .49 .60 .59 .47 .30 .53 .50 .49 .41 .51

Promotion3 .37 .79 .35 .57 .59 .32 .49 .37 .44 .34 .51 .56

3

Prestige1 .53 .34 .79 .28 .48 .62 .42 .37 .53 .60 .54 .46

Prestige2 .47 .36 .79 .38 .37 .34 .42 .32 .52 .54 .37 .59

Prestige3 .40 .54 .74 .46 .39 .52 .40 .60 .32 .47 .59 .40

4

Security1 .57 .57 .61 .82 .39 .58 .46 .57 .54 .45 .62 .34

Security2 .59 .45 .33 .82 .37 .36 .45 .34 .45 .46 .35 .59

Security3 .52 .28 .61 .75 .36 .51 .34 .48 .43 .34 .46 .42

5




6




7




8

Variety1 .33 .28 .33 .50 .36 .53 .43 .83 .49 .30 .57 .49

Variety2 .50 .53 .45 .61 .30 .31 .55 .79 .43 .59 .29 .54

Variety3 .55 .45 .33 .43 .33 .29 .41 .84 .32 .40 .51 .33

9

Creativity1 .51 .52 .31 .37 .55 .37 .61 .38 .73 .40 .34 .39

Creativity2 .38 .59 .29 .55 .41 .52 .60 .34 .83 .49 .43 .35

Creativity3 .60 .33 .40 .28 .51 .46 .34 .60 .76 .28 .37 .50

10




11

Person-org fit1 .57 .37 .56 .58 .40 .34 .30 .44 .57 .52 .84 .46

Person-org fit2 .35 .30 .54 .55 .53 .35 .62 .58 .59 .34 .80 .36

Person-org fit3 .32 .36 .62 .61 .55 .35 .29 .30 .58 .41 .76 .37

12

Person-job fit1 .32 .46 .56 .36 .57 .61 .49 .45 .49 .30 .37 .78

Person-job fit2 .52 .53 .52 .45 .51 .57 .62 .35 .49 .47 .58 .83

Person-job fit3 .41 .42 .43 .52 .34 .55 .57 .48 .43 .36 .28 .74

74

Table C1c. Study 3: Loadings and Crossloadings for Model with Pre-organizational Entry IT Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .82 .61 .55 .39 .35 .42 .38 .52 .29 .54 .41 .58

Pay2 .83 .29 .36 .44 .42 .57 .45 .48 .33 .30 .49 .36

Pay3 .78 .51 .41 .34 .28 .29 .43 .51 .40 .56 .29 .34

2

Promotion1 .36 .79 .32 .62 .51 .32 .37 .39 .57 .30 .50 .56

Promotion2 .39 .75 .32 .59 .41 .32 .55 .42 .60 .37 .49 .46

Promotion3 .61 .75 .62 .30 .45 .38 .60 .31 .57 .43 .40 .30

3

Prestige1 .58 .38 .76 .50 .49 .38 .55 .29 .39 .38 .52 .51

Prestige2 .49 .54 .78 .53 .35 .55 .33 .49 .38 .45 .55 .51

Prestige3 .52 .28 .76 .37 .41 .50 .29 .29 .31 .43 .55 .56

4

Security1 .49 .37 .40 .78 .61 .37 .46 .35 .47 .55 .53 .55

Security2 .48 .32 .43 .78 .32 .35 .47 .48 .40 .45 .29 .30

Security3 .59 .45 .45 .78 .60 .48 .55 .60 .30 .56 .47 .39

5




6




7




8

Variety1 .49 .52 .54 .52 .61 .29 .28 .81 .34 .54 .56 .40

Variety2 .28 .54 .40 .34 .29 .58 .59 .74 .28 .38 .46 .38

Variety3 .32 .59 .33 .40 .46 .50 .33 .75 .35 .60 .31 .30

9

Creativity1 .30 .60 .56 .53 .41 .61 .35 .40 .75 .58 .30 .40

Creativity2 .28 .53 .29 .40 .37 .42 .43 .35 .82 .37 .50 .33

Creativity3 .53 .46 .30 .35 .34 .38 .50 .49 .69 .59 .31 .50

10




11

Person-org fit1 .60 .58 .38 .43 .47 .28 .40 .62 .58 .38 .84 .48

Person-org fit2 .35 .42 .45 .45 .29 .56 .42 .46 .62 .28 .81 .37

Person-org fit3 .35 .52 .40 .47 .28 .50 .58 .42 .31 .33 .70 .44

12

Person-job fit1 .47 .61 .30 .62 .40 .51 .37 .56 .43 .31 .32 .73

Person-job fit2 .51 .32 .53 .43 .39 .57 .54 .57 .57 .40 .62 .83

Person-job fit3 .34 .59 .28 .56 .35 .39 .57 .44 .29 .34 .41 .81

75

Table C2a. Study 1: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12

1 Pay 4.74 1.13 .73 .70

2 Promotion 4.76 1.17 .75 .41*** .73

3 Prestige 4.84 1.24 .77 .37*** .44*** .75

4 Security 4.55 1.20 .75 .38*** .40*** .41*** .73

5 Work-life balance 5.05 1.40 .74 .13* .10 .08 .14* .71

6 Friendly co-workers 5.07 1.35 .80 .17** .12* .13* .10 .44*** .77

7 Family proximity 4.85 1.30 .84 .15* .05 .12* .07 .42*** .47*** .76

8 Variety 5.15 1.23 .81 .08 .15* .13* .10 -.05 .14* .05 .75

9 Creativity 5.13 1.27 .83 .13* .17** .15* .07 .05 .16** .07 .37*** .73

10 Skill development 4.85 1.10 .82 .15* .20** .16** -.08 .13* .10 .13* .35*** .31*** .70

11 Person-organization fit 4.84 1.06 .75 .21*** .20** .15* .13* .34*** .35*** .28*** .16** .15* .10 .91

12 Person-job fit 4.91 1.11 .77 .15* .17** .16** .13* .38*** .30*** .29*** .40*** .30*** .31*** .55*** .92

13 Gender - - - .31*** .30*** .28*** .25*** -.23*** -.17** -.20** .10 .14* .08 .24*** .30***


Table C2b. Study 2: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12

1 Pay 4.68 1.31 .77 .71

2 Promotion 4.75 1.25 .79 .34*** .74

3 Prestige 4.35 1.20 .75 .38*** .33*** .73

4 Security 4.33 1.31 .76 .35*** .37*** .36*** .71

5 Work-life balance 5.05 1.30 .77 .14* .18** .15* .12* .73

6 Friendly co-workers 4.30 1.25 .79 .21* .13* .14* .16** .34*** .75

7 Family proximity 4.27 1.06 .78 .14* .05 .13* -.07 .37*** .38*** .76

8 Variety 5.07 0.95 .75 .17** .07 .05 .10 .05 -.07 .14* .71

9 Creativity 5.01 1.09 .74 .19** .16** .13* .14* .06 -.13* .13* .37*** .74

10 Skill development 4.88 1.10 .73 .23*** .15* .14* -.13* .14* .10 .14* .35*** .33*** .71

11 Person-organization fit 4.69 1.06 .76 .24*** .17** .15* .17** .35*** .37*** .35*** .16** .14* .13* .89

12 Person-job fit 5.00 1.21 .78 .17** .15* .16** .17** .37*** .31*** .30*** .28*** .34*** .34*** .56*** .91

13 Gender - - - .35*** .30*** .28*** .17** -.21*** -.20** -.17** .08 .05 .11* .29*** .24***


76

Table C2c. Study 3: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12

1 Pay 4.74 1.35 .73 .71

2 Promotion 4.70 1.30 .71 .39*** .74

3 Prestige 4.81 1.40 .75 .32*** .31*** .73

4 Security 4.66 1.33 .78 .34*** .29*** .35*** .71

5 Work-life balance 4.31 1.25 .82 .16** .12* -.07 .08 .75

6 Friendly co-workers 4.91 1.27 .81 .17** .10 .13* .07 .37*** .76

7 Family proximity 4.88 1.20 .80 .05 .07 .15* .14* .38*** .41*** .77

8 Variety 4.35 1.28 .75 .13* -.05 .16** .17** .13* .14* .17** .79

9 Creativity 4.30 1.25 .76 .17** .14* .10 .19** .14* .14* .05 .44*** .74

10 Skill development 4.41 1.15 .75 .19** .17** .14* .10 .16** .06 .19** .34*** .42*** .73

11 Person-organization fit 4.51 1.07 .80 .24*** .21*** .17** .13* .38*** .37*** .31*** .14* .12* .14* .87

12 Person-job fit 4.56 1.23 .79 .14* .17** .07 .05 .35*** .29*** .28*** .35*** .28*** .25*** .53*** .94

13 Gender - - - .33*** .32*** .30*** .21*** -.21*** -.17** -.15* .13* .14* .05 .25*** .23***


Table C3. Formative Construct Weights for Model with Pre-organizational Entry Workers Extrinsic Outcomes Social Outcomes Intrinsic Outcomes

Pay Promotion Prestige Security Work-life bal Friendly cwrk Fam prox Variety Creative Skill dev

Study 1 .30*** .28*** .25*** .29*** .25*** .28*** .27*** .28*** .32*** .27***

Study 2 .32*** .26*** .28*** .31*** .29*** .27*** .30*** .28*** .28*** .25***

Study 3 .31*** .28*** .29*** .30*** .30*** .27*** .31*** .28*** .30*** .26***

Note: * p<.05; ** p<.01; *** p<.001.

77

Table C4a. Study 1: Loadings and Crossloadings for Model with Pre-organizational Entry Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .76 .55 .49 .39 .49 .53 .35 .61 .56 .52 .36 .48

Pay2 .70 .31 .28 .58 .37 .37 .31 .42 .44 .59 .52 .43

Pay3 .70 .35 .51 .59 .59 .31 .40 .49 .58 .53 .47 .43

2

Promotion1 .44 .80 .51 .48 .31 .47 .49 .39 .33 .34 .58 .43

Promotion2 .48 .77 .57 .62 .55 .48 .45 .36 .41 .52 .45 .35

Promotion3 .47 .82 .38 .39 .44 .50 .61 .35 .38 .52 .38 .29

3

Prestige1 .32 .29 .81 .53 .45 .57 .39 .45 .61 .42 .37 .39

Prestige2 .28 .41 .73 .37 .45 .43 .56 .38 .62 .37 .51 .46

Prestige3 .61 .44 .80 .35 .35 .55 .52 .46 .42 .32 .28 .52

4

Security1 .43 .31 .52 .79 .58 .31 .48 .53 .36 .41 .36 .41

Security2 .41 .37 .40 .80 .51 .34 .46 .35 .41 .42 .58 .49

Security3 .30 .59 .46 .84 .43 .44 .35 .32 .40 .43 .56 .56

5




6




7




8

Variety1 .41 .59 .38 .58 .57 .46 .59 .70 .56 .55 .49 .50

Variety2 .58 .28 .41 .58 .53 .49 .45 .82 .54 .54 .62 .33

Variety3 .32 .37 .28 .58 .57 .31 .43 .82 .37 .57 .47 .44

9

Creativity1 .36 .54 .42 .56 .34 .47 .29 .41 .77 .38 .36 .30

Creativity2 .29 .44 .54 .35 .51 .33 .54 .54 .71 .46 .60 .29

Creativity3 .33 .41 .60 .49 .43 .57 .53 .48 .73 .61 .44 .48

10




11

Person-org fit1 .35 .58 .44 .47 .56 .28 .55 .38 .32 .33 .82 .53

Person-org fit2 .46 .34 .30 .34 .58 .36 .42 .50 .29 .50 .84 .43

Person-org fit3 .54 .58 .41 .42 .35 .59 .52 .50 .28 .32 .84 .59

12

Person-job fit1 .44 .52 .45 .29 .50 .46 .62 .33 .51 .37 .28 .74

Person-job fit2 .52 .56 .33 .38 .60 .38 .33 .38 .38 .50 .40 .73

Person-job fit3 .42 .48 .45 .33 .58 .50 .38 .40 .36 .32 .54 .70

78

Table C4b. Study 2: Loadings and Crossloadings for Model with Pre-organizational Entry Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .71 .52 .56 .35 .51 .48 .43 .48 .47 .52 .49 .34

Pay2 .73 .42 .59 .49 .50 .47 .59 .49 .32 .57 .38 .61

Pay3 .81 .34 .37 .61 .47 .30 .53 .44 .54 .45 .61 .33

2

Promotion1 .31 .78 .42 .29 .60 .44 .56 .39 .41 .39 .56 .42

Promotion2 .32 .71 .28 .49 .46 .59 .32 .59 .60 .35 .48 .49

Promotion3 .31 .73 .43 .59 .29 .49 .60 .51 .28 .61 .59 .56

3

Prestige1 .28 .44 .73 .37 .30 .52 .28 .49 .57 .40 .51 .35

Prestige2 .31 .38 .73 .32 .48 .46 .58 .31 .50 .53 .32 .30

Prestige3 .44 .31 .76 .57 .59 .49 .31 .39 .32 .61 .53 .39

4

Security1 .40 .40 .50 .76 .44 .42 .50 .46 .40 .59 .29 .49

Security2 .61 .54 .59 .83 .42 .57 .52 .37 .58 .46 .54 .60

Security3 .51 .37 .40 .80 .50 .56 .51 .39 .60 .35 .29 .56

5




6




7




8

Variety1 .28 .38 .41 .38 .50 .31 .30 .76 .32 .47 .30 .55

Variety2 .43 .32 .54 .50 .54 .28 .62 .76 .58 .43 .35 .28

Variety3 .43 .58 .46 .41 .51 .62 .34 .79 .55 .31 .59 .52

9

Creativity1 .42 .49 .58 .45 .28 .29 .34 .33 .83 .44 .28 .34

Creativity2 .57 .60 .56 .45 .38 .50 .59 .32 .72 .39 .30 .49

Creativity3 .61 .44 .35 .44 .45 .55 .48 .38 .83 .56 .54 .54

10




11

Person-org fit1 .61 .60 .38 .29 .42 .61 .58 .35 .45 .58 .76 .43

Person-org fit2 .58 .61 .57 .39 .51 .43 .59 .51 .33 .40 .81 .39

Person-org fit3 .56 .59 .33 .61 .35 .42 .29 .37 .44 .37 .79 .54

12

Person-job fit1 .50 .38 .53 .60 .35 .31 .39 .55 .31 .38 .28 .73

Person-job fit2 .30 .58 .46 .60 .29 .51 .44 .56 .42 .41 .57 .84

Person-job fit3 .42 .40 .58 .33 .36 .54 .45 .34 .34 .49 .36 .77

79

Table C4c. Study 3: Loadings and Crossloadings for Model with Pre-organizational Entry Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .73 .33 .34 .28 .36 .50 .44 .35 .49 .51 .51 .43

Pay2 .74 .54 .42 .50 .45 .32 .39 .55 .29 .37 .33 .57

Pay3 .84 .32 .32 .55 .43 .36 .53 .59 .54 .35 .38 .41

2

Promotion1 .32 .84 .38 .55 .58 .55 .52 .46 .35 .54 .52 .37

Promotion2 .37 .71 .31 .54 .41 .32 .51 .56 .60 .33 .59 .50

Promotion3 .38 .70 .44 .43 .37 .48 .55 .50 .50 .56 .40 .47

3

Prestige1 .50 .29 .80 .46 .59 .45 .50 .40 .48 .42 .52 .52

Prestige2 .50 .44 .75 .31 .39 .43 .28 .57 .36 .57 .60 .38

Prestige3 .45 .35 .83 .46 .39 .37 .43 .33 .51 .62 .41 .31

4

Security1 .45 .52 .53 .84 .60 .36 .39 .54 .51 .61 .58 .37

Security2 .39 .39 .32 .80 .51 .50 .57 .47 .53 .55 .44 .60

Security3 .56 .35 .44 .75 .33 .39 .40 .51 .29 .35 .36 .42

5




6




7




8

Variety1 .36 .55 .43 .57 .41 .33 .57 .74 .48 .35 .41 .55

Variety2 .58 .44 .49 .60 .50 .47 .59 .83 .60 .42 .44 .58

Variety3 .60 .29 .39 .58 .31 .55 .29 .77 .48 .32 .36 .29

9

Creativity1 .37 .31 .60 .38 .34 .38 .43 .57 .70 .31 .47 .58

Creativity2 .62 .49 .57 .33 .44 .33 .47 .42 .82 .37 .31 .33

Creativity3 .48 .46 .33 .33 .38 .51 .28 .35 .82 .59 .53 .33

10




11

Person-org fit1 .49 .40 .39 .43 .39 .37 .28 .54 .39 .29 .79 .37

Person-org fit2 .31 .38 .59 .43 .37 .34 .51 .38 .61 .56 .80 .28

Person-org fit3 .56 .31 .39 .42 .59 .39 .46 .36 .45 .39 .75 .40

12

Person-job fit1 .29 .46 .34 .37 .52 .55 .36 .46 .48 .42 .44 .78

Person-job fit2 .39 .59 .50 .57 .54 .32 .50 .33 .38 .61 .37 .78

Person-job fit3 .59 .29 .37 .47 .36 .37 .44 .60 .48 .59 .44 .72

80

Table C5. Formative Construct Weights for Model with Pre-organizational Entry Workers Extrinsic Outcomes Social Outcomes Intrinsic Outcomes


Study 1 .30*** .28*** .25*** .29*** .25*** .28*** .27*** .28*** .32*** .27***

Study 2 .32*** .26*** .28*** .31*** .29*** .27*** .30*** .28*** .28*** .25***

Study 3 .31*** .28*** .29*** .30*** .30*** .27*** .31*** .28*** .30*** .26***

Note: * p<.05; ** p<.01; *** p<.001 ` Table C6a. Study 1: Descriptive Statistics and Correlations for Pre-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Pay 5.07 1.21 .73 .71

2 Promotion 5.13 1.25 .75 .43*** .74

3 Prestige 4.95 1.29 .77 .44*** .41*** .77

4 Security 4.9 1.16 .75 .40*** .38*** .35*** .77

5 Work-life balance 4.75 1.38 .74 .08 .10 .15* .15* .75

6 Friendly co-workers 4.85 1.42 .75 .14* .11* .20** .16** .41*** .72

7 Family proximity 4.80 1.40 .75 .13* .14* .17** .19** .40*** .37*** .75

8 Variety 4.51 1.21 .76 .13* .15* .21*** .15* .18** .17** .15* .74

9 Creativity 4.64 1.20 .79 .15* .17** .20** .17** .11* .11* .14* .35*** .73

10 Skill development 4.60 1.28 .82 .19** .19** .17** .19** .11* .13* .13* .38*** .39*** .70

11 Person-organization fit 5.03 1.11 .73 .13* .10 .14* .07 .25*** .26*** .29*** .07 .05 .13* .91

12 Person-job fit 5.21 1.11 .84 .11* .12* .07 .14* .28*** .21*** .20** .21*** .20** .15* .57*** .92

13 Gender - - - .28*** .31*** .32*** .25*** -.26*** -.31*** -.24*** .25*** .30*** .31*** .30*** .29*** -

14 Domain: Quantitative - - - .24*** .21*** .17** .15* -.16** -.21*** -.24*** .08 .01 .05 .07 .16* .25*** -

15 Domain: People-oriented - - - -.16** -.13* -.10 -.14* .20** .15* .16** .13* .19** .17** .19** .03 -.07 .15* -

16 Domain: IT - - - -.16** .15* .15* .01 .15* .14* .12* .21*** .20** .15* .15* .15* .03 .07 .07


81

Table C6b. Study 2: Descriptive Statistics and Correlations for Pre-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Pay 5.12 1.24 .80 .73

2 Promotion 5.16 1.29 .73 .44*** .75

3 Prestige 5.08 1.30 .74 .42*** .41*** .77

4 Security 5.10 1.29 .82 .38*** .37*** .35*** .79

5 Work-life balance 4.88 1.40 .85 .15* .16** .15* .21*** .78

6 Friendly co-workers 4.87 1.38 .74 .08 .05 .13* .20** .47*** .75

7 Family proximity 4.80 1.35 .77 .14* .19** .21*** .15* .47*** .44*** .77

8 Variety 4.70 1.29 .74 .24*** .17** .20** .07 .08 .40*** .41*** .75

9 Creativity 4.71 1.30 .77 .21*** .15* .21*** .15* .15* .10 .13* .45*** .77

10 Skill development 4.60 1.20 .78 .20** .19** .24*** .16** .08 .15* .15* .40*** .38*** .75

11 Person-organization fit 5.12 1.09 .78 .11* .14* .07 .05 .34*** .31*** .25*** .10 .08 .14* .91

12 Person-job fit 5.25 1.06 .73 .08 .10 .13* .08 .29*** .25*** .26*** .19** .21*** .24*** .55*** .92

13 Gender - - - .25*** .30*** .28*** .29*** -.25*** -.26*** -.29*** .30*** .31*** .24*** .30*** .24*** -

14 Domain: Quantitative - - - .24*** .20** .21*** .17** -.25*** -.24*** -.17** .10 .08 .10 .04 .16** .26*** -

15 Domain: People-oriented - - - -.20** -.17** -.15* -.13* .20** .19** .15* .17** .15* .13* .19** .04 -.07 .17** -

16 Domain: IT - - - .15* .13* .07 .14* .15* .10 .15* .24*** .17** .20** .17** .14* .10 .04 .08


82

Table C6c. Study 3: Descriptive Statistics and Correlations for Pre-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Pay 4.57 1.40 .83 .73

2 Promotion 4.91 1.43 .82 .38*** .74

3 Prestige 4.98 1.45 .77 .37*** .35*** .75

4 Security 5.10 1.38 .75 .36*** .39*** .38*** .76

5 Work-life balance 4.80 1.40 .77 .17** .15* .16** .17** .77

6 Friendly co-workers 4.85 1.41 .79 .15* .14* .13* .19** .38*** .78

7 Family proximity 4.69 1.35 .78 .21*** .20** .21*** .23*** .35*** .34*** .75

8 Variety 4.17 1.35 .75 .16** .19** .21*** .20** .08 .17** .13* .74

9 Creativity 4.44 1.20 .74 .17** .15* .19** .22*** .11* -.05 .15* .41*** .73

10 Skill development 4.38 1.17 .78 .17** .14* .24*** .20** .11* .08 .19** .40*** .38*** .74

11 Person-organization fit 4.60 1.12 .74 .14* .13* .17** .15* .08 .15* .20** .08 .10 .07 .88

12 Person-job fit 4.63 1.13 .79 .10 .11* .12* .07 .28*** .25*** .30*** .21*** .24*** .25*** .50*** .93

13 Gender - - - .29*** .38*** .30*** .24*** -.26*** -.30*** -.31*** .21*** .24*** .29*** .26*** .29*** -

14 Domain: Quantitative - - - .30*** .30*** .25*** .21*** -.20** -.24*** -.20** .05 .08 .08 .07 .17** .25*** -

15 Domain: People-oriented - - - -.29*** -.25*** -.20** -.21*** .24*** .21*** .24*** .21*** .22*** .20*** .21*** .15* -.08 .17** -

16 Domain: IT - - - .17** .15* .16** .13* .20** .21*** .17** .24*** .17** .16** .17** .19** .10 .01 .03


.

83

Table C7a. Study 1: Loadings and Crossloadings for Model with Post-organizational Entry Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .75 .60 .58 .50 .39 .60 .61 .59 .39 .44 .54 .42

Pay2 .74 .39 .38 .32 .50 .41 .35 .39 .38 .36 .29 .53

Pay3 .81 .56 .58 .42 .34 .39 .51 .60 .62 .53 .46 .50

2

Promotion1 .62 .80 .30 .36 .40 .40 .58 .49 .33 .41 .45 .59

Promotion2 .58 .71 .39 .32 .52 .30 .48 .49 .53 .37 .55 .34

Promotion3 .51 .76 .47 .35 .57 .31 .32 .35 .37 .34 .32 .44

3

Prestige1 .42 .45 .73 .45 .48 .56 .39 .59 .58 .42 .41 .52

Prestige2 .40 .30 .76 .50 .30 .57 .58 .49 .61 .54 .51 .47

Prestige3 .46 .57 .70 .51 .47 .46 .37 .33 .35 .32 .49 .38

4

Security1 .49 .40 .39 .71 .61 .50 .39 .56 .35 .34 .35 .36

Security2 .57 .36 .42 .82 .54 .55 .46 .61 .58 .46 .49 .33

Security3 .59 .40 .41 .70 .55 .47 .59 .44 .29 .55 .57 .46

5




6




7




8

Variety1 .51 .59 .49 .38 .38 .52 .33 .76 .53 .58 .48 .34

Variety2 .36 .40 .42 .33 .37 .39 .38 .69 .50 .49 .42 .28

Variety3 .41 .37 .38 .54 .51 .32 .46 .74 .41 .30 .54 .42

9

Creativity1 .50 .55 .61 .57 .28 .36 .34 .34 .73 .55 .40 .57

Creativity2 .53 .57 .54 .47 .57 .61 .36 .33 .77 .41 .51 .49

Creativity3 .38 .49 .41 .60 .53 .38 .51 .28 .76 .51 .30 .50

10




11

Person-org fit1 .37 .46 .37 .45 .45 .62 .40 .55 .34 .51 .84 .28

Person-org fit2 .55 .36 .52 .31 .43 .45 .61 .38 .28 .60 .77 .30

Person-org fit3 .51 .40 .60 .28 .52 .34 .43 .49 .36 .43 .76 .60

12

Person-job fit1 .47 .57 .42 .30 .42 .51 .44 .48 .60 .61 .29 .81

Person-job fit2 .41 .46 .41 .32 .47 .36 .58 .39 .31 .31 .40 .72

Person-job fit3 .59 .54 .61 .29 .47 .48 .59 .53 .32 .53 .32 .82

84

Table C7b. Study 2: Loadings and Crossloadings for Model with Post-organizational Entry Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .74 .53 .46 .28 .53 .30 .29 .29 .37 .55 .45 .54

Pay2 .74 .32 .55 .53 .62 .45 .40 .35 .49 .50 .42 .61

Pay3 .84 .30 .28 .58 .31 .31 .60 .34 .36 .45 .59 .32

2

Promotion1 .60 .71 .56 .53 .29 .32 .60 .56 .29 .29 .43 .28

Promotion2 .59 .77 .37 .48 .36 .34 .59 .61 .30 .62 .35 .59

Promotion3 .42 .69 .30 .53 .46 .43 .54 .33 .35 .48 .28 .43

3

Prestige1 .40 .59 .82 .42 .46 .62 .53 .50 .28 .50 .42 .43

Prestige2 .37 .33 .78 .47 .28 .41 .56 .56 .51 .32 .53 .33

Prestige3 .46 .35 .79 .28 .33 .38 .52 .61 .41 .57 .36 .49

4

Security1 .28 .28 .38 .82 .46 .48 .34 .50 .38 .42 .37 .54

Security2 .29 .56 .59 .70 .49 .59 .30 .53 .36 .36 .35 .29

Security3 .32 .30 .50 .74 .59 .36 .50 .52 .28 .50 .40 .46

5




6




7




8

Variety1 .42 .55 .62 .51 .53 .62 .35 .73 .37 .56 .50 .44

Variety2 .55 .31 .40 .28 .51 .59 .35 .83 .33 .32 .51 .55

Variety3 .29 .48 .56 .31 .33 .48 .30 .73 .38 .48 .39 .60

9

Creativity1 .44 .55 .29 .54 .46 .45 .29 .31 .77 .36 .33 .43

Creativity2 .52 .51 .38 .29 .45 .35 .54 .51 .84 .42 .33 .42

Creativity3 .53 .57 .47 .36 .51 .34 .29 .56 .73 .50 .55 .35

10




11

Person-org fit1 .34 .30 .46 .36 .37 .48 .38 .61 .32 .62 .78 .42

Person-org fit2 .38 .28 .35 .29 .55 .61 .40 .36 .59 .53 .79 .53

Person-org fit3 .38 .52 .40 .51 .45 .53 .55 .47 .53 .41 .69 .55

12

Person-job fit1 .36 .45 .59 .47 .28 .42 .30 .60 .55 .31 .32 .84

Person-job fit2 .31 .40 .36 .50 .47 .51 .52 .30 .31 .44 .62 .80

Person-job fit3 .46 .43 .33 .39 .37 .39 .35 .36 .47 .48 .54 .80

85

Table C7c. Study 3: Loadings and Crossloadings for Model with Post-organizational Entry Workers 1 2 3 4 5 6 7 8 9 10 11 12

1

Pay1 .83 .41 .62 .43 .61 .49 .29 .29 .47 .35 .57 .30

Pay2 .80 .30 .28 .31 .50 .51 .40 .44 .49 .53 .36 .37

Pay3 .71 .49 .57 .58 .46 .53 .34 .48 .60 .61 .56 .32

2

Promotion1 .51 .80 .62 .59 .33 .41 .35 .45 .31 .56 .42 .50

Promotion2 .39 .81 .45 .54 .41 .54 .59 .29 .43 .54 .55 .57

Promotion3 .37 .79 .58 .59 .54 .33 .54 .39 .28 .45 .47 .40

3

Prestige1 .41 .61 .73 .44 .29 .58 .53 .40 .52 .47 .36 .40

Prestige2 .58 .32 .70 .33 .50 .48 .50 .57 .44 .29 .42 .41

Prestige3 .52 .35 .73 .30 .36 .50 .52 .61 .60 .32 .32 .34

4

Security1 .60 .58 .50 .72 .54 .57 .41 .34 .49 .61 .56 .52

Security2 .50 .59 .62 .72 .57 .33 .56 .30 .51 .32 .31 .62

Security3 .40 .46 .41 .78 .32 .55 .50 .55 .37 .57 .62 .30

5




6




7




8

Variety1 .45 .56 .41 .40 .29 .39 .53 .73 .34 .42 .39 .47

Variety2 .55 .50 .38 .29 .41 .36 .44 .71 .61 .49 .39 .49

Variety3 .49 .33 .59 .30 .46 .32 .59 .74 .41 .45 .54 .47

9

Creativity1 .61 .34 .31 .29 .45 .56 .43 .37 .71 .28 .57 .58

Creativity2 .32 .30 .54 .49 .45 .54 .30 .58 .82 .46 .48 .29

Creativity3 .29 .35 .58 .45 .51 .51 .59 .57 .83 .52 .55 .32

10




11

Person-org fit1 .40 .51 .62 .58 .62 .40 .51 .51 .36 .40 .73 .30

Person-org fit2 .53 .59 .45 .43 .33 .35 .47 .33 .41 .45 .74 .57

Person-org fit3 .45 .60 .44 .38 .54 .44 .39 .53 .46 .59 .79 .31

12

Person-job fit1 .37 .40 .40 .38 .60 .51 .38 .42 .62 .28 .45 .75

Person-job fit2 .39 .60 .58 .52 .49 .33 .43 .47 .53 .51 .58 .82

Person-job fit3 .56 .40 .53 .47 .58 .56 .39 .61 .61 .38 .46 .81

86

Table C8. Formative Construct Weights for Model with Post-organizational Entry Workers Extrinsic Outcomes Social Outcomes Intrinsic Outcomes


Study 1 .28*** .31*** .26*** .25*** .26*** .29*** .34*** .25*** .30*** .28***

Study 2 .26*** .24*** .28*** .28*** .29*** .25*** .31*** .28*** .33*** .30***

Study 3 .28*** .28*** .31*** .25*** .30*** .26*** .38*** .30*** .31*** .32***

Note: * p<.05; ** p<.01; *** p<.001.

Table C9a. Study 1: Descriptive Statistics and Correlations for Post-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Pay 4.74 1.24 .77 .73

2 Promotion 4.76 1.21 .84 .40*** .77

3 Prestige 4.66 1.20 .85 .41*** .32*** .75

4 Security 4.65 1.24 .87 .35*** .33*** .32*** .71

5 Work-life balance 4.85 1.41 .80 .23*** .20** .22*** .14* .74

6 Friendly co-workers 4.60 1.38 .75 .21*** .21*** .17** .13* .35*** .70

7 Family proximity 4.66 1.35 .76 .20** .22*** .19** .17** .32*** .37*** .73

8 Variety 3.77 1.24 .77 .17** .17** .14* .13* .07 .05 .13 .74

9 Creativity 3.94 1.25 .79 .15* .13* .16** .17** .13* .12* .07 .32*** .70

10 Skill development 3.70 1.17 .82 .16** .14* .24*** .16** .13* .08 .05 .31*** .29*** .74

11 Person-organization fit 4.67 1.07 .74 .29*** .21*** .20** .17** .40*** .35*** .30*** .24*** .15* .15* .89

12 Person-job fit 4.85 1.04 .79 .14* .14* .19** .10 .35*** .28*** .25*** .19** .15* .16** .56*** .90

13 Gender - - - .37*** .34*** .31*** .33*** -.48*** -.27*** -.35*** .28*** .25*** .24*** .30*** .28*** -

14 Domain: Quantitative - - - .21*** .20** .17** .15* -.22*** -.24*** -.15* .10 .08 .17** .04 .17** .31*** -

15 Domain: People-oriented - - - .19** .19** .21*** .15* .14* .19** .20*** .15* .10 .13* .20** .08 -.10 -.19** -

16 Domain: IT - - - .16** .15* .14* .10 .24*** .21*** .14* .14* .21*** .20** .14* .16** .08 .06 .05


87

Table C9b. Study 2: Descriptive Statistics and Correlations for Post-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Pay 4.78 1.15 .75 .73

2 Promotion 4.66 1.16 .71 .31*** .70

3 Prestige 4.65 1.05 .77 .33*** .32*** .71

4 Security 4.61 1.30 .73 .30*** .25*** .26*** .74

5 Work-life balance 4.75 1.35 .75 .24*** .15* .14* .24*** .73

6 Friendly co-workers 4.77 1.37 .76 .17** .16** .17** .21*** .26*** .75

7 Family proximity 4.80 1.30 .85 .15* .15* .18** .22*** .30*** .27*** .77

8 Variety 4.01 1.25 .84 .14* .17** .19** .20** .16** .24*** .19** .71

9 Creativity 4.03 1.20 .73 .16** .08 .14* .17** .13* .21*** .13* .28*** .74

10 Skill development 3.90 1.20 .72 .13* .10 .13* .15* .14* .10 .14* .21*** .28*** .74

11 Person-organization fit 4.65 1.04 .73 .29*** .21*** .24*** .26*** .41*** .40*** .30*** .24*** .17** .17** .86

12 Person-job fit 4.77 1.05 .78 .10 .14* .08 .10 .37*** .30*** .28*** .20** .14* .13* .53*** .91

13 Gender - - - .37*** .35*** .30*** .28*** -.30*** -.32*** -.28*** .28*** .25*** .23*** .32*** .29*** -

14 Domain: Quantitative - - - .24*** .27*** .20** .20** -.17** -.24*** -.20** .25*** .08 .01 .08 .21*** .30*** -

15 Domain: People-oriented - - - -.17** -.13* -.07 -.15* .22*** .20** .17** .17** .10 .12* .21*** .03 -.10 -.20** -

16 Domain: IT - - - .14* .13* .10 .12 .16** .14* .13* .20** .15* .15* .14* .16** .03 .02 .03


88

Table C9c. Study 3: Descriptive Statistics and Correlations for Post-organizational Entry Workers M SD ICR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Pay 4.41 1.35 .77 .73

2 Promotion 4.57 1.39 .83 .28*** .72

3 Prestige 4.80 1.41 .82 .24*** .27*** .71

4 Security 4.75 1.30 .85 .21*** .24*** .26*** .74

5 Work-life balance 4.74 1.44 .77 .20** .08 .14* .06 .77

6 Friendly co-workers 4.73 1.43 .79 .15* .13* .17** .08 .29*** .70

7 Family proximity 4.65 1.35 .75 .14* .14* .14* .13* .31*** .34*** .73

8 Variety 4.04 1.24 .77 .16** .05 .10 .14* .14* .17** .15 .74

9 Creativity 3.98 1.20 .78 .17** .07 .07 .17** .15* .20** .04 .31*** .75

10 Skill development 3.97 1.23 .78 .19** .13* .05 .19** .19** .21*** .10 .37*** .36*** .70

11 Person-organization fit 4.31 1.07 .73 .29*** .21*** .20** .17** .34*** .30*** .28*** .28*** .23*** .20** .87

12 Person-job fit 4.25 1.08 .75 .15* .13* .17** .10 .34*** .38*** .24*** .21*** .21*** .17** .46*** .92

13 Gender - - - .39*** .35*** .37*** .32*** -.32*** -.30*** -.27*** .30*** .25*** .27*** .31*** .29*** -

14 Domain: Quantitative - - - .29*** .28*** .21*** .23*** -.27*** -.21*** -.20** .20** .13* .10 .03 .19** .31*** -

15 Domain: People-oriented - - - -.16** -.14* -.13* -.07 .24*** .21*** .13* .13* .14* .16** .18* .13* -.10 -.17** -

16 Domain: IT - - - .17** .14* .13* .12* .13* .14* .16** .20** .21*** .12* .18** .14* .05 .07 .06


89

Appendix D: Common Method Bias Assessment

The various post-hoc techniques used to assess CMB each have their strengths and weaknesses

(for a review, see Richardson et al. 2009). We assessed CMB using multiple methods to avoid drawing a

conclusion that may be influenced by the weakness of one particular method. First, we conducted

Harman’s single factor test (Harman 1976). More than one factor emerged from the unrotated solution.

Additionally, the first factor explained only about 20% of the variance, suggesting that CMB was not a major

concern. Second, we used the marker-variable technique (Lindell and Whitney 2001; Malhotra et al. 2006).

We selected the second-smallest positive correlation among constructs as a conservative estimate of CMB

(Lindell and Whitney 2001; Malhotra et al. 2006). We then produced a CMB-adjusted correlation matrix and

used it to estimate CMB-adjusted path coefficients and explained variances for all models. If the CMB-

adjusted path coefficients and explained variances were substantially different, then CMB may be a threat.

Our results, however, showed that the path coefficients did not change by more than .03. The explained

variance in PO fit and PJ fit in the various models did not change substantially—i.e., it changed by 1%, at

most. These results indicate that CMB is not a major influence on the results. These results also suggest

that CMB was unlikely to be a problem in our work.