Person–Organization and Person–Job Fit Perceptions of New IT 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 10 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 (1) 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; (2) 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 (3) intrinsic outcomes influenced perceptions of PJ fit for both men and women. We discuss implications for research and practice.

Full text

RESEARCH ARTICLE
PERSON–ORGANIZATION AND PERSON–JOB FIT
PERCEPTIONS OF NEW IT EMPLOYEES: WORK
OUTCOMES AND GENDER DIFFERENCES1
Viswanath Venkatesh
Walton College of Business, University of Arkansas, Fayetteville, AR 72701 U.S.A. {vvenkatesh@vvenkatesh.us}
Jaime B. Windeler
Department of Operations, Business Analytics and Information Systems, Carl H. Lindner College of Business,
University of Cincinnati, Cincinnati, OH 45221 U.S.A. {Jaime.Windeler@uc.edu}
Kathryn M. Bartol
Management and Organization Department, Robert H. Smith School of Business,
University of Maryland, College Park, MD 20742 U.S.A. {kbartol@rhsmith.umd.edu}
Ian O. Williamson
Melbourne Business School, 200 Leicester St. Carlton VIC 3053 AUSTRALIA {i.williamson@mbs.edu}
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 10 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 (1) 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; (2) 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 (3) intrinsic outcomes influenced perceptions of PJ fit for
both men and women. We discuss implications for research and practice.
1
Keywords: Work outcomes, gender differences, cross-temporal validity, person–organization, person–job
fit
1Paulo Goes was the accepting senior editor for this paper. Harrison McKnight served as the associate editor.
The appendices for this paper are located in the “Online Supplements” section of the MIS Quarterly’s website (http://www.misq.org).
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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, business units may not have the budget to replace
workers lost to voluntary turnover; replacement costs can be
high (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 recent formal training and skills 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 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. In the United States,
President Obama’s recent “Educate to Innovate” initiative has
launched an effort to improve the training of the next genera-
tion 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. Attracting and
retaining 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 2016).
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 indicates 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 work-
places 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 been shrinking, from
37% in 1985, to less than 20% in 2014 (NCWIT 2015). As a
result, many organizations are pursuing active strategies to
create a workplace that is more encouraging of women’s
participation and retention; an example is Intel’s diversity
initiative aimed at supporting women.2 Thus, investigating
gender differences in work outcome valuation is essential to
creating an equitable workplace.
Although there are many frameworks available to examine
issues related to employee attraction, motivation, and reten-
tion, 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) and
job satisfaction (e.g., Verquer et al. 2003), as well as in-
creasing 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 ap-
proaches 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 perspec-
tive (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 op-
portunities through participation in open source projects
(Mehra and Mookerjee 2012). The current work employs a
2“Diversity at Intel,” http://www.intel.com/jobs/diversity/women.htm.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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: extrinsic (e.g., pay, promotion), social (e.g.,
friendly coworkers, work–life balance), and intrinsic (e.g.,
creative work, skill development). We theorize that expecta-
tions 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; i.e.,
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 charac-
teristics and needs of IT workers in today’s business environ-
ment 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 percep-
tions 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 gener-
alizability 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 different points in time over 10
years (see Lee 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 gener-
alizable.
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 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 pro-
vide 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. 2005; Kristof-Brown et al. 2002; 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 (for reviews, see Edwards 1991;
Kristof 1996; Kristof-Brown 2000; Kristof-Brown and Bills-
berry 2013; van Vianen 2000), thus resulting in two fit con-
structs: person–organization (PO) fit and person–job (PJ) fit.
PO fit focuses on the congruence between an individual and
the broad organizational environment, and PJ fit focuses on
the congruence between an individual and the specific job
environment (Kristof-Brown 2000; Kristof-Brown and Guay
2011).
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
PJ fit relates to vocational interests, that is, 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 organiza-
tion?” 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 charac-
teristics 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., Kristof-Brown et al. 2005;
Lauver and Kristof-Brown 2001).
Understanding both types of fit is underscored by their key
roles in the nomological network of 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 2014).
Similarly, PJ fit influences a variety of outcomes including
coping, job satisfaction, intention to quit, turnover, commit-
ment (Edwards 1991; Kristof-Brown et al. 2005), organi-
zational identification (Saks and Ashforth 1997) and
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; Kristof-Brown et al. 2005; Saks and
Ashforth 1997). 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 frame-
work (see Schneider 1987).
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 and Guay 2011; Kristof-
Brown et al. 2002). 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. 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
researchers 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. 2015). We
identify a specific set of work outcomes based on the total
rewards perspective (Lawler 2011; Parus 1999). This ap-
proach 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, 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 organiza-
tions to emphasize appealing aspects of the work environment
and organization that are not just tied to financial compen-
sation, 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 psycho-
logical states (see Super 1980) that are a heuristic set of
guiding principles important to individuals to evaluate work
and/or job environments (see Ros et al. 1999). Early research
on work outcomes, such as the Minnesota Importance Ques-
tionnaire (MIQ; Gay et al. 1971), the Work Aspect Preference
Scale (Pryor 1983), and the Work Values Inventory (Super
1980), has emphasized the importance of vari ous 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 has 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 acti-
vities that are affected by interpersonal relationships and
include both work and nonwork ties, such as work–life
balance, friendliness of coworkers, and family proximity.
Finally, the role of intrinsic outcomes has been studied via
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Extrinsic
Outcomes
Social
Outcomes
Intrinsic
Outcomes
PO Fit
PJ Fit
Gender
Figure 1. Research Model
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, researchers
have 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 through-
out 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 work-
place (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 turn-
over (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 reflec-
tion of performance and an indicator of employee advance-
ment within the organization. Prestige, or the extent to which
a position elicits respect from others, is related to an indi-
vidual’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 predicts 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 pro-
cess, often see organizations as the “pay masters” and identify
specific organizations or types of organizations (e.g., con-
sulting firms) as paying higher or lower salaries in general.
Although 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, until the 1990s, IBM was well-known
for having a “no layoff” policy, whereas other organizations
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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 coworkers, 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
coworkers captures the extent to which social and work-
related support will be provided in the workplace; this is
shown to be valued for socio-emotional purposes (Minton and
Schneider 1980; Windeler et al. 2017) and for information
from others about work-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 coworkers 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 sup-
port. 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 con-
ceptualizations of PJ fit, Kristof (1996) noted that PJ fit
incorporates the congruence between the individua l and speci-
fic 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 organi-
zational 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 coworkers 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 support with 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 meth-
odologies that advocate 40-hour work weeks as an important
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 guar-
anteeing 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 under-
scored 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 coworkers 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 have
more complex responsibilities, such as software design or
project leadership, are likely to call upon their coworkers 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 (Assima-
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
kopoulos and Yan 2009). Given this professional culture and
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 with roots in psychology and OB, such as
JCM, have emphasized the importance of intrinsic work out-
comes (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 et al. 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 core job characteristics
influence employee motivation and job satisfaction. Given
that the predictors in the JCM (i.e., autonomy, feedback,
identity, significance, and variety) focus on intrinsic out-
comes, it further stands to reason that PJ fit is determined by
an individual’s valuations about how well a job 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 environments
as they will allow IT workers to perceive a promising career
path (Turmel 2011). Further, technology workers are often
innovators (see Rogers 1995) and like to “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). An employee is unlikely to per-
ceive high fit with an environment that provides work
outcomes they do not value. 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 perceive high levels
of fit. Thus, we expect employees will perceive the highest
levels of PO fit or PJ fit when they have access to outcomes
they view as important.
We argue that a key mechanism shaping the importance
placed by individuals on different work outcomes is sociali-
zation. Socialization is the process through which an indi-
vidual 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 (versus men) value different work outcomes that in
turn will cause them to form different fit perceptions.
Developmental socialization begins in an individual’s forma-
tive years and is tied to the interactions an individual has with
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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 socializa-
tion that in turn is the basis for understanding gender differ-
ences in work outcomes and their effects on fit perceptions.
Gender stereotypes take the form of norms that are prescrip-
tive (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).
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 and 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 appli-
cants may be somewhat similar to their male counterparts in
their thinking. After controlling for other variables (e.g.,
rank) no gender differences were found, thus suggesting that
sometimes women identify more with their work, rather than
gender role.
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) assess-
ment 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, atti-
tudes, behaviors, and activities for women and men (e.g.,
Konrad, Ritchie, et al. 2000), influences the importance of
instrumentality and interpersonal relations to women and men.
Social identity theory suggests that membership in social
groups affects the developmen t of self-concepts (e.g., Konrad,
Corrigall, et al. 2000). 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 socializa-
tion, play a key role in the formation of PO fit perceptions
(Cleveland 1991).
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 instru-
mental and tangible accomplishments (e.g., Cross and Mad-
son 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.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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 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 prox-
imity 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 par-
ticularly 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 contem-
porary arguments support the greater importance of work–life
balance to women in their formation of PO fit and PJ fit per-
ceptions. Compared to the valuations of men, women tend to
value affiliation and friendships more (Gati et al. 1995), value
and use social support more (Savery 1990), are more percep-
tive 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 percep-
tions 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
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 men-
tors 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 coworkers. Drawing on these justifications,
we theorize that the effect of social outcomes on PJ fit will be
strongest among women. Thus, we hypothesize
H6: The effect of social outcomes on PO fit percep-
tions will be moderated by gender, such that it
will be more important to women.
H7: The effect of social outcomes on PJ fit percep-
tions 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. (1993)
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 con-
trolling for demographics, such as age, marital status, educa-
tion, experience, organiz ational 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 college seniors who had accepted jobs and
was conducted in 2000–2001, after the dotcom bubble burst
and, thus, represents a time of significant economic recession.
The second study was also conducted among graduating
seniors but took place about six years after the first study
when the economic conditions in the United States 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 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 instru-
ment. 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 out-
lined. 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.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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
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 ac-
counting, 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 manage-
ment 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 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), and 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 diver-
gence 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.
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 United States. 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 organiza-
tional entry with a focus on expectations and after organi-
zational entry with a focus on experiences. In the first wave
of the data collection, which was conducted during the last
month of the semester, one of the authors or a research assis-
tant 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,
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
via e-mail 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 percep-
tions. Questions measuring various constructs were inter-
mixed. 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, resulting 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 break-
down was as follows: women in IT—74 (out of 173), women
in quantitative domains3—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.
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 di fferent 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, resulting 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 break-
down 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 parti-
cipated 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
began at the same time as study 2, and continued for a year
after the conclusion of 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 col-
lected data from those who were starting new jobs in three
different organizations and already had three or more years of
work experience. This allowed us to examine the generali-
zability 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 grad-
uate) 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 beyond the scope of this work. The number of partici-
pants in each domain was 202 in accounting, 237 in finance,
123 in IT, 120 in management, and 88 in marketing, resulting
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 break-
3Although economics is a quantitative domain, our study settings (study 1
and 2) did not include any economics majors.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
down 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
The items used to measure the importance of various ex-
trinsic, 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).4 The item creation
stage involved creating pools of items for each of the work
outcomes that map to their corresponding conceptual defi-
nitions. We examined existing scales and, where necessary,
created additional items to ensure content validity. Items
were drawn primarily from the Minnesota Importance Ques-
tionnaire (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 coworkers. 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 three to four 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 seven-point Likert-type agree-
ment 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 partici-
pants (two sections of an undergraduate business capstone
course and two sections of a second-year MBA elective, with
approximately 200 students total) were asked to sort the
various items into construct categories. 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 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 instruc-
tions. 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 related to expectations (used in studies 1 and
2) are shown in Table 2; the wording for the experiences
survey (study 3) was suitably adapted. Finally, we admin-
istered the questionnaire to a larger sample that was somewhat
more representative of the population (two sections of the
4Our instrument development predated the more recently described ten-step
procedure (for an example, see Hoehle and Venkatesh 2015).
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
undergraduate capstone business course, one section of a full-
time MBA elective, and one 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 3.5 Thus, we conclude that the
scales demonstrate reliability and validity.
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). 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 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, we controlled for age and the number of jobs held
prior to one’s current job. These variables were not included
as controls in studies 1 and 2 because there was 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 they had no direct or
moderating effect in any of the model tests and were thus
dropped from the analysis.
Pilot Study
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 our hypotheses. 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 proce-
dure 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
nonavailability 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 (SEM) technique. PLS has the advantage of maxi-
mizing 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 coworkers (social outcomes), variety, creativity, and
skill development (intrinsic outcomes) were modeled as
reflective. 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
5Note 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.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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 coworkers.” You will rate on a seven-point scale how much the job will provide the opportunity for
you to have “friendly coworkers.”
Pay Salary level
The opportunity to become financially wealthy
The amount of pay
Promotion Opportunities for advancement
Promotion opportunities
Chances for advancement
Prestige Having others consider my work important
Obtaining status in the eyes of others
Being looked up to by others
Job Security Being certain of keeping my job
Being sure I will always have a job
Being certain my job will last
Family
Proximity
Being in the same geographic location as my immediate family (i.e., parents, brother, sister)
Living in the same area as my immediate family
Being in very close geographical proximity of my immediate family
Friendly
Coworkers
Friendly coworkers
Collegial coworkers
Coworkers who are supportive
Work–Life
Balance
Being able to balance my family and work life
Having time for my personal life
A work environment that supports work/family balance
Variety Doing a variety of things
Doing something different every day
Doing many different things on the job
Creativity Trying out new ideas and suggestions
Creating something new
Contributing new ideas
Skill
Development
Opportunities to develop new skills
Developing new knowledge through training
Acquiring new career-relevant skills
FIT PERCEPTIONS (Scale: 7-point Likert agreement scale)
PO Fit The organization will be a total fit for me
Taking everything into account, the organization will be a complete fit for me
I would fit right into the organization
PJ Fit I would fit right in to the job
Taking everything into account, the job is a complete fit for me
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 coworkers.” You
will rate on a seven-point scale how much the job provides the opportunity for you to have “friendly coworkers.” 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 third PO fit item and first PJ fit item.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 3. Factor Analysis with Oblimin Rotation
12345678910
1Pay1 .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
2Promotion1 .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
3Prestige1 .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
4Security1 .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
5Work–life balance1 .18 .01 .18 .06 .73 .12 .26 .14 .06 .23
Work–life balance2 .14 .23 .01 .18 .79 .37 .22 .23 .28 .17
Work–life balance3 .10 .27 .21 .02 .74 .17 .12 .10 .03 .06
6Friendly coworkers1 .07 .28 .11 .04 .38 .77 .03 .19 .04 .13
Friendly coworkers2 .04 .05 .13 .22 .39 .73 .28 .30 .16 .28
Friendly coworkers3 .08 .16 .15 .01 .06 .78 .22 .06 .11 .11
7Family proximity1 .01 .16 .22 .07 .10 .12 .80 .13 .28 .03
Family proximity2 .06 .25 .26 .06 .10 .08 .84 .26 .12 .29
Family proximity3 .05 .18 .07 .09 .08 .04 .78 .28 .02 .03
8Variety1 .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
9Creativity1 .37 .11 .09 .04 .23 .17 .03 .39 .70 .16
Creativity2 .35 .20 .13 .27 .05 .15 .10 .41 .76 .16
Creativity3 .23 .13 .23 .18 .10 .24 .13 .40 .75 .26
10 Skill development1 .21 .25 .23 .06 .14 .29 .04 .04 .06 .82
Skill development2 .05 .17 .24 .11 .08 .25 .09 .13 .16 .73
Skill development3 .08 .25 .30 .23 .03 .04 .14 .09 .29 .71
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 mea-
surement model results. Support was found for measurement
invariance, as well as reliability and validity of our measure-
ment models, allowing us to proceed with examination of the
structural models.6
Tables 4, 5, and 6 present the descriptive statistics and
correlations (as they relate to the structural model variables)
6To provide further confidence in the validity of our results, we assessed the
potential for common method variance. This analysis is reported in Appen-
dix D and demonstrates that common method variance is unlikely to have
affected our results.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 4. Study 1: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers
MSDICR123456
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; fiagonal elements represent the average variance extracted (AVE);
hender was dummy-coded as 0 for women and 1 for men.
Table 5. Study 2: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers
MSDICR123456
1 Extrinsic outcomes 4.66 1.38 – .86
2 Social outcomes 4.60 1.29 – .23*** .84
3 Intrinsic outcomes 5.01 1.07 – .25*** .21*** .87
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*** –
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 6. Study 3: Descriptive Statistics and Correlations for Pre-organizational Entry IT Workers
MSDICR123456
1 Extrinsic outcomes 4.71 1.37 – .91
2 Social outcomes 4.71 1.33 – .22*** .93
3 Intrinsic outcomes 4.36 1.21 – .29*** .28*** .91
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*** –
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.
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 signi-
ficantly 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 7 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. 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
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 7. 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
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.
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.
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 8, 9, and 10 provide the descriptive statistics and
correlations, which are also highly similar to earlier results
(see Tables 4, 5, and 6). Structural model results across gen-
der and professional domain for all business domains prior to
organizational entry are shown in Tables 11, 12, and 13. 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 approxi-
mately 5% to 21%. However, we observed some interesting
differences across professional domains, when compared to
the results for IT workers. To assess whether one effect was
statistically stronger than another, we performed a series of
Chow’s tests (Chow 1960). The significance of these differ-
ences, based on the Chow’s tests, are reported in Table 14. 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.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 8. Study 1: Descriptive Statistics and Correlations for Pre-organizational Entry Workers
MSDICR1 2 3 4 5 6 789
1 Extrinsic outcomes 5.00 1.22 – .85
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*** –
8Domain: People-
oriented – – – -.13* .17** .14* .19** .03 -.07 .15* –
9 Domain: IT – – – .13* .14* .18** .15* .15* .03 .07 .07 –
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 9. Study 2: Descriptive Statistics and Correlations for Pre-organizational Entry Workers
MSDICR123456789
1 Extrinsic outcomes 5.10 1.25 – .84
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*** –
8Domain: People-
oriented –––-.17** .17** .15* .19** .04 -.07 .17** –
9 Domain: IT – – – .13* .13* .20** .17** .14* .10 .04 .08 –
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 10. Study 3: Descriptive Statistics and Correlations for Pre-organizational Entry Workers
MSDICR1 2 34 5 6 789
1 Extrinsic outcomes 4.87 1.41 – .91
2 Social outcomes 4.75 1.39 – .20*** .92
3 Intrinsic outcomes 4.46 1.24 – .26*** .26*** .89
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*** –
8Domain: People-
oriented – – – -.24*** .22*** .16** .21*** .15* -.08 .17** –
9 Domain: IT – – – .15* .19** .20** .17** .19** .10 .01 .03 –
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.
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Table 11. 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 12. Study 2: Structural Model Results for Pre-organizational Entry Workers
DV: PO Fit
Overall Gender Professional Domain Gender and Professional Domain
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***
Note: *p < .05; **p < .01; ***p < .001; Quant = Quantitative domains, People = People-oriented domains.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 13. 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*
Previous job
count -.03 -.04 .04 -.01 .01 .05 -.07 -.05 .03 .03 .07 .03
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***
Note: *p < .05; **p < .01; ***p < .001; Quant = Quantitative domains, People = People-oriented domains.
Table 14. 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
Note: *p < .05; **p < .01; ***p < .001; Quant = Quantitative domains, People = People-oriented domains.
Robustness Checks
We assessed the robustness of the model between expecta-
tions and experiences by examining the model using data
collected from respondents after organizational entry. Tables
15, 16, and 17 provide the descriptive statistics and correla-
tions, 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 18, 19, and 20. The general pattern of results
across the three studies is similar to those for the pre-
organizational entry data.
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Table 15. Study 1: Descriptive Statistics and Correlations for Post-organizational Entry Workers
MSDICR1 2 3 4 5 6 7 89
1 Extrinsic outcomes 4.71 1.21 – .83
2 Social outcomes 4.75 1.39 – .19** .82
3 Intrinsic outcomes 3.90 1.21 – .25*** .22*** .86
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*** –
7Domain:
Quantitative – – – .19** -.20*** .12* .04 .17** .31*** –
8Domain: 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 16. Study 2: Descriptive Statistics and Correlations for Post-organizational Entry Workers
MSDICR123456789
1 Extrinsic outcomes 4.73 1.18 – .82
2 Social outcomes 4.76 1.35 – .22*** .85
3 Intrinsic outcomes 3.99 1.23 – .24*** .22*** .87
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*** –
7Domain:
Quantitative – – – .22*** -.21*** .13* .08 .21*** .30*** –
8Domain: People-
oriented – – – -.14* .20** .14* .21*** .03 -.10 -.20** –
9 Domain: IT – – – .13* .15* .18** .14* .16** .03 .02 .03 –
Note: *p < .05; **p < .01; ***p < .001; ICR = Internal consistency reliability; diagonal elements represent the average variance extracted (AVE).
Table 17. Study 3: Descriptive Statistics and Correlations for Post-organizational Entry Workers
MSDICR123456789
1 Extrinsic outcomes 4.54 1.38 – .90
2 Social outcomes 4.71 1.41 – .23*** .89
3 Intrinsic outcomes 4.01 1.22 – .28*** .24*** .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*** –
7Domain:
Quantitative – – – .25*** -.24*** .14* .03 .19** .31*** –
8Domain: People-
oriented – – – -.15* .20** .14* .18* .13* -.10 -.17** –
9 Domain: IT – – – .16* .14* .18** .18** .14* .05 .07 .06 –
Note: *p < .05; **p < .01; ***p < .001; ICR = Internal consistency reliability; diagonal elements represent the average variance extracted (AVE).
MIS Quarterly Vol. 41 No. X/Forthcoming 2017 21

Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 18. 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***
Social outcomes
(H2) .24*** .08 .25*** .23*** .23*** .08 .22*** .31*** .20** .34*** .04 .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**
Note: *p < .05; **p < .01; ***p < .001; Quant = Quantitative domains, People = People-oriented domains.
Table 19. 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***
Social outcomes
(H2) .23*** .02 .29*** .23*** .25*** .08 .21*** .31*** .19** .37*** .01 .21***
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**
Note: * p<.05; ** p<.01; *** p<.001; Quant = Quantitative domains, People = People-oriented domains.
22 MIS Quarterly Vol. 41 No. X/Forthcoming 2017

Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 20. 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*
Previous job
count -.04 -.05 .03 -.03 .03 .05 -.06 -.03 .03 .04 .07 .07
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***
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*
Previous job
count -.05 -.04 .09 -.07 .06 .08 -.04 -.01 .06 .03 .05 .08
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***
Note: *p < .05; **p < .01; ***p < .001; Quant = Quantitative domains, People = People-oriented domains.
Table 21 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 11, 12, 13, 18, 19, and 20. 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 signi-
ficant 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 pre-
dicting 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 22.
These results highlight the important role played by valua-
tions 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 the
factors influencing fit perceptions.
Discussion
Drawing from prior research in IS, OB, and vocational
behavior, we hypothesized 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 these relationships were 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 rela-
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
Table 21. 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 22. Chow’s Test for Statistical Differences
Coefficients Compared Signif. of Differences
Social Outcomes  PO Fit
Pre-org vs. Post-org entry: Study 3 .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.
tionship 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 contribu-
tions 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, we contribute to a
deeper understanding of the attraction, retention, and motiva-
tion of IT workers. Ferratt and Short (1986, 1988) 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
rewards 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: (1) Does such
a pattern of results also hold in the case of IT workers with a
lengthy history working in the field? (2) How elastic is the IT
workers’ focus on intrinsic outcomes? (3) How can other
measurement approaches, such as the constant sum method
(e.g., Agarwal and Venkatesh 2002) or social network
analysis (see Sykes 2015; Sykes et al. 2009; Sykes et al.
2014) shed light on the unique aspects of IT workers’ value
systems? 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
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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 out-
comes influence fit perceptions. We observed professional
domain differences as well. The pattern of interactions sug-
gests that understanding differences based on gender and
professional domain are important to effectively manage a
workforce. Our model contributes to the literature on
employee fit percep tions 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—for example, assimilation model,
contrast model, generalized negativity model, assimilation
contrast (see Brown et al. 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 out-
comes 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 social inclusion 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 con-
sciously 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 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
perception 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 dis-
persed 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 us to 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 10 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 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 differ-
ences in employees’ reactions and beliefs across professions
(Centers and Bugental 1966; Gruenberg 1980; Williams
1972). In fact, some prior work found differences in em-
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
ployee reactions based on occupation rather than gender
(Almquist 1974). England and Stein (1961) found major
differences across occupations and called for special scales
for different occupations. More recent research has also sup-
ported 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 professiona l socialization or professional commitment, both
of which have received significant attention in OB (e.g.,
McGowen and Hart 1990; Schaubroeck et al. 2012). Profes-
sional socialization is the process by which individuals are
introduced to and familiarized with the objective and subjec-
tive 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 ac-
quiring 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 coworkers and professional societies.
Professional domains tend to have specific value systems (see
Barley 1996), thus resulting in different 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 cer-
tainly be expected to play a key role here as students get
socialized to the value system of their profession and as ex-
perienced 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 Scar-
pello 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 par-
ticular profession are socialized to a set of norms and acquire
distinct sets of skills and language causing people in a par-
ticular 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 increasing emphasis
placed on STEM education and jobs, future research 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 limita-
tion due to the 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. Longi-
tudinal data collection is needed to rule out these possibilities.
Second, although we sought ways to establish the generaliz-
ability 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—which 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 is 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.
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
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 impor-
tant 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 con-
gruence (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
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. Gender was con-
sidered a binary variable (men and women) while professional
domains were grouped into three variables (IT, people-
oriented, and quantitatively-oriented domains). This approach
limits our ability to account for those whose gender identities
fall outside this binary treatment and other business and non-
business disciplines. Future research is needed to explore
these areas, particularly in light of the diverse individual and
professional differences that abound in the workplace.
In addition to expanding the range of these two moderators,
it may be fruitful to explore other moderators, such as age,
culture, and personality. Differences in the importance of
work outcomes with age have been documented (e.g.,
increasing emphasis on family and desire 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). 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. Further
research that incorporates culture-related variables is impor-
tant, particularly in light of research showing that ethnicity
influences the work outcomes that people value (Windeler
and Riemenschneider 2016). Such research moves us closer
to the ultimate goal of a diverse and inclusive workplace.
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 to a job can be accomplished through
the selection process. Similarly, a job can be fit to an indi-
vidual through work redesign (see Furnham 2001) and
socialization (Cable and Parsons 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’ assimila-
tion 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 place more value on
social outcomes) and IT workers (who place more value on
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 interven-
tions in supporting the cultivation of friendly relationships
among coworkers. The results also have implications for
managing the needs of both men and women after organiza-
tional 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 coworkers.
By studying entry-level IT workers, our research has practical
implications for organizations that will hire the next genera-
tion of IT workers. Recent research has shown that the IT job
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Venkatesh et al./Person–Organization & Person–Job Fit Perceptions
market continues to change. As programming and user sup-
port 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
perhaps change continuously. Entry-level IT workers will
need to look at acquiring different skills sets and pursuing
different opportunities align with their work values (Heinze
and Hu 2009; Joshi et al. 2010; Mourmant et al. 2009).
Effective training and 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 socializaion 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 coworkers.
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 nonfinancial incentives, such as
consideration for work–life balance through flex-time or tele-
commuting (Levere 2011). Thus, focusing on social and
intrinsic outcomes, particularly for women and those in mar-
keting, management, and IT, can serve as a way to both
motivate employees and keep costs low. In turn, organiza-
tions 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 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 rewards 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.
Acknowledgments
We appreciate the feedback and guidance of the senior editor, Paulo
Goes, the associate editor, and reviewers. This manuscript is based
partly on 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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About the Authors
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, mark eting, and opera-
tions 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 forth-
coming in premier outlets such as Information Systems Research,
MIS Quarterly, 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 codirector of the Center for Leadership, Inno-
vation and Change at the Robert H. Smith School of Business,
University of Maryland, College Park. She completed her Ph.D. at
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, Journal of Applied Psychology, Academy of
Management Review, Personnel Psychology, 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.
34 MIS Quarterly Vol. 41 No. X/Forthcoming 2017