ArticlePDF Available

Generational Differences in Work-Related Attitudes: A Meta-analysis

Authors:

Abstract and Figures

Purpose Differences among generations on a wide variety of outcomes are of increasing interest to organizations, practitioners, and researchers alike. The goal of this study was to quantitatively assess the research on generational differences in work-related attitudes and to provide guidance for future research and practice. Design/Methodology/Approach We conducted a meta-analysis of generational differences on three work-related criteria: job satisfaction, organizational commitment, and intent to turnover. Our review of published and unpublished research found 20 studies allowing for 18 generational pairwise comparisons across four generations (Traditionals, Baby Boomers, Generation Xers, and Millennials) on these outcomes using 19,961 total subjects. Findings Corrected mean differences for job satisfaction ranged from .02 to .25, for organizational commitment they ranged from −.22 to .46, and for intent to turnover the range was −.62 to .05. The pattern of results indicates that the relationships between generational membership and work-related outcomes are moderate to small, essentially zero in many cases. Implications The findings suggest that meaningful differences among generations probably do not exist on the work-related variables we examined and that the differences that appear to exist are likely attributable to factors other than generational membership. Given these results, targeted organizational interventions addressing generational differences may not be effective. Originality/Value This is the first known quantitative review of research on generational differences in the workplace.
Content may be subject to copyright.
Generational Differences in Work-Related Attitudes:
A Meta-analysis
David P. Costanza Jessica M. Badger
Rebecca L. Fraser Jamie B. Severt
Paul A. Gade
Published online: 11 March 2012
ÓSpringer Science+Business Media, LLC 2012
Abstract
Purpose Differences among generations on a wide vari-
ety of outcomes are of increasing interest to organizations,
practitioners, and researchers alike. The goal of this study
was to quantitatively assess the research on generational
differences in work-related attitudes and to provide guid-
ance for future research and practice.
Design/Methodology/Approach We conducted a meta-
analysis of generational differences on three work-related
criteria: job satisfaction, organizational commitment, and
intent to turnover. Our review of published and unpub-
lished research found 20 studies allowing for 18 gen-
erational pairwise comparisons across four generations
(Traditionals, Baby Boomers, Generation Xers, and Mill-
ennials) on these outcomes using 19,961 total subjects.
Findings Corrected mean differences for job satisfaction
ranged from .02 to .25, for organizational commitment they
ranged from -.22 to .46, and for intent to turnover the
range was -.62 to .05. The pattern of results indicates that
the relationships between generational membership and
work-related outcomes are moderate to small, essentially
zero in many cases.
Implications The findings suggest that meaningful dif-
ferences among generations probably do not exist on the
work-related variables we examined and that the differ-
ences that appear to exist are likely attributable to factors
other than generational membership. Given these results,
targeted organizational interventions addressing genera-
tional differences may not be effective.
Originality/Value This is the first known quantitative
review of research on generational differences in the
workplace.
Keywords Generational differences Meta-analysis
Job satisfaction Organizational commitment
Intent to turnover
There is a growing sense among a group of authors, con-
sultants, trainers, and management gurus that there are
substantive and meaningful generational differences
between individuals in today’s workplaces. These differ-
ences are often summarized in terms of descriptors on sets
of characteristics that define each generation and differ-
entiate it from others. In terms of the way the generations
are sometimes described, members of the Silent (aka Tra-
ditional or Mature)
1
generation are labeled conservative
and disciplined (Strauss and Howe 1991), Baby Boomers
are called time-stressed and materialistic (Strauss and
Howe 1991), Generation Xers are identified as skeptical
and individualistic (Kupperschmidt 2000), and Millennials
are believed to be socially conscious, yet highly cynical
The views, opinions, and/or findings contained in this article are
solely those of the author(s) and should not be construed as an official
Department of the Army or DOD position, policy, or decision, unless
so designated by other documentation.
D. P. Costanza (&)J. M. Badger R. L. Fraser J. B. Severt
Department of Organizational Sciences and Communication,
The George Washington University, 600 21st St NW, #201,
Washington, DC 20052, USA
e-mail: dcostanz@gwu.edu
P. A. Gade
The U.S. Army Research Institute for the Behavioral
and Social Sciences, Arlington, VA, USA
1
Strauss and Howe (1991) use the term ‘‘Silent Generation’’ but
most of the studies that were meta-analyzed used the term ‘‘Tradi-
tional’’ and therefore we refer to this group as ‘‘Traditional’
throughout the ‘Results’ section. The term ‘‘Mature’’ is also
occasionally used.
123
J Bus Psychol (2012) 27:375–394
DOI 10.1007/s10869-012-9259-4
and narcissistic (Twenge et al. 2008). Popular-press articles
have made claims about how these differences impact
outcomes in a variety of settings; among the most cited are
the effects of generational differences on work-related
outcomes such as commitment, satisfaction, motivation,
risk-taking, and leadership style. Professional organizations
such as the Society for Human Resource Management have
conducted surveys of their members about generational
differences (Burke 2004), and practitioners and consultants
have seized on alleged generational differences developing
seminars and interventions designed to help organizations
deal with them.
Before proceeding, it is important to note that genera-
tions, as they have generally been written about, refer to
groups of individuals (i.e., cohorts) based on shared expe-
riences at similar ages. The idea is that common experi-
ences shared by individuals of a particular age at a
particular point in time create similarities (e.g., attitudes,
political orientations, general dispositions) among those in
the cohort. For generations, these common experiences
have been suggested to be events like the Depression,
World War II, the Civil Rights movement, and the Sep-
tember 11 terrorist attacks. In contrast, age refers to varia-
tion between individuals associated with aging caused by
maturation, life stage, or other developmental factors. These
two ideas are computationally connected in that age is often
used to define generational membership and the two are
sometimes used interchangeably in the generational litera-
ture. That said, because nearly all the empirical research on
generational differences uses the conceptualization of
generations as cohorts of individuals created by shared
experiences, we used that approach for the present effort.
While generational stereotypes are widely held and
promulgated (a recent Google search on ‘‘generation dif-
ferences in the workplace’’ returned over 18 million hits),
empirical evidence backing them up has been mixed at
best, and the research faces challenging conceptual, defi-
nitional, methodological, and statistical issues. Recent
reviews (e.g., Giancola 2006; Macky et al. 2008b; Parry
and Urwin 2010), several special issues of journals (e.g.,
Journal of Managerial Psychology, 2008; Journal of
Business and Psychology, 2010; Perspectives on Psycho-
logical Science, 2010), and a report by Sackett (2002)to
The National Academies have raised questions about the
empirical evidence supporting such differences and the
methodological challenges associated with studying them.
Given that most publications on generational differences
appear in the popular press, the peer-reviewed literature is
limited, and the inherent methodological challenges, there
are questions as to whether the claimed differences actually
do exist.
What is clear is that today’s workplaces include
employees with a broad range of ages and generational
membership, and this variation raises questions about the
workplace and the dynamics among employees. According
to the International Labour Organization (2010), for the
active workforce in the United States in 2008, 5% was over
65 years of age, 37% was between the ages of 46 and 65,
33% was between the ages of 30 and 45, and 25% of the
workforce was under age 30 meaning that individuals from
at least four generations are represented. Many individuals
born immediately before or during World War II are still
employed in organizations, often times in upper manage-
ment and executive positions. Those born later, in the 1960s,
1970s, 1980s, and 1990s, are entering or advancing towards
mid-career in their workplaces. All these individuals are in
the workforce at the same time, creating the potential for
cohort-based differences, difficulties, and disputes.
Similar to what happened with gender and racial
diversity in the workforce, generational variation of
workers raises questions about the nature, characteristics,
and, most importantly, the consequences of supposed
generational differences. Although a great number of pri-
mary research studies, including several meta-analyses,
have been conducted on gender (e.g., Ng and Feldman
2008) and racial differences (e.g., Roth et al. 2003) in the
workplace, there is no such comprehensive quantitative
review of the research on generational differences in work-
related outcomes (Twenge et al. 2010, addressed a few of
these issues and Parry and Urwin 2010, reviewed the lit-
erature on work values, noting general findings and trends).
The purpose of this article, therefore, is to meta-analyze
the effects of generational differences on work-related
outcomes. We begin by examining the definitional and
theoretical underpinnings of hypothesized generational
differences. Next, we review the literature on how and why
generational differences might have an impact on various
outcomes before turning to methodological issues and
challenges in conducting this type of research. Finally, we
present a meta-analysis of primary studies of generational
differences for several work-related outcomes.
Definitional and Theoretical Issues
Defining Generations
There are several definitions of the term generation that are
used in the generational differences literature. The defini-
tions are similar but have expanded over time. Mannheim
(1952) described generations as social constructions
whereby those of a particular age or set of ages are defined
by historical and social events. In essence, a generation is a
cohort of similarly aged people who experience common
historical events. This idea was echoed by Ryder (1965),
who described a generation more specifically as an
376 J Bus Psychol (2012) 27:375–394
123
‘aggregate of individuals who experienced the same event
within the same time interval’’ (p. 845). Several recent
studies have used Kupperschmidt’s (2000) definition of a
generation as ‘‘an identifiable group that shares birth years,
age, location, and significant life events at critical devel-
opmental stages’’ (p. 66), adding a developmental aspect to
the definition. Other researchers have used similar defini-
tions suggested by Strauss and Howe (1991) and Smola and
Sutton (2002).
What is consistent across these conceptualizations is that
a generation is defined as a group of individuals, who are
roughly the same age, and who experience and are influ-
enced by the same set of significant historical events during
key developmental periods in their lives, typically late
childhood, adolescence, and early adulthood. Further, these
differences are not attributable solely to an individual’s age
but rather to the common influence of shared experiences
on the cohort.
How Do Generational Cohorts Develop?
The premise behind generations is that individuals are
influenced by historical events and cultural phenomena that
occur during key developmental stages (Noble and Schewe
2003; Twenge 2000) and may lead to the formation of
impactful collective memories (Dencker et al. 2008). These
historical, social, and cultural effects, along with other
factors, have been hypothesized to impact the development
of individual’s attitudes, values, and personality charac-
teristics (e.g., Caspi and Roberts 2001; Caspi et al. 2005).
Parry and Urwin (2010) note the differences between the
more demographically framed concept of cohorts, based
solely on shared birth year, and the more sociologically
framed concept of generations, which include the historical
events that impact the cohort. The latter approach is the one
generally used by those studying generational differences.
It is worth noting that the significant historical events
that may help define generations vary greatly depending on
location and experience. Historical and cultural events
experienced by individuals growing up in the United States
in the 1950s and 1960s were very different in key ways
from those experienced by individuals growing up in
Russia, China, or Brazil, raising questions about the gen-
eralizability of generations across cultures. As generational
conceptualizations are often based on historical events in
the United States, caution must be exercised in generaliz-
ing cohorts, years, and labels to individuals who did not
experience those same events (e.g., Parry and Urwin 2010).
What Are the Different Generations?
The most common typology of generations in the United
States was suggested by Strauss and Howe (1991), who
used demographic and historical data to identify genera-
tions going back[400 years. They defined a generation as:
‘a special cohort-group whose length approximately mat-
ches that of a basic phase of life, or about twenty-two
years’’ (p. 34). Their taxonomy includes four generations
that are present in the modern workplace: Silent, Baby
Boomer, Thirteenth (aka Generation X), and Millennial,
although each of these specific labels has a history that
precedes Strauss and Howe.
2
When Are the Generations?
Though most authors across countries have adopted com-
mon labels and the same general time frames containing
their members, there is substantial variance on exactly
when each generation starts and ends. Figure 1graphically
represents the range of birth years used by various authors.
For example, the Baby Boom generation, about which
there seems to be the most agreement on start and end
dates, has starting years ranging from 1943 to 1946 and
ending years from 1960 to 1969. Generation X has starting
years varying from 1961 to 1965 and continuing on to 1975
to 1981. There is a similar pattern for the Silent and Mil-
lennial generations. It is clear that although the labels may
be generally agreed upon, the actual start and end dates
used to define each generation vary widely (Smola and
Sutton 2002). This lack of consistency has implications for
the conceptual definition of the generations, their opera-
tionalization (i.e., when they start and finish), and the
assessment of their impact on outcomes.
What Impact Do Generations Have and How?
It is the cohort-shared characteristics and experiences that
have interested researchers and popular-press writers,
speakers, and consultants. If a simple and concise des-
cription of a cohort of individuals could be generated, and
if these descriptions were broadly applicable to them, they
could have significant ramifications for their behavior in a
variety of settings. Generational membership could become
an easy and convenient proxy for the characteristics of an
entire population of individuals. Along these lines,
2
‘The Silent Generation’’ gained widespread use after it appeared in
a late 1951 TIME magazine article about ‘‘today’s youth’’ (TIME
1951, November 5), although it may have first appeared a few years
earlier. The term ‘‘Baby Boom’’ was first used to describe children
born post World War II by Westoff (1954) in a piece on differential
fertility rates. ‘‘Generation X’’ was first used in a book by Hamblett
and Deverson (1965) to describe teenagers who were living outside of
acceptable conservative mores and was popularized in Coupland’s
novel, Generation X: Tales for an Accelerated Culture (Coupland
1991). The term ‘‘Millennials’’ appeared in various popular-press
articles and was later discussed in detail in Howe and Strauss’s (2000)
book, Millennials Rising.
J Bus Psychol (2012) 27:375–394 377
123
researchers have investigated generational differences in
domains as diverse as work values (Smola and Sutton
2002), learning orientation (D’Amato and Herzfeldt 2008),
the use of influence tactics (Landry 2009), anxiety and
neuroticism (Twenge 2000), depression among children
(Twenge and Nolen-Hoeksema 2002), and narcissism
(Trzesniewski et al. 2008).
One setting in particular where generational differences
have been widely written about is the workplace. In a work
setting, such simplifications and generalized differences
among groups of employees could have major implications
for the way organizations recruit, hire, train, reward, pro-
mote, and terminate their employees. For example, if all
Generation Xers want autonomy in their jobs, work may
need to be redesigned to increase independence or if
Millennials are attracted to organizations by their level of
technological savvy, recruiting practices may need to be
modified to include virtual recruiting fairs. A recent special
issue of the Journal of Business and Psychology (2010)
examined this potential impact, dedicating the entire issue
to enhancing understanding of Millennials in the workplace
with research on topics such as work attitudes, work ethic,
career perspectives, and performance. Numerous other
authors and generational consultants (e.g., Shapira 2009,
July 9) have also written about generational differences in
the workplace and the potential benefits to organizations of
understanding and capitalizing on these differences.
Some researchers have investigated differences in work-
related outcomes across the generations. For example, a
study by Westerman and Yamamura (2007) looked at
differences in job satisfaction between Baby Boomers and
their younger counterparts of Generations X and Y,
hypothesizing that Baby Boomers would have lower sat-
isfaction than Generations X and Y. They found that there
were no significant mean differences in satisfaction
between generations. Likewise, Cennamo and Gardner
(2008) did not find significant differences in job satisfac-
tion among Baby Boomers, Generation X, and Generation
Y.
A study by D’Amato and Herzfeldt (2008) looked at
differences between early and late Baby Boomers and early
and late Generation Xers in organizational commitment,
hypothesizing that older generations would have higher
organizational commitment than younger generations.
They found that Baby Boomers reported significantly
higher organizational commitment than did Generation
Xers. However, the generalizability of these finding to
other organizational commitment research may be limited
by the fact that they conceptualized commitment differ-
ently from the normative, affective, and continuance
approach typically used. Davis et al. (2006) also hypothe-
sized that Baby Boomers would have higher affective,
normative, and continuance organizational commitment
than Generation Xers. They only found significant differ-
ences for normative commitment, and the pattern of results
was contrary to their hypotheses (Generation Xers were
higher than Boomers).
A study by Kowske et al. (2010) provided a stronger test
of differences in work attitudes among Boomers, Xers, and
Millennials by controlling for age and time period effects.
Traditionalists
Earlier
1920
1930
1940
1950
1960
1970
1980
1990
2000
Later
Baby Boomers Generation X Millennials
= Start year specified as “earlier”
= Start year
= End year
= End year specified as “later”
Fig. 1 Graphical representation
of the birth years used to define
generations in empirical studies
considered for inclusion in the
meta-analysis
378 J Bus Psychol (2012) 27:375–394
123
They used cross-classified hierarchical linear modeling
(HLM) to isolate cohort effects (i.e., generation) from age
and time-period effects. This technique estimates variance
components for each group classification variable (i.e.,
time-period and cohort) and each individual level inde-
pendent variable (i.e., age) controlling for the other two
effects. Thus, they were able to estimate the percentage of
variance attributable to cohort alone, holding age and
period constant. They found little support for differences
among the generations in work satisfaction and turnover
intentions. Of the few differences in outcomes they did
report, the effect sizes were quite small, causing the authors
to question their meaningfulness.
Other researchers have looked at different work-related
variables such as motivation (e.g., Wong et al. 2008),
training (e.g., Sayers 2007; Szamosi 2006), work life
conflict and spillover (e.g., Beutell and Wittig-Berman
2008; Dilworth and Kingsbury 2005), and leadership style
(e.g., Collins et al. 2009; Rodriguez et al. 2003), but there
are few studies in each of these areas.
It is worth noting that little of the research on genera-
tional differences has a solid, theoretical foundation
underpinning either the concept of generations or the spe-
cific hypotheses about the impact that such generations
have. For example, Cennamo and Gardner (2008) cite
general societal trends and some tangential generational
research to support their hypotheses. D’Amato and Herz-
feldt (2008) base their arguments on research on age dif-
ferences as well as popular-press pieces and age differences
research. Twenge et al. (2010) and Jurkiewicz (2000) also
relied on popular-press pieces and anecdotal evidence while
Smola and Sutton (2002) used research questions instead of
hypotheses. In turn, Westerman and Yamamura (2007) cite
Smola and Sutton’s findings to support arguments. Overall,
there is a limited theoretical support for the hypotheses
about specific differences among the generations on work-
related outcomes and the reasons for them.
Sociological research may provide some theoretical
support for specific hypotheses about generational differ-
ences. Life course theory examines how significant social–
historical events and experiences shape the behavior of
individuals and generations of individuals over their entire
lives and even across generations (e.g., Elder 1994,1998;
Gade 1991,2009; MacLean and Elder 2007). While the life
course literature does not directly address generational
differences as conceptualized in our research, this theory’s
longitudinal perspective and focus on how individuals and
their cohorts are shaped by the social–historical contexts
they experience, fits well into the broader conceptualiza-
tion of generational differences and might be used to
support specific hypotheses about how and why groups of
individuals might vary.
Issues in Studying Generational Differences
As noted above, not only is there little agreement on which
birth year ranges define the generations in question but also
a lack of consensus on what the significant shared experi-
ences are that shape generational behavior, what collective
individual differences are influenced by experience, and
what behavioral outcomes are produced by these collective
individual differences. There is also limited empirical
research on what differences do exist, how big the differ-
ences are, or what effect they have on various outcomes,
leaving many of the generalizations about these differences
largely unsupported. Further, there is substantial debate
about the best methods to test for generational differences.
Starting with the developmental and definitional issues,
some have questioned whether individuals at the same
stage of development will experience cultural and histori-
cal events similarly (e.g., Giancola 2006). In terms of
defining a generation, most researchers have accepted
Strauss and Howe’s (1991) taxonomy of generations but, as
noted above, there are varying start and end dates used.
Regarding the specific generational differences, there have
been empirical efforts designed to identify the discrimi-
nating characteristics and to quantify them but once again,
these studies are limited in number and seem to show
conflicting results when hypothesized differences are
tested.
Methodologically, the main challenge in studying gen-
erational differences seems to be disentangling the differ-
ences attributable to generational membership from those
due to other factors such as age and/or time period. Mul-
tiple researchers (Macky et al. 2008b; Rhodes 1983;
Trzesniewski and Donnellan 2010) have identified this
confound issue as the primary methodological challenge in
studying generational differences. In addition, organiza-
tional experience, tenure, and technological advancements
are also often confounded with age and generation and are
also potential explanations for observed differences.
Despite this potentially critical limitation, almost all the
studies on generational differences have conceptualized
and operationalized the differences using cross-sectional
designs. Empirical studies using longitudinal designs are
rare and studies that include a conceptualization of the
changing nature of generational differences over time are
rarer still. Besides work in life course theory that looks at
how significant social–historical events and experiences
shape the behavior of individuals and generations of indi-
viduals over their entire lives (e.g., Elder 1994,1998; Gade
1991,2009; MacLean and Elder 2007), research in gen-
erational differences has almost unanimously approached
the question cross-sectionally both in concept and in
measurement.
J Bus Psychol (2012) 27:375–394 379
123
Summary
Given all these issues, there is a need for better understanding
about generational differences. While the review above
might seem to indicate that there is not enough conceptual
and methodological clarity to justify a meta-analysis, we
argue just the opposite. First, there is precedent for con-
ducting meta-analyses even when the underlying primary
studies have systematic conceptual, methodological, or
definitional issues (e.g., Rind et al. 1998). Second, for
research areas where the primary literature has limitations,
meta-analysis can help address some of those issues by
quantifying extant findings, identifying conceptual gaps,
suggesting areas for future research, and offering guidance
for practice. The results of such a meta-analysis can therefore
serve two purposes, both to summarize existing research and
to identify gaps therein that need to be addressed by future
efforts. Thus, with the limitations in mind, the goal of our
research was to determine the extent to which research has
found generational differences.
Method
Rules for Inclusion in the Meta-analysis
In order to identify all possible studies examining genera-
tional differences in work-related criteria (e.g., job satis-
faction, organizational commitment, intent to stay/quit), the
PsycINFO, ABI/Inform Complete, and EBSCO Host dat-
abases were searched. Each search included a combination of
key terms for generation (generation, generational differ-
ences, generational cohort, birth cohort, baby boomer, gen-
eration X, generation Y, or millennial) and work-related
outcomes (job satisfaction, commitment, intent to turnover/
quit or intent to stay/remain, leader, leadership, training,
attrition, retention, promotion, rewards, motivation).
After eliminating non-scholarly pieces (e.g., newspaper
and magazine articles), our search efforts resulted in 265
abstracts (including articles, books, and dissertations). An
additional 14 studies were collected through review of the
past 3 years (2007–2009) of conference programs from the
Society of Industrial Organizational Psychology and
Academy of Management. To gather additional unpub-
lished research on generational differences or any addi-
tional published studies, several messages were posted on
two listservs, RMnet and HRDivnet; this resulted in an
additional 31 investigations. The research team also con-
tacted several journal editors and researchers in the area of
generational differences asking for unpublished or in press
pieces and obtained an additional 19 possible articles.
Overall, our efforts resulted in 329 published and
unpublished empirically based articles across all work
attitude areas searched. In reviewing all abstracts collected,
234 were eliminated because they (a) were unrelated to
the workplace (e.g., generational differences in attitudes
toward money), (b) did not include empirical quantitative
data, (c) did not include a comparison of at least two
generational cohorts (e.g., focused exclusively on Baby
Boomers),
3
(d) did not report an effect size that was meta-
analyzable (e.g., dor r), or (e) did not examine work-
related criteria.
For the remaining 95 research efforts, the full text was
reviewed to ensure that each met the criteria for inclusion.
These studies were divided among three doctoral students
to determine which should be included in the meta-analy-
sis. Each article was reviewed by two of the students; any
disagreements between reviewers were discussed with the
group until a consensus was reached. Several inclusionary
rules were established.
First, the research had to empirically and quantitatively
test hypotheses on generational differences. Several articles
focused on differences among age-range groups (dividing
individuals by birth decade or at a specific age, e.g., Mottaz
1987).
4
Of the studies that used age-range groups, several
had groups that did not map onto the generations as they
have been defined (e.g., age-range groups cut across mul-
tiple generations). One study grouped individuals such that
they were comparable with the generations but even so,
there were several age-range groups that would have been
excluded because they could not be assigned to only one
generation. This would have left just a partial set of results
from only one study. Accordingly, this study was excluded
from the meta-analysis.
Second, the articles had to examine at least one of our
focal criteria. Several articles examined tangential criteria
(e.g., satisfaction with dress code rather than job satis-
faction, or commitment to one’s occupation rather than
organizational commitment), which were excluded (e.g.,
Hu et al. 2004). Third, the study had to examine work
outcomes using measures capable of being meta-analyzed.
For example, several articles presented only ordinal data
and thus were excluded (e.g., Jurkiewicz and Brown 1998,
asked their participants to rank 15 work-related motiva-
tional factors in terms of what they wanted from their
jobs).
3
Because of variation in start and end dates across studies, we
adopted the generational assignments used by the authors of the
primary studies.
4
Ng and Feldman (2010) reported that [90% of studies using age
treated the variable as continuous and even when studies used age-
range groups, they typically calculated correlations and not group
differences. Similarly, our search revealed very few studies that used
age-range groups to make group comparisons.
380 J Bus Psychol (2012) 27:375–394
123
Finally, there needed to be a sufficient number of arti-
cles within each work outcome to include the outcome in
the study (e.g., k=2 for each comparison).
5
Based on the
selection criteria, there were a sufficient number of primary
studies to be meta-analyzed for job satisfaction, organiza-
tional commitment, and intent to stay/quit.
6
However, for
training, leadership, and motivation, there were too few
eligible studies to proceed with the meta-analysis. Studies
within each of these categories tended to include widely
varying criteria, had small sample sizes, or there were
simply no primary studies to meta-analyze.
Therefore, of the 95 studies fully reviewed, 20 met our
inclusion criteria and included sufficient information to
calculate effect sizes. After these determinations were
made, an additional faculty coder examined a subset of
randomly selected articles from those eligible for inclusion.
This rater confirmed the inclusion/exclusion determinations
in each case and detected no errors in the coding of coef-
ficients from those articles that were included.
Each of the included studies focused on at least one of
the following work outcomes: job satisfaction (k=9),
organizational commitment (including general, affective,
continuance, and normative, k=18), and/or intent to stay/
quit (k=7). Therefore, differences between generations
were compared across these three criteria using a total of
six measures. Because of the relatively small number of
effect sizes, we did not conduct any publication bias
analyses (McDaniel et al. 2006; Rothstein et al. 2005).
Research Context
The primary studies included in our meta-analysis were
conducted between 1995 and 2009. Seven of the studies
appeared in academic journals, eleven were doctoral dis-
sertations, one was a conference presentation, and one was
an unpublished study. Four of the studies were conducted
outside the United States, including one in Canada, one in
Europe, and two in New Zealand. The studies included a
mix of organization-specific and multi-organization sam-
ples. All the studies used cross-sectional designs.
Meta-analytic Procedure
We computed ds from reported means and standard devi-
ations, using the meta-analytic procedures of Hunter and
Schmidt (2004) to correct observed differences for sam-
pling error and unreliability. We used Schmidt and Le’s
(2004) software to conduct the meta-analysis and compute
credibility intervals, conducting separate meta-analyses for
each of the six possible generation pairwise compari-
sons (Traditionals-Boomers, Traditionals-Generation X,
Traditionals-Millennials, Boomers-Generation X, Boom-
ers-Millennials, and Generation X-Millennials) for each of
the six criterion variables. In each comparison, the younger
generation’s mean was subtracted from the older genera-
tion’s mean such that a positive dindicates that the older
generation’s score was higher and a negative dindicated
that it was lower on the criterion of interest.
When authors of original studies reported an internal
consistency reliability coefficient for the job satisfaction,
organizational commitment, or intent to turnover/remain
measures, we used this value to correct the observed value
for unreliability. When reliabilities were not reported, we
used the reliability value generally reported in scale
development papers for that measure. If established reli-
abilities were not available, we imputed the reliability
based on the average internal consistency of the other
studies included in the meta-analysis for each type of cri-
terion measure based on the procedure used by Judge et al.
(2002). For the 20 primary research studies, reliabilities
were published in 13 of them, generally reported reliabil-
ities were used for three, and the remaining four were
imputed.
7
Although we did correct for unreliability of the
criteria, we did not correct for range restriction.
In addition to reporting the estimated mean corrected ds,
we also report the standard deviation, the 90% confidence
intervals, and the 80% credibility intervals of the corrected
ds. The confidence interval informs conclusions about the
precision of the mean, providing an estimate of the vari-
ability around the estimated mean difference. The credi-
bility interval informs conclusions about unexplained
variance, variance that is not due to sampling error or
differences in measurement error across studies. A wide
credibility interval, or one including zero, indicates that
additional moderator variables may be affecting the rela-
tionship of interest.
Possible moderators were determined by examining the
primary studies. As all the primary studies used cross-
sectional methods, methodology could not be tested as a
moderator. Characteristics of the samples did vary some-
what but too few of the studies reported generation-specific
5
While there is no universally agreed upon criteria for the number of
studies and subjects necessary for meta-analysis, several recent meta-
analyses have been published with just 2–4 studies and with sample
sizes in the hundreds (e.g., Tourangeau and Yan 2007).
6
Studies use the terms ‘‘intent to stay/remain’’ and ‘‘intent to quit/
turnover’’ as indicative of the underlying construct turnover intentions.
Therefore, studies examining any variation of turnover intentions were
combined. Scales were reverse coded where appropriate.
7
As an alternate approach to dealing with single item scales, we
implemented Riketta’s (2008) suggested procedure. For single item
scales, he used the reliabilities imputed by Wanous and Hudy (2001),
setting single-item scale reliabilities to .7. We found that the ds never
varied [.02 after replacing the imputed reliabilities with .7. Because
our original imputation method produced more conservative esti-
mates, we report those results in the tables.
J Bus Psychol (2012) 27:375–394 381
123
means for variables such as organizational tenure, subject
age, or gender to conduct moderator analyses. Country of
data collection varied and we did run the analyses with
non-US data sets removed. Overall, because of the rela-
tively small number of primary results data and the wide
variation in information that was reported, we were able to
conduct moderator analyses only for country of data col-
lection. Ideally, we would have tested the birth years used
to define generations as a moderator; however, although the
primary studies agreed on the names of the generations, the
start and end dates for each generation varied too widely
(i.e., there was essentially no agreement) to conduct this
analysis.
Results
Table 1presents the sample and effect sizes from all the
studies used in the meta-analysis. Table 2presents addi-
tional details on the studies, data, and samples. Table 3
shows the demographics of all the primary studies in the
meta-analysis including the ks and Ns for each of the six
criteria and for each of the six generation comparisons. As
can be seen, comparisons between Boomers and Generation
X and between Generation X and Millennials were possible
for all six criteria. Boomers and Millennial comparisons
were possible for four of the six criteria and Traditionals
could only be compared with Boomers and Generation X on
job satisfaction and not with Millennials. Because of the
small number of primary studies overall, we included in the
meta-analysis generation pairs if there were as few as two
primary studies, assuming the study Ns were sufficiently
large enough to suggest stable estimates. This resulted in a
total of 18 comparisons. It is worth noting that most of the
studies had fairly large sample sizes. Had we used Huffcutt
et al.’s (1996) weighting procedure for dealing with sub-
stantial variation in sample sizes among primary studies,
every study in the meta-analysis would have been coded into
the largest group, minimizing any concerns about large
samples unduly impacting the overall results.
Table 4shows the results of the meta-analyses on the six
work-related criteria including dand corrected d, the
standard deviation of the corrected d, 90% confidence
intervals, 80% credibility intervals, and the percentage of
variance in the corrected daccounted for by sampling error
(% SE) for each of the 18 comparisons. Figure 2shows the
corrected ds along with the upper and lower 80% credi-
bility intervals for each of the generation comparisons for
each of the dependent variables. For job satisfaction, the
corrected ds ranged from .02 to .25. The general pattern
was that older generations were slightly more satisfied than
younger generations. That said, these effect sizes would be
classified as ‘‘small’’ according to Cohen (1988).
8
These
small differences along with the low percent of variance
attributable to sampling error for four of the five compar-
isons suggest that generation membership has little prac-
tical impact on job satisfaction and that other unmeasured
variables may be important for job satisfaction.
Turning to organizational commitment, the results were
similar, with corrected ds ranging from -.07 to .51 for
general commitment, .09 to .22 for affective, -.05 to .42
for normative, and -.26 to .30 for continuance. The cor-
rected dfor Boomers and Generation Xers in general
commitment (.51) could be considered moderate, indicat-
ing that Generation Xers, contrary to the popular literature,
tended to report higher levels of commitment. However,
there was no discernable pattern to the results. Older and
younger generations varied in levels of commitment, with
older generations sometimes being more and sometimes
less committed. Again, the absolute magnitudes of most of
the differences were small, many of the credibility intervals
included zero, and a number of comparisons showed low
percentage of variance attributable to sampling error.
The final criterion of interest, intent to turnover, showed
slightly larger differences with corrected ds ranging from
-.62 to .05. Two of the three generation comparisons
showed corrected dsof-.53, for Boomers and Millennials,
and -.62, for Generation X and Millennials, indicating that
younger generations were more inclined to leave their
organization than older generations. One of the three
credibility intervals included zero.
We ran several of the meta-analyses with and without
studies with large sample sizes to ensure they were not
influencing the results. For most of the meta-analyses, there
was little change (ds were within .00–.05) when these
studies were removed. However, when Dudley et al.’s
(2009) study was removed from the Boomers and Millen-
nials comparison for job satisfaction, the corrected
ddropped from .11 to -.05. Removing Wieck et al.’s
(2009) findings from the Generation X and Millennials
comparison for intent to turnover changed the corrected
dfrom -.62 to -.17 suggesting these studies dispropor-
tionately impacted the estimated effect sizes; in both cases
removing these studies lowered the estimates of d.
Given that some of the primary studies used data that
were collected in countries other than the United States,
and that the conceptualizations of the generations used in
the research were based on historical US events, we re-ran
our meta-analyses using only Anglo countries (US, Can-
ada, New Zealand) and then again after removing all non-
US countries. These results are also reported in Table 4.
For the Anglo-only analyses, the results were essentially
8
We use Cohen’s (1988) benchmarks when interpreting the effect
sizes: .2–.3 is considered small, around .5 is considered moderate, and
.8 and higher is considered a large effect.
382 J Bus Psychol (2012) 27:375–394
123
Table 1 Primary study sample characteristics and effect sizes
Study N
T
N
B
N
X
N
M
N
Total
d
Job
sat
General
comt
Affective
comt
Normative
comt
Continuance
comt
Intent to
turnover
Carley (2009)
Boomers–Gen X 131 102 233 -.27
Boomers–Millennials 131 60 191 -.03
Gen X–Millennials 102 60 162 .25
Cennamo and Gardner (2008)
Boomers–Gen X 117 288 405 .00 .16 -.30
Boomers–Millennials 117 83 200 .19 .17 -.45
Gen X–Millennials 288 83 371 .20 .00 -.15
Chan (2006)
Gen X–Millennials 60 60 120 .17
Curry (2008)
Boomers–Gen X 49 51 100 -.10
D’Amato and Herzfeldt (2008)
Boomers–Gen X 474 1,192 1,666 .53 .10
Daboval (1998)
Boomers–Gen X 44 123 167 2.08
Davis et al. (2006)
Boomers–Gen X 197 185 382 -.12 -.25 .14
Dilworth and Kingsbury (2005)
Traditionals–Boomers 441 1,463 1,904 .25
Traditionals–Gen X 441 833 1,274 .32
Boomers–Gen X 1,463 833 2,296 .09
Dudley et al. (2009)
Boomers–Gen X 2,871 2,871 5,742 .01
Boomers–Millennials 2,871 2,871 5,742 .14
Gen X–Millennials 2,871 2,871 5,742 .13
Eaton (2009)
Traditionals–Boomers 18 260 278 -.31
Traditionals–Gen X 18 106 124 -.60
Boomers–Gen X 260 106 366 -.29
Boomers–Millennials 260 46 306 -.34
Gen X–Millennials 106 46 152 -.06
Faulk (1997)
Boomers–Gen X 206 51 257 .11
Hess and Jepsen (2009)
Boomers–Gen X 100 107 207 .14 -.32
Boomers–Millennials 100 77 177 .38 -.50
Gen X–Millennials 107 77 184 .24 -.18
Hollman (2008)
Boomers–Gen X 116 219 335 .22 .20 .41
Gen X–Millennials 219 20 239 -.25 -.27 .35
Leiter et al. (2009)
Boomers–Gen X 193 255 448 .65
Macky et al. (unpublished)
Boomers–Gen X 484 399 883 .09 .19
Boomers–Millennials 484 103 587 .03 .08
J Bus Psychol (2012) 27:375–394 383
123
Table 1 continued
Study N
T
N
B
N
X
N
M
N
Total
d
Job
sat
General
comt
Affective
comt
Normative
comt
Continuance
comt
Intent to
turnover
Gen X–Millennials 399 103 502 -.06 -.11
Miller (2007)
Boomers–Gen X 61 48 109 -.02 -.21
Boomers–Millennials 61 41 102 .28 -.36
Gen X–Millennials 48 41 89 .30 -.15
Moody (2008)
Boomers–Gen X 79 48 127 .22
Boomers–Millennials 79 74 153 .33
Gen X–Millennials 48 74 122 .11
Patalano (2008)
Gen X–Millennials 100 103 203 .99 1.08 -.90
Sujdak (2003)
Boomers–Gen X 81 43 124 -.04 .18 .32
Wieck et al. (2009)
Boomers–Gen X 864 601 1,465 -.06
Boomers–Millennials 864 94 958 -.49
Gen X–Millennials 601 94 695 -.94
Boomers Baby Boomers, Gen X Generation X, N
T
sample size of Traditionals, N
B
sample size of Baby Boomers, N
X
sample size of Generation X,
N
M
sample size of Millennials, N
Total
combined sample size, job sat job satisfaction, comt commitment
Table 2 Primary study details of data and methods
Study Data
collection
method
Year of data
collection
Subject pool Jobs held by subjects
Carley (2009) Paper N/A Healthcare professionals Physicians, physicians’ assistants, nurses,
and physical/occupational therapists
Cennamo and
Gardner (2008)
Online N/A Employees from law firms, media
corporations, construction industry,
pharmaceutical distribution, and
information technology
N/A
Chan (2006) Combination N/A Employees Professional jobs
Curry (2008) Paper N/A Employees of a regional healthcare
organization
N/A
D’Amato and
Herzfeldt (2008)
Online N/A Employees Managerial jobs
Daboval (1998) Paper N/A Employees of a manufacturing company N/A
Davis et al. (2006) Online N/A Employees from various state agencies
and universities
Information technology jobs
Dilworth and
Kingsbury (2005)
Archival 1997 Employees N/A
Dudley et al. (2009) Online 2006 Employees Managerial and non-managerial jobs
Eaton (2009) Online N/A Employees of a federal organization,
including civilian, military, and contract
Secretarial, professional, and technical
jobs
Faulk (1997) Paper 1995–1996 Employees from a petroleum refinery, an
airplane manufacturer, and a university
N/A
Hess and Jepsen
(2009)
N/A N/A Employees from the following industries:
insurance, finance, non-profits/
government, manufacturing
Administrative, managerial, HR, sales/
marketing, customer service;
384 J Bus Psychol (2012) 27:375–394
123
the same, with corrected ds decreasing only slightly in both
cases (.04 and .10). For the US-only analyses, nine of the
eleven comparisons were similarly affected with changes
in corrected ds ranging from .00 to .12. Two of the cor-
rected ds did show a larger change with general commit-
ment between Boomers and Generation X increasing from
.51 to .68 and for intent to turnover for Generation X and
Millennials changing from -.62 to -.81. That said, over-
all, the changes for all 13 additional analyses were fairly
small and non-systematic suggesting that removing the
European, Canadian, and New Zealand samples did not
make a substantial difference in the meta-analytic results.
As some researchers have suggested that job satisfaction
and organizational commitment (specifically affective
commitment) are conceptually related and therefore should
be treated as a single marker of job attitudes (e.g., Harrison
et al. 2006; Le et al. 2010), and that such a combination has
been used in previous meta-analyses (Riketta 2008), we ran a
meta-analysis combining job satisfaction and affective
organizational commitment. Specifically, we meta-analyzed
affective commitment and job satisfaction together for all the
generational comparisons for which we had data on both
variables. The results of this analysis generally mirror the
results for job satisfaction, with ds not varying[.01 from the
results for job satisfaction alone (see Table 4).
Looking at the corrected ds in a different way, we see that
Traditionals were slightly more satisfied than both Boomers
(d=.18) and Generation Xers (d=.25). Boomers in turn
showed higher general commitment (d=.51) and continu-
ance commitment (d=.30) than Generation Xers. All other
Boomer–Generation X differences were essentially zero (i.e.,
\.10). Comparing Boomers and Millennials, one can see
that Boomers had slightly higher job satisfaction (d=.11),
general commitment (d=.14), and affective commitment
(d=.22) and lower intent to turnover (d=-.53). The last
set of comparisons is for Generation X and Millennials. Here,
Table 2 continued
Study Data
collection
method
Year of data
collection
Subject pool Jobs held by subjects
Hollman (2008) Online N/A Employees of UPS N/A
Leiter et al. (2009) N/A N/A Employees of various acute care facilities Nurses
Macky et al.
(unpublished)
Online 2005 Employees from a variety of
organizations
N/A
Miller (2007) Paper N/A Employees of various hotels Front office and housekeeping supervisors
and hourly employees
Moody (2008) Paper N/A Employees from various financial
services institutions
N/A
Patalano (2008) Online N/A Employees of a large internet services
company
N/A
Sujdak (2003) Online N/A Members of an information technology
professional association
Information technology jobs
Wieck et al. (2009) Online N/A Employees of a large hospital system Nurses
N/Adata was not reported in the study
Table 3 Demographics for all studies
Outcome Traditionals–
Boomers
Traditionals–
Gen X
Traditionals–
Millennials
Boomers–Gen X Boomers–
Millennials
Gen X–
Millennials
KN
T
N
B
KN
T
N
X
KN
T
N
M
KN
B
N
X
KN
B
N
M
KN
X
N
M
Job satisfaction 2 459 1,723 2 459 939 8 6,513 5,549 5 3,863 3,163 6 3,826 3,223
Organizational commitment
General 6 1,368 1,856 2 563 177 2 447 177
Affective 5 591 847 4 394 221 5 762 324
Normative 2 313 404 – 2 319 123
Continuance 2 313 404 – 2 319 123
Intent to turnover – 7 1,890 2,534 4 1,142 295 4 1,044 295
Boomers Baby Boomers, Gen X Generation X, Knumber of primary studies, – fewer than 2 primary studies, N
T
combined sample size of
Traditionals, N
B
combined sample size of Baby Boomers, N
X
combined sample size of Generation X, N
M
combined sample size of Millennials
J Bus Psychol (2012) 27:375–394 385
123
Table 4 Generational differences by criterion variable
Outcome KN
Total
dd
corrected
r
corrected
90% Confidence
Interval
% SE 80% Credibility
Interval
Lower Upper Lower Upper
Job satisfaction
Traditionals–Boomers 2 2,182 .18 .18 .18 -.12 .48 11 -.05 .42
Traditionals–Gen X 2 1,398 .24 .25 .26 -.18 .69 8 -.08 .59
Boomers–Gen X 8 10,149 .02 .02 .07 -.10 .13 46 -.07 .10
Without Dudley et al. (2009) 7 4,407 .02 .02 .10 -.17 .21 40 -.11 .15
Without Dilworth and Kingsbury (2005) 7 7,853 -.01 -.01 .06 -.13 .12 51 -.09 .08
US only 6 8,861 .01 .01 .07 -.11 .13 37 -.08 .10
Boomers–Millennials 5 7,026 .10 .11 .10 -.05 .28 26 -.01 .24
Without Dudley et al. (2009) 4 1,284 -.04 -.05 .15 -.29 .20 40 -.23 .14
US only 3 6,239 .11 .12 .11 -.06 .29 17 -.02 .25
Gen X–Millennials 6 7,049 .12 .13 .02 .07 .18 89 .10 .15
Without Dudley et al. (2009) 5 1,307 .07 .08 .05 -.04 .19 86 .01 .14
US only 4 6,176 .13 .14 .00 .14 .14 100 .14 .14
Organizational commitment
General
Boomers–Gen X 6 3,224 .46 .51 .46 -.25 1.27 4 -.08 1.10
Anglo only 5 1,558 .38 .41 .61 -.60 1.42 4 -.38 .58
US only 4 675 .63 .68 .86 -.74 2.10 4 -.43 1.79
Boomers–Millennials
a
2 740 .13 .14 .00 .12 .16 100 .14 .14
Gen X–Millennials
a
2 624 -.07 -.07 .00 -.07 -.07 100 -.07 -.07
Affective
Boomers–Gen X 5 1,438 .08 .09 .07 -.05 .24 76 .001 .19
US only 4 1,033 .05 .06 .09 -.09 .21 68 -.06 .18
Boomers–Millennials 4 615 .20 .22 .00 .22 .22 100 .22 .22
US only 3 415 .22 .24 .04 .17 .32 96 .19 .30
Gen X–Millennials 5 1,086 .19 .21 .41 -.39 .82 11 -.32 .74
US only 4 715 .29 .33 .48 -.47 1.12 10 -.29 .95
Normative
Boomers–Gen X 2 717 -.04 -.05 .22 -.42 .32 23 -.33 .23
Gen X–Millennials 2 442 .35 .42 .71 -.75 1.60 4 -.49 1.34
Continuance
Boomers–Gen X 2 717 .27 .30 .09 .13 .48 64 .19 .42
Gen X–Millennials 2 442 -.22 -.26 .65 -1.34 .81 5 -1.10 .57
Intent to turnover
Boomers–Gen X 7 4,424 .05 .05 .26 -.38 .48 10 -.28 .38
Anglo only 6 2,758 .01 .01 .33 -.53 .55 9 -.41 .43
US only 5 2,353 .06 .07 .32 -.46 .60 9 -.34 .49
Boomers–Millennials 4 1,437 -.48 -.53 .00 -.53 -.53 100 -.53 -.53
Without Wieck et al. (2009) 3 479 -.45 -.50 .00 -.50 -.50 100 -.50 -.50
US only 3 1,237 -.48 -.54 .00 -.54 -.54 100 -.54 -.54
Gen X–Millennials 4 1,339 -.57 -.62 .42 -1.32 .08 8 -1.16 -.08
Without Wieck et al. (2009) 3 644 -.16 -.17 .00 -.17 -.17 100 -.17 -.17
US only 3 968 -.72 -.81 .36 -1.41 -.22 11 -1.28 -.35
Job satisfaction and affective commitment
Boomers–Gen X 12 11182 .02 .02 .07 -.10 .14 50 -.07 .11
386 J Bus Psychol (2012) 27:375–394
123
one can see that Generation Xers were slightly more satisfied
with their jobs (d=.13) and showed higher levels of
affective (d=.21) and normative commitment (d=.42).
Although Generation Xers showed lower levels of general
(d=-.07) and continuance commitment (d=-.26), they
also demonstrated lower levels of intent to turnover (d=
-.62). Overall, the pattern of results suggests that older
generations may be slightly more satisfied with their jobs,
less likely to leave their jobs, and varied in whether they are
more, less, or not at all different in terms of their
commitment.
Discussion
The results of the meta-analysis generally do not support
the notion that there are systematic, substantive differences
among generations in work-related outcomes. The majority
of generational comparisons that were analyzed showed ds
of less than one-quarter of a standard deviation when
corrected for unreliability. Of the few differences that did
emerge, the largest ds were approximately one-half of a
standard deviation. Even then, extant research suggests that
alternate explanations besides generational membership are
plausible. Given the many and varied claims about gener-
ational differences, the generally small effect sizes identi-
fied contradict such assertions and offer an important
contribution to the scholarship in this area. Overall, our
results, like those of Sackett (2002) and others, provide
little evidence supporting the existence of significant and
meaningful differences that are attributable to generation
membership. They also raise questions about the efficacy
of organizational interventions designed to address such
differences and support the conclusions of Parry and Urwin
Fig. 2 Corrected ds and
credibility intervals for each
criteria and for all generation
comparisons. Note For
generation comparisons,
TTraditionalist, BBaby
Boomer, XGeneration X,
MMillennial. For commitment,
Ggeneral, Aaffective,
Nnormative, Ccontinuance,
CI credibility interval
Table 4 continued
Outcome KN
Total
dd
corrected
r
corrected
90% Confidence
Interval
% SE 80% Credibility
Interval
Lower Upper Lower Upper
Boomers–Millennials 8 7441 .11 .12 .10 -.04 .28 35 -.01 .25
Gen X–Millennials 10 7764 .13 .14 .16 -.12 .40 19 -.07 .34
This table excludes comparisons for which Kwas \2
Boomers Baby Boomers, Gen X Generation X, Knumber of primary studies; N
Total
combined sample size across generations, d
corrected
estimated
true score mean difference, r
corrected
standard deviation of the estimated true score mean difference, % SE the percentage of variance accounted
for by sampling error
a
For these comparisons, there was not a sufficient number of primary studies to analyze only the studies with US samples
J Bus Psychol (2012) 27:375–394 387
123
(2010) who found mixed results for studies of generational
differences.
A review of the meta-analytic results does reveal three
weak, if discernable, patterns. First, we found that older
generations were slightly more satisfied with their jobs than
younger generations. Although the ds were fairly small
(.02–.25), older generations were more satisfied with their
jobs than younger generations. However, this finding is not
surprising given that research suggests chronological age
and tenure both tend to be positively correlated with job
satisfaction (e.g., Hunt and Saul 1975; Kacmar and Ferris
1989; Ng and Feldman 2010). Because age and tenure
naturally co-vary, several authors have examined their
relative contribution in predicting job satisfaction. Some
have found that tenure is a more stable predictor of job
satisfaction than age (Bedeian et al. 1992) but others have
found the opposite (Morrow and McElroy 1987). Although
these results are conflicting, they suggest that age or tenure,
not generational membership, may explain the differences
observed in job satisfaction. This finding was confirmed by
Ng and Feldman’s (2010) meta-analysis that found the
relationship between age and job satisfaction was .18 but
dropped to .12 after controlling for tenure.
The second pattern is that while there were small to
moderate differences among generations on commitment,
the generational cohorts varied in whether they were more
or less committed, or not at all different. That is, there was
no discernable pattern of relative differences of older ver-
sus younger generations. Research on commitment has
found that while chronological age tends to be positively
correlated with organizational commitment, meta-analytic
findings suggest that it is not a strong predictor. A meta-
analysis by Ng and Feldman (2010) found relationships
between age and commitment, controlling for tenure, to be
.17 for affective, .11 for normative, and .05 for continu-
ance, which are fairly small effect sizes. Mathieu and Zajac
(1990) found that the most robust antecedents of organi-
zational commitment were individual differences (e.g.,
perceived personal competence), job characteristics (e.g.,
challenge and job scope), and leadership-related variables
(e.g., leader communication and participative leadership).
Likewise, Meyer et al. (2002) found the strongest predic-
tors of organizational commitment to be perceived orga-
nizational support, transformational leadership, role
ambiguity, and organizational justice. The meta-analytic
results herein suggest that generational membership
appears to be a mixed predictor at best and research sug-
gests that other variables are likely more responsible for
any differences.
Finally, older generations were somewhat less likely to
leave their jobs (ds of .05 to -.63) than younger genera-
tions. However, previous research on intent to leave has
shown that although chronological age tends to be
negatively related to turnover intentions, it adds little pre-
dictive value above and beyond job involvement, educa-
tion, and tenure (Parasuraman 1982). Healy et al. (1995)
found a meta-analytic correlation between age and actual
turnover of -.08, a finding confirmed by Ng and Feldman’s
(2009) meta-analytic result of -.14. Further, attitudinal
variables such as job satisfaction and organizational com-
mitment tend to be more strongly related to turnover
intentions than age (Arnold and Feldman 1982). Again,
these results suggest that other variables besides age are
stronger predictors of outcome variables such as turnover
intentions.
In addition to the above, research has suggested other
possible explanations for observed generational differ-
ences. For example, in a meta-analysis of changes in per-
sonality across the life course, Roberts et al. (2006) found
that social dominance (a facet of extraversion), conscien-
tiousness, and emotional stability tend to increase in young
adulthood. Judge et al. (2002) found that these traits are
positively related to job satisfaction, which could explain
the higher levels of job satisfaction among the older gen-
erations. This suggests that personality differences across
the life course could explain some of the generational
effects observed.
A study by Fried and Ferris (1987) examined variation
in job characteristics offers possible explanations for some
of the effects attributed to generational membership. They
found that as employees get older and progress through
their careers, their jobs may be characterized by a greater
degree of autonomy, skill variety, and task significance.
These job characteristics are positively related to job sat-
isfaction and, with the exception of task significance,
negatively related to absenteeism. A related argument is
put forth by Ng and Feldman (2010), who discuss several
theories which might explain age-based differences
including the job-congruence model (White and Spector
1987) and socioemotional selectivity theory (Carstensen
1992). While these studies looked at different outcome
variables and propose competing frameworks, they do
demonstrate that other mechanisms besides generations
may be causing observed differences.
Overall, we found little support for differences between
groups of individuals based on generational membership.
Comparing the results obtained from the present meta-
analysis and those of related primary studies and meta-
analytic efforts supports this conclusion. Further, the results
for studies using generations and those using age were very
similar, suggesting that chronological age, or some other
variable, is likely responsible for the small effects that were
observed. For example, a meta-analysis by Healy et al.
(1995) found a small, negative relationship between age and
actual turnover while Ng and Feldman’s (2009) meta-
analysis had similar results. A second meta-analysis by Ng
388 J Bus Psychol (2012) 27:375–394
123
and Feldman (2010) found small correlations between age
and job satisfaction and also between age and affective,
normative, and continuance commitment. Primary studies
by Kowske et al. (2010) found little support for generational
differences in satisfaction and turnover intentions even after
separating the effects of age and generation. The results of
these studies (meta-analytic correlations and variance-
accounted-for estimates) are very similar to the findings
reported in our study, suggesting that unique variance
attributable to generational differences is minimal.
Limitations
Meta-analytic efforts are dependent on the existence of
quality primary research that can be analyzed. In our case,
there were several limitations in the primary research that
should be mentioned. First, there were a relatively small
number of studies available. Given the extensive coverage
of generational differences in the media and popular press,
we were somewhat surprised to find only 95 studies across
six work-related outcomes that could potentially be inclu-
ded. From that group, three outcomes, including job sat-
isfaction, commitment, and intent to leave/quit among 20
primary studies provided sufficient empirical evidence to
be included in the meta-analysis. Among the 20 studies we
identified, not all generations were compared to each other
on all outcomes. As a result, we were able to make 13
comparisons involving Generation X but only two
involving Traditionals.
It is worth noting that more than half of the studies were
non-published works. On the one hand, the use of such
studies in a meta-analysis raises questions about the theo-
retical soundness and methodological rigor of the work. On
the other hand, the use of non-published works lessens the
significance of publication bias. Given that all the non-
published studies used the same methods as the published
studies and that each used a criterion measure that was
well-established and validated, we feel confident that their
inclusion strengthens the meta-analysis overall.
The small number of studies, the few work-related cri-
teria that could be analyzed, and the uneven number of
comparisons across generations all limited our effort. Even
so, our study demonstrated that empirical support for the
existence of generational differences in work-related out-
comes is far from expansive and the mixed results are
anything but conclusive. Both our review of the research on
generational differences in work outcomes and our meta-
analyses on three of those outcomes revealed little evi-
dence supporting popular press and consultant claims of
the importance of such differences.
Second, all research studies included in this meta-anal-
ysis used cross-sectional designs to assess generational
differences. Although there have been a few efforts across
domains that have used alternative approaches such as
cross-classified HLM (work attitudes; e.g., Kowske et al.
2010) or cross-temporal meta-analysis (CTMA)
9
(person-
ality; e.g., Twenge et al. 2008), most research on genera-
tional differences and almost all research focused on work-
related outcomes have employed cross-sectional designs.
As has been discussed by previous researchers (e.g., Macky
et al. 2008b; Parry and Urwin 2010; Rhodes 1983;
Trzesniewski and Donnellan 2010), there are numerous
limitations to cross-sectional research when studying gen-
erational differences, particularly the inability to separate
variance attributable to generational, age, and period
effects. However, the majority of studies have conceptu-
alized and measured generational differences in this way.
Of the few studies that did use a different methodology,
each was excluded from our analysis either because it did
not focus on work-related outcomes or because the analytic
approach produced effect sizes that did not parallel those of
the cross-sectional studies. For example, CTMA produces a
comparison of generations controlling for age. Cross-clas-
sified HLM produces an estimate of the variance accounted
for in a given outcome by generation membership, holding
age, and time-period effects constant. Neither is concep-
tually parallel to a d-score produced by the cross-sectional
studies. Nonetheless, the very small effect sizes that have
been found using alternate techniques suggest their inclu-
sion would not likely have changed the results one way or
another. The limited number of primary studies and the
cross-sectional design they employed suggests a third
limitation of our research: our inability to assess modera-
tors that may impact the relationship between generation
membership and outcomes. There were some variables
present that might have affected the results, such as the
varying range of generations’ birth years, countries in
which the data were collected, as well as the gender and
organizational tenure of generational members, but due to
the small number of primary studies and the results they
reported, no moderator analyses were possible.
We were able to run the analyses with first the European
and then all non-US samples removed (i.e., Anglo-only and
US-only, respectively). The results showed there were
relatively small changes in the effect sizes and the changes
that were present were non-systematic, suggesting that the
country in which data were collected was not an important
factor in the results. Our findings echo those of some
researchers who have similarly failed to find any cross-
cultural effects on generations (e.g., Hui-Chun and Miller
2003) but are counter to other studies which have found
9
Cross-temporal meta-analysis (CTMA) uses cross-sectional panel
data to compare members of different groups at different times when
they are at the same age (e.g., 18 year olds in 1960 vs. 18 year olds in
2000).
J Bus Psychol (2012) 27:375–394 389
123
country of origin to have an impact (e.g., Murphy et al.
2004). It is worth noting that these findings were for criteria
other than work-related outcomes, and it is unclear whether
they would generalize to those studied herein.
Overall, given the generally small effect sizes we found
and the number of comparisons that fell below Schmidt and
Hunter’s (1977) 75% rule, there are likely undetected
moderators at work. It is also possible that there are other
unknown main effects that account for most of the variance.
All these limitations again raise the broader question
about the utility of conducting a meta-analysis given the
conceptual and methodological challenges of assessing
generational differences. As noted previously, the benefits
of conducting a meta-analysis in spite of these limitations
is two-fold. First, as with all meta-analytic studies, the
results of the effort summarize the extant research given
the way the phenomena of generations and generational
differences in workplace outcomes have been studied. As
with Rind et al.’s (1998) meta-analysis of child sexual
abuse demonstrated, even when there is disagreement on
the definition of the underlying phenomenon of interest, a
meta-analysis can prove useful in summarizing extant
findings. While they had sufficient primary studies (nearly
60 studies and over 35,000 subjects) and the domain faced
fewer methodological challenges than generations, dis-
agreements about the definition and operationalization of
abuse raised questions about the efficacy of the study.
Nonetheless, their effort was key both in terms of sum-
marizing the research and in calling the question about the
problems with the underlying research.
Second, limitations in primary studies identified by a
meta-analytic effort point to areas and needs for future
research. As Rind et al. (1998) noted in their meta-analysis,
the construct they were studying was of ‘‘questionable
scientific validity’’ (p. 46) because of definitional and
conceptual issues. Because of this, they called for a more
thoughtful, comprehensive, and valid approach to studying
the phenomenon of interest, a call that could not have been
made without the meta-analytic effort. Therefore, the find-
ings of this study and the limitations that were discovered in
the process of conducting it suggest specific areas in need of
additional research which are discussed below.
Future Research and Implications
The findings of this meta-analysis and limitations noted
above point both to the need for additional research on
generational differences as well as the need for organiza-
tions to exhibit caution in adopting interventions designed
to address such differences. Clearly, there is a need for
additional, scientifically sound, primary research on gen-
erational differences in work-related outcomes. Within this
general area, we identified three specific areas that should
be investigated. First, future research on generational dif-
ferences should strive to assess more work-related criteria,
collect data on all generations in the workplace, and make
more comparisons across cohorts. Although our meta-
analytic results found few differences across groups on
three outcomes, we were unable to test additional out-
comes, to test all pairwise comparisons of generations for
each outcome, or to assess moderators that could be
affecting the underlying differences.
A second area to be addressed is the need for improved
methodological approaches for studying generational dif-
ferences. The limitations of cross-sectional research
designs in this area have been demonstrated and efforts by
Kowske et al. (2010), Twenge and Campbell (2010), and
others to employ new and better methodologies are a
positive development. That said, the recent exchange in
Perspectives on Psychological Science between Twenge,
Tresniewski, and others pointed out that there is still much
work to be done in order to identify and develop stronger
methodological approaches for studying such differences.
Finally, and perhaps most importantly, these meta-ana-
lytic results showing generally small effect sizes, along
with the mixed findings from primary studies and the many
conceptual and review pieces which raise questions over
the extent and nature of generational differences (e.g., Joshi
et al. 2010; Macky et al. 2008b; Sackett 2002), point to the
need for additional conceptual work. Much more research
is needed to fully develop the concept of generations. That
is, we need to determine exactly what generations are, what
impact they have and, most importantly, why. Such
research needs to be done before we can determine if
generational membership affects outcomes and, if so,
exactly how. It may be that there is a mechanism that has
not yet been fully explicated; or, as this meta-analysis and
other research suggests, generational membership may be,
at best, a proxy for other, more direct, proximal, and
complex causes of such differences. The fact that the
popular press and generational gurus continue to write and
consult about such differences, and that organizations are
seeking out and adopting strategies for dealing with these
perceived differences suggests there is likely some
phenomenon occurring. The question of whether these
differences are attributable to generational membership,
age, maturity, or other individual differences, remains
unanswered.
In terms of practical implications, many organizations
have begun implementing programs and interventions in an
effort to capitalize on supposed generational differences
(e.g., Shapira 2009, July 9). These strategies are often
aimed at recruiting, retaining, and motivating members of
particular generations and include or suggest specific
approaches for how members of different generations
should be treated. However, our review of the theoretical
390 J Bus Psychol (2012) 27:375–394
123
underpinnings of such differences and the results of meta-
analysis suggest that such interventions may be premature
at best. Given these findings, treating members of different
generations differently may not be an effective strategy. A
more effective approach may be to conduct needs assess-
ments that address observed differences among individuals
and develop interventions based on characteristics identi-
fied through this process. This evidence-based strategy is a
proven way to deal with individual differences rather than
relying on unsubstantiated generalizations about entire
groups of employees based on generational membership.
Conclusions
In summary, our meta-analytic effort indicates that where
generational differences do exist on work-related out-
comes, they are relatively small and the inconsistent pat-
tern of results does not support the hypothesis of systematic
differences. Given this and the general dearth of studies
supporting generational differences in work-related out-
comes, it is clear that a better conceptualization of gener-
ational phenomena and better methods for conducting
empirical research are needed. Further, the findings should
give caution to organizations looking to adopt interventions
based on the assumption that generations differ in mean-
ingful ways. For both research and practice, a better
understanding is needed of the actual role that generational
membership has on any such differences relative to the
contribution of related variables such as age, maturity,
work experience, and individual characteristics in predict-
ing work-related and other outcomes.
Acknowledgments The authors wish to thank Michael McDaniel,
Jose Cortina, Allison Brown, and the anonymous reviewers for their
very helpful advice, guidance, and feedback on this manuscript.
References
References marked with an asterisk indicate studies
included in the meta-analysis.
Arnold, H. J., & Feldman, D. C. (1982). A multivariate analysis of the
determinants of job turnover. Journal of Applied Psychology,
67(3), 350–360. doi:10.1037/0021-9010.67.3.350.
Bedeian, A. G., Ferris, G. R., & Kacmar, K. M. (1992). Age, tenure, and
job satisfaction: A tale of two perspectives. Journal of Vocational
Behavior, 40, 33–48. doi:10.1016/0001-8791(92)90045-2.
Beutell, N. J., & Wittig-Berman, U. (2008). Work-family conflict and
work-family synergy for Generation X, Baby Boomers, and
Matures: Generational differences, predictors, and satisfaction
outcomes. Journal of Managerial Psychology, 23(5), 507–523.
doi:10.1108/02683940810884513.
Burke, M. E. (2004). Generational differences survey report.
Alexandria, VA: Society for Human Resources Management.
*Carley, P. J. (2009). Generational perceptions of leadership behav-
iors and job satisfaction among healthcare professionals in
Western New England. Dissertation Abstracts International:
Section A. Humanities and Social Sciences, 70(1), 245.
Carstensen, L. L. (1992). Social and emotional patterns in adulthood:
Support for socioemotional selectivity theory. Psychology and
Aging, 7, 331–338. doi:10.1037/0882-7974.7.3.331.
Caspi, A., & Roberts, B. W. (2001). Personality development across
the life course: The argument for change and continuity.
Psychological Inquiry, 12(2), 49–66. doi:10.1207/S15327965
PLI1202_01.
Caspi, A., Roberts, B. W., & Shiner, R. L. (2005). Personality
development: Stability and change. Annual Review of Psychology,
56, 453–484. doi:10.1146/annurev.psych.55.090902.141913.
*Cennamo, L., & Gardner, D. (2008). Generational differences in
work values, outcomes and person-organization values fit.
Journal of Managerial Psychology, 23(8), 891–906. doi:
10.1108/02683940810904385.
*Chan, D. S. (2006). Relationship between generation-responsive
leadership behaviors and job satisfaction of Generations X and Y
professionals. Dissertation Abstracts International: Section A.
Humanities and Social Sciences, 66(10), 3712.
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
Collins, M. H., Hair, J. F., & Rocco, T. S. (2009). The older-worker–
younger supervisor dyad: A test of the reverse pygmalion effect.
Human Resource Development Quarterly, 20(1), 21–41. doi:
10.1002/hrdq.20006.
Coupland, D. (1991). Generation X: Tales for an accelerated culture.
New York: St Martin’s Press.
*Curry, C. J. (2008). Predicting the effects of extrinsic and intrinsic
job factors on overall job satisfaction for Generation X and Baby
Boomers in a regional healthcare organization. Dissertation
Abstracts International: Section B. Sciences and Engineering,
68(12), 8435.
*D’Amato, A., & Herzfeldt, R. (2008). Learning orientation,
organizational commitment and talent retention across genera-
tions: A study of European managers. Journal of Managerial
Psychology, 23(8), 929-953. doi:10.1108/02683940810904
402.
*Daboval, J. M. (1998). A comparison between baby boomer and
generation 9employees’ bases and foci of commitment.
Dissertation Abstracts International: Section A. Humanities
and Social Sciences, 59(2), 543.
*Davis, J. B., Pawlowski, S. D., & Houston, A. (2006). Work
commitments of Baby Boomers and Gen-Xers in the IT
profession: Generational differences or myth? The Journal of
Computer Information Systems,46(3), 43–49.
Dencker, J. C., Joshi, A., & Martocchio, J. J. (2008). Towards a
theoretical framework linking generational memories to work-
place attitudes and behaviors. Human Resource Management
Review, 18(3), 180–187. doi:10.1016/j.hrmr.2008.07.007.
*Dilworth, J. E. L., & Kingsbury, N. (2005). Home-to-Job Spillover
for Generation X, Boomers, and Matures: A Comparison.
Journal of Family and Economic Issues, 26(2), 267–281. doi:
10.1007/s10834-005-3525-9.
*Dudley, C. M., Burnfield-Geimer, J., & Erdheim, J. (2009, April).
Generational differences in federal government employee atti-
tudes and perceptions. Paper presented at the 24th annual
conference of the Society for Industrial & Organizational
Psychology, New Orleans, LA.
*Eaton, D. E. (2009). An investigation of generational differences in
job satisfaction in a bureaucratic environment. Dissertation
Abstracts International: Section A. Humanities and Social
Sciences, 69(11), 4403.
J Bus Psychol (2012) 27:375–394 391
123
Elder, G. H., Jr. (1994). Time, human agency, and social change:
Perspectives on the life course. Social Psychology Quarterly,
57(1), 4–15.
Elder, G. H., Jr. (1998). The life course as developmental theory.
Child Development, 69(1), 1–12. doi:10.1111/j.1467-8624.1998.
tb06128.x.
*Faulk, M. A. (1997). Personal and generational correlates of work
outcomes: An inter-generational exploration. Dissertation
Abstracts International: Section A. Humanities and Social
Sciences, 58(4), 1360.
Fried, Y., & Ferris, G. R. (1987). The validity of the job
characteristics model: A review and meta-analysis. Personnel
Psychology, 40, 287–322. doi:10.1111/j.1744-6570.1987.tb00
605.x.
Gade, P. A. (1991). Military service and the life-course perspective: A
turning point for military personnel research. Military Psychol-
ogy, 3(4), 187–199. doi:10.1207/s15327876mp0304_1.
Gade, P. A. (2009). The human dimension: Life course theory and
research. Arlington, VA: U.S. Army Research Institute for the
Behavioral Sciences.
Giancola, F. (2006). The generation gap: more myth than reality.
Human Resource Planning, 29(4), 32–37.
Hamblett, C., & Deverson, J. (1965). Generation X. London: Tandem
Books.
Harrison, D. A., Newman, D. A., & Roth, P. L. (2006). How
important are job attitudes? Meta-analytic comparisons of
integrative behavioral outcomes and time sequences. Academy
of Management Journal, 49(2), 305–325.
Healy, M. C., Lehman, M., & McDaniel, M. A. (1995). Age and
voluntary turnover: A quantitative review. Personnel Psychol-
ogy, 48, 335–345. doi:10.1111/j.1744-6570.1995.tb01760.x.
*Hess, J. & Jepsen, D. M. (2009). Career stage and generational
differences in psychological contracts. Career Development
International, 14(3), 261–283. doi:10.1108/13620430910966433.
*Hollman, P. J. (2009). Generational membership and organizational
commitment in a Fortune 100 firm. Dissertation Abstracts
International: Section A. Humanities and Social Sciences, 69(8),
3212.
Howe, N., & Strauss, W. (2000). Millennials rising: The next great
generation. New York: Vintage.
Hu, J., Herrick, C., & Hodgin, K. A. (2004). Managing the
multigenerational nursing team. The Health Care Manager,
23(4), 334–340.
Hui-Chun, Y., & Miller, P. (2003). The generation gap and cultural
influence: A Taiwan empirical investigation. Cross-Cultural
Management, 10, 23–41. doi:10.1108/13527600310797621.
Huffcutt, A. I., Roth, P. L., & McDaniel, M. A. (1996). A meta-
analytic investigation of cognitive ability in employment inter-
view evaluations: Moderating characteristics and implications
for incremental validity. Journal of Applied Psychology, 81(5),
459–473. doi:10.1037/0021-9010.81.5.459.
Hunt, J. W., & Saul, P. N. (1975). The relationship of age, tenure, and
job satisfaction in males and females. Academy of Management
Journal, 18(4), 690–702.
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis:
Correcting error and bias in research findings (2nd ed.).
Thousand Oaks, CA: SAGE.
International Labour Organization. (2010). LABORSTA [Database].
Retrieved from http://www.ilo.org/global/What_we_do/Statis
tics/lang-en/index.htm.
Joshi, A., Dencker, J. C., Franz, G., & Martocchio, J. J. (2010).
Unpacking generational identities in organizations. Academy of
Management Review, 35(3), 392–414.
Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of
personality and job satisfaction. Journal of Applied Psychology,
87, 530–541. doi:10.1037//0021-9010.87.3.530.
Jurkiewicz, C. L. (2000). Generation X and the public employee.
Public Personnel Management, 29(1), 55–74.
Jurkiewicz, C. L., & Brown, R. G. (1998). Generational comparisons
of public employee motivation. Review of Public Personnel
Administration, 18(4), 18–37. doi:10.1177/0734371X980180
0403.
Kacmar, K. M., & Ferris, G. R. (1989). Theoretical and methodo-
logical considerations in the age-job satisfaction relationship.
Journal of Applied Psychology, 74(2), 201–207. doi:10.1037/
0021-9010.74.2.201.
Kowske, B. J., Rasch, R., & Wiley, J. (2010). Millennials’ (lack of)
attitude problem: An empirical examination of generation effects
on work attitudes. Journal of Business and Psychology, 25(2),
265–279. doi:10.1007/s10869-010-9171-8.
Kupperschmidt, B. R. (2000). Multigeneration employees: Strategies
for effective management. The Health Care Manager, 19(1),
65–76.
Landry, D. J. (2009). Effects of generational and gender differences
on the use of influence tactics. Dissertation Abstracts Interna-
tional: Section A. Humanities and Social Sciences, 70(2), 613.
Le, H., Schmidt, F. L., Harter, J. K., & Lauver, K. J. (2010). The
problem of empirical redundancy of constructs in organizational
research: An empirical investigation. Organizational Behavior
and Human Decision Processes, 112(2), 112–125. doi:
10.1016/j.obhdp.2010.02.003.
*Leiter, M. P., Jackson, N. J., & Shaughnessy, K. (2009). Contrasting
burnout, turnover intention, control, value congruence, and
knowledge sharing between Baby Boomers and Generation X.
Journal of Nursing Management, 17(1), 100–109. doi:
10.1111/j.1365-2834.2008.00884.x.
*Macky, K., Forsyth, S., & Boxall, P. (2010) Generational differ-
ences at work: The impact of cohort on work attitudes and
outcomes. Unpublished manuscript.
Macky, K., Gardner, D., & Forsyth, S. (2008a). Generational
differences at work [special issue]. Journal of Managerial
Psychology, 23(8), 857–953.
Macky, K., Gardner, D., & Forsyth, S. (2008b). Generational
differences at work: Introduction and overview. Journal of
Managerial Psychology, 23(8), 857–861. doi:10.1108/026839
40810904358.
MacLean, A., & Elder, G. H., Jr. (2007). Military service in the life
course. Annual Review of Sociology, 36, 175–196. doi:
10.1146/annurev.soc.33.040406.131710.
Mannheim, K. (1952). The problem of generations. In K. Mannheim
(Ed.), Essays on the sociology of knowledge (pp. 276–322).
London: Routledge.
Mathieu, J. E., & Zajac, D. M. (1990). A review and meta-analysis of
the antecedents, correlates, and consequences of organizational
commitment. Psychological Bulletin, 108(2), 171–194. doi:
10.1037/0033-2909.108.2.171.
McDaniel, M. A., Rothstein, H. R., & Whetzel, D. L. (2006).
Publication bias: A case study of four test vendors. Personnel
Psychology, 59, 927–953. doi:10.1111/j.1744-6570.2006.000
59.x.
Meyer, J. P., Stanley, D. J., Herscovitch, L., & Topolnytsky, L.
(2002). Affective, continuance, and normative commitment to
the organization: A meta-analysis of antecedents, correlates, and
consequences. Journal of Vocational Behavior, 61, 20–52. doi:
10.1006/jvbe.2001.1842.
*Miller, E. J. (2007). The effect of rewards, commitment, organiza-
tional climate and work values on intentions to leave: Is there a
difference among generations? Dissertation Abstracts Interna-
tional: Section A. Humanities and Social Sciences, 68(1),
259.
*Moody, A. (2008). Examining and exploring generational differ-
ences by understanding commitment, employee satisfaction, and
392 J Bus Psychol (2012) 27:375–394
123
motivation. Dissertation Abstracts International: Section A.
Humanities and Social Sciences, 69(1), 286.
Morrow, P. C., & McElroy, J. C. (1987). Work commitment and job
satisfaction over three career stages. Journal of Vocational
Behavior, 30, 330–346. doi:10.1016/0001-8791(87)90009-1.
Mottaz, C. J. (1987). Age and work satisfaction. Work and
Occupations, 14(3), 23. doi:10.1177/0730888487014003004.
Murphy, E. F., Gordon, J. D., & Anderson, T. L. (2004). Cross-
cultural, cross-cultural age and cross-cultural generational
differences in values between the United States and Japan.
Journal of Applied Management and Entrepreneurship, 9,
21–47.
Ng, T. W. H., & Feldman, D. C. (2008). The relationship of age to ten
dimensions of job performance. Journal of Applied Psychology,
93(2), 392–422. doi:10.1037/0021-9010.93.2.392.
Ng, T. W. H., & Feldman, D. C. (2009). Re-examining the
relationship between age and voluntary turnover. Journal of
Vocational Behavior, 74, 283–294. doi:10.1016/j.jvb.2009.
01.004.
Ng, T. W. H., & Feldman, D. C. (2010). The relationships of age with
job attitudes: A meta-analysis. Personnel Psychology, 63,
677–718. doi:10.1111/j.1744-6570.2010.01184.x.
Noble, S. M., & Schewe, C. D. (2003). Cohort segmentation: An
exploration of its validity. Journal of Business Research, 56(12),
979–987. doi:10.1016/S0148-2963(02)00268-0.
Parasuraman, S. (1982). Predicting turnover intentions and turnover
behavior: A multivariate analysis. Journal of Vocational Behav-
ior, 21, 111–121. doi:10.1016/0001-8791(82)90056-2.
Parry, E., & Urwin, P. (2010). Generational differences in work
values: A review of theory and evidence. International Journal
of Management Reviews, 13, 79–96. doi:10.1111/j.1468-2370.
2010.00285.x.
*Patalano, C. (2008). A study of the relationship between genera-
tional group identification and organizational commitment:
Generation X vs. Generation Y. Dissertation Abstracts Interna-
tional: Section A. Humanities and Social Sciences, 69(2), 671.
Rhodes, S. R. (1983). Age-related differences in work attitudes and
behavior: A review and conceptual analysis. Psychological
Bulletin, 93(2), 328–367. doi:10.1037/0033-2909.93.2.328.
Riketta, M. (2008). The causal relation between job attitudes and
performance: A meta-analysis of panel studies. Journal of
Applied Psychology, 93, 472–481.
Rind, B., Tromovitch, P., & Bauserman, R. (1998). A meta-analytic
examination of assumed properties of child sexual abuse using
college samples. Psychological Bulletin, 124(1), 22–53. doi:
10.1037/0033-2909.124.1.22.
Roberts, B. W., Walton, K. E., & Veichtbauer, W. (2006). Patterns of
mean-level change in personality traits across the life course: A
meta-analysis of longitudinal studies. Psychological Bulletin,
132, 1–25. doi:10.1037/0033-2909.132.1.1.
Rodriguez, R. O., Green, M. T., & Ree, M. J. (2003). Leading
generation X: Do the old rules apply? Journal of Leadership &
Organizational Studies, 9(4), 67–75. doi:10.1177/10717919030
0900406.
Roth, P. L., Huffcutt, A. I., & Bobko, P. (2003). Ethnic group
differences in measures of job performance: A new meta-
analysis. Journal of Applied Psychology, 88(4), 694–705. doi:
10.1037/0021-9010.88.4.694.
Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2005). Publication
bias as a threat to validity. Chichester: Wiley.
Ryder, N. (1965). The cohort as a concept in the study of social
change. American Sociological Review, 30(6), 843–861.
Sackett, P. R. (2002). Letter report. Washington, DC: The National
Academies, Division of Behavioural and Social Sciences and
Education.
Sayers, R. (2007). The right staff from X to Y: Generational change
and professional development in future academic libraries.
Library Management, 28(8/9), 474–487. doi:10.1108/014351207
10837765.
Schmidt, F. L., & Hunter, J. E. (1977). Development of a general
solution to the problem of validity generalization. Journal of
Applied Psychology, 62(5), 529–540. doi:10.1037/0021-9010.
62.5.529.
Schmidt, F. L., & Le, H. (2004). Software for the Hunter–Schmidt
meta-analysis methods. Iowa City: University of Iowa, Depart-
ment of Management & Organizations.
Shapira, I. (2009). Speaking to Generation Nexus: Guru explains
Gens X, Y, Boomer to one another. The Washington Post, July 9,
C1.
Smola, K. W., & Sutton, C. D. (2002). Generational differences:
Revisiting generational work values for the new millennium.
Journal of Organizational Behavior, 23(4), 363–382. doi:
10.1002/job.147.
Strauss, W., & Howe, N. (1991). Generations: The history of America’s
future, 1584 to 2069. New York: William Morrow & Co.
*Sujdak, E. J. (2003). An investigation of the correlation of job
satisfaction, organizational commitment, perceived job opportu-
nity, organizational communications, job search behavior, and
the intent to turnover in IT professionals. Dissertation Abstracts
International, Section A. Humanities and Social Sciences, 63(8),
2940.
Szamosi, K. T. (2006). Just what are tomorrow’s SME employees
looking for? Education?Training, 48(8–9), 654–665. doi:
10.1108/00400910610710074.
TIME. (1951). The younger generation. TIME, LVIII. Retrieved from
http://www.time.com/time/magazine/article/0,9171,856950-
1,00.html.
Tourangeau, R., & Yan, T. (2007). Sensitive questions in surveys.
Psychological Bulletin, 133, 859–883. doi:10.1037/0033-2909.
133.5.859.
Trzesniewski, K. H., & Donnellan, M. B. (2010a). Rethinking
‘Generation Me’’ [special section]. Perspectives on Psycholog-
ical Science, 5(1), 58–108.
Trzesniewski, K. H., & Donnellan, M. B. (2010b). Rethinking
‘Generation Me’’: A study of cohort effects from 1976–2006.
Perspectives on Psychological Science, 5(1), 58–75. doi:
10.1177/1745691609356789.
Trzesniewski, K., H., Donnellan, M. B., & Robins, R. W. (2008). Do
today’s young people really think they are so extraordinary? An
examination of secular trends in narcissism and self-enhance-
ment. Psychological Science, 19(2), 181–188. doi: 10.1111/
j.1467-9280.2008.02065.x.
Twenge, J. M. (2000). The age of anxiety? Birth cohort change in
anxiety and neuroticism, 1952–1993. Journal of Personality and
Social Psychology, 79(6), 1007–1021. doi:10.1037/0022-3514.
79.6.1007.
Twenge, J. M., Campbell, S. M., Hoffman, B. J., & Lance, C. E.
(2010). Generational differences in work values: Leisure and
extrinsic values increasing, social and intrinsic values decreas-
ing. Journal of Management, 36, 1117–1142. doi:10.1177/014
9206309352246.
Twenge, J. M., & Campbell, W. K. (2010). Birth cohort differences in
the monitoring the future dataset and elsewhere. Perspectives on
Psychological Science, 5(1), 81–88. doi:10.1177/17456916
09357015.
Twenge, J. M., Konrath, S., Foster, J. D., Campbell, W. K., &
Bushman, B. J. (2008). Egos inflating over time: A cross-
temporal meta-analysis of the Narcissistic Personality Inventory.
Journal of Personality, 76(4), 875–902. doi:10.1111/j.1467-
6494.2008.00507.x.
J Bus Psychol (2012) 27:375–394 393
123
Twenge, J. M., & Nolen-Hoeksema, S. (2002). Age, gender, race,
socioeconomic status, and birth cohort difference on the
children’s depression inventory: A meta-analysis. Journal of
Abnormal Psychology, 111(4), 578–588. doi:10.1037/0021-
843X.111.4.578.
Wanous, J. P., & Hudy, M. J. (2001). Single-item reliability: A
replication and extension. Organizational Research Methods, 4,
361–375. doi:10.1177/109442810144003.
Westerman, J. W., & Yamamura, J. H. (2007). Generational
preferences for work environment fit: Effects on employee
outcomes. Career Development International, 12(2), 150–161.
doi:10.1108/13620430710733631.
Westoff, C. F. (1954). Differential fertility in the United States: 1900
to 1952. American Sociological Review, 19(5), 549–561.
White, A. T., & Spector, P. E. (1987). An investigation of age-related
factors in the age-job satisfaction relationship. Psychology and
Aging, 2, 261–265. doi:10.1037/0882-7974.2.3.261.
*Wieck, K. L., Dols, J., & Northam, S. (2009). What nurses want: The
nurse incentives project. Nursing Economics, 27(3), 169–177.
Wong, M., Gardiner, E., Lang, W., & Coulon, L. (2008). Generational
differences in personality and motivation: Do they exist and
what are the implications for the workplace? Journal of
Managerial Psychology, 23(8), 878–890. doi:10.1108/
02683940810904376.
394 J Bus Psychol (2012) 27:375–394
123
... Los millenial son los nacidos entre 1981 y 1996 (Costanza et al., 2012); es el grupo que llegó a cambiar todo el paradigma de las generaciones anteriores ya que surgió en la era digital y, por ende, buscan la inmediatez. Les gusta disfrutar de su vida, haciendo cosas que les brindan la sensación de disfrute y placer, se ponen a sí mismos como prioridad y no organizan su vida en torno al trabajo, lo que los lleva a ser percibidos como personas que no se comprometen laboralmente (González, 2011). ...
Article
La industria hotelera está integrada por trabajadores centennials, millenials, generación X (gen X) y baby boomers. Cada una de ellas posee cualidades, valores, expectativas y formas de trabajo diferentes, lo que representa un reto para los responsables de las empresas. El objetivo principal de este trabajo es identificar las diferencias y similitudes generacionales del capital humano en función del incremento de la productividad. Se acude a metodología cualitativa para profundizar en un estudio de caso en torno a los aspectos de 1) autoridad, 2) promociones, 3) reconocimiento, 4) relaciones interpersonales, 5) estabilidad laboral y 6) disciplina, ello por medio de entrevistas individuales a miembros representantes de cada uno de los grupos referidos. Los resultados indicaron significativas similitudes en cuanto a los puntos 2 y 5, no así para 1 y 3.
... In particular, the distinction between organizational commitment and a more individualized career commitment has been advocated in response to contemporary career trends (van Rossenberg et al., 2018), with career patterns departing from being organization-focused to pathways with less job security and more interorganizational mobility (see Greenhaus & Kossek, 2016). So far, however, there is only limited evidence for declining organizational commitment across current employee cohorts (see the meta-analysis by Costanza et al., 2012). Further, comparing mean levels of affective organizational commitment and career commitment, the former does not receive lower employee ratings than the latter (e.g., Den Hartog & Belschak, 2007;Ok & Vandenberghe, 2016;Vandenberghe & Ok, 2013). ...
Article
Postpartum mothers have to decide whether to leave the workforce for some time and, if so, when to return to paid work. Two kinds of regrets might evolve as a result of women’s leave‐related decisions: stay‐at‐home and return‐to‐work regrets. The present research investigates how these forms of regrets are associated with women’s affective organizational commitment. We conducted a four‐wave longitudinal study with female participants mostly living in Switzerland (61 %) and Germany (37 %). The first measurement occasion took place during pregnancy (N = 294), and the subsequent three postnatal measurement points were at six (n = 281), twelve (n = 254), and 24 months (n = 230) after childbirth. As expected, higher organizational commitment during pregnancy predicted stronger stay‐at‐home regrets. By contrast, women’s prenatal organizational commitment did not turn out to be predictive for lowered return‐to‐work regrets. We further assumed cross‐lagged associations between post‐birth organizational commitment and return‐to‐work regrets. Our results suggest that return‐to‐work regrets are predictive of decreases in affective organizational commitment. Reversed causation, i.e., effects from postnatal organizational commitment to return‐to‐work regrets, could not be confirmed. Results are discussed regarding theoretical, methodological, and practical implications.
... Lyons and Kuron (2014) addressed the subject with a similar sentiment, stating that studies in the domain are primarily descriptive, methodologies are inconsistent and evidence paradoxical. A metaanalysis review could not render substantial evidence to support meaningful differences among generations on work-related attitudes (Costanza et al., 2012). Likewise, Becton et al. (2014) noted no major generational differences in workplace behaviour instead of the popular stereotypes. ...
Article
Purpose The present study aims to explore the intergenerational differences in the virtue of appreciation. The construct of appreciation takes a broadened perspective with six dimensions of “have” focus, awe, ritual, present moment, loss/adversity and interpersonal appreciation. Four different generations are studied – baby boomers (above 55 years), pre-millennial or Generation X (41–55 years), millennial or Generation Y (23–39 years) and post-millennial or Generation Z (below 23 years). Design/methodology/approach The collected data is analyzed in two stages. First, the adaptability of the scale is examined using exploratory factor analysis, confirmatory factor analysis, reliability and validity estimates. And in the second stage, the variations in appreciation scores are explored using ANOVA and post hoc analysis. Findings The results reveal statistically significant intergenerational differences among four subscales of appreciation, i.e. “have” focus, awe, present moment and loss/adversity. Only one subscale of appreciation, i.e. ritual, does not vary significantly across respondents of different generations. Also, younger generations have lesser scores on subscales of appreciation than older generations. Thus, it seems that older generations are generally more appreciative than younger generations. Originality/value The present study, to the best of the authors’ knowledge, could be the first research that examines the variation among generations for the experience of broader construct of appreciation within the Indian context.
... The analysis of literature reveals that a generation is made up of people of similar age who have experienced the same historical, cultural, social, economic, technological and other events (Šakytė-Statnickė, 2020; Mencl, Lester, 2014). Currently, active generations in the labour market are: Mature / Traditional / Silent / Veterans (retirement age but still working), Baby Boom Generation, Generation X, Generation Y, and Generation Z (Costanza, et al. 2012). The current paper addresses exclusively Generation Z. ...
Conference Paper
The influence of intellectualization on the labor market has been investigated in the article. It was identified that under the present-day conditions, intellectualization is a factor of both constructive and destructive structural changes in the economy. It was revealed that in relation to the labor market intellectualization leads to reduction or complete disappearance of demand for certain professions and to the reduction in the number of jobs and soaring unemployment rate. Despite this, the intellectualization of the economy determines the development of new positions that requires the formation of new professional competencies of employees. Self-regulatory mechanisms have already been launched in the labor market and accelerated structural changes in the economy (in particular, caused by the intensive development of the gig economy). The existing analytical scenarios of possible events have been analyzed and due to this, disparities in the labor market has been determined. It is vital to make changes in institutional support (contracts, social guarantees, etc.), improve tools of state labor market regulation, foster dialogue, and public-private partnership to overcome, as well as equalize structural disparities in the economy.
Article
Full-text available
El propósito de esta investigación fue conocer si el estilo de liderazgo tiene relación con cada generación y si las diferencias generacionales influyen en la preferencia o percepción sobre sus líderes; tomando como caso de estudio una institución financiera ubicada en la provincia de Imbabura en Ecuador, empleando como instrumento el Cuestionario de Liderazgo Multifactorial MLQ, que fue aplicado a 61 empleados. Los resultados mostraron que el liderazgo predominante es transformacional, la generación que prevalece es la Millennial; el tipo de liderazgo y las generaciones no están relacionados porque la preferencia se concentró en el liderazgo transformacional independientemente de la generación a la que pertenecía el personal, por último, el tipo de liderazgo predominante consigue en las personas resultados basados en efectividad, satisfacción y esfuerzo extra, sin relacionar la generación a la que pertenecen; es decir, los comportamientos del líder, obtienen respuesta de las dos generaciones presentes en las agencias. JEL Classification: J24, M12, M54
Article
Purpose The study aims to define the term “generation” by proposing an integrated design based on age-period-cohort effects and by proposing an Indian generational cohort framework categorizing Indian generational cohorts into four categories, namely, Baby Boomers, GenX, GenY, and GenZ. The study further aimed to capture the existing generational differences between GenY and GenZ cohorts in the Indian teams on team climate, transactive memory system, and team leader humility. Design/methodology/approach For the first two objectives a literature review methodology along with the author's proposition was adopted. An integrated design was proposed by reviewing the relevant sociological literature to define generations. Thereafter, an Indian cohort framework was proposed categorizing them into four groups Baby Boomers, GenX, GenY, and GenZ. Following that, for the last objective, i.e. to identify the differences between cohorts, empirical data were collected by a structured questionnaire that was disseminated to GenY and GenZ Indian working professionals. A total of 229 responses were used for observing the differences or similarities between GenY and GenZ cohorts on the study variables by employing an independent samples t -test. Findings The study proposed an integrated design (age, period, and cohort effect). Following that, an Indian generational cohort's framework has been outlined categorizing Indian cohorts based on their birth years, age groups, developmental stages, formative years, major Indian historical events, and various characteristics possessed by them. Moreover, the empirical findings support the existing generational disparities and depict that GenZ holds a higher inclination towards transactive memory systems and team climate whereas GenY holds more inclination toward leader humility. Practical implications The study put forth its contribution to research scholars by categorizing Indian generational cohorts in a rationalized manner based on an integrated design (age-period-cohort) effect. The study would further assist concerned authorities and managers in formulating HR policies to deal with the underlying generational differences highlighted by the study. Originality/value As there lies a paucity of generational frameworks in the Indian context, this study is the first attempt in this direction which categorizes Indian generational cohorts based on a unique integrated design including age-period-cohort effects. In addition, the study also investigated these cohorts in Indian organizations to observe the existing variations.
Article
Full-text available
Objective: Many research claim that Millennials value work ethic much lower and leisure time much higher than older generations. Most of them are based on cross-sectional analyses of data collected at one time. This design confounds the COHORT effect (born in the same time period and thus exposed to the same cultural forces during their formative socialization period) and biological AGE, and it makes it impossible to separate them. Our goal is to demonstrate how to empirically separate the confounded effects of APC (biological AGE – PERIOD of measurement – COHORT) in a simple way. Methodology: Three generations (Baby Boomers, X-ers and Millennials) from the representative Polish samples of the World Value Survey, were cross-sectionally compared, and a cross-lagged comparison was made between BB in 2005 vs X in 2020, and between X in 2005 and Millennials in 2020. Findings: It was shown that significant cross-sectional differences in attitudes toward work between the 3 generation (with the highest score for Baby Boom-ers and the lowest for Millennials) cannot be explained by age differences. Over the period of 15 years, the importance of leisure time has increased for all generations (PERIOD effect). Value Added: The paper highlights significant methodological problem: the confounding effect of APC in most generational findings. It promotes the idea of using nationally representative samples from publicly available data like World Value Survey, instead of collecting convenience samples. Recommendations: Greater methodological rigour in generational studies is recommended, as their results can create/support stereotypes that tend to generate individual expectations (e.g. every Millennial is computer literate or lazy), ignoring the fact that intra-generational variability is very high.
Book
Full-text available
Student's satisfaction – measurement, models, implications Some changes in the environment that contemporary societies struggle with determine the level of quality of their life, make forecasting the future more complicated, and generate unprecedented challenges. The sources of the greatest threats and challenges for contemporary universities are related to international competition in the higher education sector, student mobility and the growing popularity of non-formal and informal learning paths. These and many other conditions for the functioning of modern universities imply the need for a holistic approach to students, and focus not only on educational aspects, but also are related to their personal and social development, and provide them with a sense of care and support from the university during their studies. It should result in students' satisfaction with their studies and a sense of conviction about making the right decision related to education at the higher level as the best path of education. The main purpose of the monograph is to identify determinants and consequences of students’ satisfaction, measure its level and develop models. The theoretical part of the work presents, inter alia, the current situation of Polish higher education, controversy related to the contemporary face of Polish universities, demographic and technological determinants of forecasts for universities, the most relevant aspects related to the quality of education, with particular emphasis on modern teaching methods, as well as the features and expectations of generation Z. The attention is also paid to contemporary concepts connected with managing university relations with students as the key group of their stakeholders. Due to the significant role of students’ satisfaction as the subject of this monograph, various ways of interpreting this concept, its determinants and consequences, methods of measurement and models, as well as benefits for the university resulting from the satisfaction of this group of its stakeholders are presented. The empirical part of the work features the assumptions, results and conclusions of the author's qualitative and quantitative research. The first, exploratory one, was carried out using the in-depth individual interview (IDI) and focus group interview (FGI) methods. The purpose of the qualitative studies was to identify the factors and consequences of students’ satisfaction and dissatisfaction. The objective of quantitative research was primarily to determine the level of students’ satisfaction with their studies, determinants and successors of this phenomenon, as well as to create models of students’ satisfaction. An attempt was also made to define the causes and consequences of dissatisfaction and post-purchase dissonance among students related to their studies. The research was carried out on a sample of 1,600 students from four universities, by means of the method of an auditorium survey. In the research such specialized methods of measuring satisfaction and loyalty of respondents as CSI, NPS, and IPA analysis were applied. The author's research has confirmed many of the research results presented in the theoretical part of the monograph, especially those relating to the factors of students’ satisfaction and its consequences. The most important differences between the research results presented in the literature and those obtained by the author concern the timetable, which turned out to be the most important factor for the author's respondents, and did not appear in any of the studies presented in the theoretical part. The most significant satisfaction factors in the author's research was also the majors offer (not present in the research of other authors). The main conclusions resulting from the secondary and primary research carried out by the author focus on the need to meet the expectations of students with regard to the quality of education that will enable them to undertake satisfactory work, as well as in the scope of such organization of studies, with particular emphasis on the timetable, so that they can take up a job while studying at the same time. As the author's research shows, the precise expectations of students denote maturity of most of them, expressed in responsible planning of their education, with a view to maximize the resulting benefits in their professional and personal life. The striving to meet their expectations, necessary in the context of students’ satisfaction, is also associated with the need to develop study programs, curricula and majors very important for students, based on monitoring of current changes in the labor market and related to professional forecasts and scenarios. It is also extremely important to use appropriate forms of educating students, based on the use of activating methods (e.g. PBL, RBL), while reducing the number of hours spent on the so-called an indicative form of education, which is a classic lecture (the least effective in terms of the results of education). The effectiveness of activities aimed at meeting the expectations of students and achieving their satisfaction and loyalty is directly related to the multi-area skills and competences of academic teachers, as well as their friendly and positive attitude towards students, which is repeatedly emphasized at work, as well as their commitment which is inspiring and motivating.
Article
Based on broaden‐and‐build theory of positive emotions, this study posits that strategic human resource management (SHRM) systems enrich positive emotions associated with psychological empowerment and resilience, and in turn, broaden employees’ momentary thought‐action repertoires. People with such positive psychological emotions build enduring personal resources to buffer the negative impacts of adversity and produce better performance outcomes. An integrative model is developed and examined through a two‐stage structural equation modeling method of meta‐analysis based on data involving 60,637 individuals in 87 independent datasets from 79 primary studies published up to September 2020. Empirical evidence suggests that psychological empowerment and resilience both independently and collectively mediate the relationship between SHRM systems and performance outcomes. This study makes an innovative attempt to incorporate theories of positive psychology into SHRM research, throwing fresh light on the promoting factors (i.e., SHRM) and psychological mechanisms (i.e., psychological empowerment) antecedent to employee resilience leading to desired performance outcomes. This study has practical implications for managers in relation to the importance of cultivating a psychologically empowered and resilient workforce, especially in difficult times such as those during the recent COVID‐19 pandemic.
Book
Full-text available
Objetivo. En la presente investigación se describen y comparan las expectativas laborales de los centennials estudiantes universitarios de negocios en Latinoamérica; además de verificar si existe un perfil hegemónico o no. Método. La data se basa en una muestra no probabilística de 5,218 estudiantes de 18 a 23 años provenientes de 22 instituciones de educación superior ubicadas en 6 países (Argentina, Colombia, Ecuador, México, Paraguay, Perú). Se ha diseñado un instrumento fiable y válido que mide 22 reactivos agrupados en tres dimensiones bipolares de las expectativas laborales: yo ideal, jefe ideal, y empresa ideal. Resultados. Considerando las expectativas en conjunto de los centennials, destacan la ética profesional (71%), la capacidad para aprender (68.7%), la capacidad para relacionarse bien con las personas (59.6%), la buena comunicación con el jefe (57.9%) y una empresa preocupada por la calidad de vida de su personal (57.1%). Se han encontrado diferencias significativas según sexo y país de procedencia. Se verifica también que el perfil de los centennials no es hegemónico. El primer perfil, orientado a las relaciones y a la justicia agrupa al 55.9%, mientras que el segundo perfil, orientado a la técnica y al éxito, agrupa al 44.1% de la muestra. Los perfiles difieren significativamente según edad, sexo y país. Palabras clave. Generación Z, centennials, estudiantes universitarios, expectativas laborales, Latinoamérica, perfiles, clúster.
Article
Full-text available
This cross-sectional study reports the work-related differences and similarities of 241 Generation X and Baby Boomer employees in the public sector. A more homogeneous pattern of what employees want across age cohorts emerges, contrary to the literature and stereotypes on generational differences. Surprising levels of similarity were found between GenXers and Boomers, with the three significant areas of difference focused on issues of personal growth. The implications for recruiting, retention, motivation, training, and human resource processes are discussed.
Article
The study presented in this paper examines generational (age-cohort) differences in the work commitments of Baby Boomer (born between 1946 and 1962) and Gen-X (born between 1963 and 1981) information technology (IT) professionals. Data were obtained from 382 IT workers in 23 state agencies and universities. The work commitments examined include work involvement job involvement, work group attachment, organizational commitment and professional commitment. Contrary to profiles of these two generations common in the popular and business press, results suggest that the work commitments of these generations of IT professionals are more homogeneous than different. Implications for research and for IT management are offered.
Article
Publication bias is the tendency to decide to publish a study based on the results of the study, rather than on the basis of its theoretical or methodological quality. It can arise from selective publication of favorable results, or of statistically significant results. This threatens the validity of conclusions drawn from reviews of published scientific research. Meta-analysis is now used in numerous scientific disciplines, summarizing quantitative evidence from multiple studies. If the literature being synthesised has been affected by publication bias, this in turn biases the meta-analytic results, potentially producing overstated conclusions. Publication Bias in Meta-Analysis examines the different types of publication bias, and presents the methods for estimating and reducing publication bias, or eliminating it altogether. Written by leading experts, adopting a practical and multidisciplinary approach. Provides comprehensive coverage of the topic including: • Different types of publication bias, • Mechanisms that may induce them, • Empirical evidence for their existence, • Statistical methods to address them, • Ways in which they can be avoided. • Features worked examples and common data sets throughout. • Explains and compares all available software used for analysing and reducing publication bias. • Accompanied by a website featuring software, data sets and further material. Publication Bias in Meta-Analysis adopts an inter-disciplinary approach and will make an excellent reference volume for any researchers and graduate students who conduct systematic reviews or meta-analyses. University and medical libraries, as well as pharmaceutical companies and government regulatory agencies, will also find this invaluable.