Content uploaded by Wendy Marcinkus Murphy
Author content
All content in this area was uploaded by Wendy Marcinkus Murphy on Jul 13, 2022
Content may be subject to copyright.
Reverse mentoring, job crafting
and work-outcomes: the mediating
role of work engagement
Neha Garg
OB-HRM, Jindal Global Business School, OP Jindal Global University, Sonipat, India
Wendy Murphy
Management, Babson College, Wellesley, Massachusetts, USA, and
Pankaj Singh
OB and HRM, Indian Institute of Management Raipur, Raipur, India
Abstract
Purpose –Reverse mentoring and job crafting are innovative, employee-driven job resources that can lead to
positive organizational outcomes. The purpose of this paper is to explore the role of work engagement in
mediating the association of these resources with work performance and work withdrawal behavior.
Design/methodology/approach –Hypotheses were tested using structural equation modeling on data
obtained from 369 software developers in India.
Findings –Findings demonstrate that reverse mentoring and job crafting are positively related to
work engagement, which, in turn, increase performance and decreases work withdrawal behaviors. Work
engagement partially mediates the association of job crafting with both outcomes. In contrast, work
engagement fully mediates the relationship between reverse mentoring and withdrawal behavior and partially
mediates the relationship between reverse mentoring and work performance.
Research limitations/implications –This study is a cross-sectional, survey design in the understudied
technical industry in India, which may limit generalizability. However, the authors also connect the previously
unrelated literatures on reverse mentoring and work engagement and develop a scale for use in future reverse
mentoring studies.
Practical implications –This study provides evidence to support practitioners in implementing resources
for reverse mentoring and job crafting to increase work engagement among employees and subsequent
positive outcomes.
Originality/value –Organizations can support reverse mentoring and job crafting as cost effective employee
development tools. The research focuses on the mentors, who tend to be the less experienced and younger
counterparts in a reverse mentoring pair and a critical part of the workforce for the growing IT industry.
Keywords Job crafting, Reverse mentoring, Work engagement, Performance, India
Paper type Research paper
Introduction
Engagement at work has been consistently shown to improve important employee outcomes,
including performance, commitment, health and retention (Halbesleben, 2010). Work
engagement is conceptualized as a positive, fulfilling, work-related state of mind
characterized by vigor, dedication and absorption (Schaufeli et al., 2002). In a growing
economy and tightening labor market worldwide (Schwab, 2017), it is advantageous for
organizations to focus on creating an engaging environment while managing costs. This
study investigates reverse mentoring and job crafting as two job resources, which may foster
work engagement and subsequently improve performance and reduce withdrawal behaviors.
Reverse mentoring, a bottom-up approach to mentoring, is an innovative tool (Murphy,
2012) to improve employees’engagement in the organization. In a reverse mentoring
relationship, the junior person serves as mentor and the senior person as mentee, which
is particularly helpful for engaging younger workers who express a desire for early
recognition (Glass, 2007) as well as more exposure to senior colleagues and career support
Reverse
mentoring and
job crafting at
work
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1362-0436.htm
Received 14 September 2020
Revised 21 December 2020
12 February 2021
Accepted 8 March 2021
Career Development International
© Emerald Publishing Limited
1362-0436
DOI 10.1108/CDI-09-2020-0233
(Chaudhuri and Ghosh, 2012;Murphy, 2012). Previous work has theorized that reverse
mentoring is associated with work engagement (Chaudhuri and Ghosh, 2012); however, no
studies to our knowledge have developed a scale or tested this association.
Through self-initiated optimization of job demands and resources, job crafting helps
employees to align their work environment with their needs and abilities (Tims and Bakker,
2010) and to achieve work autonomy and work meaningfulness fostering engagement.
Studies have reported that job crafting predicts engagement (e.g. Petrou et al., 2012;Tims
et al., 2013;Tims et al., 2015), though they were conducted in a European context, limiting
generalizability.
This study extends the extant literature in multiple ways. First, building on prior studies
by Hu et al. (2013) and Birtch et al. (2016), we integrate social exchange theory (SET) with the
job demands-resources model (JD-R) to explain the mechanism underlying an individual’s
response to job resources. Second, we develop a scale to measure reverse mentoring. Finally,
we examine the role of work engagement in mediating the relationships between reverse
mentoring and job crafting with work outcomes (work performance and work withdrawal
behaviors).
Theoretical framework
The JD-R model suggests that employees evaluate job stressors (i.e. role ambiguity and
insecurity) as potentially challenging and/or threatening. Challenge stressors (time pressure
and job crafting) are demands that are appraised as having the potential to promote work
engagement and personal development (Cavanaugh et al., 2000). While hindrance demands
(role ambiguity and job insecurity) lead to burnout (Podsakoff et al., 2007) because these
factors erode energy and exhaust mental and physical resources through sustained effort
over time (Bakker and Demerouti, 2007). Thus, the JD-R model explains psychological
conditions or antecedents for work engagement, yet it does not address the mechanism
underlying individuals’response to these factors with different engagement levels (Saks,
2006). However, SET helps explain this mechanism. A social exchange view of employment
posits that when employees receive job resources (e.g. support/autonomy) they feel obliged to
return the same to the organization in the form of increased engagement levels. Consequently,
we combine the JD-R model to explain the structural framework of relationships between job
resources, engagement and organizational outcomes with SET to explain the mechanism
behind individual’s response to the job resources in the terms of increased engagement levels.
Work engagement
In relation to the JD-R model, Schaufeli et al. (2002) defined engagement as an active and
absolute work associated mind state represented by vigor, dedication and absorption. Vigor
is marked by high levels of energy and resilience at work depicting readiness to devote efforts
in achieving work goals and endurance to face work demands; dedication refers to attaching a
strong sense of pride, enthusiasm and significance toward one’s work and absorption implies
a happy and positive state of complete engrossment in the work, wherein one loses complete
track of time and finds it difficult to detach from work. Several studies have validated this
model (e.g. Schaufeli and Bakker, 2003;Xanthopoulou et al., 2007).
The JD-R literature (Bakker and Demerouti, 2007;Van De Voorde et al., 2016) establishes
job resources as an important antecedent of work engagement and provides evidence that a
motivational process is triggered by job resources which leads to work engagement. Job
resources refer to “those physical, psychological, social, or organizational aspects of the job
that may (1) be functional in achieving work goals, (2) reduce job demands and the associated
physiological and psychological costs, and (3) stimulate personal growth, learning, and
CDI
development”(Demerouti et al., 2001, p. 501). Therefore, any resource offered by the
organization which assists employees in goal performance, learning and development and/or
coping with job demands will be deemed a job resource.
Recent research highlights that employee-driven approaches can act as sustainable
sources of work engagement leading to significant organizational outcomes (Dubbelt et al.,
2019;Rivera and Flinck, 2011). Employee-driven job resources may be located at the task
level, job/role level or the interpersonal or relational level to have motivating potential
(Demerouti and Bakker, 2011). We argue that when an organization facilitates reverse
mentoring (relational level) and job crafting (task and/or role level), employees also perceive
these job resources as strong organizational supports and feel indebted toward the
organization. According to SET, employees are guided by exchange and reciprocity norms
and hence return the favor to the organization in the form of increased engagement levels
(Saks, 2006).
Antecedents of work engagement
Reverse mentoring
Reverse mentoring refers to an approach wherein a younger, junior employee mentors an
older or hierarchically senior executive to share technological expertise, subject matter
advances and/or provide a different perspective (Murphy, 2012). The academic literature on
reverse mentoring is mostly conceptual and at a latent stage (Ka
se et al., 2019). Prior research
built a theoretical foundation by identifying the key characteristics, antecedents and
outcomes of reverse mentoring (Chaudhuri and Ghosh, 2012;Murphy, 2012) as well as
assessing its functions in contrast with traditional mentoring (Chen, 2013;Murphy, 2012).
The key aspects of reverse mentoring that differentiate it from traditional mentoring
relationships are: (1) structural role reversal wherein junior acts as mentor and senior acts a
mentee; (2) two-way knowledge sharing; (3) emphasis on mentor’s leadership and competency
development and (4) commitment toward mutual learning and support (Murphy, 2012). This
means that the support functions are slightly different, with knowledge exchange and skill
development replacing sponsorship and protection, which are only possible due to one’s role
in the organizational hierarchy.
Reverse mentoring as a practice has developed from the single focus of technological
knowledge sharing to a tool for (1) harnessing intergenerational competencies (at Microsoft
India), (2) breaking hierarchical work structures (at PwC India), (3) building leadership
pipeline and revamping performance management processes through flexibility benefits (at
Lenskart) and (4) re-orientation of recruitment and HR strategies (at Marico, Airtel and
Accenture) as reported by Mannal (2017). Procter and Gamble has successfully implemented
reverse mentoring to decrease attrition among female managers and for gender sensitization
(Zielinski, 2000); General Motors has also used it to increase productivity and enhance online
collaboration (Harvey et al., 2009). This study begins to elucidate the increasing prevalence of
reverse mentoring in industry, while broadening the scope of the academic literature.
In congruence with the JD-R model, we argue that reverse mentoring, as an interpersonal
job resource, positively affects work engagement. Given the benefits of reverse mentoring, its
availability is perceived as an organizational support by participants, which in turn makes
them feel valued. Guided by the reciprocity norms of SET, participants will thus feel obliged
to reciprocate and return the favor in the form of increased engagement levels (Saks, 2006). As
such, Chaudhuri and Ghosh (2012) proposed reverse mentoring is a tool for engaging
boomers and increasing commitment of Millennials. Similarly, practitioners offer support for
this association. “The biggest outcome of reverse mentoring is that we have a very engaged
workforce across levels”–says Anurita Chopra, area marketing lead, GSK Consumer
Healthcare (Basu and Verma, 2017).
Reverse
mentoring and
job crafting at
work
Job crafting
With reference to the JD-R framework, job crafting is described as an organizational
intervention (Van Wingerden et al., 2017), wherein an employee gets to modify the available
demands and/or resources of their job (Tims and Bakker, 2010) to increase the fit between
work and self. This fit can be achieved through four kinds of behaviors: (1) increasing
structural job resources (such as autonomy); (2) increasing social resources (such as social
support); (3) increasing challenging job demands and/or (4) decreasing hindering job
demands. Since the fourth behavior (decreasing hindering job demands) has been found to be
unrelated to work engagement (Tims et al., 2012;Tims et al., 2013), this study focuses on the
first three behaviors.
Job crafting is posited to be an effective bottom-up strategy to boost work engagement, as
it helps employees improve their person-organization fit. Fit is enhanced by creating
interventions in the form of modified job resources and job demands which enable employees
to create a resourceful and individually optimized work environment, thereby, leading to
enhanced work engagement (Tims et al., 2015). Moreover, when employees receive such
autonomy to craft their jobs, their performance (Bakker et al., 2012;Tims et al., 2012) and well-
being (Nielsen and Abildgaard, 2012) is also enhanced. Job crafting thus meets the criteria of a
job resource enabling employees to proactively modify their work environment to efficiently
achieve their work goals and stimulate their personal growth and development.
Since, the organization creates the work context, wherein employees are given the
autonomy to facilitate bottom up approaches like job crafting (Bakker, 2017); based on social
exchange theory, we argue that employees will perceive job crafting opportunities as valued
organizational support, to which they will reciprocate with increased levels of engagement.
Past studies also reveal that the magnitude of job crafting behavior carried on by an employee
is a predictor of his/her work engagement level (Bakker et al., 2012;Petrou et al., 2012;Tims
et al., 2015). Thus, we propose:
H1. (a) Reverse mentoring and (b) job crafting will be positively associated with work
engagement.
Consequences of work engagement
Work performance
Performance is an essential outcome of job-related behavior. Engaged employees deliver better
performance compared to non-engaged employees (Bakker, 2009) because they (1) experience
more positive emotions (Schaufeli and Van Rhenen, 2006), (2) have better health leading to full
utilization of available resources, (3) create their own job and personal resources leading to
efficient handling of job demands and achievement of work goals (Bakker et al., 2007) and (4)
indulge in engagement transfer to others in their work environment (Bakker and
Xanthopoulou, 2009). Several studies confirm the positive relationship between engagement
and job performance (Yalabik et al.,2013;M€
akikangas et al., 2016).
Work withdrawal behaviors
Work withdrawal behaviors are unfavorable behaviors displayed by unsatisfied employees,
wherein they tend to avoid work or minimize time devoted to work without jeopardizing
organizational membership or their work-role. It is an attempt to psychologically and
physically disengage from work by neglecting tasks, leaving work early or taking long
breaks for example (Lehman and Simpson, 1992). Withdrawal behaviors not only lead to poor
performance and disruptive organizational functioning (Hanisch and Hulin, 1990) but are also
considered worse than actual turnover (Kammeyer-Mueller and Wanberg, 2003). Scholars
argue that keeping withdrawal behaviors in check is important because mild behaviors
CDI
(e.g. lateness) can intensify into serious organizational issues (e.g. turnover) (Koslowsky et al.,
1997) and result in high costs both to the organization and to the individuals enacting them.
Engaged employees do not avoid or neglect work, rather they have a positive and proactive
attitude toward work (Zeijen et al., 2018) and exhibit more innovative behavior (Duff, 2017).
Thus, we hypothesize that:
H2. Work engagement will be (a) positively related to work performance and (b)
negatively related to work withdrawal behaviors.
Work engagement as a mediator
Past studies have found substantial evidence for the role of work engagement in mediating the
association between antecedents and work consequences (Bailey et al., 2017;Saks, 2006). Based
on the JD-R model, we similarly expect that job resources, in this study reverse mentoring and
job crafting, as antecedents predict work engagement which leads to work outcomes (work
performance and work withdrawal behaviors) (e.g. Agarwal and Gupta, 2018;Bakker et al.,
2012;Tims et al.,2012). Social exchange theory further explains the underlying mechanism
involved in these relationships. When employees receive positive benefits in the form of job
resource opportunities from the organization, they develop a feeling of obligation toward the
organization which they repay in the form of increased work engagement and enhancedwork
outcomes like performance (Cropanzano and Mitchell, 2005;Saks, 2006).
Job resources are significantly associated with work goals (Bakker and Albrecht, 2018).
Past research demonstrates the positive association between job crafting and self-reported
performance through work engagement as a mediator (Tims et al.,2013,2015). Theoretically,
when employees are given the opportunity to craft their job and improve person-job fit, it
enables them to achieve efficient and enhanced performance and to make their work more
interesting and rewarding (Tims et al., 2015), which tends to simultaneously reduce their work
withdrawal symptoms. In parallel, the positive impact of traditional mentoring on employee
performance has been demonstrated across many studies (Allen et al., 2009;Srivastava and
Thakur, 2013). Similarly, Walumbwa and Lawler (2003) found that transformational leaders
used mentoring to reduce work withdrawal behaviors among their employees. Thus, we
expect that reverse mentoring will improve performance and reduce work withdrawal
behaviors through work engagement. Hence, we propose the following hypothesis:
H3. Work engagement will mediate the association between (a) reverse mentoring and
work performance, (b) reverse mentoring and work withdrawal behaviors, (c) job
crafting and work performance and (d) job crafting and work withdrawal behaviors.
The proposed conceptual model is illustrated in Figure 1.
We investigate these relationships in the fast-growing Indian IT sector, a context that has
received limited attention in the academic literature on work engagement thus far (Gupta,
2018). The IT industry faces disengagement issues due to work uncertainty and volatility
embedded in the nature of the job (Sekhar et al., 2018). This not only prevents employees from
actively engaging in work but also hampers their productivity and gives rise to withdrawal
symptoms (Manjunath and Chandni, 2018). Therefore, finding HR strategies that facilitate
work engagement has practical significance and potential application across a variety of
industries and contexts.
Method
Participants and procedure
The survey was administered online. Participation was voluntary, and participants were
informed that their responses would remain confidential. We worked with 14 software firms
Reverse
mentoring and
job crafting at
work
to identify junior employees with a minimum of one-year work experience as software
developers, who in some way mentored their senior colleagues, in other words, were in a
reverse mentoring relationship. The team leaders in the selected software firms were
contacted and requested to identify those employees who were involved in frequent (formal/
informal) bottom-up knowledge transfer (not limited to just a technological issue) to the
seniors. These employees were then requested to respond to the survey. Of the 480 possible
respondents, 369 completed the survey which included 73.7% men and 26.3% women,
consistent with industry composition. The majority of respondents were 25–34 years-old,
single/unmarried, with 2–5 years’work experience (see Table 1). Since this is one of the few
studies to empirically test the association of reverse mentoring with work engagement and
other outcome variables hence to keep the model parsimonious, this study did not treat the
demographics as control variables.
Reverse
Mentoring
+
Work
Performa nce
Job
Crafting
Structural
Job Resources
Challenging
Job Demands
Work
Engagement
+
+
Absorption
Dedication
Vigo r
++
+
-
Work
Withd rawal
Behavi or
Psychological
Withd rawa l
Behavior
Physical
Withd rawal
Behavior
+
+
+
+
Social
Job Resources
+
+
-
-
+
Note(s): - - - Antecedents’ association with work outcomes
Variables Mean Std. Deviation Classification Frequency Percentage
Gender
a
1.26 0.44 Male 272 73.7
Female 97 26.3
Others 00 00.0
Marital Status
b
1.19 0.40 Single 298 80.8
Married 71 19.2
Divorced 00 00.0
Age (in years)
c
1.87 0.78 18–24 115 31.2
25–34 210 56.9
35–44 23 6.2
45–54 19 5.1
55þ2 0.5
Experience (in years) 2.07 0.94 1–2 99 26.8
2–5 195 52.8
5–8 24 6.5
>8 51 13.8
Note(s):
a
Scale 51–2(15Male, 2 5Female, 3 5Others)
b
Scale 51–2(15Single, 2 5Married, 3 5Divorced)
c
Scale 51–4(1518–24, 2 525–34, 3 535–44, 4 545–54)
Figure 1.
Proposed
conceptual model
Table 1.
Demographic profile
CDI
Measures
Reverse mentoring was assessed using an 11-item scale developed for this study. Based on the
extant literature, we created items to capture the characteristics and functions of reverse
mentoring (Chen, 2013;Murphy, 2012). Following a deductive approach, extensive literature
review was done to establish a clear link between the scale items and the theoretical domain of
the construct. Items were generated from the reverse mentoring literature (Chen, 2013;
Murphy, 2012), including a few items adapted from practice (Basu and Verma, 2017;Mannal,
2017), from a traditional mentoring scale (Castro et al., 2005) and from an existing untested
scale on reverse mentoring (Chen, 2014)
Content validity was assessed by having items carefully reviewed by external established
mentoring scholars to check ambiguity and interpretation of each statement. The items were
validated as representative of the reverse mentoring construct. Following best practices
(Hinkin, 1995;Wright et al., 2017), adequate precautions were taken to (1) avoiding usage of
reverse-coded items; (2) generating items such that parsimony is maintained without
compromising reliability and construct validity; (3) scaling of items to allow sufficient
variance among responses and (4) sample population and sample size.
The scale was pre-tested on a sample of 135 post-graduate management students who had a
minimum of one year work experience in the Indian IT sector and were involved in reverse
mentoring exchanges. Exploratory analysis indicated a single factor explained 55.19% variance,
with Cronbach’s alpha of 0.91. The study sample confirmed that all the items loaded on a single
factor with Cronbach’salphaof0.92(Nunnally, 1978). Confirmatory factor analysis demonstrated
convergent and discriminant validity of the construct (see Table 2) . Standardized factor loadings
Variable
No. of items
Cronbach’s
α
GFI CFI NFI RMSEA
Factor
loading
range CROriginal Retained
1. Reverse
mentoring
11 11 0.92 0.94 0.96 0.94 0.07 0.51 to 0.82 0.92
2. Job crafting
a. Challenging
job demands
5 5 0.79 0.94 0.94 0.93 0.09 0.44 to 0.83
b. Structural job
resources
5 5 0.86 0.86
3. Work engagement
a. Vigor 3 3 0.75 0.96 0.97 0.96 0.08 0.41 to 0.89
b. Dedication 3 3 0.87 0.89
c. Absorption 3 3 0.76
4. Work
performance
7 7 0.84 0.97 0.97 0.96 0.08 0.40 to 0.83 0.84
5. Work withdrawal
behavior
a. Psychological
withdrawal
behavior
8 7 0.81 0.95 0.95 0.92 0.07 0.42 to 0.73
b. Physical
withdrawal
behavior
4 4 0.77 0.87
Note(s): GFI 5goodness-of-fit index; CFI 5comparative fit index; NFI 5normed fit index; RMSEA 5root
mean square error of approximation; CR 5composite reliability
Table 2.
Scale reliability and
validity of the
constructs
Reverse
mentoring and
job crafting at
work
(Appendix) were all greater than 0.50, fit indices (GFI, NFI and CFI) were all >0.09 and RMSEA
was <0.08. The average variance extracted (AVE) was 0.51, indicating satisfactory convergent
validity of the scale (Hair et al., 2013). The square root of AVE was greater than the observed
correlations of the variable (reverse mentoring) with other variables in the model, thereby
ensuring the discriminant validity of the scale (Fornell and Larcker, 1981).
The reverse mentoring scale (Appendix) starts with the following stem: “Think of a
situation when you have trained/taught your senior (mentee) about a new technology or
transferred knowledge (KT). As a mentor, answer the following”. Sample items on the scale
include “I feel empowered updating my mentee (senior) on the latest technologies and
emerging trends”and “We both treat each other as friends”. Responses were recorded on a
five-point Likert scale ranging from strongly disagree (1) to strongly agree (5).
Job crafting was assessed using the three-dimensional job crafting scale developed by
Tims et al. (2012). Sample items for (1) increasing social job resources, include “I ask my
supervisor to coach me”and “I ask others for feedback on my job performance”; (2) increasing
challenging job demands, include “When an interesting project comes along, I offer myself
proactively as project coworker”and “I regularly take on extra tasks even though I do not
receive extra salary for them”and (3) increasing structural job resources, include “I try to
develop my capabilities”and “I try to learn new things at work”. Responses were recorded on
a five-point Likert scale ranging from never (1) to very often (5).
Work engagement was measured with the three-dimensional, nine-item Utrecht Work
Engagement Scale (UWES) by Schaufeli et al. (2006). Sample items for (1) vigor, include “At
my work, I feel bursting with energy”and “At my job, I feel strong and vigorous”; (2)
dedication, include “I am enthusiastic about my job”and “My job inspires me”and (3)
absorption, include “I am immersed in my work”and “I get carried away when I am working”.
The response was scored on a seven-point Likert scale from never (0) to every day (7).
Work performance was assessed using the seven-item scale constructed by Abramis (1985)
to determine the performance of employees during the last six months. Sample items include
“How well you were making decisions”,and“How well you were meeting deadlines”.Items
were scored on a five-point Likert scale ranging from very poor (1) to very well (5). As reported
by Kock (2017), self-reported assessment of performance not only reflects good validity and
reliability but also found to be a better measure over objective measures such as ratings given
by supervisors. Furthermore, prior studies (e.g.: Singh et al., 2012;Suar and Khuntia, 2010)
provide wide support for Abramis’(1985) self-reported measure of performance and validate it
amongst the studied sample of Indian software developers (see Singh et al.,2012).
Work withdrawal behaviors were assessed using the 12-item scale developed by Lehman
and Simpson (1992). It includes two dimensions of psychological withdrawal behaviors and
physical withdrawal behaviors during the last six months. Sample items on (1) psychological
withdrawal behaviors, include “How often have you thought of being absent”and “How often
have you left your work station for unnecessary reasons”and (2) physical withdrawal
behaviors, include “How often have you taken a longer lunch or rest break than allowed”and
“How often have you fallen asleep at work”. Items were recorded on a five-point Likert scale
ranging from almost never (1) to very often (5).
Considering the recommendations of Podsakoff et al. (2003), both procedural and
statistical remedies were followed to control common method bias. Accordingly, Harman’s
single-factor test (Harman, 1976) revealed a variance of 23.67%, thereby implying that the
data did not suffer from common method bias.
Data analysis
A multilevel confirmatory factor analysis was conducted on Amos 22.0 (Arbuckle, 2016)to
assess the fit of our measurement model (Byrne, 2001;Fu et al., 2020). The analysis indicate
CDI
that the five-factor model (proposed) fits reasonably well to the data with the RMSEA, relative
chi-square and the CFI satisfying their criteria of <0.07 (MacCallum et al., 1996), <3 (Kline,
1998) and >0.90 (Awang, 2015), respectively, and NFI and GFI were >0.80 (Forza and
Filippini, 1998). In addition, it performed better than the default model in which all the items
and nine-factor structure (i.e. reverse mentoring, challenging job demands, structural job
resources, vigor, dedication, absorption, work performance, psychological withdrawal and
physical withdrawal behavior) were analyzed in their entirety as well as the one-factor model
(see Table 2).
In addition, following the work of Biswar and Suar (2016) and Singh et al. (2012),construct
reliability and convergent validity were further assessed by conducting CFA for each variable as
reported in Table 2. GFI, CFI and NFI scores were all greater than 0.90, indicating that all items to
each construct had a good fit; RMSEA was equal to or less than 0.08 except for job crafting
whose RMSEA was 0.09 (MacCallum et al., 1996). The Cronbach’s alpha for all constructs was
greater than 0.70 (Nunnally, 1978), indicating that the items used tomeasure each construct had
good internal consistency. Items with factor loadings <0.40 were dropped as recommended by
Stevens (1992). None of the items in the increasing social job resources dimension of job crafting
had loading range >0.40; hence, this dimension was not included in the results reported.
Convergent validity was further validated by analyzing the composite reliability (CR) of all
factors. As composite reliability of all constructs ranged from 0.84 to 0.92, which surpassed the
acceptable level of 0.60 (Fornell and Larcker, 1981), convergent validity was ensured in this
study. Evidence for discriminant validity is provided when measures of constructs that
theoretically should not be highly related to each other are, in fact, unrelated. Discriminant
validity in this study was established by ensuring that there are no correlations above 0.85
between the constructs (Kline and Schermelleh-Engel, 2010;Weston and Gore, 2006). A weak
correlation between the two constructs provides evidence of discriminant difference between
them (Swank and Mullen, 2017), thereby, implying them to be conceptually different constructs.
Results
Table 4 shows the correlation matrix for all variables in the study, which were all in the
hypothesized direction.
We used latent variable structural equation modeling (LVSEM), on Amos 22.0 (Arbuckle,
2016), as shown in Figure 2. LVSEM reveals antecedent-consequence associations among the
studied variables to investigate the unreserved proposition in correlations which are
bidirectional (MacKenzie, 2001) by defining separately both the measurement model and the
structural model. Moreover, it controls measurement errors both random and systematic
(Podsakoff et al., 2003).
Path coefficients were consistent with the direct correlations between the dimensions of
work engagement, observed previously, highlighting consistency in conceptualization.
Following Schaufeli et al.’s (2006) marker for high work engagement (vigor ≥4.81,
absorption ≥4.71, dedication ≥4.21), about 57 % of participants reported high work
engagement.
Model
χ
2
df
χ
2
/df GFI CFI NFI RMSEA
Five-factor model: Proposed 1928.41 1,063 1.81 0.82 0.90 0.80 0.047
Nine-factor model: Default 2136.86 1,057 2.02 0.80 0.87 0.77 0.053
One factor model 2216.82 1,056 2.1 0.79 0.86 0.77 0.055
Note(s):
χ
2
5chi square; df 5degrees of freedom;
χ
2
/df 5relative chi square; GFI 5goodness-of-fit index;
CFI 5comparative fit index; NFI 5normed fit index; RMSEA 5root mean square error of approximation
Table 3.
Confirmatory factor
analysis
Reverse
mentoring and
job crafting at
work
Standardized path coefficients reflected that reverse mentoring (β50.40, p< 0.01) and job
crafting (β50.39, p< 0.01) were each positively associated with work engagement (see
Figure 3), supporting Hypothesis 1. Work engagement was positively associated with work
performance (β50.12, p< 0.10) and negatively associated with work withdrawal behaviors
(β50.31, p< 0.01), supporting Hypothesis 2. Following conditions of mediation specified
by Baron and Kenny (1986) and Holmbeck (1997, p. 602), the independent variables of reverse
mentoring and job crafting were first directly associated with the mediator of work
engagement, showing that both variables significantly increased work engagement. Second,
work engagement was significantly positively associated with performance and negatively
associated with withdrawal behaviors. Finally, after controlling the effects of work
Variables 1 234567 8 9
1. Reverse mentoring 1.00 0.25** 0.32** 0.27** 0.39** 0.31** 0.29** 0.19** 0.08
2. Challenging job
demands
1.00 0.57** 0.31** 0.41** 0.36** 0.33** 0.28** 0.14**
3. Structural job
resources
1.00 0.25** 0.34** 0.31** 0.43** 0.24** 0.14**
4. Vigor 1.00 0.69** 0.58** 0.26** 0.32** 0.15**
5. Dedication 1.00 0.70** 0.31** 0.38** 0.18*
6. Absorption 1.00 0.30** 0.28** 0.18**
7. Work performance 1.00 0.33** 0.24**
8. Psychological
withdrawal
Behavior
1.00 0.67**
9. Physical
withdrawal
behavior
1.00
10. Mean 3.75 3.19 3.67 4.84 4.83 5.17 4.00 2.57 2.01
11. Standard
deviation
0.62 0.77 0.71 1.32 1.59 1.40 0.57 0.71 0.81
Note(s): *p< 0.05; **p< 0.01
Revers e
Mentoring
Work
Performa nce
Job
Crafting
Work
Withdrawal
Behavior
Challenging Job
Demands
Structural
Job Resources
Psychological
Withdrawal
Behavior
Physical
Withdrawal
Behavior
0.29
–0.44
– 0.17
0.70
0.39
0.47 0.80
0.83
ξ3
ξ6
ξ5
Note(s): Numerical values over the line indicate standardized path coefficients;
_____ Significant path; ξ = Error term
ξ4
ξ2
ξ1
a
Table 4.
Pearson correlation
among variables
Figure 2.
Direct model
CDI
engagement, the original associations (see Figure 2) of reverse mentoring (β50.29, p< 0.01)
and job crafting (β50.70, p< 0.01) in increasing work performance were significantly
reduced (β50.24 and β50.36, respectively, p< 0.01; see Figure 3), suggesting partial
mediation. In addition, the relationship between job crafting and withdrawal behaviors was
significantly reduced (from β50.44, p< 0.01 to β50.18, p< 0.05), while the relationship
between reverse mentoring and work withdrawal behaviors reduced but was no longer
significant (from β50.17, p< 0.05 to β50.05, p5ns), demonstrating that work
engagement fully mediates the relationship between reverse mentoring and work withdrawal
behaviors (see Figures 2 and 3), thus Hypothesis 3 is partially supported. These results are
summarized in Table 5 including unstandardized path coefficients.
Table 6 shows that both models were highly significant (p< 0.001). Relative chi-square of
the direct model (Figure 2) was 3.48, whereas the indirect model (Figure 3) had relative chi-
square 2.92, which is within prescribed limits of 3 (Kline, 1998). In addition, both models had
GFI, CFI and NFI values > 0.90, which are acceptable (Awang, 2015). The parsimonious fit
measures (PGFI, PCFI and PNFI) of both models were also acceptable. However, since the
mediator was not included in the direct model, its parsimonious fit measures were slightly
lower. The RMSEA of the direct model was 0.08 (90%, CI: 0.04–0.13), whereas the indirect model
was 0.07 (90%, CI: 0.05–0.09), showing a better fit of the indirect model (MacCallum et al., 1996).
Discussion
This study makes several contributions to the literature on work engagement, reverse
mentoring and job crafting. Most importantly, our model integrates social exchange theory
Reverse
Mentoring
0.75
0.39
0.76
0.79
Work
Performa nce
ξ7
ξ6
Work
Withd rawal
Behavior
Psychologica l
With dra wal
Behavi or
Physical
Withdrawal
Behavior
0.85
ξ10
ξ8
Job
Crafting
Structural
Job Resources
0.74
Challenging
Job Demands
ξ2
ξ1
Work
Engagement
Absorption
Dedication
Vigo r
ξ5
ξ4
ξ3
ξ9
0.40
0.92 0.76
0.12
0.24
0.36
–0.18
–0.05 –0.31
Note(s): Numerical values over the line indicate standardized path coefficients;
_____ Significant path; _ _ _ nonsignificant path; ξ = Error term
aFigure 3.
Indirect model
Reverse
mentoring and
job crafting at
work
(SET) with the job demands and resources (JD-R) model to explain the important phenomena
of work engagement. Based on these frameworks, we examined the linkage between job
resources (reverse mentoring and job crafting) and work-related outcomes (work
performance and work withdrawal behaviors), through work engagement as a mediator.
We connect the previously unrelated literatures on reverse mentoring and work engagement
and develop a scale for use in future reverse mentoring studies.
Theoretical contributions
This research contributes to the literature on work engagement by integrating the JD-R model
with SET to explain how individuals respond to different job resources by increasing their
engagement levels. To the best of the authors’knowledge, no study in the past has combined
these two models to study the antecedents and outcomes of work engagement. As job
USTD SE CR Decision
Hypothesis 1: Antecedent association with work
engagement
H1a: Work engagement ←Reverse mentoring 0.30 0.04 7.35*** Supported
H1b: Work engagement ←Job Crafting 0.39 0.07 5.95*** Supported
Hypothesis 2: Work engagement association with
work outcomes
H2a: Work performance ←Work engagement 0.16 0.08 1.87* Supported
H2b: Work withdrawal behavior ←Work
engagement
0.34 0.08 4.14*** Supported
Hypothesis 3: Antecedents association with work
outcomes
Work performance ←Reverse mentoring 0.29 0.05 5.81*** Supported
Work withdrawal behavior ←Reverse mentoring 0.14 0.05 3.00** Supported
Work performance ←Job Crafting 1.47 0.37 4.01*** Supported
Work withdrawal behavior ←Job Crafting 0.75 0.16 4.84*** Supported
Antecedents association with work outcomes after
controlling the effects of mediator (work engagement)
Work performance ←Reverse mentoring 0.24 0.05 4.43*** Supported (Partial
Mediation)
Work withdrawal behavior ←Reverse mentoring 0.04 0.05 0.81 Supported(Full Mediation)
Work performance ←Job crafting 0.49 0.09 5.39*** Supported (Partial
Mediation)
Work withdrawal behavior ←Job crafting 0.20 0.08 2.51** Supported (Partial
Mediation)
Note(s): *p< 0.1; **p< 0.05; ***p< 0.01
USTD: Unstandardized beta coefficient; SE: Standard error; CR: Critical ratio
Model
χ
2
df
χ
2
/df GFI CFI NFI RMSEA PGFI PCFI PNFI
Direct 17.41 5 3.48 0.98 0.98 0.97 0.08 0.23 0.33 0.32
Indirect 58.34 20 2.92 0.97 0.97 0.95 0.07 0.43 0.54 0.53
Note(s): df 5degrees of freedom; GFI 5goodness-of-fit index; CFI 5comparative fit index; NFI 5normed fit
index; RMSEA 5root mean square error of approximation; PGFI 5parsimonious goodness-of-fit index;
PCFI 5parsimonious comparative fit index; PNFI 5parsimonious normed fit index
Table 5.
Path analytic results of
hypotheses
Table 6.
Fit measure of the two
models
CDI
resources, reverse mentoring (interpersonal resource) and job crafting opportunities (task or
role resource) promote positive attitudes among employees in the form of competence,
autonomy and relatedness, which provide motivation (Deci et al., 2001) and encourage
reciprocation through working harder and producing better results.
This is one of the first empirical studies to test the association between reverse mentoring and
its outcomes. We found that work engagement mediates the relationship between reverse
mentoring and critical workplace outcomes (performance and withdrawal behaviors),
which begins to explain the process of how reverse mentoring has its positive effects.
We demonstrate that when organizations ask junior employees to take on more responsibility (as
reverse mentors), not only do those employees step up to provide mentees support (Murphy, 2012)
but they also improve their own engagement and performance. In addition, a reverse mentoring
scale was developed and empirically validated, which can be used for future research.
Finally, we empirically demonstrate the role of work engagement in mediating the association
between job resources (reverse mentoring and job crafting) and work outcomes (work
performance and work withdrawal behaviors). The idea that workplace resources contribute to
engagement, performance and retention is foundational to the literature on positive
organizational scholarship which posits generative outcomes for organizations that focus on
creating positive attributes and processes for their employees (Cameron et al., 2003). Combining a
social exchange view with the predictions of the JD-R model, our study explains that reverse
mentoring and job crafting are connected to performance and retention because when
organizations offer such resources employees reciprocate with higher levels of work engagement.
Practical implications
This study provides evidence to support practitioners in implementing resources that
increase work engagement among employees. According to our study and supporting the JD-
R model, resource interventions at both the individual task and role level (job crafting) and the
interpersonal level (reverse mentoring) may provide important benefits. From a social
exchange perspective, reverse mentoring programs and job crafting training can motivate
employees to reciprocate in kind with higher levels of work engagement and consequently,
improved organizational outcomes.
Reverse mentoring is an innovative method that clearly adds value to the mentees, who
gain new knowledge and skills (Murphy, 2012); however, this study shows that the mentors
also benefit through increased engagement, performance and reduced withdrawal behaviors.
A reverse mentoring relationship serves as an interpersonal resource, which has motivating
value in and of itself as a high-quality connection (Dutton and Heaphy, 2003) that furthermore
provides support to enhance positive outcomes. By fostering opportunities for reverse
mentoring relationships, organizations stimulate learning and connections across
hierarchical levels for knowledge sharing and development.
Similarly, job crafting is a relatively new intervention for organizations who seek to
empower employees through customization of their jobs (Tims et al.,2012). Through job
crafting, employees can shape their role or adjust their tasks in order to focus on their strengths
or areas of interest for skill development to expand their contributions to the organization. Both
job crafting and reverse mentoring are developmental opportunities, which mean they foster
learning and growth. In addition, both are employee-driven practices, which mean they are also
economical tools for the organization in terms of both costs and resources.
Limitations and future research
This study, as all research, has limitations. Our study is a cross-sectional, survey design in a
technical industry. Future research with longitudinal data across industries is needed to
establish causality among the variables studied and to ensure generalizability. In addition,
including outcome data from other sources beyond self-report measures would strengthen
Reverse
mentoring and
job crafting at
work
future research. Our sample is male dominated, though it is representative of the Indian IT
industry where 72% of employees are men (Srinivas and Bansal, 2018). Future research may
look for a more gender-balanced sample.
The reverse mentoring scale developed here was focused on the mentors in the
relationship rather than the prot
eg
es, which may limit its scope of use, though it could be
modified for prot
eg
es similar to traditional mentoring scales (c.f. Allen et al., 2004). We
suggest that future studies may want to include dyadic measures of both mentees and
mentors (for example, see Murphy, 2011). In addition, researchers may consider the
developmental network of employees as broader resources for work engagement (Murphy
and Kram, 2014). In addition, given the nature and work environment of the IT sector, reverse
mentoring mostly occurs informally as formal programs are not yet widespread in India.
Future research in a context where reverse mentoring has been popularized formally as well
as supported by an inclusive, low power distance culture would significantly add to the
literature and further validate the findings of the current study. Moreover, future research
may explore the linkage of SET with individual dimensions of work engagement, as
compared to a composite aggregation measure in this study, so as to further unpack the
association. In addition, Tims et al. (2015) suggests the presence of a positive gain spiral
between job crafting and work engagement. While testing this positive gain spiral or reverse
causality of engagement with job crafting and/or reverse mentoring was beyond the scope of
our study, future work may explore and unfold these associations.
Conclusion
This study integrates social exchange theory (SET) with the job demands-resources model
(JD-R) to examine the mediating role of work engagement in the association of reverse
mentoring and job crafting with work performance and work withdrawal behavior. In
addition, this is the first empirical study to develop and validate a reverse mentoring support
scale and to offer insights into how reverse mentoring can foster positive work outcomes.
Overall, our results demonstrate that employee-driven job resources are effective practices to
improve work engagement and outcomes.
References
Abramis, D.J. (1985), Job Stressors, Strains, Job Performance, Social Support, and Social Conflict:
Causal Relationships in A Four-Wave Longitudinal Panel Study (Structural Model, Health,
Work), Doctoral dissertation.
Agarwal, U.A. and Gupta, V. (2018), “Relationships between job characteristics, work engagement,
conscientiousness and managers’turnover intentions: a moderated-mediation analysis”,
Personnel Review, Vol. 47 No. 2, pp. 353-377.
Allen, T.D., Eby, L.T., Poteet, M.L., Lentz, E. and Lima, L. (2004), “Career benefits associated with
mentoring for prot
eg
es: a meta-analysis”,Journal of Applied Psychology, Vol. 89 No. 1, p. 127.
Allen, T.D., Smith, M.A., Mael, F.A., Gavan O’Shea, P. and Eby, L.T. (2009), “Organization-level
mentoring and organizational performance within substance abuse centers”,Journal of
Management, Vol. 35 No. 5, pp. 1113-1128.
Arbuckle, J.L. (2016), IBM SPSS Amos 24.0. User’s Guide, Amos Development Corporation, Chicago.
Awang, Z. (2015), SEM Made Simple: A Gentle Approach to Learning Structural Equation Modeling,
MPWS Rich Publication, Selangor, Kuala Lumpur.
Bailey, C., Madden, A., Alfes, K. and Fletcher, L. (2017), “The meaning, antecedents and outcomes of
employee engagement: a narrative synthesis”,International Journal of Management Reviews,
Vol. 19 No. 1, pp. 31-53.
Bakker, A.B. (2009), “Building engagement in the workplace”, in Cooper, C.L. and Burke, R.J. (Eds),
The Peak Performing Organization, Routledge, pp. 50-72.
CDI
Bakker, A.B. (2017), “Strategic and proactive approaches to work engagement”,Organizational
Dynamics, Vol. 46 No. 2, pp. 67-75.
Bakker, A.B. and Albrecht, S. (2018), “Work engagement: current trends”,Career Development
International, Vol. 23 No. 1, pp. 4-11.
Bakker, A.B. and Demerouti, E. (2007), “The job demands-resources model: state of the art”,Journal of
Managerial Psychology, Vol. 22 No. 3, pp. 309-328.
Bakker, A.B. and Xanthopoulou, D. (2009), “The crossover of daily work engagement: test
of an actor–partner interdependence model”,Journal of Applied Psychology, Vol. 94 No. 6,
p. 1562.
Bakker, A.B., Hakanen, J.J., Demerouti, E. and Xanthopoulou, D. (2007), “Job resources boost work
engagement, particularly when job demands are high”,Journal of Educational Psychology,
Vol. 99 No. 2, p. 274.
Bakker, A.B., Tims, M. and Derks, D. (2012), “Proactive personality and job performance: the role of
job crafting and work engagement”,Human Relations, Vol. 65 No. 10, pp. 1359-1378.
Baron, R.M. and Kenny, D.A. (1986), “The moderator–mediator variable distinction in social
psychological research: conceptual, strategic, and statistical considerations”,Journal of
Personality and Social Psychology, Vol. 51 No. 6, p. 1173.
Basu, S.D. and Verma, P. (2017), “To tap into Gen Y ideas, leaders become mentees”,Economic Times,
May 09, available at: https://economictimes.indiatimes.com/jobs/to-tap-into-gen-y-ideas-leaders-
become-mentees/articleshow/58582916.cms.
Birtch, T.A., Chiang, F.F. and Van Esch, E. (2016), “A social exchange theory framework for
understanding the job characteristics–job outcomes relationship: the mediating role of
psychological contract fulfillment”,International Journal of Human Resource Management,
Vol. 27 No. 11, pp. 1217-1236.
Biswas, M.K. and Suar, D. (2016), “Antecedents and consequences of employer branding”,Journal of
Business Ethics, Vol. 136 No. 1, pp. 57-72.
Byrne, B.M. (2001), “Structural equation modeling with AMOS, EQS, and LISREL: comparative
approaches to testing for the factorial validity of a measuring instrument”,International Journal
of Testing, Vol. 1 No. 1, pp. 55-86.
Cameron, K.S., Dutton, J.E. and Quinn, R.E. (2003), Positive Organizational Scholarship: Foundations of
a New Discipline, Berrett-Koehler, San Francisco.
Castro, S.L., Scandura, T.A. and Williams, E.A. (2005), “Validity of Scandura and Ragins’(1993)
multidimensional mentoring measure: an evaluation and refinement”,Paper Presented at the
meeting of the Southern Management Association, San Antonio, TX.
Cavanaugh, M.A., Boswell, W.R., Roehling, M.V. and Boudreau, J.W. (2000), “An empirical
examination of self-reported work stress among US managers”,Journal of Applied
Psychology, Vol. 85 No. 1, p. 65.
Chaudhuri, S. and Ghosh, R. (2012), “Reverse Mentoring: a social exchange tool for keeping the
boomers engaged and millennials committed”,Human Resource Development Review, Vol. 11
No. 1, pp. 55-76, doi: 10.1177/1534484311417562.
Chen, Y.C. (2013), “Effect of reverse mentoring on traditional mentoring functions”,Leadership and
Management in Engineering, Vol. 13 No. 3, pp. 199-209.
Chen, Y.C. (2014), “Examining traditional mentoring functioning scale considering reverse mentoring
and the work characteristics of millennials”,International Journal of Technology, Policy and
Management, Vol. 14 No. 3, pp. 205-219.
Cropanzano, R. and Mitchell, M.S. (2005), “Social exchange theory: an interdisciplinary review”,
Journal of Management, Vol. 31 No. 6, pp. 874-900.
Deci, E.L., Ryan, R.M., Gagn
e, M., Leone, D.R., Usunov, J. and Kornazheva, B.P. (2001), “Need
satisfaction, motivation, and well-being in the work organizations of a former eastern bloc
Reverse
mentoring and
job crafting at
work
country: a cross-cultural study of self-determination”,Personality and Social Psychology Bulletin,
Vol. 27 No. 8, pp. 930-942.
Demerouti, E. and Bakker, A.B. (2011), “The job-demands resources model: challenges for future
research”,Journal of Industrial Psychology, Vol. 37 No. 2, pp. 1-9.
Demerouti, E., Bakker, A.B., Nachreiner, F. and Schaufeli, W.B. (2001), “The job demands-resources
model of burnout”,Journal of Applied Psychology, Vol. 86 No. 3, p. 499.
Dubbelt, L., Demerouti, E. and Rispens, S. (2019), “The value of job crafting for work engagement, task
performance, and career satisfaction: longitudinal and quasi-experimental evidence”,European
Journal of Work and Organizational Psychology, Vol. 28 No. 3, pp. 300-314.
Duff, A.J. (2017), “Compromising innovative behaviour with work shame”,International Journal of
Work Organisation and Emotion, Vol. 8 No. 4, pp. 249-268.
Dutton, J.E. and Heaphy, E.D. (2003), “The power of high-quality connections”, in Cameron, K. and
Dutton, J. (Eds), Positive Organizational Scholarship: Foundations of a New Discipline, Berrett-
Koehler Publishers, Oakland, CA, pp. 262-278.
Fornell, C. and Larcker, D.F. (1981), “Structural equation models with unobservable variables and
measurement error: algebra and statistics”,Journal of Marketing Research, Vol. 18 No. 3, pp. 382-388.
Forza, C. and Filippini, R. (1998), “TQM impact on quality conformance and customer satisfaction: a
causal model”,International Journal of Production Economics, Vol. 55 No. 1, pp. 1-20.
Fu, S.Q., Greco, L.M., Lennard, A.C. and Dimotakis, N. (2020), “Anxiety responses to the unfolding
COVID19 crisis: patterns of change in the experience of prolonged exposure to stressors”,
Journal of Applied Psychology, Vol. 106 No. 1, pp. 48-61.
Glass, A. (2007), “Understanding generational differences for competitive success”,Industrial and
Commercial Training, Vol. 39 No. 2, pp. 98-103.
Gupta, M. (2018), “Engaging employees at work: insights from India”,Advances in Developing Human
Resources, Vol. 20 No. 1, pp. 3-10.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2013), “Partial least squares structural equation modeling:
rigorous applications, better results and higher acceptance”,Long Range Planning, Vol. 46
Nos 1–2, pp. 1-12.
Halbesleben, J.R. (2010), “A meta-analysis of work engagement: relationships with burnout, demands,
resources, and consequences”,Work engagement: A handbook of essential theory and research,
Vol. 8 No. 1, pp. 102-117.
Hanisch, A. and Hulin, C.L. (1990), “Job attitudes and organizational Withdrawal : an examination of
retirement and other voluntary withdrawal behaviors”,Journal of Vocational Behavior, Vol. 37
No. 1, pp. 60-78.
Harman, H.H. (1976), Modern Factor Analysis, University of Chicago Press, IL.
Harvey, M., McIntyre, N., Thompson Heames, J. and Moeller, M. (2009), “Mentoring global female
managers in the global marketplace: traditional, reverse, and reciprocal mentoring”,
International Journal of Human Resource Management, Vol. 20 No. 6, pp. 1344-1361.
Hinkin, T.R. (1995), “A review of scale development practices in the study of organizations”,Journal of
Management, Vol. 21 No. 5, pp. 967-988.
Holmbeck, G.N. (1997), “Toward terminological, conceptual, and statistical clarity in the study of
mediators and moderators: examples from the child-clinical and pediatric psychology
literatures”,Journal of Consulting and Clinical Psychology, Vol. 65 No. 4, pp. 599-610.
Hu, Q., Schaufeli, W.B. and Taris, T.W. (2013), “Does equity mediate the effects of job demands and
job resources on work outcomes? An extension of the job demands-resources model”,Career
Development International, Vol. 18 No. 4, pp. 357-376.
Kammeyer-Mueller, J.D. and Wanberg, C.R. (2003), “Unwrapping the organizational entry process:
disentangling multiple antecedents and their pathways to adjustment”,Journal of Applied
Psychology, Vol. 88 No. 5, p. 779.
CDI
Ka
se, R., Saksida, T. and Miheli
c, K.K. (2019), “Skill development in reverse mentoring: motivational
processes of mentors and learners”,Human Resource Management, Vol. 58 No. 1, pp. 57-69.
Klein, A.G. and Schermelleh-Engel, K. (2010), “Introduction of a new measure for detecting poor fit due
to omitted nonlinear terms in SEM”,AStA Advances in Statistical Analysis, Vol. 94 No. 2,
pp. 157-166.
Kline, R.B. (1998), “Software review: software programs for structural equation modeling: Amos, EQS,
and LISREL”,Journal of Psychoeducational Assessment, Vol. 16 No. 4, pp. 343-364.
Kock, N. (2017), “Which is the best way to measure job performance: self-perceptions or official
supervisor evaluations?”,International Journal of E-Collaboration, Vol. 13 No. 2, pp. 1-9.
Koslowsky, M., Sagie, A., Krausz, M. and Singer, A.D. (1997), “Correlates of employee lateness: some
theoretical considerations”,Journal of Applied Psychology, Vol. 82 No. 1, p. 79.
Lehman, W.E. and Simpson, D.D. (1992), “Employee substance use and on-the-job behaviors”,Journal
of Applied Psychology, Vol. 77 No. 3, p. 309.
M€
akikangas, A., Aunola, K., Sepp€
al€
a, P. and Hakanen, J. (2016), “Work engagement–team
performance relationship: shared job crafting as a moderator”,Journal of Occupational and
Organizational Psychology, Vol. 89 No. 4, pp. 772-790.
MacCallum, R.C., Browne, M.W. and Sugawara, H.M. (1996), “Power analysis and determination of
sample size for covariance structure modeling”,Psychological Methods, Vol. 1 No. 2, p. 130.
MacKenzie, S.B. (2001), “Opportunities for improving consumer research through latent variable
structural equation modeling”,Journal of Consumer Research, Vol. 28 No. 1, pp. 159-166.
Manjunath, S. and Chandni, M.C. (2018), “Winning effective employee engagement in it sector: an
analysis on emerging trends and challenges”,ISBR Management Journal, Vol. 3 No. 2.
Mannal, S. (2017), “Look who’s mentoring the boss”,The Times of India, February 20, available at:
https://timesofindia.indiatimes.com/business/india-business/look-whos-mentoring-the-boss/
articleshow/57242551.cms.
Murphy, W.M. (2011), “From e-mentoring to blended mentoring: increasing students’developmental
initiation and mentors’satisfaction”,The Academy of Management Learning and Education,
Vol. 10 No. 4, pp. 606-622.
Murphy, W.M. (2012), “Reverse mentoring at work: fostering cross-generational learning and
developing millennial leaders”,Human Resource Management, Vol. 51 No. 4, pp. 549-573.
Murphy, W.M. and Kram, K.E. (2014), Strategic Relationships at Work: Creating Your Circle of
Mentors, Sponsors, and Peers for Success in Business and Life, McGraw-Hill, New York.
Nielsen, K. and Abildgaard, J.S. (2012), “The development and validation of a job crafting measure for
use with blue-collar workers”,Work and Stress, Vol. 26 No. 4, pp. 365-384.
Nunnally, J. (1978), Psychometric Methods, McGraw-Hill, New York.
Petrou, P., Demerouti, E., Peeters, M.C.W., Schaufeli, W.B. and Hetland, J. (2012), “Crafting a job on a
daily basis: contextual correlates and the link to work engagement”,Journal of Organizational
Behavior, Vol. 33 No. 8, pp. 1120-1141.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in
behavioral research: a critical review of the literature and recommended remedies”,Journal of
Applied Psychology, Vol. 88 No. 5, p. 879.
Podsakoff, N.P., LePine, J.A. and LePine, M.A. (2007), “Differential challenge stressor-hindrance
stressor relationships with job attitudes, turnover intentions, turnover, and withdrawal
behavior: a meta-analysis”,Journal of Applied Psychology, Vol. 92 No. 2, p. 438.
Rivera, A.P. and Flinck, J. (2011), “Employee-led, employee engagement in the federal government:
SAMHSA PeopleFirst”,Advances in Developing Human Resources, Vol. 13 No. 4, pp. 479-493.
Saks, A.M. (2006), “Antecedents and consequences of employee engagement”,Journal of Managerial
Psychology, Vol. 21 No. 7, pp. 600-619.
Reverse
mentoring and
job crafting at
work
Schaufeli, W.B. and Bakker, A.B. (2003), UWES–Utrecht Work Engagement Scale: Test Manual,
Unpublished Manuscript, Department of Psychology, Utrecht University, Utrecht, p. 8,
available at: www.schaufeli.com.
Schaufeli, W.B. and Van Rhenen, W. (2006), “Over de rol van positieve en negatieve emoties bij het
welbevinden van managers: een studie met de Job-related Affective Well-being Scale (JAWS)
[About the role of positive and negative emotions in managers’well-being: a study using the Job-
related Affective Well-being Scale (JAWS)]”,Gedrag en Organisatie, Vol. 19 No. 4, pp. 223-244.
Schaufeli, W.B., Salanova, M., Gonz
alez-Rom
a, V. and Bakker, A.B. (2002), “The measurement of
engagement and burnout: a two sample confirmatory factor analytic approach”,Journal of
Happiness Studies, Vol. 3 No. 1, pp. 71-92.
Schaufeli, W.B., Bakker, A.B. and Salanova, M. (2006), “The measurement of work engagement with a
short questionnaire: a cross-national study”,Educational and Psychological Measurement,
Vol. 66 No. 4, pp. 701-716.
Schwab, K. (2017), The Fourth Industrial Revolution, Crown Business, New York.
Sekhar, C., Patwardhan, M. and Vyas, V. (2018), “Linking work engagement to job performance
through flexible human resource management”,Advances in Developing Human Resources,
Vol. 20 No. 1, pp. 72-87.
Singh, P., Suar, D. and Leiter, M.P. (2012), “Antecedents, work-related consequences, and buffers of job
burnout among Indian software developers”,Journal of Leadership and Organizational Studies,
Vol. 19 No. 1, pp. 83-104.
Srinivas, A. and Bansal, S. (2018), “Gender gap in Indian formal sector worse than global
average”, LinkedIn data shows, available at: https://www.hindustantimes.com/india-news/
gender-gap-in-indian-formal-sector-worse-than-global-average-linkedin-data-shows/story-
yOtfsFNazeMZbOnjGL9a1I.html.
Srivastava, S. and Thakur, M.K. (2013), “Mentoring and performance: testing a mediated model in
supervisory and formal mentoring in business organisation”,International Journal of Business
and Information Technology, Vol. 6 No. 2, pp. 16-35.
Stevens, J.P. (1992), Applied Multivariate Statistics for the Social Sciences, 2nd ed., Erlbaum, Hillsdale, NJ.
Suar, D. and Khuntia, R. (2010), “Influence of personal values and value congruence on unethical
practices and work behavior”,Journal of Business Ethics, Vol. 97 No. 3, pp. 443-460.
Swank, J.M. and Mullen, P.R. (2017), “Evaluating evidence for conceptually related constructs using
bivariate correlations”,Measurement and Evaluation in Counseling and Development, Vol. 50
No. 4, pp. 270-274.
Tims, M. and Bakker, A.B. (2010), “Job crafting: toward a new model of individual job redesign”,SA
Journal of Industrial Psychology, Vol. 36 No. 2, pp. 1-9.
Tims, M., Bakker, A.B. and Derks, D. (2012), “Development and validation of the job crafting scale”,
Journal of Vocational Behavior, Vol. 80 No. 1, pp. 173-186.
Tims, M., Bakker, A.B., Derks, D. and van Rhenen, W. (2013), “Job crafting at the team and individual
level: implications for work engagement and performance”,Group and Organization
Management, Vol. 38 No. 4, pp. 427-454.
Tims, M., Bakker, A.B. and Derks, D. (2015), “Job crafting and job performance: a longitudinal study”,
European Journal of Work and Organizational Psychology, Vol. 24 No. 6, pp. 914-928.
Van De Voorde, K., Veld, M. and Van Veldhoven, M. (2016), “Connecting empowerment-focused HRM
and labour productivity to work engagement: the mediating role of job demands and
resources”,Human Resource Management Journal, Vol. 26 No. 2, pp. 192-210.
Van Wingerden, J., Derks, D. and Bakker, A.B. (2017), “The impact of personal resources and job
crafting interventions on work engagement and performance”,Human Resource Management,
Vol. 56 No. 1, pp. 51-67.
CDI
Walumbwa, F.O. and Lawler, J.J. (2003), “Building effective organizations: transformational leadership,
collectivist orientation, work-related attitudes and withdrawal behaviours in three emerging
economies”,International Journal of Human Resource Management, Vol. 14 No. 7, pp. 1083-1101.
Weston, R. and Gore, P.A. (2006), “A brief guide to structural equation modeling”,The Counseling
Psychologist, Vol. 34 No. 5, pp. 719-751.
Wright, T.A., Quick, J.C., Hannah, S.T. and Blake Hargrove, M. (2017), “Best practice recommendations for
scale construction in organizational research: the development and initial validation of the Character
Strength Inventory (CSI)”,Journal of Organizational Behavior, Vol. 38 No. 5, pp. 615-628.
Xanthopoulou, D., Bakker, A.B., Demerouti, E. and Kantas, A. (2007), “The measurement of burnout
and engagement: a cross-cultural study comparing Greece and The Netherlands”,New Review
of Social Psychology, Vol. 7 No. 2, pp. 41-52.
Yalabik, Z.Y., Popaitoon, P., Chowne, J.A. and Rayton, B.A. (2013), “Work engagement as a mediator
between employee attitudes and outcomes”,International Journal of Human Resource
Management, Vol. 24 No. 14, pp. 2799-2823.
Zeijen, M.E.L., Peeters, M.C.W. and Hakanen, J.J. (2018), “Workaholism versus work engagement and
job crafting: what is the role of self-management strategies?”,Human Resource Management
Journal, Vol. 28 No. 2, pp. 357-373.
Zielinski, D. (2000), “Mentoring up”,Training, Vol. 37 No. 10, pp. 136-141.
Further reading
Bakker, A.B. and Demerouti, E. (2008), “Towards a model of work engagement”,Career Development
International, Vol. 13 No. 3, pp. 209-223.
Appendix
Reverse mentoring scale
Think of a situation when you have trained/taught your senior (mentee) about a new technology or
transferred knowledge. As a mentor, answer the following:
Corresponding author
Wendy Murphy can be contacted at: wmurphy@babson.edu
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
S. No Items
Factor
loading
1 I feel empowered updating my mentee (senior) on the latest technologies and emerging trends 0.51
2 We both treat each other as friends 0.68
3 My mentee (senior) demonstrates a willingness to learn from me 0.75
4 I can comfortably ask for support from my mentee (senior) 0.73
5 I feel excited and involved in guiding my mentee (senior) 0.75
6 I feel competent in the content that I share with my mentee (senior) 0.64
7 We learn from each other 0.77
8 We motivate each other 0.72
9 My mentee (senior) is receptive to the feedback provided by me 0.68
10 I respect my mentee (senior) for accepting me as a mentor 0.79
11 My mentee (senior) values the ideas and suggestions that I offer 0.82
Reverse
mentoring and
job crafting at
work
