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The International Journal of Human Resource
Management
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rijh20
The impact of green HRM on green creativity:
mediating role of pro-environmental behaviors
andmoderating role of ethical leadership style
Ifzal Ahmad, Kifayat Ullah & Asif Khan
To cite this article: Ifzal Ahmad, Kifayat Ullah & Asif Khan (2021): The impact of green HRM
on green creativity: mediating role of pro-environmental behaviors andmoderating role of
ethical leadership style, The International Journal of Human Resource Management, DOI:
10.1080/09585192.2021.1931938
To link to this article: https://doi.org/10.1080/09585192.2021.1931938
Published online: 12 Jun 2021.
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THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT
The impact of green HRM on green creativity:
mediating role of pro-environmental behaviors
and moderating role of ethical leadership style
Ifzal Ahmad, Kifayat Ullah and Asif Khan
Karakoram International University, Gilgit, Pakistan
ABSTRACT
In the aftermath of global warming, organizations are com-
ing under increasing pressure to reduce carbon emissions,
minimize waste, and generally become more responsible in
their practices. To this backdrop, researchers argue that orga-
nizational HR practices have significant potential in shaping
human behaviors. Therefore, by investigating the employ-
ment of a subordinate-supervisor dyad (N = 378 (subordi-
nates), and 141 (supervisors)), this empirical study contributes
to the growing literature on Green Human Resource
Management (green HRM) and its impacts on employees’
green outcomes. In particular, this study tested a novel
model to explore the impact of green HRM on Green
Creativity (GC) of employees in mediating the role of
Pro-Environmental Behaviors (PEBs) and moderating the role
of Ethical Leadership Style (ELS). Data were collected from
organizations in the Gilgit-Baltistan (GB) region of Pakistan.
For testing the results of the proposed hypotheses, structural
equation modelling technique using SMART PLS 3 was
employed. The results indicate that green HRM has a positive
impact on employees’ GC. Furthermore, partial mediating
role of PEBs was also found. Finally, it was found that the
ELS moderates the relationship between green HRM and GC
and between green HRM and PEBs. Several theoretical and
practical implications are discussed.
Introduction
Global warming and its negative impacts have become a reality and
countries are already bearing its brunt. For example, according to National
Geographic Magazine, Cape Town is running out of water and that ‘day
zero’ is fast approaching (Welch, 2018). Similarly, Kreft et al. (2017)
© 2021 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Ifzal Ahmad ifzalmdn@gmail.com Karakoram International University, Gilgit, Pakistan.
This manuscript is in compliance with the ethical standards of International Journal of Human Resource
Management.
https://doi.org/10.1080/09585192.2021.1931938
KEYWORDS
Green HRM; green
creativity;
pro-environmental
behaviors; ethical
leadership style; social
identity theory
2 I. AHMAD ET AL.
published ‘Global Climate Risk Index (CRI)’ under GERMANWATCH
and concluded that Pakistan is the 7th most vulnerable country in the
world regarding the negative effects of global warming. Many other
countries are also on the list such as Australia, Mexico, Honduras,
Myanmar, the Philippines, and Bangladesh. To this backdrop, world lead-
ers, social experts, scientists, corporations, indigenous communities, media
and other stakeholders gathered to formulate tangible strategies that can
limit global warming by, ideally, 1.5 °C. This accord was signed by world
leaders and is called the ‘Paris Agreement’. The topic of global warming
has thus become the talk of the town because of its negative repercussions
on the eco-system and human health (Robertson & Barling, 2013).
Studies suggest that human behavior is largely responsible for the
overall global warming, increasing water and air pollution, and depleting
natural resources, which adds up to climate change (Lehman & Geller,
2004). Organizations are further compromising this already dreadful
condition through their relentless use of electricity and computer emis-
sions, thus, doubling the carbon emissions into the atmosphere (Robertson
& Barling, 2013). Consequently, these organizations are under tremen-
dous pressure to adopt such strategies that can promote pro-environmental
behaviors (PEBs) among its employees and therefore, their HR processes
must be shaped in such a way that reflects sustainability in all its prac-
tices (Jackson & Seo, 2010; Saeed et al., 2019).
To this end, academic research on green human resource manage-
ment (green HRM) practices—a sub-field of Corporate Social
Responsibility (CSR)—is gaining momentum. This trend is so important
that the Academy of Management has established a separate division
with a dedicated website in order to disseminate information about
green HRM (Renwick et al., 2016). Some journals have also published
special issues on environmental sustainability. For example, Human
Resource Management published its dedicated issue in 2012 and Journal
of Organizational Behavior published its special issue in 2013. The
assumption behind this research stream is that employees spend one
third of their daily time in the organizations and thus focusing on
their green behaviors will have a significant role in improving their
PEBs (Blok et al., 2015). Hence, green HRM has the potential to pro-
mote a culture of sustainability by initiating pro-environmental
approaches to businesses.
The term green HRM refers to such policies and practices that pro-
mote PEBs among employees and the sustainable usage of organizational
resources (Opatha & Arulrajah, 2014). Research on green HRM is
gaining momentum around the world however, a major percentage of
these studies are limited to conceptualization only (Renwick et al.,
2016). Further studies are particularly needed from relational perspective
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 3
to explore green HRM’s widespread implications (Chams &
Garcia-Blandon, 2019; Gilal et al., 2019; Tang et al., 2018). To this end,
a handful of studies have focused on the relational perspective of green
HRM (Dumont et al., 2017; Gilal et al., 2019; Kim et al., 2019; Roscoe
et al., 2019; Shen, Dumont & Deng, 2018) and emphasized the need
to explore the impact of green HRM on both green and non-green
behaviors of employees (Dumont et al., 2017; Gilal et al., 2019). In
similar lines, Jiang et al. (2012) stressed on the need to assess when
and how green HRM trigger various non-green behaviors of employees.
Other authors have termed the green and non-green outcomes as either
‘hard’ or ‘soft’ results of HR systems (Storey, 1989).
Dumont et al. (2017) studied the impact of green HRM on in-role
and extra-role behaviors, with a mediating role of psychological green
climate and a moderating role of individual green values. On the
non-green side, Shen et al. (2018) studied the impact of green HRM
on task performance, intention to quit, and organizational citizenship
behavior (OCB), with a mediating role of organizational identification
and moderating role of perceived organizational support. However, fur-
ther studies are needed to explore the full potential of green HRM and
its outcomes (both green and non-green outcomes) with various other
moderators and mediators for exploring its underlying mechanism
(Ahmad & Umrani, 2019; Kim et al., 2019; Tang et al., 2018) through
which green HRM leads to important outcomes. Ren et al. (2018) argued
that employees undertake multiple roles in parallel, i.e. recipients of
green HRM policies of an organization, implementers of green HRM
practices, and as consumers in private life and as citizens who influence
and shape policy making. Therefore, understanding the impact of green
HRM on employees is of great interest.
To this backdrop, the following are the key contributions of this study:
First, this study explores the impact of green HRM on green creativity
of employees. The term ‘green creativity (GC)’ was first coined by Chen
and Chang (2013) while using the definition of Amabile (1988) to define
GC as ‘the development of new ideas about green products, green ser-
vices, green processes or green practices that are judged to be original,
novel and useful’ (p. 109). It is widely argued that creativity is a key
for innovation and out of the box solutions to the challenges that are
faced by organizations (Wyer et al., 2010). To the best of our knowledge,
GC has attracted very limited attention in relation to green HRM, despite
its importance for innovation and sustainability. Some studies such as
Huo et al. (2020) investigated the impact of top management’s commit-
ment to HRM on green creativity and took green HRM as mediator
between the two variables. Likewise, Jia et al. (2018) studied the medi-
ating role of green HRM between Transformational leadership and green
4 I. AHMAD ET AL.
creativity. However, our model is distinct in a sense that we are testing
the impact of green HRM on green creativity with a distinct mediator
(i.e. PEBs) and moderator (i.e. ELS).
Secondly, it is believed that the role of ethical leadership style—
being conscious about the moral side of leaders’ behaviors (Nusrat et
al., 2018)—may play a key role as moderator between green HRM
and employees’ green outcomes, such that managers who possess high
ethical leadership styles will lead to enhancing the proposed outcomes
i.e. GC in this case (Ahmad et al., 2018). Surprisingly, this stream of
research is still in its nascent stages despite the huge potential of
implications for organizations (Philipp & Lopez, 2013). Extant studies
on green HRM suggest that leadership plays a key role in greening
an organization (Robertson & Barling, 2013). Likewise, Ren et al.
(2018) underlined the importance of studying the mediating process
through which green HRM leads to various green and non-green
outcomes so that policy makers can design green HRM systems in a
way that can achieve the long-term intended outcomes. Therefore,
the third contribution of this study is exploring the mediating role
of PEBs of employees (Blok et al., 2015) between green HRM and
GC (Blau, 1964). Despite the recent attraction to this field, research
on understanding the micro-level processes through green HRM leads
to outcomes is in its nascent stages (Ren et al., 2018). Past studies
have used green psychological climate (Dumont et al., 2017),
pro-environmental psychological capital (Saeed et al., 2019), organi-
zational commitment (Kim et al., 2019); OCB towards environment
(Paille et al., 2014) and environmental passion (Gilal et al., 2019) as
mediators between green HRM and outcomes. Fourth, building on
the motivational theories’ perspective of Norton et al. (2015), this
study will use social identity theory (Mael & Ashforth, 1995) to
explain the proposed model in which green HRM leads to green
outcome, i.e. GC, the mediating role of PEBs and the moderating role
of ethical leadership style. Based on this theory, it is believed that
staff who possess positive organizational values (Ashforth & Mael,
1989) are motivated to validate GC. Finally, since Pakistan is among
the most vulnerable and affected countries regarding the effects of
climate change (Kreft et al., 2017), this study signifies the need to
assess the PEBs of employees in organizations operating in this
country.
In the preceding section, discussion on conceptual framework and
hypotheses development is provided. Methodology follows suit after
which results, and findings of the study are reported and discussed.
Implications (both theoretical and practical) and limitations of the study
will conclude this paper.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 5
Literature review
Conceptual framework and hypotheses development
Companies around the globe are rapidly changing their policies and
adopting more sustainable business practices. Human resource manage-
ment (HRM), which is among the most important section of a company,
must also alter its ways by incorporating more responsible,
pro-environmental and greener policies and practices (Dumont et al.,
2017). Defined by Kramer (2014), ‘HRM activities which enhance pos-
itive environmental outcomes’ (p. 1075), green HRM has the potential
to alter the attitudes and behaviors of employees by making the company
more pro-environmental, which will consequently implement and deliver
on sustainable business practices (Renwick et al., 2013). Some of the
activities under the banner of green HRM are assessing the PEBs of
candidates at the time of recruitment, offering rewards on green behav-
iors after performance appraisal and training and development on sus-
tainable business practices (Renwick et al., 2013). The importance of
green HRM for sustainable development is best explained by Wehrmeyer
(1996) who stated that ‘if a company is to adopt an environmentally-aware
approach to its activities, the employees are the key to its success or
failure’ (7). It is due to this thinking that the focus of sustainability in
companies has been shifted from the macro level to a more micro level,
thereby highlighting the human aspect of environmental management
(Teixeira et al., 2012) and its implications in terms of various outcomes.
Social identity theory suggests that people are susceptible to classify
themselves into various groups, memberships and affiliations in order
to create a positive identification for themselves (Ashforth & Mael,
1989). Accordingly, this classification of self into a group is based on
various distinctive characteristics such as physical attributes, cognitive
abilities, psychological traits and other interests. Organizational identi-
fication is the ‘perception of oneness or belongingness to the organiza-
tions’ (Ashforth & Mael, 1989, p. 34). For example, a person classifies
him/herself to different group(s) such as ‘a Professor and an American
Citizen’. Membership in various companies is probably the most import-
ant classification that employees do (Hogg & Terry, 2000). Studies suggest
that employees define their social worth based on the status and prestige
of their companies (Farooq et al., 2014). Maxwell and Knox (2009)
supported this argument by asserting that employees will identify them-
selves with the organizations that have a favorable brand image. In
similar lines, Benkoff (1997) argued that social identity is an integral
part of an individual’s self-concept. Therefore, employees would prefer
to identify with organizations that have a high reputation and social
standing, and a good image that will ultimately enhance their self-concept,
6 I. AHMAD ET AL.
self-image and self-worth (Tajfel & Turner, 1985). Keeping this premise
in view, it is vouched that companies investing in addressing/promoting
social and environmental concerns in their practices, such as green
HRM, induce a positive image of themselves, both in the eyes of the
company’s employees and outside the company, for other stakeholders
(Farooq et al., 2014). Therefore, this theory is best suited to explain the
underlying mechanism through which green HRM may lead to important
outcomes such as GC.
GC, as explained earlier, is the development of such green products,
green services, green processes and green practices that are novel, original
and useful (Amabile, 1988), for example, increasing the usage of recyclable
material in products and services, limiting the usage of paper for printing
and instead using IT tools for communication, or turning to renewable
energy for cost reduction as well as environmental sustainability. Several
authors have highlighted the importance of creativity for the sustainable
development of organizations and for getting competitive advantages
(Ahmad et al., 2019; Amabile, 1988). However, GC is a relatively new term
used by Chen and Chang (2013) in the context of widespread qualms on
the organizational practices to behave responsibly. We believe that green
HRM practices, such as training and development about environmental
concerns, rewarding PEBs at work, and even evaluating a candidate at the
time of recruitment and selection should help to shape employees’ attitudes
and behaviors toward more green practices. According to Guerci and
Carollo (2016), green compensation, green training and green recruitment
are essential components of green HRM. These practices should also induce
the positive image of a responsible organization. Consequently, as social
identity theory suggests, employees may possibly identify themselves with
such organizations to enhance their self-image and self-worth. Their own-
ership should increase and, as a result, they would come up with unique
and environment friendly solutions to their problems, thereby enhancing
GC. They might also equate such organizations as the more ethical and
responsible ones and therefore realize suggesting out of the box solutions
would not hurt their career negatively (Ahmad et al., 2019). In similar
lines, Huo et al. (2020) argued that green HRM plays a key role in shaping
employees’ attitudes, behaviors and realizing green creativity. Hence, it is
proposed that green HRM practices would increase GC and imply that:
H1. Green HRM practices are positively associated with GC.
Mediating role of PEBs
Over the past few decades, psychologists have been searching for the
possible interventions through which employees’ PEBs can be enhanced
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 7
in their household settings, whereas, in organizational settings, this
notion has captured the attention of scholars (Blok et al., 2015). Kollmuss
and Agyeman (2002) defined PEBs as deliberate attempts by an indi-
vidual to curtail the negative impacts of his/her actions on the envi-
ronment. PEBs at work are important because people spend 1/3rd of
their time in the organization (Blok et al., 2015). Therefore, it is believed
that enhancing PEBs of employees will not only have positive implica-
tions in organizational settings, but it will also have a spill-over effect
in their household lives, thereby having a larger impact on the overall
society, too.
The concept of green HRM is introduced for the purpose of promot-
ing PEBs and values among employees (Tang et al., 2018). To achieve
this, green HRM should design and develop their training programs in
such a way that instills effective awareness about the environment, its
required business values, attitudes and knowledge (Dumont et al., 2017).
This can also be done by encouraging individuals to engage in PEBs,
such as reducing the usage of printing material, reducing unnecessary
usage of energy, minimizing, or completely stopping the usage of plastic
items, engaging in cleaning campaigns and tree planting drives. While
in so doing, companies must recognize and reward such behaviors for
reinforcement of these activities (Renwick et al., 2013). In retrospect,
PEBs have the potential to enhance GC.
While engaging in green HRM, companies will be promoting an
image of a responsible and ethical organization that could result in
shaping PEBs. Because of these ethical practices, employees would need
to feel that they are associated with green HRM, and that they identify
with them (Ahmad et al., 2019; Glavas, 2016). Enhanced PEBs of
employees should compel them to come up with unique ideas and
solutions to problems that would also be environment friendly. Moreover,
employees would also think out of the box for solutions and come up
with ideas that should help in shaping the processes to become more
environmentally friendly. This could also happen because of the enhanced
identification with green HRM by employees, as suggested by social
identity theory. Employees will be confident that their employer is more
responsible and ethical and therefore, feel it is safe to align themselves
with a responsible organization, which could result in enhancing the
employee’s image of being a responsible citizen (Frooq et al., 2013).
They could also share their unique ideas (i.e. GC) without worry of
negative repercussions because they would think that such responsible
organizations do not deceive workers, but rather encourage and offer
rewards for so doing (Ahmad et al., 2019). Previously, researchers found
that GC can lead to green product development performance (Chen &
Chang, 2013) which also lends support to our argument. Following this
8 I. AHMAD ET AL.
discussion, we propose that PEBs triggered through green HRM practices
will lead to GC. Hence, we propose that:
H2. PEBs will mediate the relationship between green HRM and GC.
Moderating role of ethical leadership style (ELS)
In the aftermaths of global corporate scandals, studies on the concept
of ELS, along with other concepts, such as green HRM, are increasing
manifold. Ethical leadership is theorized on the premise of ethics, moral-
ity and integrity which are common traits when one discusses the
concept of green HRM—a sub part of CSR. The concept of ELS is
defined by Brown and his colleagues in 2005 as ‘the demonstration of
normatively appropriate conduct through personal actions and interper-
sonal relationships, and the promotion of such conduct to subordinates
through two-way communication, reinforcement, and decision-making’
(p. 120). ELS encourages and promotes ethical and responsible business
practices through reward and punishment (Ahmad et al., 2018).
Past studies have revealed positive impacts of ELS on various out-
comes, such as employees’ well-being (Chughtai, 2015), academic citi-
zenship behavior (Arain et al., 2017), organizational citizenship behavior
(Ahmad et al., 2018), employee performance (Walumbwa et al., 2011),
and safety performance (Khan, Ahmad, & Ilyas, 2018). Surprisingly,
limited studies are available which have tested the mitigating role of
ELS despite its potential implications (Philipp & Lopez, 2013). Extant
literature on ELS suggest that ethical leaders have a sense of social
responsibility and that the various components of social responsibility,
i.e. moral conduct, legal conduct, concern for others, and self-awareness
have positive associations with ethical leadership (De Hoogh & Den
Hartog, 2008). Ethical leaders are not only concerned about their behav-
iors, but they are also concerned about the well-being of their employees
(Trevino et al., 2003).
In this study, we are exploring the mitigating role of supervisor’s ELS
between the relationship of green HRM and PEBs and between green
HRM and GC. Since green HRM is a sub-part of CSR and is based on
the ethical and social responsibility and share common traits such as
ethics, integrity and morality (Ahmad & Umrani, 2019), we therefore
expect that supervisors’ ELS also holds mitigating power between the
relationships triggered from green HRM, such that the effect of green
HRM on PEBs and GC could be stronger in cases in which a supervisor’s
ELS is high and weaker in cases in which a supervisor’s ELS is low. It
is believed that ethical leaders can serve as a balance between the mindset
of employees, which are more fixated on short-term economic goals, and
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 9
encourage more long-term focus, such as loyalty and discretionary behav-
iors (Philipp & Lopez, 2013). Therefore, it would be reasonable to expect
that ethical leadership will encourage employees’ PEBs and GC with
rewards on one hand whereas, being moral and ethical leaders, they will
also encourage loyalty and other positive behaviors—such as PEBs and
GC. Following the social identity theory (Ashforth & Mael, 1989),
employees would feel more confident working for an ethical supervisor
who can walk the talk as well as encourage PEBs and GC through reward
and punishment mechanisms stipulated by green HRM practices. Keeping
in view this discussion, we propose that:
H3: ELS will moderate the positive relationship between green HRM and PEBs such
that high ELS will strengthen and low ELS will weaken this relationship.
H4: ELS will moderate the positive relationship between green HRM and GC such
that high ELS will strengthen and low ELS will weaken this relationship.
Study context
Gilgit-Baltistan (GB) is situated in a unique, high-altitude mountainous
area consists of the Karakoram, Himalayas and Hindukush ranges. The
area is gifted with high-altitude mountains, scenic valleys, gigantic gla-
ciers, and beautiful rivers, and is a major source of water for the Indus
river of Pakistan—a key artery for Pakistan’s agriculture. Because of
global warming, variations have been noted in rainfall and snow which
affects the water supply to the communities of GB, most of whom rely
on agriculture.
Similarly, researchers have noted that the glaciers in GB are melting
at a rapid speed which threatens local habitats as well as temperature
increase in the region (USAID, 2001). According to a report by Kreft et
al. (2017), the number of casualties from climate-related disasters such
as floods, storms and heat waves are on the rise. Pakistan is on 40th
number in the list of the most affected countries, with 566 casualties and
a loss of $47.313 million US dollars to the economy. Overall, Pakistan
has lost 10,462 lives to climate-related disasters in the last 20 years, with
a loss of 3.8 billion US dollars (Abubakar, 2017). To this backdrop, the
federal and provincial governments have taken several initiatives to curtail
the effects of global warming, including the successful ‘billion tree tsunami’
project, the 10 billion trees campaign, bans on plastic and encouraging
the usage of both sides of the paper for printing purposes in organiza-
tions. Private sector organizations are also contributing their part in
promoting PEBs in Pakistan. For example, several organizations participate
in tree planting drives, give their employees time off from work to par-
ticipate in these or other cleaning drives, discarding the usage of plastic
10 I. AHMAD ET AL.
in their products and services, limiting the use of plastic bottles and cups
in meetings, and switching to renewable energy. These initiatives are
important for areas like GB, the economy of which is largely reliant on
agriculture, tourism and other small-scale businesses.
These small steps have larger impacts on the overall behavior of the
communities in GB. People are becoming more aware of the importance
of cleaning, efficient resource utilization and reducing wastes. Resultantly,
upon the insistence of these communities, the local government has
recently passed a resolution to ban the usage of plastic bags and has
declared several areas of GB as protected national parks to protect and
preserve its wildlife, greenery and its overall environment.
Methodology
In order to achieve the aims of this study (Figure 1), a quantitative
methodology involving a dyadic approach (i.e. supervisor-colleague/
employees) was adopted in which data were collected through a
pencil-paper technique from public and private sector organizations
operating in GB, Pakistan. Particularly, the study was focused on orga-
nizations from three broad sectors i.e. Banking, Education, and
Non-Governmental Organizations (NGOs). These three sectors are the
largest sources of employment in GB.
For data collection, employees and supervisors were contacted directly
through cold calling, both personal and professional contacts. All respon-
dents participated voluntarily after they were assured of confidentiality.
Each questionnaire was uniquely coded and divided into two sub parts.
The unit of analysis of this study was at the individual level, which is
a growing trend in the green HRM literature (Ren et al., 2018). Employees
were asked to respond to demographic variables, their perception of
green HRM practices of their organizations, and the ELS of their super-
visors/managers, whereas supervisors were asked to report on PEBs and
GC of those employees. All survey forms were uniquely coded for
identification and matching purposes. Only the names of employees
were shared with the supervisors so that the supervisors could rate their
PEBs and GC. Survey forms were written in English, which is the official
language of Pakistan. We distributed a total of 500 questionnaires among
subordinates and 200 questionnaires among supervisors. We made sure
that no supervisor rated more than three subordinates on average to
avoid the issue of data nesting (Ahmad et al., 2019; Naseer et al., 2016).
To further curtail the data nesting issue, we also made sure not to take
more than two supervisors who would then not rate more than three
employees each from one branch/office (Banks, schools, colleges, and
hotels have more than one branch or office in the whole GB region).
Therefore, a total of 141 supervisors rated a total of 378 respondents.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 11
Measures
Green HRM
For measuring green HRM, a six items’ scale developed by Dumont et
al. (2017) was adopted. A sample item from this scale is ‘my company
sets green goals for its employees’. The Cronbach alpha for this scale was
recorded as 0.91.
GC
For employees’ GC, the six items scale developed by Chen and Chang
(2013) was adopted with a slight change of replacing the word ‘project’
with ‘organization’. A sample item from this scale is ‘The members with
the ‘organization’ would discover original solutions for environmental
problems’. The Cronbach alpha for this scale was recorded as 0.94.
Ethical leadership style
For measuring ethical leadership style, the ten items scale developed by
Brown, Treviño, and Harrison (2005) was adopted. A sample item from
this scale is ‘my leaders listen to what employees have to say’ and the
Cronbach alpha value for this scale was recorded as 0.95.
Pro-environmental behaviors (PEBs)
Finally, the scale for measuring PEBs was adopted from a 20 items scale
created by Blok et al. (2015). A sample item from this scale is ‘[Employee
name] reduces heating in unused rooms’. The Cronbach alpha for this
scale was recorded as 0.94.
Results
Data analysis
To assess the outer model quality and to test the significance of path
coefficients in structural models, PLS technique is commonly used in
social sciences research (Ali et al., 2016; Aman et al., 2019). Due to its
ability to model latent constructs under conditions of non-normality and
small to medium sample sizes, PLS technique has become increasingly
popular in marketing and management research (Hair et al., 2019).
Structural equation modeling technique (SEM) can further be classified
in to two sub techniques, i.e. the structural model and the measurement
model. The relationship between dependent and independent latent vari-
able is determined by a structural model, while the measurement model
is used to specify the relationships among latent variables and their
observed items (Sarstedt et al., 2019; Wong, 2013). The measurement
scale in structural equation modeling can be formative or reflective in
12 I. AHMAD ET AL.
nature. The relationship among formative indicators may be positive,
negative or they may even have no relationship with each other; therefore,
indicator reliability, internal consistency reliability, and discriminant valid-
ity for formative measurement scale is not tested (Haenlein & Kaplan,
2004; Petter et al., 2007). In reflective measurement, scale indicators have
strong correlations among each other and are also interchangeable; there-
fore, their reliability and validity must be examined (Hair et al., 2013;
Petter et al., 2007). In the reflective measurement model, the direction
of the arrow is from the construct to indicators, because variations in
latent variables cause differences in the assigned indicators directly (Hair
et al., 2013). Our study is reflective in nature and we therefore followed
a standard reflective measurement procedure of SEM approach.
In the present study, we have employed SPSS version 25.0 to assess the
demographic profile of the sample and to process the descriptive statistics
analysis. A Partial Least Squares (PLS) technique was employed to inves-
tigate the research model following the recommended two stage analytical
procedure of PLS-SEM. At the first stage, we investigated the reflective
measurement model to ensure its reliability and validity and after that we
examined the structural model (Hair et al., 2014, 2017). A PLS algorithm
method was used to check the quality of the outer model, and in order
to ensure path coefficients and loadings significance, a bootstrapping
technique of 5000 subsamples was employed (Hair et al., 2014, 2017).
Findings and results
Table 1 gives the report results of the respondent’s profile including
their gender, age group, qualification, designation, and work experience.
Table 1. Respondent’s prole.
Demographic characteristics Frequency Percent (%)
Gender Male 209 55.3
Female 169 44.7
Age Under 29 years 202 53.4
30–39 years 126 33.3
40–49 years 46 12.2
Above 49 years 41.1
Qualication Intermediate 31 8.2
Bachelors 162 42.9
Masters and above 185 48.9
Designation Low level 57 15.1
Middle level 255 67.5
Upper level 66 17.5
Work experience Up to 5 years 195 51.6
6–10 years 136 36.0
Above 10 years 47 12.4
Industry Banking 139 36.8
Education 119 31.5
NGOs 120 31.7
N = 378
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 13
Evaluation of measurement/outer model (PLS algorithm)
The present study evaluated the quality of the measurement model to
confirm the validity and reliability of the analysis by using the PLS-SEM
technique through Smart PLS Algorithm. At the first stage, the convergent
validity of the measurement model was scrutinized by applying PLS-SEM
standard reliability and validity criterions i.e. Factor Loadings, Cronbach’s
Alpha, Composite Reliability and Average Variance Extracted (Hair et al.,
2017; Latan & Noonan, 2017). Table 2, which follows, reports Outer
Loadings, Cronbach’s alpha, Composite reliability (CR) and Average Variance
Extracted (AVE) for the confirmation of constructs internal consistency.
Cronbach’s alpha can be used as a conservative measure of internal
consistency and is less preferred than competitive reliability (Hair et al.,
2016). Table 3 included all item loadings that exceeded the recommended
value of 0.60 (Chin et al., 2008). Only two items of pro-environmental
behaviors i.e. (PEB 10) and (PEB 14) fall below the minimum threshold
(0.60); therefore, they were deleted from the PEBs construct. The esti-
mated values of Cronbach’s alpha and composite reliability shows the
degree of variability in the latent construct due to construct indicators
which exceeded the recommended value of 0.7. Similarly, overall vari-
ation in indicators due to construct (average variance extracted) also
exceeded its recommended value of 0.5 (Hair et al., 2014).
Discriminant validity
Discriminant validity can be evaluated by using cross-loadings,
Fornell-Larcker criterion, and HTMT as suggested by Franke and Sarstedt
(2019) and Hair et al. (2017). For the establishment of discriminant validity,
the outer loading of an item within the construct must be greater than
the cross loadings of the same item in any other construct. Results reported
in Table 4 confirmed the discriminant validity of latent variables included
in the model, as they meet discriminant validity cross loadings criterion.
Table 2. Means, SD and correlation.
Mean SD 1 2345678910
1Gender 1.45 .50 –
2Age 1.61 .74 −.12* –
3Qualication 2.40 .64 .04 .10* –
4Designation 2.02 .57 .00 .11* .44** –
5Industry 1.95 .83 .06 .12* −.04 −.05 –
6Work Exp 1.61 .67 −.14* .34** .21** .20** −.05 –
7Green HRM 2.82 1.06 .23** .13* .20** .20** .00 .08 –
8Green Creativity 2.99 1.07 .16* .17** .14** .19** .02 .14** .78** –
9PEB 2.96 .96 .18** .22** .15** .19** −.00 .13** .72** .77** –
10 ELS 3.12 1.11 .08 .12* .08 .18** .00 .08 .69** .71** .76** –
*p < 0.05.
**p < 0.01.
PEB = Pro-environmental behaviors, ELS = Ethical leadership style.
14 I. AHMAD ET AL.
Fornell-Larcker criterion
The Fornell-Larcker criterion evaluates discriminant validity by exam-
ining the square root of AVE for each latent variable. According to this
criterion, the square root of AVE of each of the latent variables should
be greater than its correlation with another latent variable. The results
confirmed that the square root of AVE of each latent variable is greater
than its correlation with other latent variables, as shown in Table 5.
Heterotrait-monotrait (HTMT)
In common research situations, the Fornell-Larcker method for the
validation of discriminant validity is not reliable (Henseler et al., 2015).
To find a reliable criterion for the validation of discriminant validity,
Table 3. Validity and reliability for constructs.
Constructs Items Loadings Cronbach’s alpha AVE CR
ELS ELS1 0.889 0.958 0.692 0.947
ELS2 0.866
ELS3 0.856
ELS4 0.884
ELS5 0.821
ELS6 0.816
ELS7 0.813
ELS8 0.786
ELS9 0.81
ELS10 0.772
GC GC1 0.908 0.943 0.733 0.943
GC2 0.843
GC3 0.853
GC4 0.849
GC5 0.864
GC6 0.817
GHRM GHRM1 0.857 0.914 0.639 0.914
GHRM2 0.757
GHRM3 0.846
GHRM4 0.801
GHRM5 0.753
GHRM6 0.777
PEB PEB1 0.725 0.946 0.523 0.946
PEB2 0.712
PEB3 0.803
PEB4 0.76
PEB5 0.725
PEB6 0.711
PEB7 0.693
PEB8 0.731
PEB9 0.654
PEB11 0.75
PEB12 0.737
PEB13 0.783
PEB15 0.762
PEB16 0.717
PEB17 0.685
PEB18 0.603
Notes: GHRM = Green HRM, ELS = Ethical leadership style, PEB = Pro-environmental
behaviors, GC = Green creativity.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 15
Henseler et al. (2015) introduced the assessment of correlations
heterotrait-monotrait ratio (HTMT) method. This method estimates the
correlation between two latent variables. Henseler et al. (2015) suggested
a threshold value of 0.90 for the heterotrait-monotrait ratio (HTMT).
Any value that exceeds this threshold value indicates a lack of discrim-
inant validity. For the validation of discriminant validity, Kline (2011)
Table 4. Cross loadings.
Items ELS GC GHRM PEB
ELS1 0.889 0.662 0.613 0.719
ELS2 0.866 0.645 0.658 0.702
ELS3 0.856 0.651 0.618 0.679
ELS4 0.884 0.688 0.659 0.688
ELS5 0.821 0.61 0.573 0.665
ELS6 0.816 0.604 0.597 0.664
ELS7 0.813 0.581 0.635 0.681
ELS8 0.786 0.582 0.583 0.638
ELS9 0.81 0.623 0.615 0.636
ELS10 0.772 0.58 0.566 0.62
GC1 0.687 0.908 0.756 0.753
GC2 0.627 0.843 0.728 0.675
GC3 0.658 0.853 0.712 0.700
GC4 0.617 0.849 0.732 0.686
GC5 0.637 0.864 0.709 0.734
GC6 0.623 0.817 0.676 0.679
GHRM1 0.678 0.693 0.857 0.691
GHRM2 0.544 0.629 0.757 0.592
GHRM3 0.645 0.703 0.846 0.66
GHRM4 0.598 0.664 0.801 0.627
GHRM5 0.544 0.658 0.753 0.554
GHRM6 0.509 0.681 0.777 0.57
PEB1 0.545 0.609 0.587 0.725
PEB2 0.581 0.581 0.549 0.712
PEB3 0.628 0.677 0.61 0.803
PEB4 0.613 0.634 0.566 0.76
PEB5 0.545 0.599 0.611 0.725
PEB6 0.503 0.619 0.578 0.711
PEB7 0.524 0.577 0.571 0.693
PEB8 0.606 0.602 0.539 0.731
PEB9 0.471 0.578 0.5 0.654
PEB11 0.633 0.601 0.572 0.75
PEB12 0.642 0.574 0.567 0.737
PEB13 0.700 0.616 0.565 0.783
PEB15 0.682 0.599 0.551 0.762
PEB16 0.601 0.574 0.553 0.717
PEB17 0.524 0.577 0.539 0.685
PEB18 0.478 0.499 0.47 0.603
Notes: GHRM = Green HRM, ELS = Ethical leadership style,
Pro-EB = Pro-environmental behaviors.
Table 5. Fornell-Larker criterion.
Constructs 1234
ELS 0.832
GC 0.749 0.856
GHRM 0.736 0.84 0.8
PEB 0.705 0.623 0.672 0.723
Notes: GHRM = Green HRM, ELS = Ethical leadership style,
PEB = Pro-environmental behaviors, GC = Green creativity.
16 I. AHMAD ET AL.
proposed a HTMT—0.85 threshold value. Table 6 shows that HTMT
criteria have been fulfilled for our PLS model.
Evaluation of structural/inner model
Each set of independent latent variables in the inner model should be
examined through a bootstrapping technique (with a resample of 5000)
for collinearity, coefficient of determination (R2), slope coefficient (beta)
and its significance (Hair et al., 2013). In addition to these, Hair et al.
(2013) also advised rashers to report the results of model’s predictive
relevance and effect size. Hair et al. (2014, 2017) suggested variance
inflation factor (VIF) for detecting collinearity among a set of predictors.
Its value should be 5 or lower (Hair et al., 2014, 2017). Table 7 reports
Variance inflator (VIF) values for the assessment of collinearity among
predictors.
The Smart PLS results in Table 7 indicate that all VIF values are
below 5 thereby proving the absence of collinearity among predictors.
The standardized root mean square residual (SRMR)
Standardized Root Mean Square Residual (SRMR) shows the average
standardized residuals between the hypothesized and observed covariance
matrices (Chen, 2007). It measures the accuracy of an estimated model
(Brown, 2006). A structural model is considered a good fit if the SRMR
value is equal to or less than 0.08. The results reported in Table 8 show
an SRMR value of 0.047, Chi-Square value 2,272.89, and NFI value of
Table 6. Heterotrait-monotrait (HTMT).
Constructs 1 2 3 4
ELS –
GC 0.748 –
GHRM 0.733 0.84 –
PEB 0.802 0.823 0.771 –
Notes: GHRM = Green HRM, ELS = Ethical leadership style,
PEB = Pro-environmental behaviors, GC = Green creativity.
Table 7. Collinearity assessment (inner VIF
values).
Reective constructs VIF values
ELS 2.457
GHRM 2.474
ELS*GHRM-PEB 1.162
ELS*GHRM-GC 1.121
PEB 2.474
Notes: ELS = Ethical leadership style, GHRM = Green
human resource management, PEB = Pro-environmental
behaviors, GC = Green creativity.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 17
0.829 which indicate that our structural model is good fit and reliable
for analysis.
Structural estimates (hypotheses testing)
To test hypothesis (H1), that green HRM practices are positively asso-
ciated with GC (GC), we looked at the relationships between two vari-
ables. The study results confirmed positive and statistically significant
positive relationships between green HRM and GC (β = 0.50, T = 7.002
and p value < 0.01).
Mediation analysis
The Smart PLS results also endorsed our hypothesis (H2) that PEBs
mediate this positive relationship significantly, as evident from the indi-
rect effect through green HRM to PEBs and from PEBs to GC (β = 0.20,
T = 4.282 and p value < 0.01). In order to test the strength of mediation
variance accounted, a (VAF) method was employed instead of the com-
monly used Sobel test, because Sobel tests do not hold for indirect
effect, and lack statistical power (Hair et al., 2016). Calculating VAF is
considered only if the indirect effect is significant (Hair et al., 2016).
The indirect effect in our mediation model (0.20) is statistically signif-
icant (p < 0.01) as evident in Table 8. VAF determines the size of the
indirect effect in relation to the total effect (direct effect + indirect effect,
which is 0.50 + 0.20 = 0.70): VAF = 0.20/0.70 = 0.28. VAF between 0.20
and 0.80 indicates partial mediation (Hair et al., 2016). Therefore, we
conclude that PEBs partially mediates the relationship between green
HRM and GC.
Moderation analysis
Hypothesis three (H3) postulates that ELS has a significant impact on
the relationship between green HRM and PEBs, i.e. ELS moderates the
existing positive relationship between green HRM and PEBs. Moderation
analysis is carried out by applying a PLS product-indicator approach.
PLS gives comparatively more reliable results of moderator effects by
Table 8. Summary of model t.
Test Value
SRMR 0.047
d_ULS 1.612
d_G 1.226
Chi-square 2,272.89
NFI 0.829
18 I. AHMAD ET AL.
incorporating the error that weakens the estimated relationships (Ali
et al., 2016; Henseler & Fassott, 2010). In order to test the moderating
effect, green HRM (predictor) and ethical leadership style (ELS) (mod-
erator) were multiplied to create an interaction construct (green
HRM*ELS) to predict PEBs. On the basis of our study results (β = 0.155;
T = 3.00; p < 0.01), we accept this hypothesis and endorse the moderating
role of ELS on the positive relationship of green HRM and PEBs. Finally,
the moderation effect (green HRM*ELS) on GC (β = 0.190; T = 2.116;
p < 0.05) is also significant indicating a positive influence of ELS on the
positive relationship between green HRM and GC.
Moreover, the overall effect size or predictive accuracy of the model
(R2) value (0.73) predicts that green HRM explains 73.0% of variance
in PEBs. Similarly, the (R2) value (0.78) explains that green HRM and
PEBs jointly contribute 78.0% of variation in GC. R2 is considered to
be substantial at a value of 0.75, moderate at 0.50, and weak at 0.26,
respectively (Hair et al., 2014, 2017). The predicted values of R2 (0.78
& 0.73) are higher than the weak threshold level (0.26); therefore, we
conclude that our model’s predictive accuracy is strong (see Table 10).
After the evaluation of overall effect size (R2) we assessed the specific
effect sizes (f2). The p-value shows the significance of the relationship
between dependent and independent variables individually, but it does not
show the size of an effect. Therefore, both substantive significance (f2) and
statistical significance (p) must be reported (Hair et al., 2013). The f2 value
has strong, moderate and weak effects at 0.35, 0.15 and 0.02 respectively
(Cohen, 1988). Results reported in Table 9 indicate that the relationship
between green HRM and GC is strong, while all other are moderate.
The predictive relevance (Q2) measures a path model’s endogenous
latent constructs quality and is estimated by a blindfolding technique
(Chin et al., 2008). Predictive relevance (Q2) can be obtained via
cross-validated redundancy procedures in Smart PLS 3.0. The value of
a predictive relevance greater than zero (Q2 > 0) is an indication of
model predictive relevance, while a predictive relevance value less than
zero (Q2 < 0) indicates that the model is lacking predictive relevance
(Ali et al., 2016). To ensure the quality of latent construct in the path
Table 9. Hypotheses testing.
Hypotheses Beta T value p value Decision
H1: GHRM GC 0.505 7.002 0.000* Supported
H2: GHRM PEB GC 0.20 4.282 0.000* Supported
H3: GHRM*ELS PEB 0.155 3.00 0.003* Supported
H4: GHRM*ELS GC 0.190 2.116 0.035** Supported
Notes: ELS = Ethical leadership style, GHRM = Green human
resource management, PEB = Pro-environmental behaviors,
GC = Green creativity.
*p < 0.01.
**p < 0.05.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 19
Figure 2. Structural model. *p < 0.01; **p < 0.05 moderation eect. Notes: ELS = Ethical
leadership style, GHRM = Green human resource management, PEB = Pro-environmental
behaviors, GC = Green creativity.
Table 10. Eect size (f2).
ELS GC GHRM PEB
ELS 0.078 0.313
GC
GHRM 0.471 0.32
GHRM*ELS 0.117 0.057
PEB 0.346
Notes: ELS = Ethical leadership style, GHRM = Green human resource man-
agement, PEB = Pro-environmental behaviors, GC = Green creativity.
Conceptual framework. Notes: GHRM = Green HRM, ELS = Ethical leadership style,
Pro-EB = Pro-environmental behaviors.
Figure 1.
model, the calculated value of predictive relevance (Q2) must be greater
than zero. It is evident from Q2 value in Figure 2, that our endogenous
variables have acceptable values predictive relevance.
20 I. AHMAD ET AL.
Discussion and implications
Past studies on the concept of green HRM are mostly limited to qualitative
approaches, whereas fewer studies (Dumont et al., 2017; Gilal et al., 2019;
Kim et al., 2019; Ojo & Raman, 2019; Shen et al., 2018) have attempted
to bring empirical evidence of its potential implications. However, very
limited studies have explored the impact of green HRM on GC. To fill
this void, our empirical study offers a unique model by exploring the
impact of green HRM on GC of employees, with a mediating role of PEBs
and a moderating role of an ethical leadership style. Data were collected
from Pakistan, the 7th most vulnerable country in the world in relation
to the effects of global warming. The study was conducted on the premise
that employees spend at least one third of their time in the organizations
where they work (Blok et al., 2015) and therefore, organizations can play
a significant role in shaping human behaviors towards more responsible
and greener activities. The results for this study were measured via SMART
PLS 3—an increasingly popular tool for studies in management, marketing
and human resource management fields (Hair et al., 2019).
Our findings suggest that green HRM has a positive impact on GC of
employees, thereby supporting hypothesis H1. Furthermore, we proposed the
mediating role of PEBs between green HRM and GC in H2. This hypothesis
was also supported from the results. Finally, we tested the moderating role
of ELS on the relationship between green HRM and PEBs and on green
HRM and GC. Both these hypotheses were also supported from the results.
Several theoretical implications from these findings can be underlined.
From the results, we derive that green HRM has a significant role in
shaping employees’ GC. This finding is in line with the previous study
on GC in the context of human resource management by Chen and
Chang (2013), who found an impact of green dynamic capabilities on
the GC of employees. Our finding is also in line with the study of Huo
et al. (2020), who found a positive impact of green HRM on GC of
employees working in Chinese coal industry. However, they studied the
intermediary relationship of green HRM between top management team
and green creativity. We believe that among the green dynamic capa-
bilities, as explained by Chen and Chang (2013), green HRM policy
and practices is also one such capability that enables and encourage
employees to act more responsibly. One important implication from this
finding is the addition of a green outcome of green HRM in the form
of GC. Past studies emphasized the exploitation of the role of green
HRM on employees’ workplace green behaviors (Dumont et al., 2017;
Shen et al., 2018). Therefore, this study addresses this void. A second
implication is that through this study, we have proved that HRM prac-
tices has a significant role in shaping individual behaviors (Becker &
Huselid, 2006).
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 21
Third, although there are very limited empirical studies available on
GC, we found that green HRM and PEBs are key determinants of GC.
Fourth, in line with previous studies (Dumont et al., 2017), this study
also provides evidence to the premise that HRM practices influence
several employees’ behaviors through some underlying mechanism (Jiang
et al., 2012), such as PEBs in our case. We found that PEBs mediates
the relationship between green HRM and GC. Fifth, past studies have
only explored PEBs as the final outcome of certain attitudes and behav-
iors (e.g. Blok et al., 2015; Robertson & Barling, 2013; Saeed et al.,
2019). Therefore, by testing its mediating role, our study provides another
novel insight into the potential that PEBs may play a significant role
in shaping individual’s other behaviors at work, too.
Sixth, although the majority of the studies on leadership are focused
on its consequences (Robertson & Barling, 2013), this study tested the
mitigating role of a supervisor’s ELS between green HRM and GC and
between green HRM and PEBs. Our findings are in line with the pre-
vious study by Philipp and Lopez (2013), who tested the moderating
role of ELS. These hypotheses were built on the assumptions that it
may not be necessary that a supervisor’s leadership style varies from
that of organizationally-encouraged or promoted behavior. On the other
hand, green HRM practices in an organization may be uniform across
the organization. We found that a supervisor’s ELS will play the mod-
erating role such that the relationship of green HRM with PEBs and
GC is stronger if a supervisor is more ethical and vice versa. Ethical
leaders have a sense of responsibility, and not only pursue the organi-
zational objectives but also care about the larger benefit of the commu-
nity (Trevino et al., 2003). Therefore, there is a natural alliance or fit
between the characteristics of an ethical leader and green HRM practices.
Seventh, this study responded to the call for empirical studies on green
HRM because a majority of the past studies on this notion are limited to
the conceptualization of its outcomes (e.g. Cherian & Jacob, 2012; Daily &
Huang, 2001; Jabbour, 2011; Jackson & Seo, 2010; Renwick et al., 2013).
Limited studies did explore the impact of green HRM on PEBs (Dumont et
al., 2017; Saeed et al., 2019; Shen et al., 2018). However, more studies are
still needed to explore the vast potential of green HRM and its implications
(Ahmad & Umrani, 2019; Shen et al., 2018). In this context, our study adds
to the extant literature on green HRM with a novel model, a strong theo-
retical underpinning, and novel consequent outcomes of green HRM.
Several practical implications can also be noted from the findings of
this study. First, this study highlights the importance of green HRM
practices in the aftermath of widespread calls from various stakeholders
of different organizations regarding responsible business practices to reduce
the impacts of global warming (Ahmad et al., 2018). Through empirical
22 I. AHMAD ET AL.
evidence, this study has proved that green HRM practices are among
those responsible initiatives that organizations can adopt. This will not
only help in addressing the concerns of stakeholders, but organizations
can also extract numerous benefits from the same in the form of various
positive outcomes such as enhancing employees’ GC and their PEBs.
Organizations can also help in improving the relationships with their
employees through green HRM practices. We utilized social identity
theory to demonstrate that employees make fine judgements regarding
the organizational policies and practices and make decisions based on
their judgements. Therefore, while working in an organization where
responsible business actions are encouraged through green HRM, employ-
ees will be more engaged, and they will feel pride by having an association
with such an organization. Studies suggests that the stronger the identi-
fication of an employee with the organization, the stronger will be his/
her commitment and performance (Farooq et al., 2014). Therefore, fol-
lowing setting up the green agenda and targets, organizations must adopt
green HRM policies and practices for achieving the same (Shen et al.,
2018). Resultantly, the business case for green HRM can also be proved
from this study, which may give a competitive advantage in the future.
In order to enable employees to work on green agendas, organizations
should arrange various developmental and awareness sessions. Similarly,
at the time of recruitment and selection, assessing candidates on green
attributes and behaviors is important to hire people with the right green
attitude. Moreover, for reinforcing greener attitudes and behaviors, employ-
ees must be apprised and rewarded based on their greener achievements.
Another important dimension of practical implications is the supervisor’s
style of management, which can play a significant role in shaping employees’
behaviors to achieve a green agenda. For this purpose, supervisors must
adopt an ethical leadership style in their day to day dealings with the
employees. Since ELS is rooted in ethics and morality, where leaders behave
responsibly by taking care of all the stakeholders, this premise is in line
with green HRM and therefore, employees expect such a responsible attitude
from their supervisors as well as the top management. Hence, top man-
agement who sets green agenda/targets must ensure that supervisors also
understand its importance and ensure the same behaviors. A strong ethical
culture must be promoted at all levels of the organization and supervisors
must also be held responsible for adhering to this culture. Similarly, for
promoting PEBs among employees, supervisors need to adopt a more ethical
leadership style, as supported by the results of this study.
Finally, this study has important practical implications for the policy
makers of the Pakistani government in particular, and for the rest of
the world generally. Being the 7th most vulnerable country regarding
the negative effects of global warming, it is now time for the country
to have a holistic and strategic approach for addressing the challenge
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 23
of global warming. The Government of Pakistan (GoP) must encourage
public and private sector organizations to set green targets and contribute
their fair share towards protecting the carbon footprint. For this purpose,
they must strengthen the existing policies and their implications in true
spirit. Similarly, for organizations to play a positive role, they can be
given tax breaks or rebates as incentives for achieving their green targets.
Limitations and future research
This study was conducted in a Pakistani context only and therefore, it
may have limited generalizability for other countries. Similar studies
may be repeated in other developed countries and the results compared
to further the generalizability of our study. Secondly, we have taken all
the precautions to minimize the issue of common method variance
(CMV)—a natural tendency in the cross-sectional study. For example,
we collected date from two sources (i.e. employee and supervisor dyed).
Resultantly, the outcomes suggest that CMV is not a grave concern in
the present study. However, in order to completely eliminate CMV,
longitudinal studies on this topic are recommended in the future. Thirdly,
we have taken only one green outcome in our study. Future studies may
include other green outcomes to fully understand its potential. One such
variable can be green recovery performance, which relies on the green
behaviors of employees. Fourth, we collected data from various organi-
zations from three broad sectors (i.e. banking, NGOs and education)
which condenses our ability to understand the role of one particular
sector. As such, in its present form, our study is more general than
specific in nature, as far as the industry is concerned. This limitation
also curtailed our ability to understand the difference of green HRM
practices across different organizations. We do, however, control for the
organization type and other demographic variables such as age, gender
qualification, designation, industry, and work experience. Future studies
are encouraged to choose one particular industry/organization and
explore the impacts of green HRM in the same. One such industry in
particular is the manufacturing sector; for example, steel and chemical
factories, which are damaging the environment (Van & Hai, 2015) could
be reviewed. Fifth, the unit of analysis in this study is at an individual
level, which is a growing trend in green HRM scholarship (Ren et al.,
2018). More multi-level studies may be needed to further illuminate the
relationship between green HRM practices from the organization level
and its impacts on the individual. Finally, the study took only green
outcome, i.e. GC. Dumont et al. (2017) argued that there may other
non-green outcomes as well that will be affected from green HRM
practices. Therefore, future studies on green HRM should also explore
its impacts on non-green attitudes and behaviors of employees.
24 I. AHMAD ET AL.
Disclosure statement
No potential conflict of interest was reported by the authors.
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Appendix: Testing of moderation via PROCESS macro (model 1 &
model 7)
We performed additional tests of moderation in order to bring more rigor to our study
as well as to tackle the challenge of controlling the eects of demographic variables.
e results of the same are given below.
Moderating role of ELS between green HRM and green creativity
For testing the moderating role of ELS between green HRM and green creativity,
PROCESS macro using model 1 was employed. e results indicate (Table A1) that the
moderating role of ELS between green HRM and green creativity is sustained at all lev-
els because the estimates did not contain zero i.e. [ELS = 0.5705, p < 0.01, ULCI = 0.7116,
LLCI = 0.4294 at 1.8; 0.4577, p < 0.01, ULCI = 0.5457, LLCI = 0.3698 at 3.3; and 0.3826,
p < 0.01, ULCI = 0.4790, LLCI = 0.2861 at 4.3]. erefore, the moderating role of ELS is
conrmed between the direct relationship between green HRM and green creativity.
Full Result
Run MATRIX procedure:
**************** PROCESS Procedure for SPSS Version
3.1 ******************
Written by Andrew F. Hayes, Ph.D. www.afhayes.com
Documentation available in Hayes (2018). www.guilford.
com/p/hayes3
********************************************************
******************
Model : 1
Y : GreenCre
X : GreenHRM
W : ELS
Covariates:
Gender Age Qualic Designat Industry Work.Exp PEB
Table A1. Conditional eects of the focal
predictor at values of the moderator(s).
ELS Eect se tpLLCI ULCI
1.8000 .5705 .0717 7.9526 .0000 .4294 .7116
3.3000 .4577 .0447 10.2359 .0000 .3698 .5457
4.3000 .3826 .0491 7.7994 .0000 .2861 .4790
30 I. AHMAD ET AL.
Sample
Size: 378
********************************************************
******************
OUTCOME VARIABLE:
GreenCre
Model Summary
RR-sq MSE Fdf1 df2 p
.8474 .7180 .3345 93.4477 10.0000 367.0000 .0000
Model
Coe se tpLLCI ULCI
constant −.5213 .3123 −1.6692 .0959 −1.1355 .0928
GreenHRM .7058 .1219 5.7878 .0000 .4660 .9456
ELS .3362 .0939 3.5793 .0004 .1515 .5209
Int_1 −.0752 .0316 −2.3753 .0180 −.1374 −.0129
Gender .0005 .0642 .0082 .9935 −.1258 .1268
Age .0123 .0447 .2760 .7827 −.0756 .1003
Qualic −.0518 .0537 −.9651 .3351 −.1574 .0538
Designat .0195 .0594 .3286 .7426 −.0973 .1364
Industry .0304 .0367 .8277 .4084 −.0418 .1025
Work.Exp .0588 .0472 1.2466 .2134 −.0340 .1515
PEB .3744 .0551 6.7984 .0000 .2661 .4826
Product terms key:
Int_1 : GreenHRM x ELS
Test(s) of highest order unconditional interaction(s):
R2-chng Fdf1 df2 p
X*W .0043 5.6421 1.0000 367.0000 .0180
----------
Focal predict: GreenHRM (X)
Mod var: ELS (W)
Conditional eects of the focal predictor at values
of the moderator(s):
ELS Eect se tpLLCI ULCI
1.8000 .5705 .0717 7.9526 .0000 .4294 .7116
3.3000 .4577 .0447 10.2359 .0000 .3698 .5457
4.3000 .3826 .0491 7.7994 .0000 .2861 .4790
*********************** ANALYSIS NOTES AND ERRORS
************************
Level of condence for all condence intervals in
output:
95.0000
W values in conditional tables are the 16th, 50th,
and 84th percentiles.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 31
NOTE: Variables names longer than eight characters
can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
Moderating role of ELS between the mediated relationship of green HRM and
green creativity via PEB
For testing the moderating role of ELS between the indirect relationship of green
HRM and green creativity through PEB, PROCESS macro using model 7 was employed.
e results indicate (Table A2) that the moderating role of ELS between the indirect
relationship of green HRM and green creativity through PEB is sustained at all levels
because the estimates did not contain zero i.e. [ELS = 0.4553, p < 0.01, ULCI = 0.5804,
LLCI = 0.3302 at 1.8; 0.3095, p < 0.01, ULCI = 0.3865, LLCI = 0.2325 at 3.3; and
0.2123, p < 0.01, ULCI = 0.3010, LLCI = 0.1236 at 4.3]. erefore, the moderating role
of ELS is conrmed between the indirect relationship between green HRM and green
creativity through PEB.
Run MATRIX procedure:
**************** PROCESS Procedure for SPSS Version
3.1 ******************
Written by Andrew F. Hayes, Ph.D. www.afhayes.com
Documentation available in Hayes (2018). www.guilford.
com/p/hayes3
********************************************************
******************
Model : 7
Y : GreenCre
X : GreenHRM
M : PEB
W : ELS
Covariates:
Table A2. Conditional eects of the focal
predictor at values of the moderator(s).
ELS Eect se tpLLCI ULCI
1.8000 .4553 .0636 7.1557 .0000 .3302 .5804
3.3000 .3095 .0391 7.9076 .0000 .2325 .3865
4.3000 .2123 .0451 4.7074 .0000 .1236 .3010
32 I. AHMAD ET AL.
Gender Age Qualic Designat Industry Work.Exp
Sample
Size: 378
********************************************************
******************
OUTCOME VARIABLE:
PEB
Model Summary
RR-sq MSE Fdf1 df2 p
.8246 .6800 .2998 86.9020 9.0000 368.0000 .0000
Model
Coe se tpLLCI ULCI
constant −.5971 .2940 −2.0307 .0430 −1.1752 −.0189
GreenHRM .6303 .1107 5.6952 .0000 .4127 .8479
ELS .6860 .0814 8.4276 .0000 .5260 .8461
Int_1 −.0972 .0295 −3.2921 .0011 −.1553 −.0391
Gender .1836 .0600 3.0579 .0024 .0655 .3017
Age .1418 .0417 3.4034 .0007 .0599 .2238
Qualic .0061 .0508 .1194 .9050 −.0939 .1060
Designat .0272 .0562 .4838 .6288 −.0834 .1378
Industry −.0186 .0347 −.5355 .5926 −.0868 .0497
Work.Exp .0296 .0446 .6641 .5070 −.0581 .1174
Product terms key:
Int_1 : GreenHRM x ELS
Test(s) of highest order unconditional interaction(s):
R2-chng Fdf1 df2 p
X*W.0094 10.8382 1.0000 368.0000 .0011
----------
Focal predict: GreenHRM (X)
Mod var: ELS (W)
Conditional eects of the focal predictor at values
of the moderator(s):
ELS Eect se tpLLCI ULCI
1.8000 .4553 .0636 7.1557 .0000 .3302 .5804
3.3000 .3095 .0391 7.9076 .0000 .2325 .3865
4.3000 .2123 .0451 4.7074 .0000 .1236 .3010
********************************************************
******************
OUTCOME VARIABLE:
GreenCre
Model Summary
RR-sq MSE Fdf1 df2 p
.8403 .7061 .3468 110.8195 8.0000 369.0000 .0000
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 33
Model
Coe se tpLLCI ULCI
constant .1721 .1919 .8969 .3704 −.2052 .5494
GreenHRM .4815 .0419 11.4823 .0000 .3990 .5639
PEB .4889 .0464 10.5296 .0000 .3976 .5802
Gender −.0478 .0642 −.7445 .4570 −.1740 .0784
Age .0012 .0454 .0266 .9788 −.0881 .0905
Qualic −.0517 .0539 −.9584 .3385 −.1578 .0544
Designat .0229 .0603 .3806 .7037 −.0956 .1415
Industry .0302 .0373 .8090 .4190 −.0432 .1036
Work.Exp .0637 .0479 1.3306 .1842 −.0305 .1579
****************** DIRECT AND INDIRECT EFFECTS OF X
ON Y *****************
Direct eect of X on Y
Eect se tpLLCI ULCI
.4815 .0419 11.4823 .0000 .3990 .5639
Conditional indirect eects of X on Y:
INDIRECT EFFECT:
GreenHRM -> PEB -> GreenCre
ELS Eect BootSE BootLLCI BootULCI
1.8000 .2226 .0513 .1326 .3317
3.3000 .1513 .0326 .0947 .2228
4.3000 .1038 .0297 .0505 .1686
Index of moderated mediation:
Index BootSE BootLLCI BootULCI
ELS −.0475 .0186 −.0863 −.0139
---
*********************** ANALYSIS NOTES AND ERRORS
************************
Level of condence for all condence intervals in
output:
95.0000
Number of bootstrap samples for percentile bootstrap
condence intervals:
5000
W values in conditional tables are the 16th, 50th,
and 84th percentiles.
NOTE: Variables names longer than eight characters
can produce incorrect output.
Shorter variable names are recommended.
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