Adopting human resource information system and work-related outcomes in emerging market SMEs: unified theory of acceptance and use of technology

Abstract

Purpose
This study investigates the psychological adoption of technology in relation to employees' mental beliefs about using technology in their workplace, because it is necessary to investigate the direct and indirect effects of information systems (IS) on employees' work-related results that underpin creativity and engagement.

Design/methodology/approach
Using a cross-sectional design, data were collected from 153 human resource (HR) employees who used human resource information systems (HRIS) in small- and medium-sized enterprises (SMEs) in Malaysia.

Findings
The results show that effective acceptance and adoption of an HRIS enables HR employees and management in SMEs to be creative, balanced and engaged. Facilitating conditions and task-technology fit positively affect the behavioral intention to accept and adopt an HRIS. Additionally, organizational citizenship behavior moderates the relationship between the behavioral intention to accept and adopt an HRIS and employee creativity.

Originality/value
This study significantly advances the fields of human resource management and IS by elucidating the factors influencing employees' adoption of technology. In an effort to address a research gap in existing research, it introduces a unified theory of acceptance and use of technology, which precedes the psychological adoption process by individuals. Furthermore, it offers both empirical and theoretical insights into the interplay between technology adoption factors and their subsequent impact on work-related outcomes.

Full text

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Adopting human resource information system and work-related outcomes in emerging
market SMEs: The unified theory of acceptance and use of technology
Javad Shahreki
Lecturer (Assistant Professor)
Faculty of Business Management and Professional Studies,
Management and Science University, 40100 Shah Alam, Malaysia
E-mail: Javad_shahreki@msu.edu.my
Jeoung Yul Lee*
Chongqing Bayu Chair Professor
School of Business Administration
Chongqing Technology and Business University
Chongqing 400067, China
&
Professor of International Business
School of Business Management, Hongik University
Sejong 30016, South Korea
E-mail: jeoungyul@hongik.ac.kr
Running Head: Software Solution and HR
*Corresponding author

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Adopting human resource information system and work-related outcomes in emerging
market SMEs: The unified theory of acceptance and use of technology
Abstract
Purpose – This study investigates the psychological adoption of technology in relation to
employees’ mental beliefs about using technology in their workplace, because it is necessary
to investigate the direct and indirect effects of information systems (IS) on employees’ work-
related results that underpin creativity and engagement.
Design/methodology/approach – Using a cross-sectional design, data were collected from
153 human resource (HR) employees who used human resource information systems (HRIS)
in small- and medium-sized enterprises (SMEs) in Malaysia.
Findings – The results show that effective acceptance and adoption of an HRIS enables HR
employees and management in SMEs to be creative, balanced, and engaged. Facilitating
conditions and task-technology fit positively affect the behavioral intention to accept and
adopt an HRIS. Additionally, organizational citizenship behavior moderates the relationship
between the behavioral intention to accept and adopt an HRIS and employee creativity.
Originality/value – This study significantly advances the fields of human resource
management and IS by elucidating the factors influencing employees’ adoption of
technology. In an effort to address a research gap in existing research, it introduces a unified
theory of acceptance and use of technology, which precedes the psychological adoption
process by individuals. Furthermore, it offers both empirical and theoretical insights into the
interplay between technology adoption factors and their subsequent impact on work-related
outcomes.
Keywords Technology adoption factors, e-HRM, Psychological adoption, Behavioral
intention, Creativity, Employee engagement, Work-life balance
Paper type Research paper

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1. Introduction
The rapid growth of information technologies facilitates human resource (HR) functions
through the Human Resource Information System (HRIS). Owing to the increasing tendency
of organizations to rely mostly on information and communication technologies (Bondarouk
and Brewster, 2016), most companies have been exploring the use of HRIS to manage, support,
and integrate their HR to boost their HR performance (Shahreki et al., 2019; Maier et al., 2013).
This acceptance and adoption of new information technologies facilitates HR employees in
changing their roles from administrative to strategic business functions (Shahreki, 2019b;
Obeidat, 2012; Troshani et al., 2011; Quaosar et al., 2018; Ribeiro-Navarrete et al., 2021).
According to Johnson et al. (2016), the HRIS facilitates employee interactions and effective
HR outcomes, and improves employees’ work-life balance. HRIS technologies are popularly
used in large organizations that adopt them at a rapid pace, but small and medium-sized
enterprises (SMEs) have not yet adopted them, refusing to invest in them because of a lack of
resources (Esangbedo et al., 2021).
Organizations have experienced numerous changes over the past 20 years as a result of
factors such as globalization, rapid technological improvement, emergence of a knowledge-
based economy, and competitiveness. Consequently, to keep pace with organizational changes,
HR operations have evolved quickly. Consequently, the previously practiced traditional HR
system has become obsolete and insufficient over time (Noutsa et al., 2017; Bondarouk et al.,
2017a; Marler and Parry, 2016; Strohmeier and Kabst, 2014). An HR management system, also
known as an HRIS or Human Capital Management, is a type of HR software-based system that
includes several systems and processes to facilitate the administration of HR, business
processes, and data (Shahreki et al., 2019; Oehlhorn et al., 2020; Johnson et al., 2016). Thus,
an HRIS is a system that collects and stores information about an organization’s employees,
such as their name, age, address, income, performance reviews, benefits, time, and attendance

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(Shahreki et al., 2023; Zhou et al., 2022; Buzkan, 2016). Thus, HRIS can efficiently assist
businesses in managing and automating basic HR procedures. These HR software solutions
help companies and their HR with benefits administration, payroll, time and attendance, and
other activities, as well as the storage of employee data, such as personal, demographic, and
salary information (Obeidat, 2012; Hussain et al., 2007). Thus, an HRIS is a system for
gathering, storing, managing, analyzing, retrieving, and disseminating data about a company’s
HR (Kavanagh and Johnson, 2017). Therefore, an HRIS is not just computer equipment and
related software that can be used to manage HR in companies. It consists of people, forms,
rules and processes, and data, in addition to hardware and software.
Furthermore, the goal of HRIS is to serve the system’s “clients” by giving them accurate
and timely information. HR data can be utilized for strategic, tactical, and operational decision-
making because they have a wide range of possible consumers (e.g., HR data’s necessity to
prepare for many workers who are needed in a merger and acquisition). Further, HR data are
needed to avoid legal action (e.g., to discover discrimination issues in hiring); to assess policies,
practices, or programs (e.g., to determine whether a training program is effective); and to
facilitate HR’s daily activities (e.g., to aid HR management in keeping track of their workers’
attendance and timekeeping). Considering all these applications, it follows that reliable and
timely data and reports, as well as clients’ comprehension of the information’s intended use,
are essential (Shahreki et al., 2019; Stone et al., 2015; Strohmeier and Piazza, 2013; Shahreki
et al., 2023; Buzkan, 2016; Kovach and Cathcart Jr, 1999; Hannon et al., 1996).
Moreover, HR functions are developed and updated using HRIS-based platforms
(Buzkan, 2016; Maier et al., 2013). Strohmeier (2020) and Shahreki et al. (2019) proposed that
the triple influence of using information and communication technology on the HR task and
their findings revealed that HRIS has an impact on the HR function in relational (e.g.,
recruiting, training, and performance management) and operational (e.g., activities related to

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administration, payroll, and employee information) ways for efficient HR management. HRIS
plays an essential role in the success of HR functions, and leads to accurate employee
performance (Marler and Parry, 2016; Maamari and Osta, 2021). According to Strohmeier
(2007) and Obeidat (2012), the benefits from utilizing HRIS can facilitate access to
information, increase speed of response, administrative efficiency, and improve decision-
making and reporting. Moreover, HRIS influences the HR operational and strategic
management (Stone and Dulebohn, 2013). However, employees must accept HRIS technology
for their adoption (Marler and Parry, 2016; Strohmeier and Kabst, 2014). Moreover, this
disagreement affects employees’ outcomes, such as doing their duties well, or their job
satisfaction (Strohmeier, 2009), creativity, productivity, and engagement (Bondarouk et al.,
2009; Strohmeier, 2007; Ruel et al., 2007).
Furthermore, nowadays, SMEs play a crucial role in their country’s social and
economic development. With the emergence of cloud computing, these organizations are
interested in adopting new technologies, such as HRIS, which greatly improve their daily
activities. However, most organizations are reluctant to adopt these new technologies because
of their employees’ unwillingness. Thus, organizations’ managers should improve HRIS
adoption levels by enhancing employees’ perceptions of psychological adoption.
Behavioral intention is the motivational element that influences a certain behavior in a
way that the stronger the intention to execute the behavior, the more likely for it to be performed
(Bondarouk et al., 2017b; Bondarouk and Brewster, 2016). The desire or interest to undertake
specific behaviors, often known as someone’s willingness to perform the behavior, is referred
to as behavioral intention. As such, “the unified theory of acceptance and use of technology
(UTAUT)” argues and demonstrates that behavioral intention has a direct positive impact on
the adoption of technology (Suki and Suki, 2017; Rana and Dwivedi, 2015; Venkatesh et al.,
2003). The UTAUT model explains and predicts individuals’ behavioral intentions to use a

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new technology and their subsequent technology utilization. This model has been extensively
employed and expanded in various research contexts to understand and predict technology
adoption in different domains. In this UTAUT model, the four dimensions of psychological
adoption are enthusiasm, mental acceptance, usage commitment, and effort worthiness.
Heightened enthusiasm indicates how eagerly a person approaches technologically related
behaviors. In principle, the extent to which a user perceives an artifact as a good concept is
referred to as mental acceptance. The level of commitment to using technology, regardless of
it being mandated, is referred to as use commitment. A user’s positive judgement of the return
on resources expended to use a technology is referred to as effort worthiness (Karahanna and
Agarwal, 2006b; Heidenreich and Talke, 2020b; Li et al., 2013). In this vein, the present study
focuses on employees’ psychological adoption of technology to analyze the usefulness of HRIS
in relation to employee’s behaviors. Employees take a long time to adopt new technology, and
they adopt it psychologically in certain way. Accordingly, psychological adoption means
users’ mental evaluation of the technology which they employ at work.
Indeed, several studies have investigated the role of Information System (IS) in
creativity (Pacauskas and Rajala, 2017; Tiwana and McLean, 2005) and employee engagement
(Molino et al., 2020; Strom et al., 2014). A literature review revealed that only 6% of creativity
is based on each individual, and the remaining 94% is provided by system-technological
support, processes, and other variables, such as the rewards system, environment, and training
(Pacauskas and Rajala, 2017; Seidel et al., 2010). Therefore, it is necessary to investigate the
direct and indirect effects of IS on employees’ work-related results that underpin creativity and
engagement (Pacauskas and Rajala, 2017; Njoku and Ebie, 2015; Olszak et al., 2018; Chandna
and Krishnan, 2009).
This study used UTAUT to check employees’ psychological adoption of an HRIS and
their work-related results, because previous studies only focused on the very simple meaning

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of psychological adoption (Virdyananto et al., 2016; Prasanna and Huggins, 2016; Dey and
Saha, 2020; Poba-Nzaou et al., 2016), rather than psychological adoption with work-related
results. Therefore, to fill these research gaps, this study investigated the psychological adoption
role of HRIS and the aforementioned elements. In sum, it examined prior HRIS psychological
adoption and its impact on employees’ work-related creativity, engagement, and work-life
balance.
2. Theoretical Background
HRIS is a united system that accelerates, automates, and helps HR employees manage
information and facilitate their duties (Shahreki et al., 2019). It is a critical tool for HR
employees to facilitate data transfer (Maier et al., 2013) along with organizations’ procedures
and policies (Bondarouk and Brewster, 2016). Additionally, HRIS instruments help reach
strategic value for all HR performance (Hussain et al., 2007), such as tracking employees’
recruitment, engagement level, performance, payroll, innovativeness, and turnover (Shahreki
et al., 2020b; Dey and Saha, 2020; Bondarouk and Brewster, 2016; Maier et al., 2013; Obeidat,
2012). Overall, HRIS can improve the effectiveness of all HR functions, such as easing
employees’ daily activities, providing a problem-solving dashboard, and simplifying complex
procedures precisely at a low cost to support HR strategies that organizations use to achieve
their goals (Shahreki, 2019a; Marler and Parry, 2016; Schalk et al., 2013). According to
Johnson et al. (2016), a combination of the HR and HRIS functions allows employees to
perform their tasks accurately and immediately. Thus, HR employees can access information
using HRIS to simplify their decisions (Esangbedo et al., 2021; Quaosar et al., 2018).
Therefore, according to Hussain et al. (2007), HRIS technologies facilitate workforce
procedures, administration, and benefits; they offer a higher analysis of performance
management, suggesting that all of them have been performed using HRIS. Despite all the

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advantages that HRIS provides to organizations, SMEs do not adopt it in their workplaces
(Bondarouk et al., 2017a). Employee acceptance plays an important role in the acceptance and
adoption of new technologies (Alhammadi et al., 2023; Sun et al., 2023). A review of the
existing literature reveals that 50% of IS has failed because of employee resistance and refusal
(Heidenreich and Talke, 2020a; Bartis and Mitev, 2008; Pan et al., 2008).
Several models and theories have been proposed in the literature to explore employees’
acceptance of new information technology, such as the UTAUT, innovation diffusion theory
(IDT), theory of reasoned action (TRA), technology acceptance model (TAM), theory of
planned behavior (TPB), combined TAM and TPB (C-TAM-TPB), social cognitive theory
(SCT), model of PC utilization (MPCU), and motivational model (MM). Among these, the
UTAUT model has gained attention as an integrated model that combines all prior models
(Venkatesh et al., 2012) in the literature. According to Lai (2017), the UTAUT (Venkatesh et
al., 2003), TPB (Ajzen, 1991), and TAM (Davis, 1989) are the most popular models of relevant
topics. Despite its popularity, the TAM has been criticized by many researchers because it does
not consider social and human factors (Boonsiritomachai and Pitchayadejanant, 2019).
Therefore, this study uses the UTAUT model and information quality construct as the
background for the psychological adoption of HRIS. According to the UTAUT theoretical
model, behavioral intention controls the actual use of technology. The UTAUT has contributed
to the literature in several ways. By comparing well-known technology acceptance theories
that frequently offer contradictory or partial viewpoints on the subject, the model provides
empirical insights into technology acceptance (Venkatesh et al., 2003; Venkatesh et al., 2012).
The UTAUT reveals that the proposed components explain 70% of the variance in usage
intention (Venkatesh et al., 2003), providing greater predictive power than other models that
analyze technological adoption (e.g., Davis, 1993).

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Indeed, the purpose of the UTAUT is to clarify user intentions when using an IS and
their subsequent usage patterns. The idea was created by reviewing and combining the eight
models’ constructs, which have been used by earlier studies to explain how people used IS
(Venkatesh and Bala, 2008; Venkatesh et al., 2003; Venkatesh et al., 2012). Owing to the large
number of components, the UTAUT model has the best explanatory power of any standard
acceptance model, and thus aids in the technology development process. According to the
UTAUT theoretical model, behavioral intention determines real technology use (Suki and Suki,
2017; Rana and Dwivedi, 2015; Venkatesh et al., 2012; Venkatesh et al., 2003).
In summary, compared to other information technology acceptance models, the
UTAUT model proposes more precise outcomes (Venkatesh et al., 2012), despite attempts to
combine it with other models to improve its instructive power (Shahreki et al., 2019; Quaosar
et al., 2018; Virdyananto et al., 2016; Attuquayefio and Addo, 2014). Therefore, our UTAUT
model and information quality constructs based on the IS success model are to investigate the
psychological adoption factors of HRIS employees. The UTAUT includes social influence,
effort expectancy, facilitating conditions, and performance expectancy. When effort
expectation is absent, employees’ intentional behavior is facilitated because it is based on the
effort expectancy construct (Venkatesh et al., 2012; Venkatesh and Bala, 2008). This study
investigates the relationship between information quality, effort expectancy, performance
expectancy, social influence, facilitating conditions, and task technology fit with behavioral
intention to accept and adopt HRIS, work-related engagement, work-life balance, and
creativity.
2.1 Adoption of Information System (IS)
IS affects HR performance and employees’ work-related results (Aydiner et al., 2019; Laumer
et al., 2012; Cho et al., 2006; Martinez-Simarro et al., 2015). Several studies support HRIS

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adoption and explore its relationship with engagement (Molino et al., 2020; Ibrahim et al.,
2020; Albrecht et al., 2015; Strom et al., 2014; Rurkkhum and Bartlett, 2012; Heslina and
Syahruni, 2021), creativity (Hughes et al., 2018; Pacauskas and Rajala, 2017; Njoku and Ebie,
2015; Squalli and Wilson, 2014; Seidel et al., 2010; ), and work-life balance (Ratna and Kaur,
2016; Gopinathan and Raman, 2016; Adisa et al., 2017; Bardoel and Drago, 2016; Oosthuizen
et al., 2016).
Performance Expectancy (PE)
PE is defined as employees’ beliefs that accepting and adopting HRIS would improve their job
functions. Venkatesh et al. (2012) defined PE as “the degree to which an individual believes
that using the system will help him or her to attain gains in job performance.” According to
Venkatesh and Bala (2008) and Davis (1993), TAM perceived usefulness is fundamental in
PE, where perceived usefulness is “the degree to which a person believes that using a particular
system would enhance his or her job performance.” Effectively, this factor is based on the
psychological adoption of technology in an organization (Prasanna and Huggins, 2016;
Venkatesh et al., 2012). The outcome expectations of accepting and adopting the new IS impact
HR employees because they enhance work autonomy and provide opportunities for employees
by decreasing their anxiety and facilitating work-life balance. Bauwens et al. (2020) showed a
relationship between employees’ work–life balance and performance expectancy.
Engagement with learning, using, and having fun accepting and adopting a new IS,
especially HRIS modules, should be interesting for employees (Heslina and Syahruni, 2021;
Molino et al., 2020; Strom et al., 2014). Therefore, the benefits of HRIS make employees more
engaged with their work-related performance. Many studies have examined the relationship
between employees’ engagement and IS, revealing a significant relationship between

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employees’ engagement and performance expectancy (Molino et al., 2020; Strom et al., 2014;
Rurkkhum and Bartlett, 2012; Heslina and Syahruni, 2021).
System-technological support accounts for 94% of creativity (Pacauskas and Rajala,
2017). Other factors, such as training, reward systems, and the environment, crucially influence
creativity. According to Eisenberger and Aselage (2009) and Shahreki et al. (2020a), extrinsic
motivation is an inspiring factor that enhances employees’ creativity. Overall, employees can
be effective problem-solvers when they perceive performance expectancy, which is positively
related to their rewards and results. Therefore, we posit:
H1. There is a positive relationship between PE and a) creativity, b) engagement, and c)
work-life balance.
Effort Expectancy (EE)
According to Venkatesh et al. (2012), EE is the employees’ beliefs toward HRIS’s ease of use,
which means “the degree of ease associated with the use of the system,” and it is founded on
the perceived ease of TAM use. Perceived ease of use can be defined as “the degree to which
a person believes that using a system would be free of effort” (Venkatesh and Bala, 2008;
Rauniar et al., 2014; Marangunić and Granić, 2015). A significant relationship exists between
employees’ psychological adoption of IS and this construct (Virdyananto et al., 2016; Prasanna
and Huggins, 2016). This system’s ease of use leads to employees’ greater self-efficacy and
lower anxiety (Aruldoss et al., 2021; Adnan Bataineh, 2019). Additionally, it increases
employees’ self-efficacy by decreasing their stress, as new IS eases their duties. In general,
new IS increases employees’ dedication, absorption, work performance, and engagement to be
wholly present in their workplace.
According to Venkatesh et al. (2012), effort expectancy is another factor that increases
employees’ accessibility to the complete usage of any IS. Employees’ engagement with the
new IS leads to their active response to work-related functions by providing higher creativity

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and new solutions to organizational issues (Liu et al., 2016; Ferreira et al., 2020). Information
technology automation decreases the number of daily tasks, and offers more opportunities to
think and apply their full creativity (Lee et al., 2020; Olszak et al., 2018; Pacauskas and Rajala,
2017; Abdullah et al., 2016). Therefore, we propose:
H2. There is a positive relationship between EE and a) creativity, b) engagement, and c)
work-life balance.
Social Influence (SI)
SI is an employee’s perception that others’ suggestions are important, implying that others
believe that employees should apply new IS. According to Venkatesh et al. (2012), SI is “the
degree to which an individual perceives that important others believe that he or she should use
a new system.” It is “the person’s perception that most people who are important to him think
he should or should not perform the behavior in question” (Venkatesh et al., 2003). Employees’
intentions to apply new technology are mostly inspired by their perceptions and thoughts about
their immediate situation (Myllymaki, 2021; Bondarouk et al., 2017a), which is a strong
anticipator of their psychological adoption of IS (Heslina and Syahruni, 2021; Hussain et al.,
2007). Employees’ perceptions refer to their social belief that they need a new IS, such as HRIS
(Shahreki et al., 2019; Johnson et al., 2016), that directs employees to a more satisfying and
productive environment. Therefore, employees engage in duties that are important to others
(Venkatesh and Bala, 2008) to increase their engagement with the new IS. Moreover, SI leads
employees to focus on creating valuable and novel ideas (Lee et al., 2020; Hughes et al., 2018).
Therefore, we propose:
H3. There is a positive relationship between SI and a) creativity, b) engagement, and c)
work-life balance.

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Information Quality (IQ)
IQ can be considered as an employee’s perception of the characteristics of IS outcomes
(Aydiner et al., 2019; Uwizeyemungu et al., 2018). According to DeLone and McLean (2003),
IQ is “the desirable characteristics of the system output.” This output should have appropriate
characteristics, such as being useable, comprehensive, easy-to-understand, relevant, concise,
accurate, and timely (Tummers et al., 2019; Martins et al., 2019; Bondarouk and Brewster,
2016). Muller and Ulrich (2013), defined ISs as “problems, provokes opportunities, compiles
relevant information, generates new ideas or concepts, and evaluates and prioritizes ideas for
implementation.” IQ facilitates employees’ duties, reduces stress, supports healthy
relationships with other staff, and aids in managing others and organizations (Gopinathan and
Raman, 2016; Kankanhalli et al., 2011). According to Prasanna and Huggins (2016) and
Virdyananto et al. (2016), the IQ is a predictor of psychological adoption. Stvilia et al. (2007)
and Gandhi et al. (2015) proposed that a good IS helps employees perform their tasks faster
with more dedication, to engage with it completely, which leads to their acceptance and
adoption, cognitive, and absorption commitment (Maier et al., 2013; Laumer et al., 2012. Thus,
we posit:
H4. There is a positive relationship between IQ and a) creativity, b) engagement, and c)
work-life balance.
2.2 Behavioral Intention to Accept and Adopt HRIS
Motivating elements affect a certain activity, where the stronger the intention to engage in a
behavior, the more likely for the behavior to be performed. For instance, a person is more likely
to have a favorable attitude toward a behavior if family, friends, and colleagues approve of it,
which will strengthen their intention to see the particular action through completion ( Dey and
Saha, 2020; Noutsa et al., 2017). Klonglan and Coward (1970) describe psychological
adoption as the mental acceptance of innovation as a good idea for HR. As stated by Nelson et

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al. (2014), the behavioral intention to accept and adopt an HRIS can be defined as a highly
motivated psychological state that represents a user’s mental evaluation on the technology and
its utilization. HRIS affects employees’ HR functions and work-related results within an
organization (Kavanagh and Johnson, 2017; Buzkan, 2016; Teo et al., 2007).
In this sense, the behavioral intention to accept and adopt HRIS plays a significant role
in the actual adoption of technology, which comprises four dimensions: use commitment,
mental acceptance, heightened enthusiasm, and effort worthiness. Use commitment refers to
“the degree to which one is committed to the use of the technology independent of whether it
is mandated or not.” Mental acceptance is “the extent to which a user views the artifact, in
principle, as a good idea.” Heightened enthusiasm refers to “the eagerness with which a user
approaches the behaviors associated with technology use.” Effort worthiness is “the user’s
positive evaluation of the return on resources expended to be able to use the technology”
(Nelson et al., 2014; Heras-Saizarbitoria and Boiral, 2015; Karahanna and Agarwal, 2006a;
Virdyananto et al., 2016).
Owing to the compulsory nature of IS, psychological adoption has been applied to
analyze it (Nelson et al., 2014; Karahanna and Agarwal, 2006a; Prasanna and Huggins, 2016;
Teo et al., 2007). Thus, HRIS technology impacts employees’ HR tasks and work-related
results throughout the organization (Buzkan, 2016; Shahreki et al., 2019; Bondarouk et al.,
2017a; Marler and Parry, 2016). In summary, this study focused on employees’ creativity,
engagement, and work-life balance as work-related outcomes of the adoption of new
information technology.
Work-life balance refers to employees’ perceptions of life and work responsibilities.
As stated by Kalliath and Brough (2008), work-life balance refers to the “individual perception
that work and non-work activities are compatible and promote growth in accordance with an
individual’s current life priorities,” which is subjective to their work and personal lives (Brough

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et al., 2014; Oosthuizen et al., 2016; Gopinathan and Raman, 2016; Nha Trang et al., 2022).
Consequently, the employees’ jobs are relevant to their responsibilities and roles toward their
family and friends, and IS assists them in keeping, facilitating, and maintaining their work-life
balance, which reduces their anxiety and stress.
According to Saks (2006), engagement refers to the “emotional and intellectual
commitment toward the organization, or the amount of discretionary effort exhibited by
employees in their job.” Schaufeli et al. (2002) offered an engagement model and introduced
absorption, dedication, and vigor as its essential factors. Schaufeli et al. (2002) defined
absorption as “being fully concentrated, happy, and deeply engrossed in one’s work whereby
time passes quickly”; dedication as “a sense of significance, enthusiasm, inspiration, pride, and
challenge”; and vigor as “high levels of energy and mental resilience while working, the
willingness to invest effort in one’s work, and persistence in the face of difficulties.” HRIS
technology is considered a self-empowering instrument for intervening, facilitating, and
promoting employee engagement in technology. The behavioral intention to accept and adopt
HRIS is influenced by HRIS technology, which improves employee engagement.
Creativity is another critical element, often described as the process by which an
individual or a small team collaboratively generates ideas that are both novel and useful
(Amabile et al., 2005). Information technology decreases the amount of employees’ daily
work, offers them the opportunity to use their full cognitive capabilities, and generates more
ideas that lead to increased creativity (Yang et al., 2018; Lee et al., 2020; Pacauskas and Rajala,
2017; Liu et al., 2016; Shahreki et al., 2021). Moreover, good interaction between HRIS
technology and organizational advancement can lead to an increase in employees’ creativity.
According to Shahreki et al. (2019), HRIS implementation gathers information and data,
leading to an increase in creativity and supporting an increase in the use of systems, easing
tasks and work routes.

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In summary, effort expectancy, information quality, social influence, and performance
expectancy contribute to HRIS psychological adoption (Heidenreich and Talke, 2020a;
Virdyananto et al., 2016; Prasanna and Huggins, 2016; Heras-Saizarbitoria and Boiral, 2015;
Nelson et al., 2014), which influences work-related outcomes such as creativity, engagement,
and work-life balance (Liu et al., 2016; Marler and Parry, 2016; Njoku and Ebie, 2015; Heslina
and Syahruni, 2021; Dey and Saha, 2020; Bondarouk and Brewster, 2016; Maier et al., 2013;
Troshani et al., 2011). A literature review revealed that few studies have been conducted on
employees’ work-related outcomes (Maier et al., 2013; Shahreki et al., 2019) and
psychological adoption (Virdyananto et al., 2016; Prasanna and Huggins, 2016). Therefore,
this study hypothesizes that psychological HRIS adoption facilitates the relationship between
work-related outcomes and technology adoption factors. Thus, the second objective of this
study is to investigate the mediating effect of technology adoption factors and work-related
outcomes. Therefore, we propose:
H5. There is a positive relationship between the a and a) creativity, b) engagement, and c)
work-life balance.
H6a. Behavioral intention to accept and adopt HRIS mediates the relationship between PE
and a) creativity, b) engagement, and c) work-life balance.
H6b. Behavioral intention to accept and adopt HRIS mediates the relationship between EE
and a) creativity, b) engagement, and c) work-life balance.
H6c. Behavioral intention to accept and adopt HRIS mediates the relationship between SI
and a) creativity, b) engagement, and c) work-life balance.
H6d. Behavioral intention to accept and adopt HRIS mediates the relationship between IQ
and a) creativity, b) engagement, and c) work-life balance.
Technical assistance and technicians can help unfamiliar users with the use of a new IS
(Kwak et al., 2011; Venkatesh, 2000). With all this support, receiving the new IS proves easier
for users. Therefore, this facilitating condition can impact user acceptance, and we propose:
H7. There is a positive relationship between facilitating conditions and behavioral intention
to accept and adopt HRIS.

17
The relationship between technology and duty can encourage users to adopt and utilize
new technologies (Legris et al., 2003; Venkatesh, 2000; Venkatesh et al., 2012). Goodhue and
Thompson (1995) defined Task Technology Fit (TTF) as the relationship between technology
and tasks impacted by technology and task features (Isaac et al., 2019; Rai and Selnes, 2019;
Erskine et al., 2019). Therefore, we posit:
H8. There is a positive relationship between the TTF and behavioral intention to accept and
adopt HRIS.
2.3 Moderating Role of Organizational Citizenship Behavior (OCB)
The notion of organizational citizenship is influenced by the concept of extracurricular
activities suggested by Katz (1964). OCBs are non-essential behaviors for the job, including
showing consideration for other staff members and being considerate of co-workers and the
organization ( Baeza et al., 2022; Jayaweera et al., 2021). In informal organizations, this
voluntary, spontaneous, and contributing behavior is the result of personal initiative, and
exceeds the organization’s contractual obligations (Choi and Sy, 2010).
Katz and Kahn (1978) state that since OCB is the result of spontaneous, “innovative”
behavior, it may promote creativity (Hu et al., 2022). Employee creativity and OCB are
beneficial for organizations (Hu et al., 2022). As Chang et al. (2007) state, OCB enables
employees to propose innovative ideas, and Singh and Srivastava (2016) affirm that employees
use OCB to provide co-workers with creative suggestions. Citizenship behaviors may catalyze
innovation as they move through the organization’s social structure, changing the overt and
covert behavior of others on a path that is good, and encourages unplanned creative activity
(Kianto et al., 2017). When humanity and courtesy are combined, a new element called
“helping” can take the form of one of OCB’s dimensions, and the presence of a great level of
“helping behavior” within an organization will aid in the purging of distrust, fear, and

18
dissatisfaction-based knowledge creation processes (Rurkkhum and Bartlett, 2012).
Consequently, we propose the following hypothesis:
H9. OCB positively moderates the relationship between the behavioral intention to accept
and adopt HRIS and employee creativity.
Based on these above hypotheses, we propose the conceptual framework shown in Figure 1.
[Insert Figure 1 about here]
3. Methodology
This study employed a descriptive research design that described naturally occurring events or
circumstances (Kothari, 2004). For sampling, the purposive sampling method was used, which
is a type of non-probability sampling method used to select participants through specific criteria
(Hair et al., 2019a; Kothari, 2004) such as Malaysian employees in SMEs who use HRIS in
daily activities. Furthermore, a quantitative research methodology and a deductive approach
were adopted as it is a theory that guides research (Bell et al., 2022). Bailey and Pearson’s
(1983) information quality survey was adapted for this study, and an exploratory factor analysis
was conducted for this scale. Reliability of the questionnaire was tested by 12 human resource
management (HRM) professors. A pilot study was conducted online with 60 respondents who
used HRIS in daily activities to check the validity and reliability of the instruments and
research, and some modifications were made based on their feedback (Appendix A). We
informed the SMEs’ HR departments about the study’s objectives and assured the
confidentiality of their data. The questionnaire consisted of two sections. The first section
included 44 items rated on a 7-point Likert-type scale (1 = “strongly disagree” to 7 = “strongly
agree”) for the 11 modeled constructs. The second section contained the demographic
information of the respondents.

19
In total, 155 questionnaires were collected from manufacturing and service companies,
two of which were deemed invalid. Only 153 questionnaires were deemed valid. Around 69.3%
of the respondents were men and 30.7% were women (Table 1). According to their education,
62.7% of the respondents had a bachelor’s degree, and 45.8% were 30–40 years old.
Furthermore, 82.4% were managers, 54.9% had 5–20 years of experience, and 66.7% had used
HRIS for more than four years (Table 1).
4. Results
Structural equation modelling (SEM) using PLS version 3.2.9 software and the Statistical
Package for the Social Sciences (SPSS) were used to conduct the data analysis. Additionally,
an exploratory factor analysis (EFA) was performed to check the validity of the information
quality measures. The EFA results revealed a one-dimensional scale for information quality.
The total explained variance was 59.30%.
4.1 Common method bias
To examine whether common method bias augmented the relationships, we first conducted
Harman’s single-factor test on the items included in our model (Podsakoff and Organ, 1986).
If common method bias exists in the data, either a single factor emerges from a factor
analysis of all measurement items included in the study or one general factor accounts for
most of the variance. Factor analysis revealed all factors with eigenvalues greater than 1, the
first of which (eigenvalue =2.40) explained 11.34% of the total variance. Hence, the factor
analysis does not indicate the presence of a single background factor, and supports the
validity of the data. Second, we resent the same questionnaire to different Malaysian
employees in SMEs who use HRIS in the daily activities of 47 sample firms whose

20
employees responded to our earlier survey. We obtained 19 responses and found no
significant differences between the two participants from each firm (Lee et al., 2022).
4.2 Measurement model evaluation
This model observes whether the experimental variables are effective in assessing inactive
variables. Three standards must be available to examine the measurement model: all t-values
should be ≥ 1.96, all variance errors should be positive, and all standardized loading factors
should be ≥ 0.50 (Hair et al., 2019b; Ringle et al., 2020). Table 2 reveals that the loadings
were significant, and all were greater than 0.7. Additionally, the average variance extracted
(AVE) was more than 0.5 for all constructs. The evaluation of composite reliability and
Cronbach’s alpha showed high scale reliability for each construct. However, the skewness (−1
to 1) and kurtosis (−2 to 2) results revealed that the data were normally distributed. Table 3
reveals that the square root of each construct’s AVE is greater than its respective inter-
correlation, and HTMT is below 0.9. Overall, the results demonstrated high reliability and
validity (Sarstedt et al., 2020).
[Insert Tables 2 and 3 about here]
4.3 Structural model assessment
Table 4 shows the path coefficients (β) of the measurement model. Hypothesis 1 exerts a very
strong significant relationship between performance expectancy and a) CR, b) EN, and c) WLB
(β = 0.750, β = 0.747, β = 0.753, p < 0.01). Hypothesis 2 reveals a very strong significant
relationship between effort expectancy and a) CR, b) EN, and c) WLB (β = 0.794, β = 0.792,
β = 0.796, p < 0.01). Hypothesis 3 exerts a moderate effect on the relationship between social
influence and a) CR, b) EN, and c) WLB (β = 0.475, β = 0.473, β = 0.477, p < 0.01). Hypothesis
4 exerts a strong significant relationship between information quality and a) CR, b) EN, and c)

21
WLB (β = 0.581, β = 0.579, β = 0.582, p < 0.01). Hypothesis 5 exerts a very strong significant
relationship between behavioral intention to accept and adopt HRIS and a) CR, b) EN, and c)
WLB (β = 0.762, β = 0.759, β = 0.764, p < 0.01). Hypothesis 7 reveals a very strong significant
relationship between facilitating conditions and the behavioral intention to accept and adopt
HRIS (β = 0.801, p < 0.01). Hypothesis 8 proposes a moderating effect on the relationship
between TTF and behavioral intention to accept and adopt HRIS (β = 0.486, p < 0.01).
Additionally, the effect size (f2) test showed that 0.219 to 0.321 had a medium to strong impact
based on Cohen’s cut-offs, with a small effect equal to 0.02, medium effect equal to 0.15, and
strong effect equal to 0.35. Furthermore, we examined the predictive power of the model by
assessing the R² and Q² standards for the predictor factors. All R² values were greater than the
0.10 threshold and all Stone-Geisser’s Q2 values were positive for our endogenous constructs
(Hair et al., 2019b).
[Insert Table 4 about here]
4.4 Mediating effect of behavioral intention to accept and adopt HRIS
Bootstrapping-based resampling was used to assess the significance of the indirect effects using
the product-of-coefficients approach (Hayes and Scharkow, 2013). According to Baron and
Kenny (1986), the mediation effect of an instrument should be determined by examining a
satisfactory relationship of the mediator with outcome and predictor variables. Performance
expectancy was found to indirectly impact a) CR, b) EN, and c) WLB through behavioral
intention (BI) (β = 0.273, 0.275, 0.271). Furthermore, the direct effects of PE on a) CR, b) EN,
and c) WLB were statistically significant (β = 0.131, 0.133, 0.129). Hypothesis 6a is supported
by partial mediation. Effort expectancy was found to indirectly affect a) CR, b) EN, and c)
WLB through BI (β = 0.301, 0.303, 0.305). Moreover, the direct effects of EE and a) CR, b)
EN, and c) WLB were not statistically significant (β = 0.062, 0.064, 0.061), Hypothesis 6b is

22
supported by full mediation. Social influence indirectly affected a) CR, b) EN, and c) WLB
through BI (β = 0.344, 0.346, 0.342). Furthermore, the direct effects of SI and a) CR, b) EN,
and c) WLB were not statistically significant (β = 0.072, 0.075, 0.070). Thus, Hypothesis 6c is
supported by full mediation. Information quality indirectly affected a) CR, b) EN, and c) WLB
through BI (β = 0.264, 0.266, and 0.262). Additionally, the direct effects of IQ and a) CR, b)
EN, and c) WLB were not statistically significant (β = 0.053, 0.055, 0.052), Hypothesis 6d is
supported by full mediation (Table 5).
[Insert Table 5 about here]
4.5 Moderating effect of Organizational Citizenship Behavior (OCB)
To investigate the role of OCB as a moderator, we used the two-stage approach of Fassott et
al. (2016) to test for interaction effects. The results indicate a positive and significant
interaction, and moderator effect for the effect of BI on employees’ creativity (Hypothesis 9),
illustrating that its greater impact on OCB plays a major moderating role in these connections
(Table 6).
[Insert Table 6 about here]
5. Discussion and conclusion
This study contributes mostly to HRM and IS research that focuses on SMEs because they do
not adopt new technologies owing to their lack of HR experts and technological and financial
readiness (Noutsa et al., 2017; Virdyananto et al., 2016; Shahreki et al., 2019; Bondarouk and
Brewster, 2016; Troshani et al., 2011). The models used in this study were adopted from
Venkatesh et al. (2003), named Model-UTAUT factors, such as effort expectancy, performance
expectancy, and social influence. To measure information quality, DeLone and McLean (2003)
IS Success Model was used. In HR functions, the HRIS plays an important role, which is

23
difficult to adopt in SMEs (Noutsa et al., 2017; Shahreki, 2019b; Quaosar et al., 2018; Buzkan,
2016; Hussain et al., 2007). SMEs use part of HRIS modules as an example employee
attendance and payroll mapping or their record-keeping functions ( Obeidat, 2012; Maier et al.,
2013). This issue remains in Malaysian SMEs; therefore, this study was conducted there, and
its findings revealed a favorable connection between behavioral intention to accept and adopt
HRIS, information quality, effect expectancy, social influence, and performance expectancy,
and employees’ creativity, engagement, and work-life balance. Another outcome of this study
revealed a favorable link between facilitating conditions, TTF, and behavioral intention to
accept and adopt HRIS, indicating that facilitating conditions and TTF are influential factors
in employees’ HRIS acceptance and performance improvement. The behavioral intention to
accept and adopt an HRIS more significantly impacts employees’ creativity for high (vs. low)
OCB, suggesting that HR managers should establish a setting that actively promotes good OCB
and encourages their staff by providing non-cash rewards for respectful behavior, promoting
OCB to their personnel by providing training.
Moreover, a favorable connection was found between information quality, social
influence, effort expectancy, and performance expectancy with the psychological adoption of
HRIS to explore its predecessor factors. For the data analysis, SEM was used to represent a
positive relationship between information quality, performance expectancy, social influence,
effort expectancy, facilitating conditions, and TTF with the behavioral intention to accept and
adopt HRIS. Thus, a positive relationship improves end-users’ psychological adoption of HRIS
and helps decrease employee resistance. Additionally, results of the SEM evaluation revealed
a direct relationship between creativity, engagement, and work-life balance with psychological
HRIS adoption. Therefore, when HRIS technology is psychologically adopted by employees,
they begin to implement it to achieve their daily work tasks, which helps advance their work-
related outcomes, including creativity, engagement, and work-life balance. When employees

24
adopt HRIS technology psychologically, they have a better work-life balance. These findings
support Bauwens et al. (2020) study, which showed a relationship between teachers’
acceptance of IS and performance expectancy and their work-life balance. Furthermore, it
supports the findings of Gopinathan and Raman (2016), who showed the positive impact of
information quality on individual work-life balance. Employees improve their job-related
outcomes, such as work-life balance, engagement, and creativity, as they simultaneously adopt
HRIS technology psychologically, and begin using it to complete their everyday work
responsibilities. The results show that psychologically adopting HRIS technology improves
their work-life balance (Bauwens et al., 2020). Organizations should encourage end users to
increase their confidence in their IS. Employees gain a sense of belonging when they have
access to resources, information, and assistance from senior management, and are treated as
essential members.
By contrast, this study showed a positive correlation between employee engagement
and HRIS adoption factors, supporting other studies that have proposed a positive relationship
between work engagement and technology adoption (Molino et al., 2020; Yoo and Lee, 2019;
Heslina and Syahruni, 2021; Albrecht et al., 2015). Although there are few studies that show a
positive relationship between creativity and employees’ technology adoption factors (Suki and
Suki, 2017), this study shows both indirect and direct impacts of information quality, social
influence, effort expectancy, and performance expectancy on employees’ creativity,
engagement, and work-life balance by investigating the mediating role of behavioral intention
to accept and adopt HRIS.
5.1 Theoretical and empirical implications
The findings of this study contribute to the HRM and IS fields by demonstrating the importance
of employees’ technology adoption through its impact on their behavioral intention to accept

25
and adopt HRIS. It reveals the relationship between the behavioral intention to accept and adopt
HRIS and work-related outcomes, which can be considered a new contribution to the HRM
and IS literature. It explores psychological adoption procedures and enhances HRIS research.
Moreover, this study introduces the behavioral intention to accept and adopt HRIS as a
mediating variable, and expands its horizons on HRM and IS. Thus, it facilitates an
understanding of how information quality and employees’ technological adoption variables are
crucial in improving employees’ creativity, engagement, and work-life balance by supporting
positive results such as psychological HRIS adoption. Furthermore, it confirms a positive
relationship between employees’ information quality, performance expectancy, effort
expectancy, social influence, facilitating conditions, and TTF with behavioral intention to
accept and adopt HRIS. DeLone and McLean’s model of information quality is considered an
antecedent factor of technology adoption (Prasanna and Huggins, 2016) that affects employees’
work-related outcomes.
These findings reveal that information quality, effort expectancy, social influence, and
performance expectancy are involved in decreasing end-users’ disagreement, and are beneficial
for their psychological adoption to improve their work-related outcomes, such as creativity,
engagement, and work-life balance. The impact of information quality, performance
expectancy, social influence, and effort expectancy is related to HRIS psychological adoption
(Prasanna and Huggins, 2016; Virdyananto et al., 2016), which affects the technology
acceptance domain and employees’ psychological adoption (Karahanna and Agarwal, 2006a;
Venkatesh et al., 2012; Venkatesh and Bala, 2008; Venkatesh et al., 2003; Venkatesh, 2000).
5.2 Practical implications
The scope of this study is Malaysian employees of SMEs and the psychological adoption
factors that motivate and help them adopt new technologies, as Malaysian companies are the

26
most emerging and promising organizations. Thus, the results of this study can help increase
employees’ technology adoption by increasing their psychological adoption. Overall, they can
encourage organizational management to implement more IS modules and attract more
essential advantages from HRIS interventions to inspire SMEs to become pioneers in this
competitive environment. They can persuade their employees to enhance and raise their loyalty
to organizational IS and increase their sense of belonging, as they have access to information
and data, while top management supports them. To this end, employees and organizations are
involved in a positive interchangeable relationship, and achieve the advantages of using IS to
boost productivity, increase strategy, and receive accurate data from HRIS technology
(Shahreki et al., 2019; Virdyananto et al., 2016; Hussain et al., 2007; Bondarouk and Brewster,
2016; Maier et al., 2013; Troshani et al., 2011).
5.3 Limitations and future research
This study is subject to certain limitations. One primary constraint is the reliance on a single
source for survey data collection, which could introduce common method bias. This approach
was necessitated due to the challenges associated with employing alternative methodological
designs to investigate the intricate dynamics of HRIS. Furthermore, the self-assessment method
used to measure creativity presents difficulties. This approach is inherently subjective and may
not accurately reflect true creativity levels. In addition, the survey items, rather than directly
measuring creativity, appear to gauge an “innovative orientation” – an individual’s tendency
to seek novel methods or approaches. However, the pursuit of new approaches does not
inherently equate to creativity. Creativity, more accurately, is often determined based on
outcomes assessed by others, while our survey primarily measured personal inclinations.
Future research should, therefore, consider external assessments of creativity. Additionally, the

27
study’s sample is limited to SMEs in Malaysia, which may affect the generalizability of the
findings. Given these limitations, further research in this area is recommended.
Specifically, to enhance the generalizability of our findings, it is imperative to extend
the scope of testing beyond SMEs to include large enterprises. Moreover, there is a need to
evaluate these organizations in both emerging and developed economies to better contextualize
the results within the Malaysian framework. Future research should also consider investigating
the influence of moderating factors such as HR expertise, HRIS training, top management
support, and employees’ age, involvement, and experience to deepen our understanding of how
employees’ technology adoption factors affect work-related outcomes. Additionally, the
adoption of cloud-based HRIS in both SMEs and large enterprises warrants exploration.
Investigating the HRIS adoption mechanism across both small-to-medium and large-scale
organizations could provide valuable insights into the similarities and differences between
these two contexts. Finally, other work-related outcomes such as productivity, satisfaction, and
effective communication should be subjects of inquiry in future studies.
Appendix A. Questionnaire items
Constructs
Items
Source
Performance Expectancy
The HRIS is very beneficial in my workplace
(Venkatesh et al.,
2003, Shahreki et
al., 2019)
When I use HRIS I complete my duties very fast
My productivity is increased when I use HRIS
I have a chance of being promoted when I use HRIS
Effort Expectancy
I have a clear and comprehensible interaction with HRIS
(Venkatesh et al.,
2003, Shahreki et
al., 2019)
Being skillful is easy by using HRIS
The HRIS is easy to apply
It’s easy to learn to work with HRIS
Social Influence
When I use HRIS, people think my behavior is influenced by it
(Venkatesh et al.,
2003, Shahreki et
al., 2019)
Because of the people who are significant to me, I should use HRIS
The senior management facilitates the HRIS usage
Our organization encourages us to use HRIS
Information Quality
Using HRIS offers precise output data
(Bailey and
Pearson, 1983,
HRIS output data are accessible

28
HRIS output data is dependable and consistent
Shahreki et al.,
2019)
HRIS layout and material design of output display are satisfactory
Facilitating Condition
Adequate resources encourage me to accept and apply HRIS
(Venkatesh et al.,
2003, Shahreki et
al., 2019)
My knowledge encourages me to accept and apply HRIS
The organization supports and encourages me to implement HRIS
There is a relationship between HRIS and previous information systems
Task Technology Fit
The HRIS data are based on my needs and duties.
(Goodhue and
Thompson, 1995,
Shahreki et al.,
2019)
Data in HRIS are accessible and understandable.
Using HRIS is easy and comfortable
The IS departments understand my duties and goals
Behavioral Intention
It is exciting to use HRIS
(Karahanna and
Agarwal, 2006a,
Shahreki et al.,
2019)
to Accept and Adopt HRIS
I am eager to HRIS
In my opinion, the HRIS is a vital tool
I use HRIS if it is required
Creativity
I always propose new approaches to reach our goals
(Zhou and
George, 2001)
I often have innovative and unique ideas
I work with innovative approaches to problems
I propose some new ways to do work duties
Engagement
I feel strong and dynamic at my workplace
(Schaufeli et al.,
2002)
I am keen on going to work every day
My job motivates me
Working passionately makes me happy
Work-Life Balance
I can strike a balance between working time and daily life
(Brough et al.,
2014)
It’s difficult to find a balance between working time and daily life
I feel my working time and daily life are balanced
Overall, I believe my working time and daily life are balanced
Organizational Citizenship
Make suggestions on how to make the organization run more smoothly
(Lee and Allen,
2002)
Behaviour
Protect the organization from potential issues by taking action
Help others with their responsibilities
Remain up to date with the company's developments
Source: Created by authors.
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Table 1. Demographic information
Variables
Category
Frequency
%
Gender
Male
106
69.3
Female
47
30.7
Education
Diploma
27
17.6
Bachelor’s
96
62.7
Master’s
24
15.7
Ph.D.
6
3.9
Age
Under 30
30
19.6
30–40
70
45.8
41–50
43
28.1
Above 51
10
6.5
Designation
Manager
126
82.4
Executive
22
14.4
Others
5
3.3
Experience
Below 5 Years
24
15.7
5–20 Years
84
54.9
More than 20 Years
45
29.4
HRIS Utilization
Less than two years
7
4.6

39
Between two and four years
38
24.8
More than four years
102
66.7
Others
6
3.9
Source: Created by authors.

40
Source: Created by authors.
Table 2. Construct validity and reliability
Constructs
Loadings
AVE
CR
α
Skewness
Kurtosis
Performance Expectancy (PE)
0.802
0.912
0.903
PE1
0.826
−0.506
−0.610
PE2
0.831
−0.143
−0.130
PE3
0.901
−0.254
−0.633
PE4
0.805
0.641
−0.701
Effort Expectancy (EE)
0.791
0.835
0.871
EE1
0.925
−0.324
−0.799
EE2
0.856
−0.460
−0.761
EE3
0.902
−0.253
1.226
EE4
0.923
−0.321
−0.796
Social Influence (SI)
0.788
0.903
0.909
SI1
0.821
0.814
1.547
SI2
0.940
−0.612
−0.224
SI3
0.922
−0.243
0.924
SI4
0.823
0.812
1.545
Information Quality (IQ)
0.781
0.912
0.906
IQ1
0.886
−0.314
−0.624
IQ2
0.961
0.325
−0.912
IQ3
0.920
−0.730
−0.917
IQ4
0.866
0.351
−0.944
Facilitating Condition (FC)
0.779
0.914
0.918
FC1
0.832
0.825
1.558
FC2
0.951
−0.623
−0.235
FC3
0.934
−0.254
0.937
FC4
0.836
0.828
1.558
Task Technology Fit (TTF)
0.813
0.925
0.914
TTF1
0.837
−0.517
−0.623
TTF2
0.842
−0.154
−0.142
TTF3
0.916
−0.265
−0.641
TTF4
0.817
0.652
−0.712
Behavioural Intention
0.817
0.923
0.915
to Accept and Adopt HRIS (BI)
BI1
0.925
0.231
0.436
BI2
0.916
−0.237
−0.923
BI3
0.819
−0.431
−0.326
BI4
0.758
−0.152
−0.442
Creativity (CR)
0.824
0.921
0.914
CR1
0.848
−0.516
−0.622
CR2
0.843
−0.153
−0.141
CR3
0.904
−0.264
−0.640
CR4
0.806
0.651
−0.710
Engagement (EN)
0.831
0.911
0.903
EN1
0.916
0.242
0.457
EN2
0.927
−0.248
−0.944
EN3
0.810
−0.442
−0.345
EN4
0.769
−0.163
−0.463
Work-Life Balance (WLB)
0.824
0.915
0.911
WLB1
0.842
−0.531
1.301
WLB2
0.868
−0.119
−0.154
WLB3
0.917
0.464
−0.241
WLB4
0.842
−0.531
1.302
Organizational Citizenship
0.824
0.901
0.909
Behavior (OCB)
OCB1
0.854
0.620
−0.831
OCB2
0.889
−0.838
1.519
OCB3
0.884
−0.769
0.510
OCB4
0.829
0.601
−0.802
Notes: α = “Cronbach’s alpha”; AVE = “average variance extracted”; CR = “composite reliability”. **p < 0.01.

41
Table 4. Structural model results
Hypothesis
Path
Estimate
t-statistics
f2
Decision
H1
Performance Expectancy→ a) CR, b) EN, c) WLB
a) 0.750**
b) 0.747**
c) 0.753**
7.65
7.63
7.67
0.301
0.298
0.302
Supported
H2
Effort Expectancy→ a) CR, b) EN, c) WLB
a) 0.794**
b) 0.792**
c) 0.796**
10.87
10.85
10.89
0.311
0.310
0.312
Supported
H3
Social Influence→ a) CR, b) EN, c) WLB
a) 0.475**
b) 0.473**
c) 0.477**
6.43
6.41
6.44
0.221
0.219
0.222
Supported
H4
Information Quality→ a) CR, b) EN, c) WLB
a) 0.581**
b) 0.579**
c) 0.582**
7.12
7.09
7.14
0.233
0.231
0.236
Supported
H5
Behavioral Intention to Accept and Adopt HRIS→ a) CR, b) EN, c)
WLB
a) 0.762**
b) 0.759**
c) 0.764**
7.78
7.76
7.81
0.304
0.301
0.306
Supported
H7
Facilitating Condition→ Behavioral Intention to Accept and Adopt
HRIS
0.801**
12.23
0.321
Supported
H8
Task Technology Fit→ Behavioral Intention to Accept and Adopt
HRIS
0.486**
6.91
0.228
Supported
Notes: a) Creativity (CR): R² = 0.685; Q² = 0.163, b) Engagement (EN): R² = 0.608; Q² = 0.161, c) Work-Life Balance (WLB): R² =
0.709; Q² = 0.158, Behavioral Intention to Accept and Adopt HRIS: R² = 0.711; Q² = 0.159. **p < 0.01. Source: Created by authors.
Table 3. Assessment of discriminant validity
Constructs
1
2
3
4
5
6
7
8
9
10
11
1. PE
0.85
0.52
0.49
0.55
0.51
0.59
0.58
0.52
0.51
0.53
0.58
2. EE
0.58
0.84
0.55
0.56
0.55
0.57
0.48
0.60
0.53
0.52
0.51
3. SI
0.51
0.60
0.82
0.51
0.52
0.53
0.55
0.56
0.54
0.55
0.49
4. IQ
0.56
0.59
0.54
0.88
0.50
0.52
0.51
0.55
0.48
0.47
0.48
5. FC
0.53
0.58
0.55
0.54
0.89
0.56
0.59
0.56
0.51
0.53
0.58
6. TTF
0.62
0.61
0.55
0.58
0.60
0.86
0.59
0.58
0.54
0.47
0.45
7. BI
0.60
0.49
0.60
0.56
0.61
0.60
0.80
0.57
0.56
0.57
0.55
8. CR
0.53
0.62
0.59
0.61
0.58
0.60
0.59
0.83
0.52
0.51
0.47
9. EN
0.54
0.56
0.57
0.50
0.53
0.56
0.59
0.56
0.81
0.50
0.52
10. WLB
0.60
0.57
0.60
0.51
0.58
0.50
0.60
0.55
0.54
0.84
0.53
11. OCB
0.62
0.55
0.58
0.54
0.60
0.52
0.58
0.52
0.57
0.58
0.88
Notes: “Diagonal measures (bold) are the square root of the average variance extracted (AVE) for
every construct, while the other entries represent the correlations”. HTMT ratios are above the
bold diagonal factors. Source: Created by authors.

42
Table 6. Moderating effects
Path
β
SD
t-Value
p-Value
Moderation
H9: BI × OCB → CR
0.356
0.222
3.350**
0.003
Yes (Supported)
Note: Behavioural Intention to Accept and Adopt HRIS (BI); Organizational Citizenship Behavior
(OCB); Creativity (CR); **p < 0.01. Source: Created by authors.
Table 5. Mediation test results
95% Bootstrapped Confidence Interval
Path
Indirect
Effect
SE
LL
UL
Direct
Effect
SE
LL
UL
Mediation
H6a: PE →BI→ a) CR, b) EN, c) WLB
a) 0.273
b) 0.275
c) 0.271
0.050
0.052
0.049
0.174
0.176
0.172
0.311
0.313
0.309
0.131
0.133
0.129
0.056
0.058
0.055
0.020
0.022
0.019
0.251
0.253
0.249
Yes (Partial)
Yes (Partial)
Yes (Partial)
H6b: EE →BI→ a) CR, b) EN, c) WLB
a) 0.301
b) 0.303
c) 0.305
0.064
0.066
0.062
0.184
0.186
0.183
0.345
0.347
0.343
0.062
0.064
0.061
0.060
0.062
0.058
−0.026
−0.028
−0.025
0.104
0.106
0.102
Yes (Full)
Yes (Full)
Yes (Full)
H6c: SI →BI→ a) CR, b) EN, c) WLB
a) 0.344
b) 0.346
c) 0.342
0.066
0.068
0.065
0.233
0.235
0.231
0.370
0.372
0.368
0.072
0.075
0.070
0.062
0.064
0.061
−0.032
−0.034
−0.031
0.164
0.166
0.162
Yes (Full)
Yes (Full)
Yes (Full)
H6d: IQ →BI→ a) CR, b) EN, c) WLB
a) 0.264
b) 0.266
c) 0.262
0.050
0.052
0.049
0.317
0.319
0.315
0.292
0.294
0.291
0.053
0.055
0.052
0.074
0.076
0.073
−0.250
−0.252
−0.249
0.144
0.146
0.143
Yes (Full)
Yes (Full)
Yes (Full)
Note: LL: Lower Limit; UL: Upper Limit. Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI),
Information Quality (IQ), and Behavioral Intention to Accept and Adopt HRIS (BI). Source: Created by authors.

43
Effort Expectancy
Social Influence
Performance Expectancy
Information Quality
Behavioral Intention to
Accept and Adopt HRIS
Work-Life Balance
Organizational Citizenship
Behavior
Facilitating Condition
Engagement
Creativity
H7
H8
H1
H2
H3
H4
H5
H6a
H6b
H6c
H6d
H9
Task Technology Fit
Figure 1. Conceptual framework
Source: Created by authors.