Int. Journal of Business Science and Applied Management, Volume 17, Issue 1, 2022
A Study of the relationship between the Symbolic Adoption
of Human Resource Information Systems’, Technology
Adoption factors, and Work-Related Outcomes
Sonalee Srivastava
Humanities and Social Sciences, Jaypee Institute of Information Technology
A-10, Noida-62, 201309, India
Tel: +91-120-2594302
Email: sonalee.bhu@gmail.com
Badri Bajaj
Humanities and Social Sciences, Jaypee Institute of Information Technology
A-10, Noida-62, 201309, India
Tel: +91-120-2594302
Email: badri.bajaj@jiit.ac.in
Abstract
Over recent years, the Human Resource Information System (HRIS) has gained tremendous impetus in
industry. Still, the literature states that most information system failures result from employee
resistance and subsequent opposition behaviour. To address the gap in the literature, this study has
targeted employees' technological symbolic adoption to analyze employee resistance behaviour. As
employees’ technological symbolic adoption occurs much before their actual technology adoption.
Symbolic adoption means users’ mental evaluation of the technology used for their work. This study
has taken the perspective of HRIS symbolic adoption and identifies its antecedents and outcome
factors, i.e., technology adoption factors and work-related outcomes. Thus, this study investigates the
relationship between technology adoption factors (performance expectancy, effort expectancy, social
influence, and information quality) and work-related outcomes (work-life balance, engagement, and
creativity). Furthermore, this study examines the mediating effect of HRIS symbolic adoption between
the above-stated factors. 415 HRIS end-users from the HR department of small-to-medium-sized
organizations were recruited to collect data. This paper offers a theoretical and practical contribution,
extending the line of research on the end user's symbolic adoption domain, and helping small and
medium-size organizations to better understand end-users' technological adoption factors. In light of
this study's findings, HR practitioners and management should focus on effective HRIS interventions in
small and medium-sized organizations to stay ahead with engaged, creative, and balanced employees.
Keywords: human resource information system, symbolic adoption, work-life balance, engagement,
creativity
Sonalee Srivastava and Badri Bajaj
45
1. INTRODUCTION
With the emergence of information technologies, Human Resource Information Systems (HRIS)
facilitates Human Resources functions; thus the organization relies more on Information and
Communication Technologies (Bal, Bozkurt, and Ertemsir, 2012). A remarkable shift marks the
changing role of Human Resources from an administrative function to a strategic business decision-
making function (Ngai and Wat, 2006; Al-Dmour, Love and Al-Zu’bi, 2013; Aeron and Jain, 2015;
Masum et al., 2018; Arefin and Hosain, 2019; Bayraktaroglu et al., 2019; Fenech, Baguant and Ivanov,
2019; Shahreki et al., 2019). Human Resources have been revolutionized globally with the arrival of
information technology. So organizations started investing in HRIS (Brandon-Jones and Kauppi, 2018)
to support, manage and integrate their HR functions (Bondarouk, Parry, and Furtmueller, 2017).
According to Hendrickson (2003), “a well-designed HRIS can serve as the management tool in the
alignment or integration of the human resource department goals with the goals of long-term corporate
strategic planning" (Hendrickson, 2003). Tannenbaum (1990) defined HRIS as “a system used to
acquire, store, manipulate, analyze, retrieve and distribute pertinent information about an
organization’s human resources” (Noutsa, Robert and Wamba, 2017). Though larger organizations are
quickly adopting HRIS technologies, the same is not applicable for small-to-medium-sized
organizations (Noutsa, Wamba and Robert, 2017; Bayraktaroglu et al., 2019), as they are witnessing
low utilization and low return on investment despite HRIS technologies phenomenal benefits (Al
Debei, Al Dmour, and Love, 2016).
Employees have to accept the organizational information systems (Nah, Tan, and Teh, 2004;
Virdyananto et al., 2017). The current research focuses on the information systems, where the
difference between employees’ symbolic adoption and actual use behaviour creates dissonance (i.e.,
when employees symbolically reject technology). Further, this might result in dissatisfaction and
misuse of technology (Karahanna and Agarwal, 2006; Marikyan, Papagiannidis, and Alamanos, 2020).
The discord between employees' symbolic perceptions of the new information system and its actual
usage alters their attitude regarding technology. They might fear losing their job or be habitually
attached to the traditional way of doing things (Ghobakhloo et al., 2011).
Further, dissonance not only impacts employees’ adoption decisions but also affects their work-
related outcomes like well-being, satisfaction (Marikyan, Papagiannidis, and Alamanos, 2020),
productivity, creativity, and employee engagement (Njoku and Ebie, 2016; Noutsa, Robert and
Wamba, 2017; Pacauskas and Rajala, 2017). Recently, researchers have started exploring the role of
information systems along with employees’ engagement (Silic and Back, 2017; Mohamad Nor,
Arokiasamy and Balaraman, 2018; Yoo and Lee, 2019; Molino, Cortese and Ghislieri, 2020) and
creativity (Pacauskas and Rajala, 2017). The literature has noticed that information systems’ direct and
indirect effects on employees' work-related outcomes like employee engagement and creativity should
be examined (Njoku and Ebie, 2016; Pacauskas and Rajala, 2017; Olszak, Bartuś, and Lorek, 2018).
Additionally, the literature shows that only 6% of creativity is person-driven. The remaining 94% is
facilitated by process, system-technological support, and external factors like the environment, rewards
system, and training (Muller and Ulrich, 2013; Pacauskas and Rajala, 2017). Moreover, the literature
states that HRIS influences human resource functions, helps maintain employee relations, and enhances
employees' work-life balance (Buzkan, 2016).
The current study examined the information system research with the Unified Theory of
Acceptance and Use of Technology (UTAUT) concerning HRIS symbolic adoption and employees’
work-related outcomes. Though studies have focused on the symbolic adoption (Prasanna & Huggins,
2016; Virdyananto et al., 2017), they have not focused on end-users' symbolic adoption with work-
related outcomes. This study addresses these gaps and further posits and examines the mediating role of
HRIS symbolic adoption between the above-stated factors. Thus, this study's objective is to examine
the antecedent factors of HRIS symbolic adoption and its impact on employees’ work-related outcomes
like work-life balance, engagement, and creativity.
This study also suggests that the UTAUT factors are the antecedent to the end user's symbolic
adoption. Further, the study contributes to the HRIS literature by integrating the UTAUT model with
information quality from DeLone and McLean's IS success research (DeLone and McLean, 2003). The
information quality set forth by DeLone and McLean's model has been shown to be one of the
antecedent factors of technology adoption and it influences employees' work-related outcomes.
The rest of the paper is organized as follows: theoretical background and hypothesis development,
methodology, analysis and interpretation of the data, followed by discussion, implications, and
conclusion (limitations and future research directions).
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2. THEORETICAL BACKGROUND AND HYPOTHESIS DEVELOPMENT
HRIS automates, accelerates, and facilitates human resources in disseminating information and
easing their work activities (Bayraktaroglu et al., 2019). In general, HRIS is a critical tool in the hands
of HR that helps in transferring employees' data into accessible information (Wiblen, Grant and Dery,
2010) along with assimilating organizations' policies and procedures (Hendrickson, 2003). HRIS tools
assist in achieving strategic value for all HR functions (Troshani, Jerram, and Hill, 2011), such as
tracking employees’ performance, engagement level, payroll, recruitment, and even managing
employee turnover (Troshani, Jerram and Hill, 2011). Thus, the effectiveness of almost all HR
functions could be enhanced through one tool, i.e., HRIS. HRIS facilitates users in their day-to-day
activities and offers an indicative dashboard for aligning HR strategies with organizational objectives.
HRIS adoption will simplify the organization's complex calculations with precision and less cost
(Hendrickson, 2003; Bondarouk, Parry and Furtmueller, 2017; Bayraktaroglu et al., 2019). The
integration of HRIS with HR functions enables HR to do work swiftly and accurately (Hosnavi and
Ramezan, 2010); HR has easy access to information, which facilitates decision-making (Lengnick-Hall
and Moritz, 2003; Ben Moussa and El Arbi, 2020). Further, HRIS facilitates HR quality, productivity,
and innovativeness (Lengnick-Hall and Moritz, 2003; Davarpanah and Mohamed, 2013; Mauro and
Borges-Andrade, 2020). Thus, HRIS supports workforce planning, benefits, and administrations, and
provides superior analysis for performance management (Hendrickson, 2003). All the complex
management entities are planned and managed through HRIS (Ankrah and Sokro, 2016). HRIS
automates and facilitates almost all the HR functions and influences their work-related outcomes like
work-life balance, engagement, creativity, productivity, performance, and satisfaction (Muller and
Ulrich, 2013; Buzkan, 2016; Ratna, 2016; Noutsa, Robert and Wamba, 2017; Pacauskas and Rajala,
2017; Alboloushi et al., 2018; Molino, Cortese and Ghislieri, 2020). Despite HRIS phenomenal
benefits and usages, small and medium-sized organizations face problems in its adoption (Noutsa,
Robert, and Wamba, 2017). Additionally, literature reports that around 50% of information system
failures are due to employee resistance and subsequent opposition behaviour (Arekete, Ifinedo, and
Akinnuwesi, 2015; Haddara and Hetlevik, 2016; Mahmud, Ramayah and Kurnia, 2017; Heidenreich
and Talke, 2020).
There are various theories and models in the literature for examining users’ acceptance of
information technology. These theories and models are the Technology Acceptance Model (TAM), an
adaptation of the theory of reasoned action (TRA), the theory of planned behavior (TPB), Combined
TAM and TPB (C-TAM-TPB), Innovation Diffusion Theory (IDT), the Social Cognitive Theory
(SCT), the motivational model (MM), the Model of PC Utilization (MPCU), and the Unified Theory of
Acceptance and Use of Technology (UTAUT) model. The UTAUT model is a unified model that
incorporates all the previous theories (Venkatesh et al., 2003). TAM(Davis, 1989), TPB (Ajzen, 1991),
and UTAUT (Venkatesh et al., 2003) are the widely accepted theories in the field of information
systems (Vega and Chiasson, 2021). Though the technology acceptance model (TAM) is widely
accepted by various researchers (Noutsa, Robert and Wamba, 2017; Lu, 2021), it has been criticized by
many for not considering human and social factors (Boonsiritomachai and Pitchayadejanant, 2017).
The current study considered the widely accepted UTAUT model and the information quality construct
as antecedents to HRIS symbolic adoption.
The Unified Theory of Acceptance and Use Technology (UTAUT) model explains more accurate
results as compared to other technological acceptance theories like TAM (Venkatesh et al, 2003).
Recently, there has been an attempt to integrate the UTAUT Model with other models to enhance the
model's explanatory power (Shibly, 2011; Al-Khowaiter, W., Dwivedi, Y., Willams, 2014; Aletaibi,
2016). We have used the UTAUT model and information quality constructs from the IS Success model
to identify users’ HRIS symbolic adoption determinants for this study. UTAUT includes performance
expectancy, effort expectancy, social influence, and facilitating conditions. Facilitating conditions
become predictive to intention behaviour in the absence of effort expectancy, as the core concept of
facilitating conditions is predominantly captured by the effort expectancy construct (Venkatesh et al.,
2003; Aeron and Jain, 2015). The current study examined the relationship between performance
expectancy, effort expectancy, social influence, and information quality with HRIS symbolic adoption
and work-related outcomes (work-life balance, engagement, and creativity).
2.1. Information System (IS) adoption
Information systems influence human resource functions and employees’ work-related outcomes
(Nielson, Grant-Vallone, and Jackson, 2002; Buzkan, 2016). Prior studies supported HRIS adoption
and its relationship with work-life balance (Müller and Ulrich, 2013; Ratna, 2016), engagement
(Molino, Cortese and Ghislieri, 2020), and creativity (Bondarouk, Parry and Furtmueller, 2017;
Pacauskas and Rajala, 2017; Alboloushi et al., 2018).
Sonalee Srivastava and Badri Bajaj
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2.1.1. Performance Expectancy
Performance expectancy refers to employees' perceived beliefs that HRIS technology usage would
improve employees’ job performance. Performance expectancy (PE) is defined as “the degree to which
an individual believes that using the system will help him or her to attain gains in job performance”
(Venkatesh et al., 2003). Performance Expectancy is based on the perceived usefulness of the
Technology Acceptance Model (TAM), where perceived usefulness is “the degree to which a person
believes that using a particular system would enhance his or her job performance” (Davis, 1989;
Venkatesh et al., 2003). This factor represents a robust antecedent of technology symbolic adoption
(Venkatesh et al., 2003; Prasanna and Huggins, 2016) within organizations. Employees are mainly
influenced by the outcome expectations with the new information system. This information system
allows autonomy of work and provides employees with ample opportunities for growth via reducing
their anxiety about the new information system, thus easing employees' work-life balance. One such
linkage between performance expectancy and employee work-life balance has been shown by Bauwens
et al. about teachers’ acceptance of information systems and their impact on work-life balance, where
performance expectancy stimulates teachers' work-life balance (Bauwens et al., 2020).
Further, it could be stated that when employees get acquainted with the new information system
and have fun learning using HRIS modules, they absorb and flow with the technology (Rheinberg,
2010; Mahnke, Hess and München, 2014). So, engagement with information systems is likely to be in a
playfulness state (Webster, 1997; Rheinberg, 2010; Mahnke, Hess and München, 2014). Furthermore,
if employees perceive that HRIS brings performance-related benefits, we expect them to be more
engaged with their work. Recently researchers have started exploring the role of information systems
along with employee engagement and showed a significant relationship between performance
expectancy and employees' engagement (Silic and Back, 2017; Mohamad Nor, Arokiasamy and
Balaraman, 2018; Yoo and Lee, 2019; Molino, Cortese and Ghislieri, 2020).
Furthermore, it has been argued that 94% of creativity among people is facilitated by process,
system-technological support, and external factors like the environment, rewards system, and training
(Muller and Ulrich, 2013; Pacauskas and Rajala, 2017). Thus extrinsic motivation is conducive to
inspiring creativity among individuals (Eisenberger and Aselage, 2008). When employees perceive that
performance expectancy is positively related to rewards/outcomes, they try to find new and unique
solutions to the problems or suggest new ideas for their work assignment. Thus, we hypothesize the
following
H1: Performance expectancy has a significant relationship with a) work-life balance, b)
engagement and c) creativity.
2.1.2. Effort Expectancy
Effort expectancy refers to employees' perceived beliefs that HRIS technology is easy to use. It
has been defined as “the degree of ease associated with the use of the system” (Venkatesh et al., 2003).
Effort expectancy is based on perceived ease of use from TAM, where perceived ease of use is “the
degree to which a person believes that using a system would be free of effort” (Davis, 1989; Venkatesh
et al., 2003; Williams, Rana, and Dwivedi, 2014; Dwivedi et al., 2017). The construct has been strongly
associated with employees’ symbolic adoption of information systems (Prasanna and Huggins, 2016;
Virdyananto et al., 2017). The easier the system is to use, the greater the users’ perceived self-efficacy
and lesser would-be employees’ anxiety (Edmunds, Ntoumanis, and Duda, 2008; Bataineh, 2019). It
proactively enhances employee self-efficacy by reducing stress as the new information system has
facilitated their work. With the arrival of a new information system, employees could perform their
work effortlessly and immerse themselves wholly in their work, enhancing their absorption, dedication,
and engagement towards their work.
Further, effort expectancy enhances the accessibility of employees towards full utilization of any
system (Venkatesh et al., 2003). Employees flow with the new information system and respond
actively towards work-related challenges via demonstrating higher creative thinking by providing
unique and novel ideas/solutions to the work-related issues (Liu et al., 2016; Nyesiga Catherine et al.,
2017). Information technology automation often reduces the amount of routine work that has to be
done, potentially providing more opportunities for individuals to think and use their full cognitive
capacities and thus enhancing their creativity (Müller and Ulrich, 2013; Pacauskas and Rajala, 2017).
Thus, we hypothesize the following
H2: Effort expectancy has a significant relationship with a) work-life balance, b) engagement and
c) creativity.
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2.1.3. Social Influence
Social Influence refers to an employee’s perception that people important to him or her believe
that they should use the new information system. It is defined as “the degree to which an individual
perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003).
Further, it has been defined as “the person’s perception that most people who are important to him
think he should or should not perform the behavior in question” (Fishbein and Ajzen, 1975; Davis,
1989; Todd, 1995; Venkatesh et al., 2003; Aletaibi, 2016). The intention of people to use new
technology is usually influenced by the thoughts and perceptions of their immediate environment
(Aletaibi, 2016). This construct is a strong antecedent to the employees' symbolic adoption of
technology (Venkatesh et al, 2003; Prasanna and Huggins, 2016; Virdyananto et al., 2017).
The employees' perception is that people within their social and work circle believe that they
should use a new information system. The workplace norms help employees positively associate
themselves with information systems like HRIS (Adkins and Premeaux, 2014; Bauwens et al., 2020),
which channelized employees towards a more productive and satisfying work environment.
Further, employees indulge and engage themselves in the activities that the most important people
think they should do (Venkatesh et al., 2003). Thus, employee involvement with the new information
system enhances their engagement. Additionally, social influence directs employees’ attention and
cognitive energy towards generating new and valuable ideas (Zhou and George, 2001; Hughes et al.,
2018). Thus, we hypothesize the following
H3: Social Influence has a significant relationship with a) work-life balance, b) engagement and
c) creativity.
2.1.4. Information Quality
Information quality refers to employees' perception of information system output in terms of its
characteristics (Korunka and Hoonakker, 2014; Gopinathan, 2016). Information quality is defined as
“the desirable characteristics of the system output” (DeLone and McLean, 2003). Further, the
information system's output should have desirable characteristics, such as, it should be relevant, easy to
understand, accurate, concise, complete, timely and useable (DeLone and McLean, 2003; Al-mamary,
Shamsuddin and Aziati, 2014). Information quality supports employees' work activities and thus
reduces stress and helps in maintaining healthy relationships among co-workers and management.
Sound quality information systems are needed to ensure better work possibilities within an organization
(Kankanhalli et al., 2012). Only one previous study has suggested it is one of the antecedent factors to
symbolic adoption (Prasanna and Huggins, 2016).
Further, with better information system support, employees channel their energy towards fulfilling
their work assignments with dedication (Gandhi et al., 2016). The new information system engages and
captivates employees so much that they flow along with it and reach a state of cognitive absorption,
which leads to information system usage, its absorption, and cognitive engagement (Webster, 1997;
Mahnke, Hess and München, 2014). Moreover, information systems defines “problems, provokes
opportunities, compiles relevant information, generates new ideas or concepts, and evaluates and
prioritizes ideas for implementation” (Müller and Ulrich, 2013). Thus, we hypothesize the following
H4: Information quality has a significant relationship with a) work-life balance, b) engagement
and c) creativity.
2.2. Mediation Effect (HRIS symbolic adoption)
HRIS symbolic adoption, “a peak motivational state reflective of a user’s mental evaluation of the
technology and its use as a worthwhile concept” (Karahanna and Agarwal, 2006), plays a vital role in
the actual adoption of the technology. Symbolic adoption constitutes four dimensions: heightened
enthusiasm, mental acceptance, use commitment, and effort worthiness. Heightened enthusiasm
represents “the eagerness with which a user approaches the behaviors associated with technology use.”
Mental acceptance means “the extent to which a user views the artifact, in principle, as a good idea.”
Use commitment is “the degree to which one is committed to the use of the technology independent of
whether it is mandated or not.” Effort worthiness refers to “the user’s positive evaluation of the return
on resources expended to be able to use the technology” (Karahanna and Agarwal, 2006; Wang and
Hsieh, 2006).
Additionally, symbolic adoption has been used for analyzing information systems due to the
mandatory nature of the information systems (Rawstorne, Jayasuriya and Caputi, 1998; Nah, Tan and
Teh, 2004; Karahanna and Agarwal, 2006; Prasanna and Huggins, 2016; Virdyananto et al., 2017).
HRIS influences employees' human resource functions and work-related outcomes within an
organization (Nielson, Grant-vallone, and Jackson, 2002; Buzkan, 2016). The current study has taken
Sonalee Srivastava and Badri Bajaj
49
employees’ work-life balance, engagement, and creativity as a work-related outcome to information
technology adoption.
Work-life balance is an employee’s cognitive perception of work and life roles. Work-life balance
is defined as the “individual perception that work and non-work activities are compatible and promote
growth in accordance with an individual’s current life priorities” (Kalliath and Brough, 2008). Further,
it is a subjective perception of an employee’s professional and personal lives (Brough, Timms and
Driscoll, 2014). Though professionally employees are related to their work roles and responsibilities,
they are attached to their friends and family. Information systems play a vital role in easing,
maintaining, and sustaining employees' work-life balance. With the new information system,
employees assess abundant knowledge and learning, which clarifies all their doubts regarding the
information system and their work activities. Therefore, it reduces the stress and anxiety among
employees by establishing their work-life balance.
Engagement is the “emotional and intellectual commitment towards the organization or the
amount of discretionary effort exhibited by employees in their job” (Saks, 2005). Schaufeli et al. (2002)
proposed an engagement model with vigor, dedication, and absorption as the main elements of the
model. Vigor is “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”; dedication is “a sense of significance,
enthusiasm, inspiration, pride, and challenge”; and absorption is “being fully concentrated, happy, and
deeply engrossed in one's work whereby time passes quickly” (Schaufeli, 2002). HRIS delivered self-
empowering tools that facilitate, intervene, and elevate employee engagement towards technology and
thus facilitate better employee engagement via positively impacting their HRIS symbolic adoption.
Creativity refers to the novel and valuable ideas created by an individual or group of individuals. It
is defined as the “production of novel & useful ideas by an individual or small group of individuals
working together” (Amabile et al., 2005). Information technology automation often reduces the amount
of routine work that has to be done, potentially providing more opportunities for individuals to think
and use their full cognitive capacities and thus enhancing their creativity. IS tools support idea
generation and the enhancement of creativity within individuals and groups (Yang, Lin, and Xue,
2018).
Additionally, it has been reported that a good exchange between organizational, technological
advancement in HRIS could promote creativity among employees. Creativity could result from the
learning and experience gathered from HRIS use, enabling employees to expand the current use of the
system and modify task and work procedures. They surely enhance their creative thinking and provide
unique and novel ideas/ solutions to work-related issues (Liu et al., 2016).
Hence, performance expectancy, effort expectancy, social influence, and information quality
contribute to HRIS symbolic adoption (Arekete, Ifinedo, and Akinnuwesi, 2015; Prasanna and
Huggins, 2016; Virdyananto et al., 2017), which further contributes to work-related outcomes like
work-life balance, engagement, and creativity (Nielson, Grant-vallone and Jackson, 2002; Buzkan,
2016; Liu et al., 2016; Noutsa, Wamba and Robert, 2017; Nyesiga Catherine et al., 2017). So, in this
study, HRIS symbolic adoption was hypothesized to mediate the relationship between technology
adoption factors and work-related outcomes.
Therefore, performance expectancy, effort expectancy, social influence, and information quality
would predict users' HRIS symbolic adoption, which would, in turn, predict their work-related
outcomes. There has been little attention paid to the end-users 'symbolic adoption (Arekete, Ifinedo,
and Akinnuwesi, 2015; Prasanna and Huggins, 2016; Virdyananto et al., 2017) and work-related
outcomes (Maier, 2012). Thus, the study's second objective is to examine the mediation effect between
the above-stated variables. Hence, we formulate the following hypotheses:
H5: HRIS symbolic adoption mediates the relationship between performance expectancy and a)
work-life balance, b) engagement, and c) creativity.
H6: HRIS symbolic adoption mediates the relationship between effort expectancy and a) work-life
balance, b) engagement, and c) creativity.
H7: HRIS symbolic adoption mediates the relationship between social and a) work-life balance, b)
engagement, and c) creativity.
H8: HRIS symbolic adoption mediates the relationship between information quality and a) work-
life balance, b) engagement, and c) creativity.
Figure 1. Research Model
Note: Performance expectancy, effort expectancy, social influence, and information quality have a relationship with a) work-life balance, b) engagement and c) creativity, respectively.
Symbolic Adoption mediates the relationship between performance expectancy with a) work-life balance, b) engagement and c) creativity; effort expectancy with a) work-life balance, b)
engagement and c) creativity; social influence with a) work-life balance, b) engagement and c) creativity; and information quality with a) work-life balance, b) engagement and c) creativity,
respectively.
Performance Expectancy
Effort Expectancy
Social Influence
HRIS Symbolic Adoption
Work-Life
balance
Engagement
Creativity
Information Quality
51
3. METHODOLOGY
The study used a descriptive research design, which portrays naturally happening events or
situations (Kothari, 2004). The purposive/judgmental sampling method is a sort of non-probability
sampling wherein researchers can use their judgment to select respondents for the sample by following
specific criteria (Kothari, 2004). The criteria are that only those employees have been taken as
respondents (nominated by the respective organization’s HR Department) who often use HRIS modules
in their day-to-day activities. Employees who work with small and medium-size organizations in the
National Capital Region, India, the Ministry of Micro, Small, and Medium Enterprises registered under
the Udyog Aadhaar Memorandum and MSME Annual Reports and NASSCOM database were used as
a sampling frame to select the organizations. The study adopted a quantitative research methodology
along with a deductive approach, where theory guides research (Bryman and Bell, 2011)
Survey items were taken from previously validated scales. Minor modifications were made to the
wording of the scale to suit the HRIS adoption; however, the information quality scale has been
adapted from the Bailey and Pearson Scale (1983) (Bailey and Pearson, 1983; Almutairi and
Subramanian, 2005); thus, EFA was conducted for this scale. Measurement items with factor loading
and Cronbach’s Alpha values are shown in Table 2.
The questionnaire reliability has been analyzed and pre-tested by 5 assistant professors of
management disciplines. Further, online pilot testing has been done with 50 samples randomly taken
from our survey population. We modified our survey instrument based on the feedback given by
academicians, HR experts, and Managers (see the Appendix for the final set of questionnaires). Then,
the final data was collected using the pen-pencil method. The HR department of small and medium
organizations was briefed about the purpose of the study and was ensured about their data
confidentiality. A total of 500 questionnaires were distributed, 440 were returned, and finally, 415
useable data-sets were taken for analysis. Table 1 outlines the demographic profile of the participants.
Table 1: Demographic Profile of the Respondents n=415
Variable
Category
Frequency
Percentage
Gender
Male
276
66.5
Female
139
33.5
Qualification
Graduate
161
38.8
Postgraduate
Other
239
15
57.6
3.6
Age
Age 20-30 (years)
62
14.9
Age 31-40 (years)
268
64.6
Age 41-50 (years)
81
19.5
> 51 (years)
4
1.0
Work Experience
Exp 1-10 (years)
210
50.6
Exp 11-20 (years)
191
46
Exp 21-30 (years)
6
1.4
> 30 (years)
8
1.9
4. ANALYSIS AND RESULTS
Data analysis has been done with the help of statistical tools like Statistical Package for the Social
Sciences (SPSS) and Structural Equation Modelling using AMOS version 21. To ensure the validity of
the Information quality measures (adapted scale), Exploratory Factor Analysis (EFA) was conducted.
The EFA result showed a one-dimensional scale of information quality. The total variance explained
was 67.20%. Further to establish convergent validity, mainly three criteria are used: “Factor loadings
>0.7>, Average Variance Extracted (AVE) >0.50 and Composite Reliability (CR)>0.7” (Sarstedt,
Ringle and Hair, 2017; Hair et al., 2018). The results are under the prescribed threshold limits. Table 2
displays the factor loadings along with Cronbach’s Alpha values. The correlation matrix, composite
reliability (CR), and AVE of the study variables are shown in Table 3.
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Table 2: Measurement Items, Cronbach’s Alpha and Factor Loadings
Variable/Scale/ (α)
Measurement Items
FL
Performance Expectancy
I find HRIS useful in my job
0.73
Venkatesh et al. (2003)
Using HRIS enables me to accomplish tasks more quickly
0.86
(α)= 0.84
Using HRIS increases my productivity
0.7
If I use HRIS, I will increase my chances of getting a raise
0.75
Effort Expectancy
My interaction with HRIS is clear & understandable
0.75
Venkatesh et al. (2003)
It is easy for me to become skillful by using HRIS
0.70
(α)=0.79
I find HRIS easy to use
0.7
Learning to operate HRIS is easy for me
0.71
Social Influence
People who influence my behavior think I should use HRIS
0.79
Venkatesh et al. (2003)
People who are important to me think that I should use HRIS
0.85
(α)=.090
The senior management of this organization has been helpful in the use of HRIS
0.85
In general, the organization has supported the use of HRIS
0.87
Information Quality
HRIS provides accurate output information
0.7
Bailey and Pearson (1983),
HRIS output information is available at a time suitable for its use
0.93
Almutairi, H., &
HRIS output information is precisely what it purports to measure
0.74
Subramanian, G. H. (2005)
HRIS output information is consistent & dependable
0.73
(α)=0.87
HRIS material design & layout display of the output is satisfactory
0.83
HRIS Symbolic Adoption
I am excited that I can use HRIS
0.68
Karahanaa and Agarwal
(2003)
I am always looking forward to using HRIS
0.69
(α)=0.85
I view the use of HRIS with enthusiasm
0.70
In my mind, I am convinced that HRIS is a vital technology
0.69
I don't view HRIS as an essential concept
0.85
The only way I will use HRIS is if it is mandated
0.67
If I can choose what I use, I will not choose HRIS
0.87
If I have a choice, I do not use HRIS
0.87
Learning to use HRIS was worth the effort I put in
0.69
My investment in learning HRIS was worthwhile
0.84
Work-Life Balance
I am currently able to balance the time at work and time for non-work activities
0.66
Brough, Timms, and Driscoll
(2014)
I have difficulty in balancing my work & non-work activities
0.92
(α)=0.83
I feel that the job and other non-work activities are currently balanced
0.78
Overall, I believe that my work & non-work life are balanced
0.67
Engagement
At my work, I feel energetic
0.89
Schaufeli (2006)
At my job, I feel strong & vigorous
0.94
(α)=0.91
When I get up in the morning, I look forward to going to work
0.93
My job inspires me
0.91
I am enthusiastic about my job
0.91
I am proud of the work that I do
0.78
I feel happy when I am working intensely
0.84
I am engrossed in my work
0.86
Time flies when I am working
0.71
Creativity
I suggest new ways to achieve goals or objectives
0.89
Sonalee Srivastava and Badri Bajaj
53
Zhou and George (2001)
I come up with new and practical ideas to improve performance
0.78
(α)=0.91
I search out new technologies, processes, techniques, and product ideas
0.72
I am a good source of creative ideas
0.79
I develop adequate plans & schedules for the implementation of new ideas
0.87
I often have new and innovative ideas
0.94
I come up with creative solutions to problems
0.77
I suggest new ways of performing work tasks
0.78
Note: (α)=Cronbach’s Alpha; FL= Factor Loading
Table 3: Correlation, CR, AVE
S.No.
Correlations, CR, AVE
Variables
CR
AVE
1
2
3
4
5
6
7
8
1 Performance Expectancy 0.84
0.58 0.76
2 Effort Expectancy 0.79
0.51 .208
**
0.71
3 Social Influence 0.9 0.7 .173
**
.556
**
0.83
4 Information Quality 0.89
0.62 .127*
*
.222
**
.410
**
0.78
5 HRIS Symbolic Adoption 0.92
0.57 .249
**
.239
**
.330*
*
.342
**
0.75
6 Work-Life Balance 0.84
0.58 .233
**
.179
**
.195
**
.192
**
.220
**
0.76
7 Engagement 0.96
0.75 .422
**
.359
**
.388
**
.212
**
.287
**
.206
**
0.86
8 Creativity 0.94
0.67 .271
**
.248
**
.340
*
.300
**
.179
**
.197
**
.353
**
0.81
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Note: Square root of AVE on the diagonal
Confirmatory factor analysis (CFA) using AMOS version 21 was performed. Internal reliability
and construct validity have been examined to assess the measurement model. To ensure the study
variables’ reliability, the Cronbach's Alpha coefficients should be ≥0.7 (Sarstedt, Ringle and Hair,
2017; Hair et al., 2018). The measurement model was tested, and if it turned out to be satisfactory, then
the structural model could be tested (Anderson and Gerbing, 1988). For model fit, four fitness criteria
were examined (chi-square statistics; Root- Mean-Square Error of Approximation (RMSEA) ≤. 08;
Standardized Root-Mean-Square Residual (SRMR) of ≤.08; and Comparative Fit Index (CFI) ≥ 0.9)
(Hu and Bentler, 1999). For HRIS symbolic adoption, information quality, engagement, and creativity,
parcels of the items were formed; this was done to control inflated measurement errors caused by
multiple items for the latent factors. Four HRIS Symbolic adoption dimensions were used as indicators
to create the HRIS Symbolic Adoption latent factor; three employee engagement dimensions were used
as indicators to create the employee engagement latent factor; three item parcels were made for the
creativity scale and information quality scale using a random assignment approach (Little, Cunningham
and Shahar, 2002).
4.1. Measurement Model
The measurement model consists of performance expectancy, effort expectancy, social influence,
information quality, HRIS symbolic adoption, work-life balance, engagement, and creativity, and it
revealed a satisfactory overall model fit with the data: CMIN/DF=2.251, RMSEA = .055; SRMR = .04;
and CFI=.93.
4.2. Structural Model
The structural model was tested to establish a relationship between independent and dependent
variables. The structural model of the relationship between performance expectancy with work-life
balance, engagement, and creativity (with a path coefficient β=0.26, β =0.34, β =0.30 p< .001
Int. Journal of Business Science and Applied Management / Business-and-Management.org
54
respectively) provided support to accept hypothesis H1. Further, the structural model of the relationship
between effort expectancy and work-life balance, engagement, and creativity (with a path coefficient
β=0.21, β =0.41, β =0.28 p< .001 respectively) provided support to accept hypothesis H2. The
structural model of the relationship between social influence and work-life balance, engagement, and
creativity (with a path coefficient β=0.22, β =0.41, β =0.37 p< .001 respectively), provided support to
accept hypothesis H3. Lastly, the structural model of the relationship between information quality and
work-life balance, engagement, and creativity (with a path coefficient β=0.22, β =0.21, β =0.33 p< .001
respectively) provided support to accept hypothesis H4.
4.3. Mediation Effect of HRIS Symbolic Adoption
The mediation effect was examined by satisfying a relationship between predictor and mediator
variable and between mediator and outcome variables (Baron and Kenny, 1986). The result indicates a
significant positive relationship between performance expectancy, effort expectancy, social influence,
and information quality with HRIS symbolic adoption, as the direct path shows β=.29, β=.29, β=.39,
and β=.40 respectively. Further, the SEM result shows the direct path between HRIS symbolic adoption
and work-life balance (β=.27), engagement (β=.34), and creativity (β=.22). Five thousand
bootstrapping samples were generated from the original data, N-415, with a confidence interval of 95
%. The result of this test is shown in Table 4; when there are significant direct and indirect effects,
there is partial mediation, and when the indirect effect is significant, but the direct effect is not
significant, then full mediation is assumed (Cheung, 2009; Shankar, Jebarajakirthy and Ashaduzzman,
2020). Hence, H5 concerning a) work-life balance, b) engagement, and c) creativity; H6 concerning a)
work-life balance, b) engagement and c) creativity; H7 concerning a) work-life balance, b) engagement
and H8 concerning a) work-life balance, b) engagement have been accepted. However, H7 c) and H8 c)
concerning creativity have been rejected. A summary of the mediation effects is shown in Table 4.
Table 4. Mediation Effect
Direct Effect
Indirect Effect
Results
PE→HRIS SA→WLB
.196*
.062**
Yes (Partial)
PE→HRIS SA→ENG
.362***
.069***
Yes (Partial)
PE→HRIS SA→CR
.255***
.042*
Yes (Partial)
EE→HRIS SA→WLB
.151*
.065***
Yes (Partial)
EE→HRIS SA→ENG
.342***
.069***
Yes (Partial)
EE→HRIS SA→CR
.238***
.043*
Yes (Partial)
SI→HRIS SA→WLB
.138*
.084***
Yes (Partial)
SI→HRIS SA→ENG
.327***
.081**
Yes (Partial)
SI→HRIS SA→CR
.338**
.034ns
No
IQ→HRIS SA→WLB
.141*
.086**
Yes (Partial)
IQ→HRIS SA→ENG
.091ns
.121***
Yes (Full)
IQ→HRIS SA→CR
.283***
.042ns
No
Note: ***p < 0.001; **p < 0.01; *p < 0.05; ns=not significant, PE=Performance Expectancy,
EE=Effort Expectancy, SI=Social Influence, IQ= Information Quality, HRIS SA= Human Resource
Information System Symbolic Adoption, Work-Life Balance,
ENG= Engagement, CR=Creativity
5. DISCUSSION
Our study contributes to information system research concerning small and medium-sized
organizations. These organizations are reluctant to adopt new technologies due to their financial and
technological readiness, lack of HR expertise, and resistance from employees (Al Debei, Al Dmour,
and Love, 2016; Noutsa, Robert and Wamba, 2017; Virdyananto et al., 2017). Our study is based on
Venkatesh’s IT adoption Model-UTAUT factors (performance expectancy, effort expectancy, social
influence) and Information quality from DeLone and McLean’s IS Success Model (DeLone and
McLean, 2003; Venkatesh et al., 2003). HRIS is essential for HR functions, and knowing its
importance, organizations are adopting it. However, for some, it is easier to adopt, while for others, like
small and medium-sized organizations, it has been difficult (Al Debei, Al Dmour and Love, 2016;
Sonalee Srivastava and Badri Bajaj
55
Noutsa, Wamba and Robert, 2017; Bayraktaroglu et al., 2019). Most small and medium-sized
organizations use standalone modules of HRIS, such as employee record-keeping applications, or
payroll and attendance mapping applications (Kinnie and Arthurs, 1996; Ball, 2001; Ngai and Wat,
2006; Kundu S.C, 2012). In India, as a developing country, the adoption of HRIS is still a problem for
small and medium organizations, and this needs to be analyzed. The study shows that the performance
expectancy, effect expectancy, social influence, and information quality positively correlate with work-
related outcomes, work-life balance, engagement, and creativity. We formulated a positive relationship
between performance expectancy, effort expectancy, social influence, and information quality with
HRIS symbolic adoption to examine its antecedent factors. The result was analyzed through structural
equation modelling, which portrays a significant positive relationship between performance
expectancy, effort expectancy, social influence, and information quality with HRIS symbolic adoption.
Thus, the positive perception towards performance expectancy, effort expectancy, social influence and
information quality enhances end-users’ symbolic adoption of HRIS technology. Thus helpful in
reducing employees' reluctance to use HRIS technology. Further, the SEM result shows the direct path
between HRIS symbolic adoption and work-life balance, engagement and creativity. Hence, as soon as
employees adopt HRIS technology symbolically and start its usage for achieving their daily work
assignments, they elevate their work-related outcomes, such as work-life balance, engagement and
creativity.
The findings suggest that employees have a better work-life balance when they symbolically adopt
HRIS technology. Prior studies revealed a linkage between performance expectancy and employee
work-life balance (β =0.27) concerning teachers’ acceptance of information systems (Bauwens et al.,
2020). Further, the study conducted by Gopinathan, 2016, reveals a positive influence of information
quality on employees' work-life balance with β =0.508 (Gopinathan, 2016). Thus, the current study at
the same time resembles and differs from prior research and found that all the factors, i.e., performance
expectancy, effort expectancy, social influence, and information quality, have a significant relationship
with employees’ work-life balance.
Additionally, the current study has shown a positive relationship between technology adoption
factors and employee engagement, and it aligns with prior researchers’ findings that establish a positive
association between technology adoption and work engagement (Silic and Back, 2017; Mohamad Nor,
Arokiasamy and Balaraman, 2018; Yoo and Lee, 2019; Molino, Cortese and Ghislieri, 2020).
Furthermore, the study has shown a positive relationship between technology adoption factors and
employee creativity. However, there has been very little empirical evidence has shown of a relationship
of technology adoption factors with creativity. (Suki and Suki, 2017). The current study investigated
both direct and indirect effects of the performance expectancy, effort expectancy, social influence, and
information quality on employees’ work-life balance, engagement, and creativity via examining the
mediating role of HRIS symbolic adoption.
The findings suggest that the hypothesized relationships have been supported. The indirect
effects of performance expectancy, effort expectancy, social influence, and information quality on
work-related outcomes have been established. These findings lead us to conclude that performance
expectancy, effort expectancy, social influence, and information quality are instrumental in reducing
end-users' dissonance via impacting their symbolic adoption, which is influential in enhancing work-
related outcomes like work-life balance, engagement, and creativity.
6. THEORETICAL AND PRACTICAL CONTRIBUTION
The current study is a significant addition to the field of Information systems. It empirically
demonstrates the role of end-users in technology adoption via affecting their HRIS symbolic adoption.
Additionally, the study has shown a relationship of HRIS symbolic adoption with work-related
outcomes as a novel contribution to the literature on information systems. The study uniquely explores
the symbolic adoption process and significantly advances human resource information system research.
Notably, the study identifies the mediating mechanisms of HRIS symbolic adoption and broadens the
research on IS. The study helps better understand how end users’ technological adoption factors and
information quality become instrumental in alleviating employees' work-life balance, employee
engagement, and employees’ creativity by facilitating positive outcomes such as HRIS symbolic
adoption. The findings confirmed that end users' performance expectancy, effort expectancy, social
influence, and information quality positively correlate with HRIS symbolic adoption. Further,
information quality set forth by DeLone and McLean's model has been shown to be one of the
antecedent factors of technology adoption (Prasanna and Huggins, 2016) and it influences employees'
work-related outcomes.
The current study found that the effects of the performance expectancy, effort expectancy, social
influence, and information quality are relevant for HRIS symbolic adoption (Arekete, Ifinedo, and
Int. Journal of Business Science and Applied Management / Business-and-Management.org
56
Akinnuwesi, 2015; Prasanna and Huggins, 2016; Virdyananto et al., 2017). Thus, the study has
contributed to and extended the line of research on end-users' symbolic adoption and the technology
acceptance domain (Venkatesh et al., 2003; Karahanna and Agarwal, 2006).
The study targeted small and medium-sized organizations' employees and the symbolic adoption
factors that help and trigger them to adopt such technology. Small and medium organizations in India
are the most promising and emerging organizations. The findings are helpful to these organizations in
elevating employees’ technology adoption via enhancing employees’ symbolic adoption. The study
findings could encourage organizations and their management to incorporate more information system
modules to gain inherent benefits of the systems. Therefore, in light of the study's findings,
practitioners and organizations need to focus on effective HRIS interventions to help small and
medium-sized organizations stay ahead in competition with engaged, creative, and balanced
employees.
Organizations should motivate end-users to develop and nurture faith in the organizational
information system. When employees have access to resources, information, and support from top
management and are considered an integral part, this cultivates a sense of belonging. Eventually,
employees and organizations indulge in a positive exchange relationship. With the emergence of
information technologies, these organizations will significantly gain advantages if they use information
systems to enhance their strategy, boost their productivity, and obtain accurate information from the
HRIS technology (Bhatti, 2017; Johnson and Diman, 2017).
7. CONCLUSION, LIMITATIONS, AND FUTURE DIRECTIONS
The study collected primary data from HRIS end-users working in various small and medium-size
organizations from India’s National Capital Region. The present study uses a single source for data
collections, which may cause common method variance. Thus, more robust findings may be obtained if
the exogenous, mediator and endogenous variables are collected from multiple sources. Adopting
cross-sectional design poses difficulty in interpreting the direction of causality, as there may be a
possibility of alternative paths among the variables.
To better understand the role of end users' technology adoption factors in predicting work-related
outcomes, future research may explore moderating variables like top management support, HRIS
training, employee involvement, age and experience, HR expertise, which accentuate or mitigate the
strength of the relations hypothesized in the present study. Future researchers should examine the
cloud-based human resource information system adoption by small and medium-sized organizations.
Furthermore, a comparative study could be done between the HRIS adoption mechanism of large-scale
organizations and small-to-medium-sized organizations. Researchers could explore more work-related
outcomes like satisfaction, productivity, and effective communication in the future. Additionally, small
and medium-sized organizations could be segregated sector-wise, like the service or manufacturing
sector. Then data could be analyzed to give specific outcomes for a particular sector or industry type.
Finally, researchers could see differences in HRIS symbolic adoption from employees’ demographic
profiles.
Small and medium organizations highly contribute to the Country’s economic and social
development. Organizations are focused on adopting HRIS modules to enhance their day-to-day
activities. Additionally, with the arrival of cloud computing, these organizations are better positioned to
adopt new technologies. Still, many of these organizations are reluctant to adopt HRIS due to their lack
of financial and technological readiness, dearth of HR expertise, and employees' unwillingness to adopt
any new technology. Therefore, organizations should enhance the adoption level of HRIS by targeting
employees’ symbolic adoption perception. HRIS is an emerging IT solution for small-to-medium
organizations, and employees’ symbolic perception can trigger their intention towards HRIS
technology adoption. This research has extended the understanding of HRIS symbolic adoption in
Indian small and medium-sized organizations. The results indicate that all the studied factors are
significantly associated with work-related outcomes. The mediation effect of HRIS symbolic adoption
between technological factors, information quality, and work-related outcomes has been established.
The findings contribute to the HRIS symbolic adoption phenomenon and the technology acceptance
literature.
Sonalee Srivastava and Badri Bajaj
57
REFERENCES
Adkins, C. L. and Premeaux, S. A. (2014) ‘The Use of Communication Technology to Manage Work-
Home Boundaries’, Journal of Behavioral and Applied Management, 15(2), pp. 82–100. Available
at: http://search.proquest.com/docview/1503117410?accountid=14682.
Aeron, P. and Jain, S. (2015) ‘The Extent of Adoption of HRIS : An Empirical Investigation with
Mediators & Moderators’, Twenty-first Americas Conference on Information Systems, pp. 1–17.
Ajzen, I. (1991) ‘The Theory of Planned Behavior’, Organizational Behavior and Human Decision
Processes, 50, pp. 179–211.
Al-Dmour, R. H., Love, S. and Al-Zu’bi, Z. (2013) ‘Factors Influencing the Adoption of HRIS
Applications : A Literature Review’, International Journal of Management & Business Studies,
3(4), pp. 9–25.
Al-Khowaiter, W., Dwivedi, Y., Willams, M. (2014) ‘Examining the Adoption of Human Resource
Information System in the Context of Saudi Arabia’, Association for Information System s, pp. 1–
21.
Al-mamary, Y. H., Shamsuddin, A. and Aziati, A. H. N. (2014) ‘Key factors enhancing acceptance of
management information systems in Yemeni companies’, Journal of Business and Management
Research, 5, pp. 108–111.
Alboloushi, B. et al. (2018) ‘Administrative Innovation in Human Resource Information Systems :
Exploring Post-Adoption Usage in Kuwait ’ s Public Sector’, in iConference 2018 Proceedings,
pp. 1–4. Available at: http://hdl.handle.net/2142/100263.
Aletaibi, R. G. (2016) ‘An analysis of the adoption and use of HRIS in the public Universities in Saudi
Arabia’. Available at: https://core.ac.uk/download/pdf/78653029.pdf.
Almutairi, H. and Subramanian, G. H. (2005) ‘An empirical application of Delone and Mclean model
the Kuwaiti private sector’, Journal of Computer information System, 4(3), pp. 113–122. doi:
10.1080/08874417.2005.11645849.
Amabile, T. M. et al. (2005) ‘Affect and Creativity at Work’, Administrative Science Quarterly, 50, pp.
367–403.
Anderson, J. and Gerbing, D. (1988) ‘Structural Equation Modeling in Practice : A Review and
Recommended Two-Step Approach’, Psychological Bulletin, 103(3), pp. 411–423.
Ankrah, E. and Sokro, E. (2016) ‘Intention and Usage of Human Resource Information Systems among
Ghanaian Human Resource Managers’, International Journal of Business and Management, 11(2),
p. 241. doi: 10.5539/ijbm.v11n2p241.
Arefin, A. and Hosain, S. (2019) ‘The role of human resource information system on organizational
performance: Evidence from Bangladeshi pharmaceutical industry’, European Journal of Applied
Business …, 5(1), pp. 37–53. Available at:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3415066.
Arekete, S., Ifinedo, P. and Akinnuwesi, B. (2015) ‘Antecedent Factors to End-Users ’ Symbolic
Acceptance of Enterprise Systems : An Analysis in Nigerian Organizations’, in Conference: 2014
IEEE 6th International Conference On Adaptive Science & Technology (ICAST)At: Covenant
University, Ota, Ogun State, Nigeria. doi: 10.13140/2.1.3370.8484.
Bailey, J. E. and Pearson, S. W. (1983) ‘Development of a Tool for Measuring and Analyzing
Computer User Satisfaction’, Management Science, 29(5), pp. 530–545. doi:
10.1287/mnsc.29.5.530.
Bal, Y., Bozkurt, S. and Ertemsir, E. (2012) ‘The Importance of Using Human Resources Information
Systems(HRIS) and a Research on Determining the Success of HRIS’, Management Knowledge
and Learning International Conference, 2012, pp. 53–62.
Ball, K. S. (2001) ‘The use of human resource information systems: A survey’, Personnel Review,
30(6), pp. 677–693. doi: 10.1108/EUM0000000005979.
Baron, R. M. and Kenny, D. A. (1986) ‘The Moderator-Mediator Variable Distinction in Social
Psychological Research: Conceptual, Strategic, and Statistical Considerations’, Journal of
Personality and Social Psychology, 51(6), pp. 1173–1182. doi: 10.1177/1350506818764762.
Bataineh, K. (2019) ‘Impact of Work-Life Balance , Happiness at Work , on Employee Performance’,
International Business Research, 12(2), pp. 99–112. doi: 10.5539/ibr.v12n2p99.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
58
Bauwens, R. et al. (2020) ‘Teachers’ acceptance and use of digital learning environments after hours:
Implications for work-life balance and the role of integration preference’, Computers in Human
Behavior, 112(January), p. 106479. doi: 10.1016/j.chb.2020.106479.
Bayraktaroglu, S. et al. (2019) ‘Application of Expanded Technology Acceptance Model for Enhancing
the HRIS Usage in SMEs’, International Journal of Applied Management and Technology, 18(1),
pp. 48–66. doi: 10.5590/ijamt.2019.18.1.04.
Bondarouk, T., Parry, E. and Furtmueller, E. (2017) ‘Electronic HRM: four decades of research on
adoption and consequences’, International Journal of Human Resource Management, 28(1), pp.
98–131. doi: 10.1080/09585192.2016.1245672.
Boonsiritomachai, W. and Pitchayadejanant, K. (2017) ‘Determinants affecting mobile banking
adoption by generation Y based on the Unified Theory of Acceptance and Use of Technology
Model modified by the Technology Acceptance Model’, Kasetsart Journal of Social Sciences,
(November). doi: 10.1016/j.kjss.2017.10.005.
Brandon-Jones, A. and Kauppi, K. (2018) ‘Examining the antecedents of the technology acceptance
model within e-procurement’, International Journal of Operations and Production Management,
38(1), pp. 22–42. doi: 10.1108/IJOPM-06-2015-0346.
Brough, P., Timms, C. and Driscoll, M. P. O. (2014) ‘Work-life balance: a longitudinal evaluation of a
new measure acroess Austrail and New Zealand workers’, The International Journal of Human
resource Management. doi: 10.1080/09585192.2014.899262.
Bryman, A. and Bell, E. (2011) Business Research Methods. 3rd Editio. Oxford University Press.
Buzkan, H. (2016) ‘The Role of Human Resource Information System (HRIS) in Organizations: A
Review of Literature’, Academic Journal of Interdisciplinary Studies, 5(1), pp. 133–138. doi:
10.5901/ajis.2016.v5n1p133.
Cheung, M. (2009) ‘Comparison of methods for constructing confidence intervals of standardized
indirect effects’, Behavior Reserach Methods, 41(2), pp. 425–438. doi: 10.3758/BRM.41.2.425.
Davarpanah, A. and Mohamed, N. (2013) ‘Human Resource Information Systems (HRIS) success
factors in a public higher education institution context’, International Conference on Research and
Innovation in Information Systems, ICRIIS, 2013, pp. 79–84. doi: 10.1109/ICRIIS.2013.6716689.
Davis, F. D. (1989) ‘Perceived Usefulness , Perceived Ease of Use , and User Acceptance of
Information Technology’, MIS QUARTERLY, 13(3), pp. 319–340. doi: 10.2307/249008.
Al Debei, M. M., Al Dmour, R. H. and Love, S. (2016) ‘Factors influencing the organisational
adoption of human resource information systems: a conceptual model’, International Journal of
Business Innovation and Research, 11(2), p. 161. doi: 10.1504/ijbir.2016.10000012.
DeLone, W. H. and McLean, E. R. (2003) ‘The DeLone and McLean model of information systems
success: A ten-year update’, Journal of Management Information Systems, 19(4), pp. 9–30. doi:
10.1080/07421222.2003.11045748.
Dwivedi, Y. K. et al. (2017) ‘Re-examining the Unified Theory of Acceptance and Use of Technology
(UTAUT): Towards a Revised Theoretical Model’, Information Systems Frontiers, pp. 1–16. doi:
10.1007/s10796-017-9774-y.
Edmunds, J., Ntoumanis, N. and Duda, J. L. (2008) ‘Testing a self-determination theory-based teaching
style intervention in the exercise domain’, European Journal of Social Psychology, 38(1), pp. 375–
388. doi: 10.1002/ejsp.463.
Eisenberger, R. and Aselage, J. (2008) ‘Incremental effects of rewrad on experienced performance
pressure: positive outcomes for intrinsic interest and creativity’, Journal of Organizational
Behavior. doi: 10.1002/job.
Fenech, R., Baguant, P. and Ivanov, D. (2019) ‘The Changing Role Of Human Resource Management
In An Era of Digital Transformation’, International Jornal of Entrepreneurship, 22(2), pp. 166–
175.
Fishbein, M. and Ajzen, I. (1975) ‘Formation of Intentions’, in Belief, Attitude, Intention, and
Behavior: An Introduction to Theory and Research, pp. 288–333. doi: 10.5465/256770.
Gandhi, A. et al. (2016) ‘Information quality assessment for user perception on Indonesia Kreatif web
portal’, ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and
Information Systems, Proceedings, (July), pp. 171–176. doi: 10.1109/ICACSIS.2015.7415182.
Sonalee Srivastava and Badri Bajaj
59
Ghobakhloo, M. et al. (2011) ‘Information Technology Adoption in Small and Medium-sized
Enterprises; An Appraisal of Two Decades Literature’, Interdisciplinary Journal of Research in
Business, 1(7), pp. 53–80.
Gopinathan, S. (2016) ‘Information System Quality In Work-Life Balance’, KNOWLEDGE
MANAGEMENT & E-LEARNING, 8(2), pp. 216–226. doi: 10.1177/1524839914548451.
Haddara, M. and Hetlevik, T. (2016) ‘Investigating the Effectiveness of Traditional Support Structures
& Self-organizing Entities within the ERP Shakedown Phase’, Procedia Computer Science,
100(December), pp. 507–516. doi: 10.1016/j.procs.2016.09.189.
Hair, J. F. et al. (2018) MULTIVARIATE DATA ANALYSIS Multivariate Data Analysis. 8th edn.
Cengage Leraning EMEA.
Heidenreich, S. and Talke, K. (2020) ‘Consequences of mandated usage of innovations in
organizations: developing an innovation decision model of symbolic and forced adoption’, AMS
Review. doi: 10.1007/s13162-020-00164-x.
Hendrickson, A. R. (2003) ‘Human resource information systems: Backbone technology of
contemporary human resources’, Journal of Labor Research, 24(3), pp. 381–394. doi:
10.1007/s12122-003-1002-5.
Hosnavi, R. and Ramezan, M. (2010) ‘Measuring the effectiveness of a human resource information
system in National Iranian Oil Company: An empirical assessment’, Education, Business and
Society: Contemporary Middle Eastern Issues, 3(1), pp. 28–39. doi:
10.1108/17537981011022797.
Hu, L. and Bentler, P. M. (1999) ‘Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives’, Structural Equation Modeling A Multidisciplinary
Journal, 6(1), pp. 1–55.
Hughes, D. J. et al. (2018) ‘Leadership , creativity , and innovation : A critical review and practical
recommendations’, The Leadership Quarterly, (December). doi: 10.1016/j.leaqua.2018.03.001.
Kalliath, T. and Brough, P. (2008) ‘Work – life balance : A review of the meaning of the balance
construct’, Journal of Management & Organization, 14(July 2008), pp. 323–327. doi:
10.5172/jmo.837.14.3.323.
Kankanhalli, A. et al. (2012) ‘Interaction of individual and social antecedents of learning effectiveness:
A study in the IT research context’, IEEE Transactions on Engineering Management, 59(1), pp.
115–128. doi: 10.1109/TEM.2011.2144988.
Karahanna, E. and Agarwal, R. (2006) When the Spirit is Willing : Symbolic Adoption and Technology
Exploration.
Kinnie, N. J. and Arthurs, A. J. (1996) ‘Personnel specialists’ advanced use of information technology:
Evidence and explanations’, Personnel Review, 25(3), pp. 3–19. doi:
10.1108/00483489610147933.
Korunka, C. and Hoonakker, P. (2014) The Impact of ICT on Quality of Working Life.
Kothari, C. R. (2004) Research Methodology Methods and Techniques. 2nd edn. New Age
International (P) Ltd.
Kundu S.C, K. R. (2012) ‘Applications of HRIS in Human Resource Management in India: A Study’,
European Journal of Business and Management, 4(21), pp. 34–41. doi: ISSN 2222-1905 (Paper)
ISSN 2222-2839 (Online).
Lengnick-Hall, M. L. and Moritz, S. (2003) ‘The impact of e-HR on the human resource management
function’, Journal of Labor Research, 24(3), pp. 365–379. doi: 10.1007/s12122-003-1001-6.
Little, T. D., Cunningham, W. A. and Shahar, G. (2002) ‘To Parcel or Not to Parcel : Exploring the
Question , Weighing the Merits’, Structural Equation Modeling A Multidisciplinary Journal, 9(2),
pp. 151–173. doi: 10.1207/S15328007SEM0902.
Liu, D. et al. (2016) ‘Motivational mechanisms of employee creativity: A meta-analytic examination
and theoretical extension of the creativity literature’, Organizational Behavior and Human
Decision Processes, 137, pp. 236–263. doi: 10.1016/j.obhdp.2016.08.001.
Lu, Y. (2021) ‘Examining user acceptance and adoption of the internet of things’, International Journal
of Business Science and Applied Management, 16(3), pp. 1–17.
Mahmud, I., Ramayah, T. and Kurnia, S. (2017) ‘To use or not to use: Modelling end user grumbling
as user resistance in pre-implementation stage of enterprise resource planning system’,
Information Systems, 69, pp. 164–179. doi: 10.1016/j.is.2017.05.005.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
60
Mahnke, R., Hess, T. and München, L. (2014) ‘Flow Experience in Information Systems Research :
Revisiting its Flow Experience in Information Systems Research : Revisiting its Conceptualization
, Conditions , and Effects’, in Proceedings of the 2014 International Conference on Information
Systems (ICIS 2014)At: Auckland, New Zealand Volume: 35. doi: 10.13140/2.1.4852.0486.
Maier, C. et al. (2012) ‘Analyzing the impact of HRIS implementations on HR personnel’s job
satisfaction and turnover intention’, Journal of Strategic Information Systems, 22(3), pp. 193–207.
doi: 10.1016/j.jsis.2012.09.001.
Marikyan, D., Papagiannidis, S. and Alamanos, E. (2020) ‘Cognitive Dissonance in Technology
Adoption: A Study of Smart Home Users’, Information Systems Frontiers, 1. doi:
10.1007/s10796-020-10042-3.
Masum, A. K. et al. (2018) ‘Intelligent human resource information system (i-HRIS): A holistic
decision support framework for HR excellence’, International Arab Journal of Information
Technology, 15(1), pp. 121–130.
Mauro, T. G. and Borges-Andrade, J. E. (2020) ‘Human resource system as innovation for
organisations’, Innovation & Management Review, 17(2), pp. 197–214. doi: 10.1108/inmr-03-
2019-0037.
Mohamad Nor, N. ‘Izzah, Arokiasamy, L. and Balaraman, R. A. (2018) ‘The influence of Internet of
Things on employee’s engagement among generation Y at the workplace’, in SHS Web of
Conferences, p. 03003. doi: 10.1051/shsconf/20185603003.
Molino, M., Cortese, C. G. and Ghislieri, C. (2020) ‘The promotion of technology acceptance and work
engagement in industry 4.0: From personal resources to information and training’, International
Journal of Environmental Research and Public Health, 17(7), pp. 2–15. doi:
10.3390/ijerph17072438.
Ben Moussa, N. and El Arbi, R. (2020) ‘The impact of Human Resources Information Systems on
individual innovation capability in Tunisian companies: The moderating role of affective
commitment’, European Research on Management and Business Economics, 26(1), pp. 18–25.
doi: 10.1016/j.iedeen.2019.12.001.
Muller, S. D. and Ulrich, F. (2013) ‘Creativity and Information Systems in a Hypercompetitive
Environment : A Literature Review’, Communications of the Association for Information
Systems, 32. doi: 10.17705/1CAIS.03207.
Müller, S. D. and Ulrich, F. (2013) ‘Creativity and information systems in a hypercompetitive
environment: A literature review’, Communications of the Association for Information Systems,
32(1), pp. 175–200. doi: 10.17705/1CAIS.03207.
Nah, F. F.-H., Tan, X. and Teh, S. H. (2004) ‘An Empirical Investigation on End-Users’ Acceptance of
Enterprise Systems’, Information Resources Management Journal, 17(3), pp. 32–53. doi:
10.4018/irmj.2004070103.
Ngai, E. W. T. and Wat, F. K. T. (2006) ‘Human resource information systems: A review and empirical
analysis’, Personnel Review, 35(3), pp. 297–314. doi: 10.1108/00483480610656702.
Nielson, T. R., Grant-vallone, E. and Jackson, M. (2002) ‘Tales from the Hiring Line : Effects of the
Internet and Technology on HR Processes’, Organizational Dynamics, 31(3), pp. 224–244.
Njoku, E. and Ebie, S. (2016) ‘Shaping Innovativeness and Creativity with Electronic Human Resource
Management (e-HRM): Exploring a Conceptual Framework’, Knowledge Management: An
International Journal, 15(3), pp. 1–14. doi: 10.18848/2327-7998/cgp/v15i03/50834.
Noutsa, A., Robert, K. and Wamba, S. F. (2017) ‘Acceptance and Use of HRIS and Influence on
Organizational Performance of SMEs in a Developing Economy:The case of Cameroon’, Recent
Advances in Information Systems and Technologies, 571(March). doi: 10.1007/978-3-319-56541-
5.
Noutsa, A., Wamba, S. F. and Robert, K. (2017) ‘Exploring Factors Affecting the Adoption of HRIS in
SMEs in a Developing Country: Evidence from Cameroon’, Lecture Notes in Information
Systems and Organisation, 30(March), pp. 281–295. doi: 10.1007/978-3-030-10737-6_18.
Nyesiga Catherine, B. et al. (2017) ‘Effort Expectancy, Performance Expectancy, Social Influence and
Facilitating Conditions as Predictors of Behavioural Intentions to use ATMS with Fingerprint
Authentication in Ugandan Banks’, Global Journal of Computer Science and Technology, 17(5),
pp. 5–23.
Sonalee Srivastava and Badri Bajaj
61
Olszak, C. M., Bartuś, T. and Lorek, P. (2018) ‘A comprehensive framework of information system
design to provide organizational creativity support’, Information and Management, 55(1), pp. 94–
108. doi: 10.1016/j.im.2017.04.004.
Pacauskas, D. and Rajala, R. (2017) ‘Information system users’ creativity: A meta-analysis of the link
between IT use and creative performance’, Information Technology and People, 30(1), pp. 81–
116. doi: 10.1108/ITP-04-2015-0090.
Prasanna, R. and Huggins, T. J. (2016) ‘Factors affecting the acceptance of information systems
supporting emergency operations centres’, Computers in Human Behavior, 57, pp. 168–181. doi:
10.1016/j.chb.2015.12.013.
Ratna, R. (2016) ‘The impact of Information Technology on Job Related Factors lik e Health and
Safety, Job Satisfaction, Performance, Productivity and Work Life Balance’, Journal of Business
& Financial Affairs, 05(01). doi: 10.4172/2167-0234.1000171.
Rawstorne, P., Jayasuriya, R. and Caputi, P. (1998) ‘Association for Information Systems AIS
Electronic Library (AISeL) An Integrative Model of Information Systems Use in Mandatory
Environments Recommended Citation’. Available at: http://aisel.aisnet.org/icis1998/32.
Rheinberg, F. (2010) ‘Intrinsische Motivation und Flow-Erleben’, in Motivation und Handeln. 4th edn.
SpringerEditors: J. Heckhausen & H. Heckhausen. doi: 10.1007/978-3-642-12693-2.
Saks, A. M. (2005) ‘Antecedents and consequences of employee engagement’, Journal of
Organizational Effectiveness, 6(1), pp. 19–38. doi: 10.1108/JOEPP-06-2018-0034.
Sarstedt, M., Ringle, C. M. and Hair, J. F. (2017) Partial Least Squares Structural Equation Modeling.
Hnadbook o. Springer International Publishing. doi: 10.1007/978-3-319-05542-8.
Schaufeli, W. B. et al. (2002) ‘The Measurement of Engagement and Burnout: A Two Sample
Confirmatory Factor Analytic Approach’, Journal of Happiness Studies, 3(1), pp. 71–92.
Shahreki, J. et al. (2019) ‘The effect of human resource information system application on employee
satisfaction and turnover intention’, Entrepreneurship and Sustainability Issues, 7(2), pp. 1462–
1479. doi: 10.9770/jesi.2019.7.2(47).
Shankar, A., Jebarajakirthy, C. and Ashaduzzman, M. (2020) ‘Journal of Retailing and Consumer
Services How do electronic word of mouth practices contribute to mobile banking adoption ?’,
Journal of Retailing and Consumer Services, 52(June 2019). doi:
10.1016/j.jretconser.2019.101920.
Shibly, H. (2011) ‘Human Resources Information System Success Assessment: An Integrative Model’,
Australian Journal of Basic and Applied Sciences, 5(MAY), pp. 157–169.
Silic, M. and Back, A. (2017) ‘Impact of Gamification on Users Knowledge-Sharing Practices:
Relationships between Work Motivation, Performance Expectancy and Work Engagement’,
Proceedings of the 50th Hawaii International Conference on System Sciences (2017), (January).
doi: 10.24251/hicss.2017.156.
Suki, Norbayah Mohd and Suki, Norazah Mohd (2017) ‘Determining students’ behavioural intention to
use animation and storytelling applying the UTAUT model: The moderating roles of gender and
experience level’, International Journal of Management Education, 15(3), pp. 528–538. doi:
10.1016/j.ijme.2017.10.002.
Todd, T. (1995) ‘Understanding Information Technology Usage: A test of Competing Models’, pp.
144–176.
Troshani, I., Jerram, C. and Hill, S. R. (2011) ‘Exploring the public sector adoption of HRIS’,
Industrial Management and Data Systems, 111(3), pp. 470–488. doi:
10.1108/02635571111118314.
Vega, A. and Chiasson, M. (2021) ‘Complementary adoptions and the diffusion of information systems
in small and medium enterprises: An exploratory study’, International Journal of Business Science
and Applied Management, 16(3), pp. 18–38.
Venkatesh et al (2003) ‘User Acceptance Of Information Technology: Toward A Unified View’, MIS
QUARTERLY, 27(3), pp. 425–478.
Virdyananto, A. et al. (2017) ‘User acceptance of human resource information system: An integration
model of Unified Theory of Acceptance and Use of Technology (UTAUT), Task Technology Fit
(TTF), and Symbolic Adoption’, 2016 International Conference on Information Technology
Systems and Innovation, ICITSI 2016 - Proceedings, pp. 1–6. doi: 10.1109/ICITSI.2016.7858227.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
62
Wang, W. and Hsieh (2006) ‘Beyond Routine : Symbolic Adoption , Extended Use , and Emergent Use
of Complex Information Systems in the Mandatory Organizational Context Beyond Routine :
Symbolic Adoption , Extended Use , and Emergent Use of Complex Information Systems in the
Mandator’, in ICIS 2006 Proceedings. Available at: http://aisel.aisnet.org/icis2006/48%0A.
Webster, J. (1997) ‘Audience Engagement in Multimedia Presentations’, The DATA BASE for
Advances in Information Systems, 28(2).
Wiblen, S. L., Grant, D. and Dery, K. (2010) ‘Transitioning to a new HRIS : the reshaping of Human
Resources and information technology talent Transitioning to a new HRIS : the reshaping of
Human Resources and information’, Journal of Electronic Commerce Research, 11(4), pp. 251–
267.
William DeLone, E. M. (1992) ‘Information Systems Success:The Quest for the Dependent Variable’,
Information Systems Research, 3(1), pp. 60–95.
Williams, M. D., Rana, N. P. and Dwivedi, Y. K. (2014) ‘The unified theory of acceptance and use of
technology (UTAUT): A literature review’, Journal of Enterprise Information Management, 28(3),
pp. 443–448. doi: 10.1108/JEIM-09-2014-0088.
Yang, X., Lin, L. and Xue, P. C. (2018) ‘Examining creativity through a virtual reality support system’,
Educational Technology Research and Development, 66(5), pp. 1231–1254. doi: 10.1007/s11423-
018-9604-z.
Yoo, K. and Lee, K. (2019) ‘Core Self-Evaluation and Work Engagement : Moderated Mediation
Model of Career Adaptability and Job Insecurity’, Frontiers in Psychology, 10(September), pp. 1–
9. doi: 10.3389/fpsyg.2019.02093.
Zhou, J. and George, J. M. (2001) ‘When job dissatisfaction leads to creativity : Encouraging the
expression of Voice’, Academy of Management Journal, 44(4), pp. 682–696.
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Appendix: Final Research Questionnaire
Part A
Name (optional): -------------------------------------------------------------------
Organization Name (Required): -------------------------------------------------------------------
Designation/Department (Required)-------------------------
Qualifications: Graduate ( ) Post Graduation ( ) Other ( )
Experience (years): 1-10 ( ) 11-20 ( ) 21-30 ( ) Above 30 ( )
HRIS Experience (years) -----------------------
Gender: Male ( ) Female ( ) Other ( )
Age (years): 20-30 ( ) 31-40 ( ) 41-50 ( ) Above 51 ( )
Part B
Below are the statements regarding employee perception of Human Resource Information System.
Using a 1-7 scale, mark your agreement with each statement
Items
Strongly
Disagree
1
Disagree
2
Somewhat
Disagree
3
Neutral
4
Somewhat
Agree
5
Agree
6
Strongly
Agree
7
I find the HRIS helpful system
in my job
Using the HRIS system enables
me to accomplish tasks more
quickly
Using the HRIS system
increases my productivity
If I use the HRIS system, I will
increase my chances of getting
a raise
My interaction with the HRIS
system is clear &
understandable.
It is easy for me to become
skillful at using the HRIS
system
I find the HRIS system easy to
use
Learning to operate an HRIS
system is easy for me
People who influence my
behavior think I should use the
HRIS system.
People who are important to me
think that I should use the
HRIS system.
The senior management of this
organization has been helpful in
the use of the HRIS system
In general, the organization has
supported the use of the HRIS
system.
Part C
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64
Below are the statements regarding your level of HRIS symbolic adoption; here, symbolic adoption
means one’s mental acceptance of technology. Using a 1-7 scale, mark your agreement with each.
Items
Strongly
Disagree
1
Disagree
2
Somewhat
Disagree
3
Neutral
4
Somewhat
Agree
5
Agree
6
Strongly
Agree
7
I am excited that I can use
HRIS
I am always looking forward to
using HRIS
I view the use of HRIS with
enthusiasm
In my mind, I am convinced
that HRIS is a vital technology
I don't view HR
IS as an
essential concept
The only way I will use HRIS
is if it is mandated
If I can choose what I use, I
will not choose HRIS
If I have a choice, I do not use
HRIS
Learning to use HRIS was
worth the effort I put in
My investment in learning
HRIS was worthwhile
Part D
Below are the statements regarding the quality of the information system's output, which can be from
reports or online screens. Using 1-7 scales, mark each statement.
Items
Strongly
Disagree
Somewhat
Neutral
Somewhat
Agree
Strongly
Disagree
1
2
Disagree
3
4
Agree 5
6
Agree 7
HRIS provides accurate
output information
HRIS output information
is available at a time
suitable for its use
HRIS output information
is precisely what it
purports to measure
HRIS output information
is consistent &
dependable
HRIS material design &
layout display of the
output is satisfactory
Part E
Sonalee Srivastava and Badri Bajaj
65
Below are the statements regarding employee work engagement, i.e., individual’s involvement,
satisfaction and enthusiasm for work.
Using a 0-6 scale, mark your agreement with each statement
Items
Never
0
Almost
Never 1
Rarely
2
Someti
mes
3
Often
4
Very
Often
5
Always
6
At my work, I feel energetic
At my job, I feel strong & vigorous.
When I get up in the morning, I look
forward to going to work
My job inspires me
I am enthusiastic about my job
I am proud of the work that I do
I feel happy when I am working
intensely
I am engrossed in my work
Time flies when I am working
Part F
Below are the statements regarding work-life
When I reflect on my work and non-work activities (your
regular activities outside of work such as family, friends, sports, study, etc.), over the past six months, I conclude
that:
Items
Strongly
Disagree
Disagree
Neutral
Agree
Strongly
Agree
1
2
3
4
5
I am currently able to balance the
time at work and time for non –work
activities
I have difficulty balancing my work
and non-work activities.
I feel that the jobs and other non-
work activities are currently balanced
Overall, I believe that my
Work and non-work life are
balanced.
Part G
How well do the following statements describe you?
Items
Not at all
Characteri
stic
1
Slight
Characteristic
2
Somewhat
Characteristic
3
Moderate
Characteristic
4
Very Much
Characteristic
5
I suggest new ways to achieve goals
or objectives
I come up with new and practical
ideas to improve performance
I search out new technologies,
processes, techniques, and product
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66
ideas
I am a good source of creative ideas
I develop adequate plans and
schedules for the implementation of
new ideas
I often have new and innovative
ideas
I come up with creative solutions to
problems
I suggest new ways of performing
work tasks
