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nt. J. Business Information Systems, Vol. 43, No. 1, 2023
Copyright © 2023 Inderscience Enterprises Ltd.
The effect of technology readiness on technology
acceptance in the electronic human resources
management field
Javad Shahreki*, Audrey Lim Li Chin and
Hasmida Jamaluddin
Faculty of Business,
Multimedia University, Malaysia
Email: Javad.shahreki@mmu.edu.my
Email: lclim@mmu.edu.my
Email: hasmida.jamaluddin@mmu.edu.my
*Corresponding author
Abdollah Shahraki
Department of Agricultural Economics,
Ferdowsi University of Mashhad, Iran
Email: ab.shahraki@mail.um.ac.ir
Thanh Thuy Nguyen
College of Business and Law,
RMIT University, Australia
Email: thuy.nguyen21@rmit.edu.au
Ali Afzalian Mand
Faculty of Information Science and Technology,
Multimedia University, Malaysia
Email: ali.afzalian@mmu.edu.my
Abstract: This study attempted to determine the influence of ‘technology
readiness’ on ‘technology acceptance’ in the electronic human resources
management (e-HRM) field. A sample of 167 HRs from Fortune Global 500
companies in Malaysia were selected for the study. The research model of this
study was developed using two theories namely, the technology acceptance
model created by Davis and the technology readiness index created by
Parasuraman. The findings of the current study revealed that, four categories of
technology readiness namely, innovativeness, optimism, insecurity and
discomfort dimensions, have a significant effect on e-HRM apparent ease of
use as well as apparent usefulness.
Keywords: technology readiness index; TRI; electronic human resources
management; e-HRM; technology acceptance model; TAM.
The effec
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of technology readiness on technology acceptance 75
Reference to this paper should be made as follows: Shahreki, J., Chin, A.L.L.,
Jamaluddin, H., Shahraki, A., Nguyen, T.T. and Mand, A.A. (2023) ‘The effect
of technology readiness on technology acceptance in the electronic human
resources management field’, Int. J. Business Information Systems, Vol. 43,
No. 1, pp.74–86.
Biographical notes: Javad Shahreki is a Lecturer at Multimedia University,
Malaysia. He holds a PhD in Human Resource Management from University
Technology Malaysia. His research interests are in human resource
management and human resource information system.
Audrey Lim Li Chin is a Lecturer at Multimedia University, Malaysia. She
holds a DBA in Finance and Management from Northern University of
Malaysia (UUM), Malaysia. Her research interests are in data analytics,
blockchain technology and management.
Hasmida Jamaluddin is a Lecturer at Multimedia University, Malaysia. She
holds a PhD in Management from Multimedia University, Malaysia. Her
research interests are in information systems and organisational behaviour.
Abdollah Shahraki is a PhD student in Agricultural Economics from Ferdowsi
University of Mashhad, Iran. His research interests are in agricultural
economics and management.
Thanh Thuy Nguyen holds a Doctor of Science in Logistics Sciences from
Kobe University, Japan. She is a lecturer at RMIT University, Australia. Her
research interests are in logistics and supply chain management, and human
resource management.
Ali Afzalian Mand is a Lecturer at Multimedia University, Malaysia and
graduated from Multimedia University in Master of Science in Information
Technology. His research interests are in Information Technology and
management.
1 Introduction
In order for an organisation to participate successfully in the current competitive market,
human resource services of the organisation must use IT effectively. Using IT enables
organisations to fulfil HR practices effectively and efficiently. Thus, increasingly more
organisations are attempting to implement ‘electronic human resources management’
(e-HRM) (Lin, 2011) and internet-based IT in human resource management (HRM)
policies practices, and procedures. Although e-HRM systems have been implemented in
organisations, there is a paucity of information on the significance of e-HRM
implementation and its impact on individuals and organisations, possibly due to the
relatively recent acceptance of using IT in HRM systems by HR employees. Thus, the
objective of the current paper is to examine the influence of readiness of technology on
adoption of technology. Based on the objectives, two theories have been applied in the
present research namely; the ‘technology readiness index (TRI)’ by Parasuraman (2000)
and the ‘technology acceptance model (TAM)’ by Davis (1989). A review of the
published papers revealed that, there are only a couple of studies that have been carried
out to calculate acceptance of technology and readiness of technology (Kuo et al., 2013;
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Kwahk and Lee, 2008; Massey et al., 2007; Richey et al., 2007; Shahreki, 2019a;
Tsourela and Roumeliotis, 2015). These studies focused on e-HRM as well. The current
study aims to examine e-HRM ‘apparent ease of use’ and ‘apparent usefulness’.
Therefore, it will be the first practical attempt to evaluate the procedure, as well as to
assess TRIs impact on attitude toward implementing e-HRM. Based on the researcher’s
observations, large organisations in developing countries have established HRM systems
and are interested in applying IT in HRM practices. In summary, investigating the impact
of readiness of technology on adoption of technology in the HRM context is very
important, thus, a research model should be formulated. This model will be established
using two theories, the TAM and TRI, which will be elaborated on, in the following
sections.
2 Review of literature
2.1 e-HRM
The phrase e-HRM can be used interchangeably with web-based HRM, HR portals, via
HR intranet, virtual HRM, and computer-based HRMS (Bissola and Imperatori, 2014;
Wiblen, 2016). According to Marler and Fisher (2013), e-HRM refers to, the HR function
administrative support in organisations through internet technology usage. It can
additionally be described as, a method of using HR policies, practices, and strategies in
companies by direct support and application of internet-based channels (Yusliza et al.,
2011). Furthermore, e-HRM can be explained as, using computer techniques,
telecommunication systems and collaborative electronic media to achieve HR functions
(Afshan et al., 2018; Parry, 2011; Parry and Tyson, 2011; Puspitasari and Jie, 2020;
Shahreki, 2019b).
2.2 TAM
The various factors that influence technology usage, should be examined to predict and
demonstrate users’ acceptance and implementation of IS and IT. The generic ‘theory of
reasoned action (TRA)’ generated by Fishbein and Ajzen (1975), is the first of its kind in
this particular field, that has attempted to describe user’s attitude regarding application of
technology in organisations. According to the TRA, a user’s behaviour can be anticipated
by their behavioural intention. Presently, the most common model applied in this field is,
the ‘TAM’, which is a modification of the ‘reasoned action theory’. In 1985, Davis
developed TAM to demonstrate an individual’s intent to consent to and apply novel
technology in companies. The TAM consists of three important variables namely;
‘apparent usefulness’, ‘apparent ease of use’, and ‘behavioural intention to use’.
‘Apparent ease of use’ is the extent to which an individual anticipates the certain system
to be void of exertion. ‘Apparent usefulness’ describes a user’s individual ability to apply
a certain system to facilitate accomplishing their job execution in an organisational
context. Finally, ‘behavioural intention to use’ is explained as person’s demands and
attempts to carry out a behaviour. The TAM describes the impact of outside variables
based on user behaviour, which is facilitated by attitudes and beliefs of users. The
rationale behind selecting TAM for the current study, was that it has previously been
evaluated empirically and has been maintained by functions, replications and validations
The effec
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(Abdullah and Ward, 2016; Holden and Karsh, 2010; King and He, 2006; Pitafi et al.,
2020; Shahreki et al., 2019, 2020a, 2020b; Shahreki and Nakanishi, 2016; Venkatesh and
Bala, 2008; Wu and Chen, 2017). According to King and He (2006), TAM amongst the
models can be described as being extremely robust, strong, and parsimonious to predict
user approval, particularly in the IS setting. Lee et al. (2003) proposed that the TAM
parsimony along with its predictive ability can be applied very easily in various settings.
2.3 TRI
Review of related literature revealed that, every person has a different idea and attitude
towards using technology (Rogers, 2010), so technology readiness has a direct effect on
IS and IT acceptance. Thus, a scale has been defined and developed by Parasuraman
(2000), known as the TRI scale. This scale measures a user’s level of readiness to apply
technology as well as their characteristics towards using technology, and not their
competency in using it (Avila and Garcés, 2018; Lin et al., 2007; Parasuraman and
Colby, 2015; Venkatesan and Sridhar, 2019). Thus, four groups of users are defined by
TRI based on, an individual’s personality traits such as, innovativeness, optimism,
insecurity and discomfort. Innovativeness can be defined as a user’s propensity to be the
primary ones who use a novel technology. Optimism refers to a user’s positive belief
towards using technology to improve flexibility, productivity, control, and efficacy.
Insecurity refers to mistrusting technology for privacy and security reasons. Discomfort
can be defined as, a need to manage and a sense of feeling overwhelmed. In general, a
number of studies have been carried out to evaluate technology acceptance and
technology readiness (Asadi et al., 2018; Kwahk and Lee, 2008; Lin et al., 2007;
Parasuraman and Colby, 2015; Richey et al., 2007; Tsourela and Roumeliotis, 2015) but
most of these studies were not carried out in developing countries. According to the
researcher’s observation, HRM systems have been applied in large organisations of
developing countries, and they intend to use IT in HRM, thus investigating the impact of
technology readiness on adoption of technology, is very important. Therefore, the
subsequent hypotheses were established, to examine the impact of technology readiness
on apparent ease of use as well as apparent usefulness in the e-HRM context. Figure 1
illustrates the models used in the present study, the TAM and TRI.
H1a A positive association exists amongst optimism toward technology and apparent
ease of use.
H1b A positive association exists amongst optimism toward technology and apparent
usefulness.
H2a A positive association exists amongst innovativeness toward technology and
apparent ease of use.
H2b A positive association exists amongst innovativeness toward technology and
apparent usefulness.
H3a A negative association exists amongst discomfort toward technology and apparent
ease of use.
H3b A negative association exists amongst discomfort toward technology and apparent
usefulness.
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H4a A negative association exists amongst insecurity toward technology and apparent
ease of use.
H4b A negative association exists amongst insecurity towards technology and apparent
usefulness.
H5 A positive association exists amongst apparent ease of use and apparent
usefulness.
H6 A positive association exists amongst apparent ease of use and attitude to use.
H7 A positive association exists amongst apparent usefulness and attitude to use.
Figure 1 Conceptual model
Technology readiness index
Optimism H1a
Innovativeness
Discomfort
Insecurity
Apparent
usefulness
Apparent ease
of use
Attitude to use
H1b
H2a
H2b
H3a
H3b
H4a
H4b
H5
H6
H7
4 Methodology
The applied methodology of this study including sampling procedure, data collection, and
statistical analysis will be elaborated on, in this section
4.1 Sampling and collection of data
All the 167 participants of this study were HR employees selected from the largest
private sector companies in Malaysia. The HR positions held by the participants included,
managers (38%), directors (29.7%), and experts (32.3%) with an average age of
47.2 years. Of the participants sampled, 32.8% were women and 67.2% were men.
Furthermore, more than 89.7% of them had at least a university degree.
The effec
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4.2 Research instrument
The survey used comprised of two major parts; one was based on technology readiness
using the ‘TRI’ by Parasuraman (2000) and the other on technology adoption using the
‘TAM’ by Davis (1989).
4.2.1 Technology readiness
The original TRI by Parasuraman (2000) comprised of 36 items that were separated into
four dimensions namely; innovativeness (6 items), optimism (7 items), insecurity
(5 items) and discomfort (3 items). The five-point Likert style reply structure
self-assessment type, was applied to measure the participants’ technology readiness.
4.2.2 Technology acceptance
Davis’s TAM is comprised of three factors and 13 items including; apparent ease of use
(five items), apparent usefulness (five items), and attitude to use (three items). All items
in the five-point Likert response format self-assessment type, were used to measure the
participants’ technology acceptance. Each questionnaire was accompanied with a page
explaining the study objectives, as well as a guarantee that all respondents’ would remain
anonymous, the information would remain confidential, and participation would be solely
on voluntary basis. The survey questionnaires were distributed to a total of 500 selected
participants but only 167 (33.4%) responded and returned the answered questionnaires by
e-mail or post.
5 Analysis
In the present study, the Smart PLS 3.0 software was applied to evaluate the suggested
model (Hair et al., 2016) and to evaluate the parameters in the inner and outer model.
Applying the PLS software is an effective way of maximising the variance to
demonstrate the dependent variables. The PLS path modelling proposes the various
advantages of the model, based on the type of variables, distribution needs, size of the
sample and the model complexity. This modelling with a path-estimating scheme was
used for the internal estimation (Chin, 2010; Hair, 2017). Subsequently, the
non-parametric bootstrapping approximation was used with 500 resampling to determine
the evaluation standard errors.
5.1 Model assessment measurement
In the current study, initially the ‘convergent validity’ was evaluated, a term that
describes the extent to which various items evaluating the same idea concur. The
‘composite reliability (CR)’, ‘Cronbach’s alpha’ (α) the ‘factor loadings’, and ‘average
variance extracted (AVE)’ were used in the present research, to assess ‘convergence
validity’. All the items’ loading values were greater than the suggested value of 0.5. In
Table 1, it is demonstrated how the CR measures represent the extent to which the
construct values disclosed the ‘latent construct’, who’s values were between 0.87 to 0.92,
higher than the value of 0.7, which was the suggested value. On the other hand, the AVE
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represents the total degree of variance in the pointers through the ‘latent construct’, which
were between 0.62 and 0.66, higher than 0.5, the suggested value (Hair, 2017; Hair et al.,
2016).
Table 1 Loadings, validity, AVE, CR, α
First-order constructs Items Loadings AVE CR
α
Optimism (Opt) Opt 1 0.81 0.66 0.91 0.87
Scale type: reflective Opt 2 0.84
Opt 3 0.79
Opt 4 0.87
Opt 5 0.84
Opt 6 0.82
Opt 7 0.79
Innovativeness (Inn) Inn 1 0.79 0.64 0.89 0.84
Scale type: reflective Inn 2 0.87
Inn 3 0.83
Inn 4 0.84
Inn 5 0.78
Inn 6 0.75
Discomfort (Dis) Dis 1 0.73 0.65 0.90 0.86
Scale type: reflective Dis 2 0.79
Dis 3 0.78
Insecurity (Ins) Ins 1 0.79 0.64 0.88 0.84
Scale type: reflective Ins 2 0.87
Ins 3 0.86
Ins 4 0.82
Ins 5 0.82
Apparent usefulness (Pus) Pus 1 0.87 0.67 0.92 0.88
Scale type: reflective Pus 2 0.73
Pus 3 0.74
Pus 4 0.83
Pus 5 0.77
Apparent ease of use (Peu) Peu 1 0.76 0.62 0.87 0.82
Scale type: reflective Peu 2 0.84
Peu 3 0.80
Peu 4 0.81
Peu 5 0.75
Attitude to use (Att) Att 1 0.75 0.63 0.88 0.83
Scale type: reflective Att 2 0.83
Att 3 0.81
The effec
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of technology readiness on technology acceptance 81
5.2 Constructs’ discriminant validity
‘Discriminant validity’ is the degree to which measurement items vary from one to the
other. Consequently, the AVE square root is the diagonal of the latent variable
association (Table 2). Since these values of the square root are higher than the equivalent
construct associations (Fornell and Larcker, 1981; Hulland, 1999; Schaupp et al., 2009),
the conclusion can be drawn that, the condition has been met and consequently the
measurement model is validated.
Table 2 Assessment of discriminant validity
Opt Inn Dis Ins Pus Peu Att
Opt 0.82
Inn 0.48 0.81
Dis 0.62 0.56 0.76
Ins 0.45 0.62 0.51 0.83
Pus 0.53 0.52 0.54 0.48 0.79
Peu 0.49 0.54 0.51 0.52 0.48 0.79
Att 0.52 0.52 0.44 0.62 0.44 0.44 0.80
Notes: Diagonal values (italics) represent the average variance extracted (AVE) square
root for each construct; Opt = optimism; Dis = discomfort; Inn = innovativeness;
Ins = insecurity; Peu = apparent ease of use; Pus = apparent usefulness;
Att = attitude to use.
Table 3 Results of hypotheses with PLS (basic model – bootstrap data).
Constructs Hypothesis Sign Estimate t-value Remarks
Opt → Pus H1a + 0.27** 3.66 Supported
Opt → Peu H1b + 0.25** 3.11 Supported
Inn → Pus H2a + 0.26** 3.35 Supported
Inn → Peu H2b + 0.28** 3.93 Supported
Dis → Pus H3a - –0.20** 2.18 Supported
Dis → Peu H3b - –0.23** 2.78 Supported
Ins → Pus H4a - –0.21** 2.31 Supported
Ins → Peu H4b - –0.22** 2.49 Supported
Peu → Pus H5 + 0.31** 4.31 Supported
Peu → Att H6 + 0.39** 5.63 Supported
Pus → Att H7 + 0.33** 4.91 Supported
Notes: **p < 0.01; *p < 0.05; (*) p < 0.10. Opt = optimism; Inn = innovativeness;
Dis = discomfort; Ins = insecurity; Pus = apparent usefulness; Peu = apparent
ease of use; Att = attitude to use.
5.3 Assessment of structural model
To check and analyse the hypotheses of the current study, the structural model was
examined. According to Table 3 and Figure 2, all the hypotheses were supported, where
optimism and innovativeness, displayed a significantly positive impact on apparent ease
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of use as well as on apparent usefulness. Whereas, discomfort and insecurity showed a
significantly negative impact on apparent ease of use and apparent usefulness.
Furthermore, apparent ease of use demonstrated a significant consequence on apparent
usefulness positively, and apparent ease of use also showed a substantial impact on the
attitude to use, positively. Apparent usefulness displayed a noteworthy positive effect on
attitude to use. Thus, all the hypotheses developed in the present study were supported.
The data showed that, 51% of the variance in attitude towards using technology is
attributed to usefulness and ease of use. In addition, the R2 values for apparent ease of use
and apparent usefulness were 41% and 43%, respectively (Figure 2).
Figure 2 Structural model
Technology readiness index
Optimism
Innovativeness
Discomfort
Insecurity
Apparent
usefulness
R
2
= 0.43
Perceived
ease of use
R
2
= 0.41
Attitude to use
R
2
= 0.51
β
= 0.31**
β
= 0.39**
Notes: **p < 0.01; *p < 0.05.
6 Discussion and conclusions
Although there is a substantial amount of information and studies on e-HRM in the
existing literature, the current study has contributed to this field of research further by
providing a practical approach in evaluating e-HRM apparent ease of use and apparent
usefulness while examining TRIs that affect the attitude regarding e-HRM usage.
6.1 Implications theoretically
The current study proposed four kinds of technology readiness, which have distinct
influences on e-HRM apparent usefulness and apparent ease of use. The results revealed
that, innovativeness and optimism as technology readiness dimensions, demonstrated
positive influences on ease of use and apparent usefulness. The results are in
corroboration with the reports of Kuo et al. (2013), Lin and Chang (2011) and Walczuch
The effec
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of technology readiness on technology acceptance 83
et al. (2007). Conversely, insecurity and discomfort dimensions demonstrated a negative
impact on apparent usefulness and apparent ease of use, which is in agreement with the
general perception that they have negative effects. However, the data obtained in this
study revealed that, TRI factors play a major role in evaluating ease of use (R2 = 0.41)
and apparent usefulness (R2 = 0.43) apparent ease of use. Additionally, usefulness as well
as ease of use has demonstrated a noteworthy impact on an individual’s attitude regarding
applying technology (R2 = 0.51). Thus, an HR staff’s perceived TRI factors towards
e-HRM, has a substantial control on e-HRM ease of use as well as usefulness.
6.2 Managerial implications
As a user’s personality has a significant influence on using technology, organisational
managers should be conscious of this association when introducing IS. Accordingly, it is
imperative that organisational managers implement specific strategies to encourage
technology adoption among their employees, based on their personalities. The different
personality types may influence the use and acceptance of IT either positively or
negatively. Thus, when an organisation intends to implement an e-HRM system, it is the
duty of HR managers to anticipate whether the novel system will be readily adopted by
HR employees. This is imperative as implementation of new technology and systems
require not only substantial amount of finances but is also a time-consuming effort. Thus,
investigating user acceptance is a significant factor when an organisation intends to
implement a new IT.
6.3 Limitations and prospective research
One of the limitations of this study is that, the results and the derived conclusions cannot
be generalised to other contexts because the respondents were limited to HRM from
private sector companies in Malaysia, so additional investigations need to be carried out
into the electronic HRM of other organisations. The scope within the current study was to
investigate the impact of technology readiness on e-HRM apparent usefulness as well as
apparent ease of use, so further research should be conducted to explore the impact of
other elements of technology adoption in various structural settings. The findings
revealed that, the ease of use dimension, which was used in current study, was distinct
from prior results, thus the ease of use dimension in particular ought be assessed in
prospective investigations.
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