Technology Adoption and HRM and AI

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/364160066
Technology Adoption and Human Resource Management Practices: The Use of
Artificial Intelligence for Recruitment in Bangladesh
Article in South Asian Journal of Human Resources Management · October 2022
DOI: 10.1177/23220937221122329
CITATIONS
READS
0
84
5 authors, including:
Abdullah Al Mamun
Samina Afrin
University of Chittagong
University of Chittagong
4 PUBLICATIONS 9 CITATIONS
5 PUBLICATIONS 26 CITATIONS
SEE PROFILE
SEE PROFILE
Md. Aftab Uddin
University of Chittagong
91 PUBLICATIONS 892 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Resilience of Social Enterprises in Bangladesh View project
Leading employees' creative engagement toward innovative behavior by their psychological empowerment View project
All content following this page was uploaded by Abdullah Al Mamun on 11 October 2022.
The user has requested enhancement of the downloaded file.
Research article
Technology Adoption
and Human Resource
Management Practices:
The Use of Artificial
Intelligence for
Recruitment in
Bangladesh
South Asian Journal of Human ­Resources
Management
1­–26
© The Author(s) 2022
Reprints and permissions:
https://doi.org/10.1177/23220937221122329
in.sagepub.com/journals-permissions-india
DOI: 10.1177/23220937221122329
journals.sagepub.com/home/hrm
Muhaiminul Islam1 , Abdullah Al Mamun2
Samina Afrin3, G. M. Azmal Ali Quaosar4
and Md. Aftab Uddin5
,
Abstract
Artificial intelligence (AI) is now considered indispensable in undertaking
operational activities, especially in the area of human resource analytics.
However, in practice, the rate of the adoption of such modern algorithms in
organisations is still in its early stages. Consequently, the primary objective
of this study is to identify the main antecedents of the adoption of AI-based
technologies in recruitment, using the lens of the unified theory of acceptance
and use of technology (UTAUT) model, alongside perceived credibility and
moderating variables, in the context of an emerging nation in South Asia, namely
Bangladesh. Data were collected from 283 human resource professionals
employed in different manufacturing and service firms in Bangladesh through the
administration of a questionnaire, which was analysed by applying PLS-SEM. The
outcomes of the study show that all the direct hypothesised relationships were
found to be significant, apart from the extended variable of perceived credibility.
Lecturer, Department of Organization Strategy and Leadership, University of Dhaka, Dhaka,
Bangladesh
2 Assistant Professor, Department of Human Resource Management, University of Chittagong,
Chattogram, Bangladesh
3 Associate Professor, Department of Human Resource Management, University of Chittagong,
Chattogram, Bangladesh
4 Associate Professor, Department of Management Studies, Comilla University, Cumilla, Bangladesh
5 Professor, Department of Human Resource Management, University of Chittagong, Chattogram,
Bangladesh
1
Corresponding author:
Md. Aftab Uddin, Professor, Department of Human Resource Management, University of
Chittagong, Chattogram, Chittagong 4331, Bangladesh.
E-mail: [email protected]
2
South Asian Journal of Human Resources Management
However, no moderating effect of gender or firm size was found in any of the
hypothesised propositions. Finally, policy implications and recommendations for
future researchers are proposed.
Keywords
Actual use, artificial intelligence, Bangladesh, human resource professionals,
intention to use, recruiting talents, UTAUT, SEM-PLS
Introduction
Human resources are considered the strategic resources in business organisations
and the ultimate source of sustainable competitive advantage (Black & Van Esch,
2020; Patel et al., 2019). However, recruiting the right talent is still a far cry
(Vardarlier & Zafer, 2020) because LinkedIn and other traditional methods to
attract and recruit the right employees have yet to be successful (Iqbal, 2018). In
this context, artificial intelligence (AI) can be useful for attracting, retaining and
empowering skilled workers for ensuring a win-win situation for both employers
and applicants (Adikaram et al., 2021; Geetha & Bhanu, 2018; Tambe et al., 2019;
Uddin et al., 2021; Vardarlier & Zafer, 2020). Compared to other methods, AI has
been proven to be beneficial and effective in recruitment (Black & Van Esch,
2020; Hemalatha et al., 2021; O’Connor, 2020; Vardarlier & Zafer, 2020).
In the area of recruitment, AI refers to intelligent machines and software which
behave and act like humans without any human intervention (Lisa & Talla Simo,
2021; Uddin et al., 2021). With the aid of simple filtering algorithms, applicants
are automatically assessed with regard to several factors, such as the university
they attended, and business and industry-wide experience. This simple AI
algorithm allows organisations such as Unilever (Vardarlier & Zafer, 2020),
L’Oréal (Sharma, 2018) and SAT Telecom (Kmail et al., 2015), among others, to
fill employee vacancies faster, economise time and effort, and source appropriate
candidates capable of addressing strategic problems and developing long-term
bonds with new changes (Bhalgat, 2019; Pan et al., 2022; Upadhyay & Khandelwal,
2018). Therefore, using AI can be justified, as the automated system can save time
and costs in hiring the right talent and solve the problems of manual sorting
(Soleimani et al., 2022; Tambe et al., 2019; Vedapradha et al., 2019).
Historically, the recruitment and selection process in Bangladesh in both public
and private sectors is traditional and politically motivated (Absar, 2012; Mahmood
& Nurul Absar, 2015). The large supply of applications to the relatively scarce
employment opportunity for managerial and non-managerial positions causes the
recruiters in the private sector fall prey to nepotism (Mahmood & Nurul Absar,
2015). However, the recruitment and selection process of MNCs in Bangladesh
happens based on the compliance of headquarters office’s directions (Mahmood,
2015). The strength of using AI to recruit in either cases prevents the organisations
from unwanted error and violations in transparent recruitment. However, there
has been minimal use of AI in Bangladesh, although together with the IoT, big
Islam et al.
3
data and block chains, it is gradually gaining in popularity (Uddin et al., 2021). To
remain resilient in a technology-driven economy, HR professionals need to adopt
AI to recruit talents (McGovern et al., 2018; Sen, 2018). However, the use and
adoption of such technology by HR managers for recruitment is still extremely
low throughout the world (Albert, 2019). Besides the low rate of adoption, there
have been relatively few studies on the issue in different countries (Cruz-Jesus et
al., 2018; Muduli & Trivedi, 2020; Palshikar et al., 2019; Quaosar, 2018; Salleh
& Janczewski, 2019; Van Esch et al., 2019).
The adoption of AI in the Asian continent is still in the early stage and slow
(Mehrotra & Khanna, 2022). For example, Pillai and Sivathanu (2020) mentioned
that 22% of Indian organisations are using AI to solve problems in business
processes. The recent study by Hossin et al. (2021) documented the potential
factors which prevent the widespread acceptance of AI in HR management
practice. Despite the fact that professionals are convinced of the benefits of
adopting AI, its use remains relatively rare (Hossin et al., 2021). The biggest
challenge to AI adoption among professionals is their phobia regarding the
replacement of humans with AI and possible widespread job cuts in the future
(Babu, 2021). The potential job loss is not the whole truth of AI adoption because
the WEF (2018) reported that apart from causing few job cuts (75 million jobs),
AI will generate so many new job opportunities (133 million new jobs) and boost
the world’s economy by saving cost and improving quality (ICTD, 2019). Notably,
the lack of studies on the adoption of AI for recruitment from the company and
HR manager viewpoints can be considered to be a potential research gap (Uddin
et al., 2021). Moreover, there is also a dearth of studies in developing countries
which identify predictors of AI adoption and consequences of the former.
Therefore, this study aims to establish the predictors of behavioural intention and
actual use of AI in recruiting talents by HR professionals using the unified theory
of acceptance and use of technology (UTAUT) lens in an emerging country
context in a moderated mechanism.
The article is organised as follows. The second section covers the theoretical
background and study hypotheses, while the third section explains the research
methods and research and sampling design. In the fourth section, the results are
considered, followed by the analytical tools, method bias, and the evaluation of
the models and estimates in relation to the proposed hypotheses. Finally, the fifth
section discusses the findings and presents the contributions, managerial
implications and limitations of the study.
Theoretical Background and Study Hypotheses
The study of the adoption of and intention to use technology has been based on a
variety of theories. Numerous studies have demonstrated that the use of a multitheory approach in a variety of contexts is able to develop an integrated model and
increase its explanatory power (Barrane et al., 2018). However, their empirical
findings show that the integration of fragmented theories to generate a holistic
4
South Asian Journal of Human Resources Management
view of understanding the intention and actual use of a given technology has been
unsuccessful due to the diverse assumptions and beliefs of the disjointed theories
(Bhatiasevi, 2016). Therefore, Venkatesh et al. (2003) developed a new approach,
UTAUT, to describe the intention to use and actual use of technology. It includes
performance expectancy, effort expectancy, facilitating conditions and social
influence as direct predictors of users’ intention to use, which eventually predicts
the actual use behaviour (Patil et al., 2020). The advantage of UTAUT is that it
describes 70% of the variance in intention to use and 50% of actual use (Venkatesh
et al., 2003). Moreover, its generalizability has also been confirmed by many
other studies (Jahanshahi et al., 2020; Patil et al., 2020). Considering the early
stage of AI in developing countries’ context, the present study used the original
UTAUT to explore the intention to use and actual use of AI. Apart from the
original UTAUT, the extant literature showed that the perceived credibility is an
important determinant of adopting a new technology because the security and
trust issues from end users’ perspective regulate their intention to use and actual
use of AI (Gupta et al., 2019). Following the prior studies, the moderating effect
of gender, along with the moderating effect of firm size, was also investigated
because literature shows that the large the firm’s size, the more they use AI (Pillai
& Sivathanu, 2020; WEF, 2018). Thus, the original UTAUT model was employed
in this study, together with the gender of the respondents and firm size, to explain
the actual use of AI.
Performance Expectancy and Behavioural Intention
Performance expectancy refers to end users’ expectation of enhancing their
functional capabilities through the adoption of technologies in a given situation
(Uddin et al., 2020). Venkatesh et al. (2003) explain that it concerns the degree to
which system users perceive that the use of a given technology will help them to
find a solution to a problem. Several studies have shown that performance
expectancy significantly influences the behavioural intention to use technologies
(Dey & Saha, 2020; Suki & Suki, 2017). Alam et al. (2020) conducted a study of
recruiters in Bangladesh and observed a significant impact of the predictors of
UTAUT on the behavioural intention to use AI-enabled technologies in the
recruitment of talent. Another study by Uddin et al. (2021) also found that the
performance expectancy from AI technology improved the behavioural intention
to use it. Therefore, the following hypothesis is proposed:
H1: Performance expectancy influences HR professionals’ behavioural intention to
use AI for recruitment.
Effort Expectancy and Behavioural Intention
Effort expectancy is the degree of ease in using a technology (Venkatesh et al.,
2003). It concerns the user-friendly features of systems which significantly affect
Islam et al.
5
users’ interest in adopting new technologies (Samat et al., 2020). Similar to the
findings previously made related to performance expectancy, effort expectancy is
considered to be an influencer of behavioural intention to use technology (Alam
et al., 2020). Furthermore, Dey and Saha (2020) conducted a study related to
human resources information system (HRIS) adoption in Bangladeshi private and
public hospitals using the UTAUT model; their findings show that effort
expectancy predicted the behavioural intention of HR managers to adopt HRIS.
Similarly, the findings of Onaolapo and Oyewole (2018) demonstrate that effort
expectancy predicted the behavioural intention to use smartphones in postgraduate
distance learning. In addition, various studies have shown similar relationships in
technology adoption, such as in mobile banking (Ghalandari, 2012); enterprise
resource planning (ERP; Rajan & Baral, 2015) and internet use (Isaac et al.,
2019). In the context of Bangladesh, Alam et al. (2020) and Uddin et al. (2021)
found that effort expectancy significantly influenced the behavioural intention to
use AI to recruit talents. Therefore, the following relationship can be hypothesised:
H2: Effort expectancy predicts HR professionals’ behavioural intention to use AI in
recruitment.
Social Influence and Behavioural Intention
Social influence refers to the expectation or pressure on technology adopters
which comes from social factors, subjective norms and perceived images of the
use of new technologies. It can be considered as the level of belief of an individual
regarding the actual use or non-use of such technologies (Alam & Uddin, 2019;
Venkatesh et al., 2003). Individuals’ intentions can be influenced by different
social entities, such as friends, peers, relatives and neighbours. In addition, the
literature also acknowledges that the adoption of new technologies depends on
normative judgements and expectations of the user’s surroundings (Uddin et al.,
2020). In the African context, a study on the behavioural intention to use
technologies in the adoption of social networking sites reported that social
pressure affected the behavioural intention to use SNS (Kaba & Touré, 2014).
Furthermore, the research findings of Shiferaw and Mehari (2019) on Ethiopian
electronic medical recording systems revealed that the behavioural intention to
use AI was positively affected by the social influence perceived by doctors and
nurses. Likewise, studies in Bangladesh on recruiters have shown that social
influence has a significant effect on the behavioural intention to use AI to recruit
employees (Alam et al., 2020; Uddin et al., 2021). Therefore, we hypothesise that:
H3: Social influence affects HR professionals’ behavioural intention to use AI.
Facilitating Conditions and Behavioural Intention
Venkatesh et al. (2003) reported in the UTAUT that facilitating conditions
significantly affected the behavioural intention to use technology, including the
6
South Asian Journal of Human Resources Management
availability of technical and organisational infrastructures. Researchers have
argued that without adequate technical and infrastructural support, along with
knowledge sharing and physical resources, it is highly likely that ambiguity will
be generated at the time of technology acquisition and implementation (Dey &
Saha, 2020; Mamun, 2022; Rajan & Baral, 2015; Suki & Suki, 2017). Dwivedi et
al. (2017) conducted a study on e-government adoption from an Indian perspective;
their findings show a positive association between the facilitating conditions and
the behavioural intention to use. In addition, another study highlighted that to
predict the behavioural intention to use AI of recruiters, facilitating conditions
fuel the adoption of AI-based software in Bangladesh (Alam et al., 2020).
Furthermore, similar research findings have been made in the studies of Shiferaw
and Mehari (2019) and Uddin et al. (2020), respectively. The following hypothesis
is therefore posited:
H4: Facilitating conditions influence the behavioural intention to use AI in
recruitment.
Perceived Credibility and Behavioural Intention
Perceived credibility concerns systems users’ perceived belief in the privacy,
security and credibility of new systems, including the trustworthiness of
technologies and the ability to perform operational activities and transactions with
business partners (Argyris et al., 2021; Cukurova et al., 2020). It creates a sense
of faith and dependency in the minds of users, whereas its absence leads to the
creation of worries regarding the leakage of confidential information without the
knowledge of clients (Gupta et al., 2019; Wang et al., 2003). Related research has
highlighted the association between perceived credibility and behavioural
intention to use online banking, e-payment systems and mobile apps (Gupta et al.,
2019; Palau-Saumell et al., 2019; Yu, 2012). In addition, a recent cross-cultural
study based on bank customers in Pakistan and Turkey and their acceptance of
online banking showed that perceived credibility influenced their behavioural
intention to use it (Khan et al., 2021). However, no studies have considered such
relationships in the adoption of AI-based software by HR professionals. Therefore,
this research gap motivated us to develop a further hypothesis by extending the
UTAUT model:
H5: Perceived credibility influences the behavioural intention to use AI in
recruitment.
Behavioural Intention and Actual Use
The behavioural intention to use measures the degree of subjective probability
and willingness of system users to employ new technologies; it is treated as a
sustainable and consequential forecaster of actual use (Venkatesh & Davis, 2000;
Venkatesh et al., 2003). Previous studies have demonstrated from different
Islam et al.
7
perspectives that the actual use of technologies is significantly influenced by the
behavioural intention to use them (Alam et al., 2020; Rajan & Baral, 2015; Suki
& Suki, 2017). The study of Alam et al. (2020) found a conclusive association
between behavioural intention to use and actual use of AI among recruiters in
Bangladesh. Therefore, we propose the following direct hypothesis:
H6: Behavioural intention to use has a significant relationship with actual use in the
adoption of AI in recruitment.
Gender as a Moderator
In previous technology adoption research, gender has been considered as a
significant control variable in exploring the relationship between constructs
other than AI adoption in recruitment. It has been found that males are more taskorientated, have a greater interest in innovation and novelty, have less anxiety
over technology use, focus more on online purchases and feel less perceived
social pressure than females in the adoption of technologies (Venkatesh & Davis,
2000; Venkatesh et al., 2003; Yu, 2012). On the other hand, female users place
more emphasis on their achievement needs, consider the opinions of those
surrounding them, have more technology anxiety, and perceive more risk in
online transactions than male users in relation to technology adoption and
implementation (Venkatesh & Davis, 2000; Venkatesh et al., 2003; Yu, 2012).
Alam et al. (2020) conducted a study of 296 respondents from Bangladeshi
m-Health service providers using the extended UTAUT model and found a
significant effect of gender on m-Health service adoption. In other studies,
gender was shown to significantly predict mobile banking adoption in the context
of Taiwan (Yu, 2012) and students’ behavioural intention to use animation and
storytelling in Malaysia (Suki & Suki, 2017). In terms of AI adoption in talent
acquisition, the effects of such kinds of control variables are yet to be explored
by researchers with reference to UTAUT. Such research findings motivated the
researchers to test the moderating effect of gender on the adoption of AI.
Therefore, we propose the following hypotheses.
Gender moderates the relationship between performance expectancy and
behavioural intention to use (H7); between effort expectancy and behavioural
intention to use (H8); between social influence and behavioural intention to use
(H9); between facilitating conditions and behavioural intention to use (H10);
between perceived credibility and behavioural intention to use (H11); and between
behavioural intention to use and actual use (H12) in the adoption of AI.
Firm Size as Moderator
Firm size is considered to be an influential predictor in technology adoption (Pan
et al., 2022), but few research studies were found which apply it as a moderator,
especially using the UTAUT model. Previous studies have evidenced that in
8
South Asian Journal of Human Resources Management
general, large firms hold a significant amount of financial, human, physical and
information resources compared to small and medium-sized enterprises (SMEs;
Jacobsen, 2018; Senarathna et al., 2018). This leads to significant investment in
technological advancement and innovation, whereas small firms suffer from a
lack of resources (Ahmadi et al., 2018). Salah et al. (2021) conducted a study on
SMEs operating in Palestine to identify the dominant factors affecting customer
relationship management with the moderation influence of firm size; they reveal
that it significantly influenced the adoption of customer relationship management.
On the contrary, the study of Alhammadi et al. (2015) showed that firm size had
no significant impact on cloud computing adoption in Saudi Arabia. A recent
study by Pan et al. (2022) tested the effect of firm size in AI adoption in Chinese
recruitment but found no significant impact. The consequences of firm size have
also been explored in numerous studies in the area of e-commerce (Ramayah et
al., 2016), ICT adoption (Alshamaila et al., 2013), e-marketing (El-Gohary, 2012)
and ERP (Uddin et al., 2020). However, no empirical studies have explored the
ramifications of firm size as a moderator by extending the UTAUT model. We
therefore hypothesise the following.
Firm size moderates the relationship between performance expectancy and
behavioural intention to use (H13); between effort expectancy and behavioural
intention to use (H14); between social influence and behavioural intention (H15);
between facilitating conditions and behavioural intention to use (H15); between
perceived credibility and behavioural intention to use (H17); and between
behavioural intention to use and actual use (H18) in the adoption of AI.
Methods
Research Design
The researchers designed a quantitative study in which deductive reasoning was
employed to investigate the hypothesised relationships. According to Saunders et
al. (2009), the deductive technique based on a positivist philosophy is justified in
the testing of any theory. A cross-sectional survey with multi-item instruments
was employed to collect the data. For ease of response, it was translated into
native Bangla using the back-translation approach, strictly adhering to Brislin
(1970). In addition, the survey instruments were sent to academics and industry
professionals for face and content validity. Subsequently, some modifications
were made to allow for better comprehension by the respondents, who were
native Bangladeshis.
Data Collection Procedure
A variety of organisations in Bangladesh, both service and manufacturing-based
ones, which have made considerable use of AI were targeted for the study. The
convenience sampling technique was followed to identify HR professionals
Islam et al.
9
working in those organisations who have been working at Chattogram, the
commercial capital and financial hub of Bangladesh (Azim et al., 2019). The
rationale for using HR professionals was their direct attachment to the recruitment
process, so those with no role in recruitment talents were excluded. Notably,
74.44% professionals responded that they were using a few features of AI, such as
application tracking systems, sourcing talents and targeted advertising. The data
were collected through mailed questionnaires sent to each organisation’s HR
department, together with a cover letter and a self-addressed stamped envelope.
From the 390 questionnaires, 283 replies were received, a response rate of 72%.
Among these responses, 13 were deleted due to outliers or incompletion.
Therefore, the final number of responses was 270, which was deemed sufficient
to run the PLS path model (Azim et al., 2019; Fan et al., 2019).
The data were gathered from a varied set of recruiting professionals in terms of
gender, age, tenure, educational background, and firm size and type. Surprisingly,
the majority were male (80%), with ages ranging from 23 to 60, and a mean age
of 33.33, indicating that the HR profession is in the infancy stage. In addition,
most respondents (80%) had completed postgraduate education, and their average
service length was 6.07 years. Moreover, the study covered all types of
organisations, including small (22), medium (98) and large (150) ones. The
majority of these were service-oriented (65%), with the remaining 35%
manufacturing-based.
Measures
Measurement tools from previous research were employed, and all the items were
scored on a 5-point Likert scale (from 5 = ‘strongly agree’ to 1 = ‘strongly
disagree’). Modifications were made to ensure the respondents’ face validity,
particularly error-free meaning (Rubel et al., 2019). The items for measuring
performance expectancy, effort expectancy, facilitating conditions, social
influence and behavioural intention to use were adapted from Venkatesh et al.
(2003) and Venkatesh et al. (2012), whereas those for perceived credibility and
actual use were adopted from Nisha et al. (2019) and Rajan and Baral (2015).
Results
Analytical Tools
Smart PLS-3 was chosen to analyse the data in the structural equation modelling
(SEM). Gudergan et al. (2008) stated that PLS-based SEM is a second-generational
regression tool for assessing complicated cause and effect relationships in
management research. Moreover, due to the goodness of the results, the researchers
chose this approach over basic regression as it examines the entire model rather
than just one path (Azim et al., 2019; Uddin et al., 2019).
10
South Asian Journal of Human Resources Management
Common Bias Method
To ensure that bias remained within a fair spectrum, procedural integrity was
strictly maintained. The survey was entirely voluntary, and those who participated
were assured that their identity would remain anonymous. Moreover, the
questionnaire was constructed using multiple reverse-coded items, considering
the respondents’ monotonous tendencies. Additionally, Harman’s one-factor test
showed that the first factor explained 27.07% of the total variance, which is
lower than the 50% threshold. Furthermore, since the research employed the
PLS–SEM approach, Kock’s (2015) suggestion was also considered for assessing
and controlling method bias (CMB). According to Kock (2015), when a variance
inflation factor (VIF) exceeds 3.3, it indicates extreme collinearity and that the
model is affected by CMB. All the total collinearity variance inflation factor
(VIF) values were lower than 3.3, indicating that there was no concern about
CMB issues.
Measurement Issues
The two-step procedure mentioned in Henseler et al. (2009) was followed to
analyse the data. The measurement model was evaluated with average variance
extracted (AVE), composite reliability (CR) and discriminant validity (Henseler
et al., 2009). CR refers to the indications of model reliability, with any value of 0.7
or greater showing a sufficient degree of reliability (Chin, 2010). The CR values
of all the constructs are shown in Table 1, where it can be seen that they exceed
the threshold value of 0.7, hence demonstrating adequate reliability. In general,
validity is categorised into conceptually different segments: convergent validity
and discriminant validity. Convergent validity reflects whether or not the item
truly represents the underlying construct, as assessed through AVE and outer
loadings, whereas discriminant validity indicates the constructs’ distinctiveness,
in line with Fornell and Larcker’s criterion, the HTMT ratio and cross-loadings
(Hair Jr et al., 2017). A construct’s AVE score which exceeds 0.5 represents
convergent validity (Hair Jr et al., 2017), even if any factor loading is equal to or
greater than 0.5 (Byrne, 2016). As shown in Table 1, the AVE scores of all the
constructs ranged from 0.582 to 0.771, thus confirming convergent validity.
In addition, the Fornell and Larcker criterion and HTMT ratio were considered
to examine discriminant validity. According to Fornell and Larcker (1981), each
construct’s square root of AVE must be higher than its correlation with the
remaining constructs—likewise, HTMT values lower than 0.8, as recommended
by Henseler et al. (2015). Table 2 shows that the square root of each construct’s
AVE (represented by the diagonal scores in italics) is higher than the values below
it, while Table 3 shows that all the constructs’ HTMT scores are below 0.85.
Therefore, there is no issue with validity, and the model is both reliable and valid
for subsequent analysis.
11
Islam et al.
Table 1. Convergent Validity: Measurement Items with Factor Loading, AVE, CR.
Construct
Effort expectancy
Facilitating condition
Performance expectancy
Social influence
Perceived credibility
Behavioural intention
Actual use
Item
Loading
EE1
0.740
EE2
0.821
EE3
0.812
EE4
0.668
FC1
0.846
FC2
0.904
PE1
0.810
PE2
0.849
PE3
0.799
PE4
0.688
SI1
0.826
SI2
0.785
SI3
0.806
SI4
0.715
PC1
0.855
PC2
0.810
PC3
0.873
BI1
0.793
BI2
0.813
BI3
0.871
AU1
0.849
AU2
0.912
AU3
0.873
CR
AVE
0.847
0.582
0.868
0.767
0.868
0.622
0.864
0.614
0.883
0.716
0.866
0.683
0.910
0.771
Source: The authors.
Note: Two items from facilitating conditions were deleted to confirm convergent validity;
CR = composite reliability; AVE = average variance extracted.
Table 2. Discriminant Validity with Fornell and Larcker Criterion.
Latent Variable
1
2
3
4
5
6
1. Actual behaviour
2. Behavioural intention
0.406
3. Effort expectancy
0.260
0.696
4. Facilitating condition
0.577
0.625
0.599
5. Perceived credibility
0.039
0.260
0.328
0.217
6. Performance expectancy
0.282
0.646
0.724
0.450
0.308
7. Social influence
0.423
0.551
0.519
0.601
0.266
Source: The authors.
0.517
7
12
South Asian Journal of Human Resources Management
Table 3. Discriminant Validity with HTMT Ratio.
Latent variable
1
2
3
4
5
6
7
1. Actual behaviour
2. Behavioural intention
0.406
3. Effort expectancy
0.260
0.696
4. Facilitating condition
0.577
0.625
0.599
5. Perceived credibility
0.039
0.260
0.328
0.217
6. Performance expectancy
0.282
0.646
0.724
0.450
0.308
7. Social influence
0.423
0.551
0.519
0.601
0.266
0.517
Source: The authors.
Evaluation of the Structural Model
The model’s explanatory power can be assessed by two widely used approaches,
namely R2 and Q2, and SRMR (standardised root mean residual; Rabiul & Yean,
2
2021). Cohen (1977) postulated that R v� alues of 0.10, 0.25 and 0.30 should be
considered as small, moderate and significant, respectively. However, Hair Jr
2
et al. (2014) state that an R value above 0.20 is acceptable in behavioural
2
science. As illustrated in Figure 1 and Table 4, the R value of behavioural
intention to use is 0.459, indicating that 45.9% of it can be explained by
independent variables, whereas that for actual use is 0.129, indicating that 12.9%
of it can be explained by behavioural intention to use. Any SRMR value below
0.080 is desirable (Hair et al., 2017); in this case, it is 0.071, as shown in Table 4,
2
indicating a good model fit. Additionally, Q works as a measure to project the
model’s predictive value beyond its sample. Table 4 reports a low-to-moderate
predictive relevance (Hair et al., 2017). VIF is a collinearity indicator, and any
value greater than 3 leads to a greater error in regression weight (Mahmood et al.,
2019). The largest VIF in this study, as shown in Table 4, is 1.675 (effort
expectancy), indicating that the study has no multicollinearity issue. Moreover,
the inclusion of control variables, such as age, gender, education, experience and
industry type, showed their insignificant influence on the actual use of AI.
Testing of the Direct Effects
Consistent bootstrapping was employed to test the hypotheses, using 5,000
samples of the reflective measurement model, as suggested by Dijkstra and
Henseler (2015), while leaving other conditions as default. Regarding the direct
effects, five hypotheses (H1: β = 240; p = .001; H2: β = 0.217; p = .000; H3: β =
0.249; p = .000; H4: β = 0.128; p = .049; and H6: β = 0.336; p = .00) are supported
(p < .05; Figure 1 & Table 5). However, perceived credibility does not have a
significant effect on behavioural intention to use (β = 0.021, p = .639 or p > .05).
Therefore, H5 is not supported because the respondents believed that technology
was more reliable as it performs in a bias-free manner.
13
Islam et al.
Effort
Expectancy
β=0.240
p=0.001
Facilitating
Condition
β=0.217
p=0.000
Performance
Expectancy
Behavioral Intention
R2 = 0.459
β=0.249
p=0.000
β=0.336
p=0.000
Actual Behavior
R2 = 0.129
β=0.021
p=0.639
Perceived
Credibility
β=0.128
p=0.049
Experience
Social
Influence
Figure 1. Structural Model with Path Estimates.
Source: The authors.
Table 4. Quality of the Model and Fit Indices.
Construct
R2
VIF
Q2
SRMR
Actual behaviour
0.129
–
0.084
0.071
Behavioural intention
0.459
1.000
0.272
–
Effort expectancy
–
1.675
–
–
Facilitating condition
–
1.395
–
–
Perceived credibility
–
1.104
–
–
Performance expectancy
–
1.567
–
–
Social influence
–
1.433
–
–
Source: The authors.
Table 5. Test Statistics of the Direct Effects.
β
CI
0.24
(0.105, 0.372)
3.484
.001
Supported
FC → BI
0.217
(0.111, 0.318)
4.101
.000
Supported
PE → BI
0.249
(0.108, 0.384)
3.517
.000
Supported
0.128
(0.004, 0.260)
1.966
.049
Supported
PC → BI
0.021 (–0.067, 0.104)
0.47
.639
Not supported
BI → AU
0.336
6.49
.000
Supported
Hypothesis Relation
H1
H2
H3
H4
H5
H6
EE → BI
SI → BI
(0.231, 0.432)
T Value P Value Decision
Source: The authors.
Note: EE = effort expectancy; FC = facilitating condition; PE = performance expectancy; SI = social
influence; PC = perceived credibility; BI = behavioural intention; AU = actual use.
14
South Asian Journal of Human Resources Management
Testing of the Moderating Effects
In line with the hypothesised research model, we examined the moderating effect
of firm size and gender on the predictors of AI adoption, behavioural intention to
use and actual use. Since firm size and gender were employed as moderator
variables, multi-group analysis was used to examine the relationship (Hair et al.,
2017; Sarstedt et al., 2011). We divided responses into two mutually exclusive
groups based on gender: male (215, 80%) and female (55, 20%). Table 6 shows
the moderating effect of gender on effort expectancy, facilitating conditions,
perceived credibility, performance expectancy and social influence on behavioural
intention to use, together with the influence of behavioural intention to use on
actual use. The results of the multi-group analysis shown in the table reveal that
gender has an insignificant impact on all the relationships between effort
expectancy and behavioural intention to use (male: β = 0.252; p = .001; female: β
= 0.169; p = .273); performance expectancy and behavioural intention to use
(male: β = 0.213; p = .001; female: β = 0.256; p = .057); social influence and
behavioural intention to use (male: β = 0.225; p = .005; female: β = 0.399; p =
.005); facilitating conditions and behavioural intention to use (male: β = 0.213; p
= .001; female: β = 0.256; p = .057); perceived credibility and behavioural
intention to use (male: β = 0.041; p = .406; female: β = –0.024; p = .834); and
behavioural intention to use and actual use (male: β = 0.358; p = .000; female: β
= 0.266; p = .101).
Moreover, we also conducted multi-group analysis between firms, such as
small and medium organisations and large organisations. Since each respondent
represented a distinct organisation and each organisation was counted as a
sample, the researchers divided the respondents into two mutually exclusive
groups based on firm size: large organisations (150, 56%) and medium to smallsized organisations (120, 44%). Table 7 shows that firm size has an insignificant
effect on all the relationships between effort expectancy and behavioural intention
to use (large organisations: β = 0.258; p = .003; small and medium-sized
organisations: β = 0.253; p = .023); performance expectancy and behavioural
intention to use (large organisations: β = 0.247; p = .002; small and mediumsized organisations: β = 0.218; p = .065); social influence and behavioural
intention to use (large organisations: β = 0.134; p = .089; small and mediumsized organisations: β = 0.187; p = .046); facilitating conditions and behavioural
intention to use (large organisations: β = 0.243; p = .002; small and mediumsized organisations: β = 0.137; p = .119), perceived credibility and behavioural
intention to use (large organisations: β = –0.020; p = .757; small and mediumsized organisations: β = 0.040; p = .540); and behavioural intention to use and
actual use (large organisations: β = 0.336; p = .000; small and medium-sized
organisations: β = 0.358; p = .000). Therefore, it can be concluded that
organisations of all sizes share a similar perspective towards the adoption of AI
for recruiting personnel.
PE → BI
SI → BI
FC → BI
PC → BI
BI → AU
Relationship
EE → BI
Men
STDE
0.078
0.08
0.074
0.061
0.05
0.057
B
0.252
0.225
0.127
0.213
0.041
0.358
0.005
0.087
0.000
0.406
0.000
P
0.001
0.399
0.096
0.256
–0.024
0.266
B
0.169
0.143
0.118
0.132
0.115
0.16
Women
STDE
0.151
0.005
0.417
0.053
0.834
0.097
P
0.263
–0.175
0.031
–0.042
0.066
0.092
Difference
0.083
.288
.83
.741
.593
.562
P Value
.61
Not supported
Not supported
Not supported
Not supported
Not supported
Decision
Not supported
Relationship
EE → BI
PE → BI
SI → BI
FC → BI
PC → BI
BI → AB
B
0.253
0.218
0.187
0.137
0.040
0.358
SME
STDE
0.112
0.114
0.093
0.088
0.066
0.08
P
0.023
0.055
0.045
0.120
0.540
0.000
B
0.258
0.247
0.134
0.234
–0.02
0.336
LO
STDE
0.088
0.080
0.091
0.074
0.063
0.067
P
0.003
0.002
0.140
0.001
0.757
0.000
Difference
0.004
0.029
–0.053
0.098
–0.060
–0.022
P Value
.98
.837
.683
.392
.513
.828
Decision
Not supported
Not supported
Not supported
Not supported
Not supported
Not supported
Note: EE = effort expectancy; FC = facilitating condition; PE = performance expectancy; SI = social influence; PC = perceived credibility; BI = behavioural intention; AU = actual
use; SME = small and medium-sized enterprise; LO = large organisation; STDE = standard error.
Source: The authors.
Hypothesis
H7
H8
H9
H10
H11
H12
Table 7. Moderating Effect of Firm Size on Hypothesised Relationships.
Note: EE = effort expectancy; FC = facilitating condition; PE = performance expectancy; SI = social influence; PC = perceived credibility; BI = behavioural intention; AU = actual
use; SME = small and medium-sized enterprise; LO = large organisation; STDE = standard error.
Source: The authors.
H14
H15
H16
H17
H18
Hypothesis
H13
Table 6. Moderating Effect of Gender on the Hypothesised Relationships.
16
South Asian Journal of Human Resources Management
Discussion
Among IT-based HRM technologies, AI has grown rapidly in the recruitment
industry, and system experts are claiming that its adoption and implementation
will be the most appropriate strategic weapon to incorporate talent hunting
programmes in the future. This study has analysed the major factors which are
essential in endorsing AI-supportive technologies in employee recruitment by
applying the extended UTAUT model in a developing country context. To ensure
the originality and robustness of the findings, the moderation effects of firm size
and gender were tested in all the hypothesised relationships.
The results show that performance expectancy, effort expectancy, social
influence and facilitating conditions have a significant impact on behavioural
intention to use, and that behavioural intention to use subsequently influences the
actual adoption of AI-based technologies in recruitment. However, our findings
also indicate that perceived credibility has no significant effect on behavioural
intention to use. Surprisingly, the moderation effect (Tables 6 & 7) shows that
none of the moderating variables makes a significant intervention in regulating
the adoption of AI in recruitment. In the adoption of technology, users’ perceived
belief relating to their ability to solve any given problems is referred to as
performance expectancy, meaning that the perceived functional capability of the
system has predicting agility in behavioural intention to use. In our study, the
hypothesised relationship between performance expectancy and behavioural
intention to use is supported and is consistent with the studies of Uddin et al.
(2021), Alam et al. (2020) and Alam and Uddin (2019). Therefore, the study
endorses that AI-enabled technologies in Bangladesh can assist organisations to
conduct talent acquisition effectively and can replace traditional recruitment
processes.
The study also supports the proposed relationship in which the extent of the
user-friendly characteristics of AI-facilitated technologies perceived by
Bangladeshi HR professionals has an influential impact on the behavioural
aspects. This implies that if professionals believe that the adoption of AI-based
technologies is difficult, they may exhibit repulsive behaviour; this is in line with
the findings of Onaolapo and Oyewole (2018) and Ghalandari (2012). In our
study, the hypothetical relationship between social influence and behavioural
intention to use AI in the context of HR professionals in Bangladesh indicates that
social influence significantly leads to behavioural intention to use, which implies
that AI acquisition and adoption are backlashed by HR professionals’ peers,
colleagues, supervisors and friends. Similar results were obtained in the studies of
Kaba and Touré (2014) and Shiferaw and Mehari (2019).
Moreover, the adoption of AI-enabled technologies calls for facilitating
conditions, meaning infrastructural and technical support from top management,
vendors, users, importers or suppliers. Our study supports the tenets of facilitating
conditions and behavioural intention to use, which indicate that when recruiters in
Bangladesh receive sufficient technological support in the adoption of AI for
Islam et al.
17
talent acquisition, their motivation will increase; our findings are in line with
those of Uddin et al. (2020). The extended construct, perceived credibility, is not
supported in our study, which shows that HR professionals in Bangladesh do not
consider it to be an influential factor in adopting AI in recruitment. This finding is
contrary to those of Gupta et al. (2019), Khan et al. (2021) and Alam et al. (2020).
Naturally, the present generation in Bangladesh enjoys the formal learning
opportunities provided by technology in educational institutions and social
groups, which proxies their absorption capacity to reshape their attitudes and
perception to overcome techno-centric obstacles. They strongly believe that their
tech-directed knowledge encourages them to adopt AI-based technologies in
changing situations (Jilani et al., 2022).
Regarding the moderation effect, it is noticeable that none of the moderating
variables was found to be significant. First, gender does not have a significant
effect in the relationships between performance expectancy, effort expectancy,
social influence, facilitating conditions, perceived credibility and behavioural
intention to use, and between behavioural intention to use and actual use. These
findings are contrary to those of Venkatesh et al. (2003), Venkatesh et al. (2012),
Tsourela and Roumeliotis (2015) and Alam et al. (2020). One possible reason for
the discrepancy is that the majority of respondents were male, with low
participation of female HR professionals, which prevented the distinctive
moderation effect of gender on the adoption of AI-centric technologies. Second,
similar to the moderation effect of gender, the effect of firm size shows no
interaction effect on performance expectancy, effort expectancy, social influence,
perceived credibility or behavioural intention to use, nor on behavioural intention
to actually use. This finding is contrary to the studies of Salah et al. (2021) and
Wang et al. (2018) but consistent with that of Pan et al. (2022). The reason behind
the inconsistency might be that the majority of respondents represented medium
and large firms. As a result, the intervening effect of firm size is not evident in any
of the hypothesised relationships.
Theoretical Contributions of the Study
The study makes several notable contributions. It sheds light on the use of AI to
recruit talents using the lens of the UTAUT model. This model has been previously
used in the study of the adoption of AI, but mostly in advanced countries.
Therefore, this study is original as it is from the developing country perspective
which will undoubtedly contribute to advancing the current body of knowledge of
AI adoption to recruit talents. Moreover, this study extends the original UTAUT
model with the inclusion of perceived credibility as a predictor variable, and
gender and firm size as moderators. Surprisingly, the study echoes and validates
the results of original UTAUT adoption in previous studies and disproves the use
of perceived credibility and moderators such as gender and firm size. Another
important contribution is the methodological novelty of using the responses from
18
South Asian Journal of Human Resources Management
HR professionals, which play an important role during the recruitment process.
Finally, it suggests the necessity to restructure the function of human resources,
especially in the recruitment of talent. It is also expected that the conceptual and
practical insights of the study will boost the adoption rate of AI in terms of both
frequency and appropriateness in developing countries. Moreover, the study
highlights the adoption of AI from the HR professionals’ perspective, and is not
limited to any particular sector, which is also a significant contribution to
advancing understanding of AI adoption using the tenets of UTAUT.
Managerial Implications of the Study
The outcomes of the study provide a number of insights for AI system designers,
developers, importers, vendors and HR professionals concerning the adoption of
AI-enabled technologies in organisations. First, system designers and developers
should concentrate more on the functional capability of AI-enabled technologies
in the context of emerging economies in South Asia, such as Bangladesh.
Table 5 demonstrates that the value of the beta coefficient is high for the
constructs of performance expectancy (β = 0.249) and effort expectancy
(β = 0.240) among the four dependent variables of the original UTAUT model.
In brief, importers, vendors and HR business partners should concentrate more
on individual-level variables rather than group or organisational-level ones.
Second, the findings of the study provide important insights for the practising
HR professionals who are directly involved with recruitment and selection.
Findings of this study will assist them to construct and use AI at their organisation
by considering the antecedents of intention to use and actual use of AI. Moreover,
the study will pave the way for strategic HR leaders to analyse HR training needs
and identify gaps based on the different antecedents of AI adoption in employee
recruitment. Henceforth, the results of this study will assist them in designing the
appropriate training and development programmes to reshape the behavioural
patterns of employees, which in turn may accelerate the adoption rate of AI in
Bangladesh. Third, the moderation effect in the study shows that AI-enabled
system developers, designers and vendors need not consider either gender or
firm size in the development phase; rather, they should concentrate more on the
social influence and facilitating conditions of AI-enabled technologies. Finally,
the study enriches the literature in the area of information system, especially in
technology acceptance, giving academics, researchers, entrepreneurs and
students comprehensive conceptualisation of AI-based technologies.
Limitations and Future Research Directions
The study is not without its limitations, despite its original contributions to the
body of knowledge. First, it attempts to examine the behavioural intention to
Islam et al.
19
use and actual use of AI for talent recruitment in Bangladesh, an approach which
is still in its infancy, thus implying the possibility of national bias. Second, the
respondent pool was confined to HR professionals from various firms in
Bangladesh, the majority of whom were male and on average 33 years old,
raising concerns about generalizability and industry comprehensiveness. Third,
rather than relying on the three key moderators in the original UTAUT model in
addition to gender, firm size was used as a moderator. However, the respondents
were predominantly from service organisations (68%), limiting the
generalizability of the findings. The same holds true for the intervening effect
of gender, as the participants were overwhelmingly male (80%). Moreover, the
cross-sectional data raise concerns about the credibility of the study. Therefore,
adopting longitudinal and repeated longitudinal approaches with a larger sample
size and even distribution of respondents’ demographic characteristics in similar
contexts may be a viable avenue for future researchers. In addition, the territorial
restrictions of the study provide opportunities for further investigation in other
emerging regions. In conclusion, although the predictive power of the
behavioural intention of the model is satisfactory (41.5%), several potential
elements (e.g., self-efficiency and technology trust) could be included to
improve its explanatory power.
Conclusion
The present study provides useful insights into AI adoption for recruiting talents
in the context of a developing country, Bangladesh, using the lens of UTAUT
which was not addressed elsewhere. Particularly, the research attempted the
original UTAUT model with few modifications, including the relevant control
variables, for AI adoption. The results obtained showed that all the predictor
variables of original UTAUT variables are found significantly associated and the
influence of perceived credibility on behavioural intention to use is not supported.
Moreover, the multi-group analysis showed that there is no moderating influence
of gender and organisational size. The findings of the study imply that the original
UTAUT model explains the adoption of AI technology in recruiting talents in the
Bangladesh context. However, future researchers are recommended to attempt
UTAUT2 and other models with different research designs if the attempted
findings bring any difference in theory and practice.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of
this article.
20
South Asian Journal of Human Resources Management
ORCID iDs
Muhaiminul Islam
Abdullah Al Mamun
https://orcid.org/0000-0002-5927-3762
https://orcid.org/0000-0001-9042-8163
G. M. Azmal Ali Quaosar
Md. Aftab Uddin
https://orcid.org/0000-0001-9125-7378
https://orcid.org/0000-0002-9101-7451
References
Absar, M. M. N. (2012). Recruitment & selection practices in manufacturing firms in
Bangladesh. Indian Journal of Industrial Relations, 47(3), 436–449.
Adikaram, A. S., Priyankara, H. P. R., & Naotunna, N. P. G. S. I. (2021). Navigating
the crises of COVID-19: Human resource professionals battle against the pandemic.
South Asian Journal of Human Resources Management, 8(2), 192–218. https://doi.
org/10.1177/23220937211018021
Ahmadi, H., Nilashi, M., Shahmoradi, L., Ibrahim, O., Sadoughi, F., Alizadeh, M., &
Alizadeh, A. (2018). The moderating effect of hospital size on inter and intra-organizational factors of hospital information system adoption. Technological Forecasting
and Social Change, 134, 124–149. https://doi.org/10.1016/j.techfore.2018.05.021
Alam, M. S., Dhar, S. S., & Munira, K. S. (2020). HR professionals’ intention to adopt and
use of artificial intelligence in recruiting talents. Business Perspective Review, 2(2),
15–30.
Alam, M. S., & Uddin, M. A. (2019). Adoption and implementation of enterprise resource
planning (ERP): An empirical study. Journal of Management and Research, 6(1),
1–33.
Albert, E. T. (2019). AI in talent acquisition: A review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215–221. https://doi.org/10.1108/
SHR-04-2019-0024
Alhammadi, A., Stanier, C., & Eardley, A. (2015). The determinants of cloud computing
adoption in Saudi Arabia. https://airccj.org/CSCP/vol5/csit54506.pdf
Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud computing adoption by SMEs in
the north east of England. Journal of Enterprise Information Management, 26, 250275. http://dx.doi.org/10.1108/17410391311325225
Argyris, Y. A., Muqaddam, A., & Miller, S. (2021). The effects of the visual presentation of
an influencer’s extroversion on perceived credibility and purchase intentions—moderated by personality matching with the audience. Journal of Retailing and Consumer
Services, 59, 102347. https://doi.org/10.1016/j.jretconser.2020.102347
Azim, M. T., Fan, L., Uddin, M. A., Jilani, M. M. A. K., & Begum, S. (2019). Linking
transformational leadership with employees’ engagement in the creative process.
Management Research Review, 42(7), 837–858. http://dx.doi.org/10.1108/MRR-082018-0286
Babu, K.-E.-K. (2021). Artificial intelligence in Bangladesh, its applications in different
sectors and relevant challenges for the government: An analysis. International Journal
of Public Law and Policy, 7(4), 319–333. https://doi.org/10.1504/ijplap.2021.118891
Barrane, F. Z., Karuranga, G. E., & Poulin, D. (2018). Technology adoption and diffusion: A new application of the UTAUT model. International Journal of Innovation
and Technology Management, 15(06), 1950004–1950023. https://doi.org/10.1142/
s0219877019500044
Bhalgat, K. H. (2019). An exploration of how artificial intelligence is impacting recruitment and selection process. Dublin Business School.
Islam et al.
21
Bhatiasevi, V. (2016). An extended UTAUT model to explain the adoption of
mobile banking. Information Development, 32(4), 799–814. https://doi.
org/10.1177/0266666915570764
Black, J. S., & Van Esch, P. (2020). AI-enabled recruiting: What is it and how should a
manager use it? Business Horizons, 63(2), 215–226.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Crosscultural Psychology, 1(3), 185–216.
Byrne, B. M. (2016). Structural equation modelling with AMOS: Basic concepts, applications, and programming (3rd ed). Routledge.
Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin,
J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690).
Springer.
Cohen, J. (1977). Statistical power analysis for the ehavioral sciences. Academic Press.
Cruz-Jesus, F., Oliveira, T., & Naranjo, M. (2018). Understanding the adoption of business analytics and intelligence. World Conference on Information Systems and
Technologies (pp. 1094–1103). Springer.
Cukurova, M., Luckin, R., & Kent, C. (2020). Impact of an artificial intelligence research
frame on the perceived credibility of educational research evidence. International
Journal of Artificial Intelligence in Education, 30(2), 205–235. https://doi.org/10.1007/
s40593-019-00188-w
Dey, T., & Saha, T. (2020). Implementation of HRIS by hospitals in Bangladesh: An analysis using the UTAUT model. https://www.irjet.net/archives/V7/i1/IRJET-V7I1341.pdf
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS
Quarterly, 39(2), 297–316.
Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M.
(2017). An empirical validation of a unified model of electronic government adoption
(UMEGA). Government Information Quarterly, 34(2), 211–230.
El-Gohary, H. (2012). Factors affecting e-marketing adoption and implementation in
tourism firms: An empirical investigation of Egyptian small tourism organisations.
Tourism Management, 33(5), 1256–1269.
Fan, L., Mahmood, M., & Uddin, M. A. (2019). Supportive Chinese supervisor, innovative
international students: A social exchange theory perspective. Asia Pacific Education
Review, 20(1), 101–115. http://dx.doi.org/10.1007/s12564-018-9572-3
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and neasurement error. Journal of Marketing Research, 18(1), 39–50.
Geetha, R., & Bhanu, S. R. D. (2018). Recruitment through artificial intelligence: A conceptual study. International Journal of Mechanical Engineering and Technology,
9(7), 63–70.
Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social
influence and facilitating conditions on acceptance of e-banking services in Iran: The
moderating role of age and gender. Middle-East Journal of Scientific Research, 12(6),
801–807.
Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis
in PLS path modeling. Journal of Business Research, 61(12), 1238–1249.
Gupta, K. P., Manrai, R., & Goel, U. (2019). Factors influencing adoption of payments
banks by Indian customers: Extending UTAUT with perceived credibility. Journal of
Asia Business Studies. https://doi.org/10.1108/JABS-07-2017-0111
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated
and expanded assessment of PLS-SEM in information systems research. Industrial
Management & Data Systems, 117(3), 442–458.
22
South Asian Journal of Human Resources Management
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least
squares structural equation modeling (PLS-SEM): Sage publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least
squares structural equation modeling (PLS–SEM) (2nd ed.). SAGE Publications.
Hemalatha, A., Kumari, P. B., Nawaz, N., & Gajenderan, V. (2021). Impact of artificial
intelligence on recruitment and selection of information technology companies. Paper
presented at the 2021 International Conference on Artificial Intelligence and Smart
Systems (ICAIS), 25–27 March.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy
of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path
modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New
challenges to international marketing. Emerald Group Publishing.
Hossin, M. S., Ulfy, M. A., & Karim, M. W. (2021). Challenges in adopting artificial
intelligence (AI) in HRM practices: A study on Bangladesh perspective. International
Fellowship Journal of Interdisciplinary Research, 1(1), 66–73.
ICTD. (2019). National strategy for artificial intelligence of Bangladesh. https://ictd.
portal.gov.bd/sites/default/files/files/ictd.portal.gov.bd/page/6c9773a2_7556_4395_
bbec_f132b9d819f0/Draft%20-%20Mastering%20National%20Strategy%20for%20
Artificial%20Intellgence%20-%20Bangladesh.pdf
Iqbal, F. M. (2018). Can artificial intelligence change the way in which companies recruit,
train, develop and manage human resources in workplace? Asian Journal of Social
Sciences and Management Studies, 5(3), 102–104.
Isaac, O., Abdullah, Z., Aldholay, A. H., & Ameen, A. A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the unified theory of acceptance and use of technology (UTAUT) model. Asia Pacific
Management Review, 24(4), 335–354.
Jacobsen, D. I. (2018). Adopting and refining e-services: The role of organization size.
Public Organization Review, 18(1), 111–123.
Jahanshahi, D., Tabibi, Z., & Van Wee, B. (2020). Factors influencing the acceptance and
use of a bicycle sharing system: Applying an extended unified theory of acceptance
and use of technology (UTAUT). Case Studies on Transport Policy, 8(4), 1212–1223.
Jilani, M. M. A. K., Moniruzzaman, M., Dey, M., Alam, E., & Uddin, M. A. (2022).
Strengthening the trialability for the intention to use of mHealth apps amidst pandemic: A cross-sectional study. International Journal of Environmental Research and
Public Health, 19(5), 2752.
Kaba, B., & Touré, B. (2014). Understanding information and communication technology
behavioral intention to use: Applying the UTAUT model to social networking site
adoption by young people in a least developed country. Journal of the Association for
Information Science and Technology, 65(8), 1662–1674.
Khan, I. U., Hameed, Z., Khan, S. N., Khan, S. U., & Khan, M. T. (2021). Exploring the
effects of culture on acceptance of online banking: A comparative study of Pakistan
and Turkey by using the extended UTAUT model. Journal of Internet Commerce,
1–34. https://doi.org/10.1080/15332861.2021.1882749
Kmail, A. B., Maree, M., Belkhatir, M., & Alhashmi, S. M. (2015). An automatic online
recruitment system based on exploiting multiple semantic resources and concept-relatedness measures. Paper presented at the 2015 IEEE 27th International Conference on
Tools with Artificial Intelligence (ICTAI), 9 November.
Islam et al.
23
Kock, N. (2015). Common method bias in PLS–SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.
org/10.4018/ijec.2015100101
Lisa, A. K., & Talla Simo, V. R. (2021). An in-depth study on the stages of AI in recruitment process of HRM and attitudes of recruiters and recruitees towards AI in Sweden.
(Independent thesis advanced level, degree of master, student thesis), Umea University,
Sweden. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184521
Mahmood, M. (2015). Strategy, structure, and HRM policy orientation: Employee recruitment and selection practices in multinational subsidiaries. Asia Pacific Journal of
Human Resources, 53(3), 331–350. https://doi.org/https://doi.org/10.1111/17447941.12057
Mahmood, M., & Nurul Absar, M. M. (2015). Human resource management practices in
Bangladesh: Current scenario and future challenges. South Asian Journal of Human
Resources Management, 2(2), 171–188. https://doi.org/10.1177/2322093715599481
Mahmood, M., Uddin, M. A., & Fan, L. (2019). The influence of transformational leadership on employees’ creative process engagement: A multi-level analysis. Management
Decision, 57(3), 741–764.
Mamun, A. A. (2022). Human resource professionals’ intention to use and actual use
of human resource information systems. International Journal of Technology and
Human Interaction, 18(1), 1–18.
McGovern, S. L., Pandey, V., Gill, S., Aldrich, T., Myers, C., Desai, C., Gera, M., &
Balasubramanian, V. (2018). The new age: Artificial intelligence for human resource
opportunities and functions. EY. https://www.studocu.com/in/document/gujarat-technological-university/masters-of-business-administration/ey-the-new-age-artificialintelligence-for-human-resource-opportunities-and-functions/8439195
Mehrotra, S., & Khanna, A. (2022). Recruitment through AI in selected Indian companies.
Metamorphosis, 21(1), 31–39. https://doi.org/10.1177/09726225211066220
Muduli, A., & Trivedi, J. J. (2020). Recruitment methods, recruitment outcomes and information credibility and sufficiency. Benchmarking: An International Journal. https://
doi.org/10.1108/bij-07-2019-0312
Nisha, N., Iqbal, M., & Rifat, A. (2019). The changing paradigm of health and mobile
phones: An innovation in the health care system. Journal of Global Information
Management (JGIM), 27(1), 19–46.
O’Connor, S. W. (2020). Artificial intelligence in human resources management: What
HR professionals should know. Northeastern University Graduate Programs. https://
www.northeastern.edu/graduate/blog/artificial-intelligence-in-human-resource-management/
Onaolapo, S., & Oyewole, O. (2018). Performance expectancy, effort expectancy, and
facilitating conditions as factors influencing smart phones use for mobile learning by
postgraduate students of the University of Ibadan, Nigeria. Interdisciplinary Journal
of e-Skills and Lifelong Learning, 14(1), 95–115.
Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User
acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2.
Sustainability, 11(4), 1210.
Palshikar, G. K., Srivastava, R., Pawar, S., Hingmire, S., Jain, A., Chourasia, S., & Shah,
M. (2019). Analytics-led talent acquisition for improving efficiency and effectiveness.
In A. K. Laha (Ed.), Advances in analytics and applications (pp. 141–160). Springer.
Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International
24
South Asian Journal of Human Resources Management
Journal of Human Resource Management, 33(6), 1125–1147. https://doi.org/10.1080
/09585192.2021.1879206
Patel, C., Budhwar, P., Witzemann, A., & Katou, A. (2019). HR outsourcing: The impact
on HR’s strategic role and remaining in-house HR function. Journal of Business
Research, 103, 397–406. https://doi.org/10.1016/j.jbusres.2017.11.007
Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer
adoption of mobile payment in India: Extending meta-UTAUT model with personal
innovativeness, anxiety, trust, and grievance redressal. International Journal of
Information Management, 54, 102144.
Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599–
2629. https://doi.org/10.1108/BIJ-04-2020-0186
Quaosar, G. M. A. A. (2018). Adoption of human resource information systems in developing countries: An empirical study. International Business Research, 11, 133. https://
doi.org/10.5539/ibr.v11n4p133
Rabiul, M. K., & Yean, T. F. (2021). Leadership styles, motivating language, and work
engagement: An empirical investigation of the hotel industry. International Journal of
Hospitality Management, 92(January 2021), 102712.
Rajan, C. A., & Baral, R. (2015). Adoption of ERP system: An empirical study of factors
influencing the usage of ERP and its impact on end user. IIMB Management Review,
27(2), 105–117. https://doi.org/10.1016/j.iimb.2015.04.008
Ramayah, T., Ling, N. S., Taghizadeh, S. K., & Rahman, S. A. (2016). Factors influencing
SMEs website continuance intention in Malaysia. Telematics and Informatics, 33(1),
150–164.
Rubel, M. R. B., Kee, D. M. H., & Rimi, N. N. (2019). Matching people with technology:
Effect of HIWP on technology adaptation. South Asian Journal of Human Resources
Management, 7(1), 9-33. https://doi.org/10.1177/2322093719875567
Salah, O. H., Yusof, Z. M., & Mohamed, H. (2021). The determinant factors for the adoption of CRM in the Palestinian SMEs: The moderating effect of firm size. PLOS One,
16(3), e0243355.
Salleh, K. A., & Janczewski, L. (2019). Security considerations in big data solutions adoption: Lessons from a case study on a banking institution. Procedia Computer Science,
164, 168–176. https://doi.org/10.1016/j.procs.2019.12.169
Samat, M. F., Awang, N. A., Hussin, S. N. A., & Nawi, F. A. M. (2020). Online distance
learning amidst COVID-19 pandemic among university students. Asian Journal of
University Education, 16(3), 220–233.
Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in partial
least squares (PLS) path modeling: Alternative methods and empirical results. In
Measurement and research methods in international marketing (Vol. 22, pp. 195–
218). Emerald Group Publishing.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students.
Pearson Education.
Sen, S. (2018). AI and automation in HR: Impact, adoption and future workforce. AIHR
Digital. https://www.digitalhrtech.com/ai-in-hr-impact-adoption-automation/
Senarathna, I., Wilkin, C., Warren, M., Yeoh, W., & Salzman, S. (2018). Factors that
influence adoption of cloud computing: An empirical study of Australian SMEs.
Australasian Journal of Information Systems, 22. https://doi.org/10.3127/ajis.
v22i0.1603
Islam et al.
25
Sharma, A. (2018). How AI reinvented hiring practice at L’Oréal. https://www.peoplematters.in/article/technology/how-the-worlds-largest-cosmetic-company-transformedits-hiring-practice-with-ai-19006
Shiferaw, K. B., & Mehari, E. A. (2019). Modeling predictors of acceptance and use of
electronic medical record system in a resource limited setting: Using modified UTAUT
model. Informatics in Medicine Unlocked, 17, 100182.
Soleimani, M., Intezari, A., & Pauleen, D. J. (2022). Mitigating cognitive biases in developing AI-assisted recruitment systems: A knowledge-sharing approach. International
Journal of Knowledge Management (IJKM), 18(1), 1–18. https://doi.org/10.4018/
IJKM.290022
Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioural intention to use
animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3),
528–538. https://doi.org/10.1016/j.ijme.2017.10.002
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human
resources management: Challenges and a path forward. California Management
Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Tsourela, M., & Roumeliotis, M. (2015). The moderating role of technology readiness,
gender, and sex in consumer acceptance and actual use of Technology-based services.
The Journal of High Technology Management Research, 26(2), 124–136.
Uddin, M. A., Alam, M. S., Hossain, M. K., Islam, T., & Hoque, M. S. A. (2021). Artificial
intelligence (AI) in recruiting talents: Recruiters’ intention and actual use of AI. In The
essentials of machine learning in finance and accounting (pp. 211–232). Routledge.
Uddin, M., Alam, M. S., Mamun, A. A., Khan, T.-U.-Z., & Akter, A. (2020). A study of
the adoption and implementation of enterprise resource planning (ERP): Identification
of moderators and mediator. Journal of Open Innovation: Technology, Market, and
Complexity, 6(1), 2.
Uddin, M. A., Priyankara, H. R., & Mahmood, M. (2019). Does a creative identity encourage innovative behaviour? Evidence from knowledge-intensive IT service firms.
European Journal of Innovation Management, 23(5), 877–894.
Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: implications
for recruitment. Strategic HR Review, 17(5), 255–258.
Van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase
in job application and selection. Computers in Human Behavior, 90, 215–222.
Vardarlier, P., & Zafer, C. (2020). Use of artificial intelligence as business strategy in
recruitment process and social perspective. In Digital business strategies in blockchain ecosystems (pp. 355–373). Springer.
Vedapradha, R., Hariharan, R., & Shivakami, R. (2019). Artificial intelligence: A technological prototype in recruitment. Journal of Service Science and Management, 12,
382–390. https://doi.org/10.4236/jssm.2019.123026
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology.
MIS Quarterly, 36(1), 157–178.
26
South Asian Journal of Human Resources Management
Wang, J., Zhang, Y., & Goh, M. (2018). Moderating the role of firm size in sustainable performance improvement through sustainable supply chain management. Sustainability,
10(5), 1654.
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: An empirical study. International Journal of Service
Industry Management, 14(5), 501–519.
WEF. (2018). Is artificial intelligence replacing jobs? Here’s the truth. https://www.weforum.org/agenda/2018/09/is-artificial-intelligence-replacing-jobs-truth/
Yu, C.-S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2),
104–123.
View publication stats