Technology Adoption and HRM and AI

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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
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Abdullah Al Mamun
University of Chittagong
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https://doi.org/10.1177/23220937221122329
South Asian Journal of Human Resources
Management
1 –26
© The Author(s) 2022
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DOI: 10.1177/23220937221122329
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Research article
Technology Adoption
and Human Resource
Management Practices:
The Use of Artificial
Intelligence for
Recruitment in
Bangladesh
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.
5 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
2 Assistant Professor, Department of Human Resource Management, University of Chittagong,
Chattogram, Bangladesh
1 Lecturer, Department of Organization Strategy and Leadership, University of Dhaka, Dhaka,
Bangladesh
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 multi-
theory 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