Enabling organizational use of artificial intelligence: an employee perspective
| DOI | https://doi.org/10.1108/JABS-09-2020-0372 |
| Published date | 11 February 2021 |
| Date | 11 February 2021 |
| Pages | 245-266 |
| Subject Matter | Strategy,International business |
| Author | Amal Dabbous,Karine Aoun Barakat,May Merhej Sayegh |
Enabling organizational use of artificial
intelligence: an employee perspective
Amal Dabbous, Karine Aoun Barakat and May Merhej Sayegh
Abstract
Purpose –As artificial intelligence (AI) has become increasingly popular and accessible, most
companies have recognizedits far-reaching potential. However, despite numerousresearch papers on
organizational adoption of new technologies including AI, little is known about individual employees’
intentionsto use them. Given that organizationalinnovations are of limited valueif they are not adopted by
employees, the purpose of this study is to understand the underlying factors that push employees to
make use of these new technologiesin the workplace.
Design/methodology/approach –This study builds on previously developed technology acceptance
modelsto providea new theoreticalmodel. The model is then tested using data collected froma survey of
203 employeesand analyzed through structuralequation modeling.
Findings –Findings show that five factors affect employees’ intention to use AI either directly or as
mediators. Organizational culture and habit exert a positive impact on employees’ intention to use AI,
whereas job insecurity has a negative impact. Perceived self-image and perceived usefulness fully
mediate the relation between job insecurity and intention to use. Moreover, perceived self-image and
perceivedusefulness partially mediate the relationshipbetween habit and intentionto use.
Originality/value –To the best of the authors’ knowledge,this study is among the first to determine the
factors that influenceemployees’ intention to use AI in general and more particularlychatbots within the
workplace.
Keywords Mediation, Organizational culture, Artificial intelligence, Jobinsecurity, Habit
Paper type Research paper
Introduction
In recent years, artificial intelligence (AI) has emerged as a phenomenon of economic and
organizational significance (Von Krogh, 2018) in large part owing to the transformational
impact this new technology has had in increasing efficiency and productivity within
organizations (Bhattacherjee and Sanford, 2006). Although AI is not new, during the past
five years remarkable progress has been achieved in several areas like speech recognition
and machine translation industries (Varian, 2018), this has paved the way for a change in
the decision-making process, led to the creation of new business models and allowed
companies to realize things that were considered not possible to achieve before. Despite
the fact that some of the AI applications such as chatbots could be seen as replacements
for human jobs, AI can also be a tool to augment and improve the work done by workers as
it can provide instant and accurate access to the knowledge and information employees
need to perform their tasks rapidly and reduces the time spent on collecting and analyzing
data (Burgess, 2017). AI can assist employees in overcoming difficult situations by offering
diversified and different solutions(Jarrahi, 2018), and as a result it can ensure rigid inputs in
decision-making processes (Bader and Kaiser, 2019). Indeed, AI is at the heart of a digital
disruption that has enabled companies to drive better customer engagement and led to
accelerated rates of innovation, higher competitiveness, higher margins and more
productive employees (Yablonsky, 2019).
Amal Dabbous,
Karine Aoun Barakat and
May Merhej Sayegh all are
based at the Faculty of
Business Administration
and Management, Saint
Joseph University, Beirut,
Lebanon.
Received 23 September 2020
Revised 12 November 2020
Accepted 14 December 2020
DOI 10.1108/JABS-09-2020-0372 VOL. 16 NO. 2 2022, pp. 245-266, ©Emerald Publishing Limited, ISSN 1558-7894 jJOURNAL OF ASIA BUSINESS STUDIES jPAGE 245
However, over the years, one of themajor problems organizations have faced has been the
low adoption and high underutilization of this type of new technology (Jasperson et al.,
2005). These obstructions have oftenbeen the result of employees’ unwillingness to accept
and use these new technologies, given the factthat AI has the potential to replace the work
that is currently being realized by a large number of workers (Burgess, 2017). This is
especially true in the case of companieswhich operate in developing countries where many
observers argue that the current wave of automation will have significant effects (Alonso
et al.,2020
). Sachs (2019) and Yusuf (2017) suggest profound implications for
development pathways and strategies, along with reductions in demand for unskilled labor
within these countries. Nedelkoska and Quintini (2018) find that developing countries are
more vulnerable to automation, based on the way work is organized and their greater
dependence on unskilled labor. Furthermore, according to Makridakis (2017), the adoption
of AI is even more challenging in these countries in particular owing to the high cost of
investing in this technology and the threat posed to human labor that will lead to increased
unemployment.
Although several theories have been proposed to explain technology adoption in general,
researchers have only recently begun to examine organizational readiness to embrace AI
(Pumplun et al., 2019;Alsheibani et al.,2018), and to date little is known about employees’
intention to use AI within the context of organizations, especially those operating in
developing countries. In addition, even though technologies and applications of AI have
been widely studied in the literature, the factors that influence the AI adoption within
organizations remain understudied. In light of the fact that AI adoption in the workplace can
only be successful if employees accept and efficiently use this technology (Lee et al.,
2006), it appears important to focus on employees, and warrants further researchregarding
the role of organizational, individual and social factors in affecting individual adoption of
such new technologies (Schepers and Wetzels, 2007;Yi et al.,2006). This study therefore
proposes to answer the following research question: What are the factors that influence
employees’ intention to use AI in the context of companies operating in developing
countries?
To do so, the study builds on previously developed technology acceptance models (TAM)
and the theory of reasoned action (TRA). Because of the lack of grounded theory in
information system research, various intention models such as TRA, theory of planned
behavior and TAM from the field of social psychology are used to provide an explanation
about the factors predicting individuals’ acceptance behavior (Kaur and Arora, 2020;
Venkatesh et al., 2012). Within this stream, the TRA (Fishbein and Ajzen, 1975) has long
advocated that individual behavior is based on reasoned and planned action resulting from
and consistent with people’s conscious intentions regarding that behavior. Such reasoned
action is at the heart of information technologies (IT) acceptance models, such as TAM and
UTAUT. This study therefore extends the technology adoption literature by proposing a
behavioral model that takes into account the specificities of AI. The model is tested through
structural equation modeling (SEM) using data collected from 203 questionnaires
addressed to employees workingin Lebanon.
The paper offers both theoretical and practical implications. It is to the best of the authors’
knowledge the first to propose a model explaining employees’ intention to use AI in the
workplace based on five factors which are perceived usefulness, job insecurity, self-image,
habit and organizational culture. Moreover, it is one of the few studies that addresses
employees’ AI adoption in the context of a developing country, thus providing insight into
this type of behavior andpaving the way for comparative studies with other countries.
The paper is structured in the following manner. Section 2 presents the theoretical
development, hypotheses and proposed model. Section 3 describes the research
methodology. Section 4 illustrates the data analysis. Section5 exhibits the study results, key
findings and discussion. Finally, Section 6 outlines the theoretical and managerial
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