Int. Journal of Business Science and Applied Management, Volume 17, Issue 1, 2022
Key elements in transferring knowledge of the AI
implementation process for HRM in COVID-19 times: AI
consultants’ perspective
Mohand Tuffaha
Business Administration and Management School, Universitat Politècnica de València
Aremoda 45056 Valencia, Spain
Email: mohtuf@hotmail.com
M Rosario Perello-Marin
Business Administration and Management School, Universitat Politècnica de València
Camino de Vera s/n E-46022 Valencia, Spain
Email: rperell@upvnet.upv.es
Esperanza Suarez-Ruz
School of Industrial Engineering, Universitat Politècnica de València
Camino de Vera s/n E-46022 Valencia, Spain
Email: esuarezruz@omp.upv.es
Although artificial intelligence (AI) is transforming the workplace structure, very little is known about
the strategy that facilitates AI implementation in organizations. The purpose of this paper is to explore
key elements in transferring knowledge of the AI implementation process in human resource
management (HRM) from the perspective of AI consultants. This study utilizes qualitative data
analysis techniques. We first review the literature and then conduct in-depth semistructured interviews
with eight AI consultants. We analyze transcripts using the ATLAS.ti software. First, this research
reveals that AI implementation is affected by a shortage of employee data, no clear vision, a limited
understanding of the AI decisions framework and managers’ desire to bypass AI decisions. Second, the
combination of an intensive training program and assigning AI specialists is the best way to transfer the
knowledge of AI implementation processes to HR managers. Third, HR managers should create
communication channels and enhance employees’ awareness of the positive impact that AI solutions
have on smooth collaboration with AI-employees. The paper also reveals that accelerating the process
of implementing AI applications has no negative impact in COVID-19 times. However, an AI bias may
be considered a potential threat for AI implementation. This paper attempts to provide a practical
understanding of the elements that facilitate AI implementation in the HRM process. It provides vital
insights for HR managers and AI developers to benchmark their activities when designing and adopting
AI solutions. It also contributes to the literature by responding to the question of how AI
implementation should be provided to HR managers and employees.
Keywords: Artificial Intelligence, Implementation, HR Manager, Employee, COVID-19
Int. Journal of Business Science and Applied Management / Business-and-Management.org
Data is becoming the most valuable asset for any organization and might be its only truly
inimitable asset (de Medeiros et al., 2020). Organizations have ample access to massive volumes of
data from several sources, which, when reliably and quickly processed, significantly increases the
likelihood of obtaining valuable insights to guide decision makers and employee performance in the
HR department (Wan & Liu, 2021). HR data plays a crucial role in guiding the performance measure in
a competitive business environment. This includes employee data, such as employee performance,
employee behavioural patterns, attendance, compensation and other personal data (Pillai & Sivathanu,
2021). Researchers have noticed that data strongly influences strategy formulation because the increase
in data and analytical capabilities is redefining innovation, competition and productivity (Nisar, et al.,
2021). According to an interview published by the McKinsey Global Institute, conducted by Barr Seitz
and Rob Roy: “Having a data-first mentality is a crucial first step, but then you need to put in place the
processes and capabilities to be able to use this data” (Seitz & Roy, 2018). Therefore, HR data needs to
be appropriately managed to maintain data quality depending on two assets: emerging technology and
human asset (Yablonsky, 2021).
Artificial intelligence (AI) is considered the most advanced technology that transforms the nature
of the workplace and the relationships among employees. Based on a study conducted by McKinsey
and Company in 2020 about the state of AI, revenue increased from adopting AI in human resources by
55% and adopting AI across several teams led to 33% increased team efficiency (McKinsey, 2020).
These advantages of AI depend on a large set of technologies that allow the computer to perform many
HR tasks that generally require human intervention. These tasks extend to covering data mining in
recruitment and selection (Allal-Chérif et al., 2021), employee turnover (Zhang et al., 2021) and data
extraction, with an emphasis placed on employee performance and productivity (Arslan et al., 2021).
For instance, IBM and Microsoft are using AI and data mining to identify suitable candidates for
particular jobs by standardizing candidate sourcing and C.V screening methods for all their subsidiaries
(Garg et al., 2021). Similarly, Human Capital Management (HCM) from Oracle is using data-driven
insights, which help with talent acquisition and advanced HR metrics as a part of AIHR process
integration (Fernandez J. , 2019).
HR data-driven solutions have a constructive effect on survival during times of crisis and
pandemics (Vahdat, 2021). COVID-19 is a contagious infectious pandemic that negatively influences
major economic sectors globally. This influence is driven by a series of widespread lockdowns of
various sizes, marking it as the first crisis and life-changing phenomenon of its type for nearly 7 billion
people around the world (Nguyen et al., 2021; Nizamidou & Vouzas, 2018). This crisis has, in turn,
posed financial challenges for organizations as a result of a massive decrease in product demand and
sharp declines of in-house employee numbers due to isolation and social distancing, which inevitably
leads to fewer investments (Adikaram et al., 2021). On the one hand, companies are concerned to
ensure their employees’ health and safety. On the other hand, curtailing the spread of this virus means
having to make drastic changes in the workplace structure and employee performance (He et al., 2021).
Disruption in the workplace, such as that caused by COVID-19, prompts the need for a novel
solution to facilitate remote working options. The AI-powered solution is bridging the physical world
and the digital world by strengthening human-machine interactions and fostering automation through
integrations between smart machines and HR tasks (Pereira et al., 2021). Accordingly, AI is considered
the recent key answer in the unexpected situations faced by individuals and corporations related to
business survival in disasters (Kashyap & Raghuvanshi, 2020). In fact 52% of US companies are
accelerating their AI investments in the wake of the COVID-19 crisis in different company areas (PwC,
2020). One international bank has created a source of truth from datasets and launched an AI-powered
chatbot to respond to customer queries. These efforts not only helped customers, but also demonstrated
to employees the role of AI in facilitating job demands (McKinsey, 2020). Therefore, AI
implementation acts as the optimum solution for organizations and technology developers to bypass
COVID-19 challenges.
On the other side of the story, human assets, HR managers and employees are playing a crucial
role in maintaining the quality of data and uploading these data in AI-powered solutions to leverage
these solutions (Wiblen & Marler, 2019). Both HR managers and employees have witnessed the
advanced role of AI applications in several human resource management (HRM) functions. From a
manager’s perspective, AI facilitates the functions of collecting, managing, analyzing and visualizing
large amounts of data to generate recommendations and insights (de Medeiros et al., 2020). From an
employee’s perspective, AI enables employees to work in both physical and virtual spaces. These
facilities will help employees to save useless commuting time, provide them with more flexibility,
enable them to manage work and collaborate with no time and place constraints (Malik et al., 2021).
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
Organizations have taken advantage of AI in different ways, which has led to a massive change in
the landscape of HR processes. However, there are still challenges and difficulties to be addressed with
AI advantages (Tambe et al., 2019), for instance: the data generation stage, employees' learning
capabilities, the accountability questions associated with fairness and ethical constraints, possible
adverse employee reactions to management’s decisions via data-based AI (Harney & Collings, 2021).
These challenges have brought about a need to enhance HR managers’ knowledge about AI and how
this technology should be adopted among their employees. Kolbjørnsrud et al. (2017) report that
managers are not confident enough about AI. Their readiness and enthusiasm for AI may vary
extensively across organizational levels, which raises serious questions about the optimum strategy for
sharing knowledge of the AI implementation process.
The importance of extending HR managers' and employees’ knowledge about AI implementation
has been further highlighted by Chang (2020). He calls for further studies to investigate the best HR
manager-employee balance for AI implementation and how knowledge about AI implementation
processes can be provided to HR managers and employees. At the same time, the current massive
challenges posed by COVID-19 provide an opportunity for management scholars to extend research
efforts and turn them into actionable insights. These initiatives will support organizations in handling
one of the greatest challenges in modern history by identifying the potential impact of COVID-19 on
AI implementation (Hamouche, 2021).
Despite several studies having been conducted on AI implementation in HR tasks, such as
recruiting, selecting and performance management (Wall & Schellmann, 2021; Tuffaha & Perello-
Marin, 2021; Xiong & Xia, 2020), there is still a gap in the literature in terms of understanding the best
HR manager-employee balance for AI implementation. To overcome these gaps, the following research
questions (RQs) are raised:
RQ1: How can the knowledge of AI implementation process be transferred to HR managers?
RQ2: What kind of strategies can HR managers adopt for smooth AI implementation among
Our analysis contributes to the literature about HRM in AI in several ways. First, we develop a
practical understanding of the elements facilitating AI implementation in the HR department during a
pandemic. Second, our study responds to Chang’s (2020) request to research the best-demand balance
for AI implementation. Finally, this novel research will be beneficial for HR managers and AI
developers when designing and adopting AI solutions.
The article is organized as follows. We first provide a literature review. Then we discuss the
methods. Next we analyze the categories reported by the participants. This is followed by a section
with discussion and implications. The final section offers some conclusions.
This section attempts to look closely at what is currently known about AI solutions in HR, the role
of AI in COVID-19 times and the effects of AI implementation on HR managers and employees. We
review the literature and technical reports on AI and HRM. HRM and COVID-19 are also considered.
2.1. Impact of technologies on HRM
For several decades, scholars have been studying the impact of information technology (IT) on
HRM. One of the main study areas is E-HRM: the planning and implementing of IT among employees
for collective actors of HR activities (Poba-Nzaou et al., 2020). Subsequently, scholars have focused on
big data and the development of HR analytics in organizations (Dahlbom et al., 2020). Later scholars
have centred on the implementation of AI, data mining, HRM cloud computing and algorithms in
functioning HR tasks (Alrashedi & Abbod, 2021; Black & van Esch, 2021; Marin et al., 2021), and
access to and the creation of structured and unstructured HR-specific datasets with growing
dependence on advanced digitalized HRM and AI applications to generate insights, solve issues and
participate in HR decision making (Caruso, 2018; Prikshat et al., 2021; Vrontis et al., 2021). The
diffusion and applications of AI-innovated database management are demonstrated by emergent AI-HR
solutions, such as SAP SuccessFactors, ERP and CloudHR (Oracle, 2022; SAP, 2022). Google,
Microsoft, IBM and LinkedIn are among the IT behemoths that have also created such applications.
The job of HR professionals involves redefining and restructuring as a result of greater digitization and
data utilization by AI-powered HR solutions (Qamar et al., 2021).
AI is defined as a smart machine-based system’s ability to correctly interpret data, find patterns
and adopt the resulting information to fulfill specific goals and to perform tasks (Arslan et al.,2021). AI
uses several techniques, such as the Internet of Things (IoT), deep learning, pattern recognition,
Int. Journal of Business Science and Applied Management / Business-and-Management.org
machine learning and artificial neural networks (ANN) (Ali & Frimpong, 2020; Lu, 2021). The main
advantages of AI, such as high-speed processing, big data mining and sophisticated prediction and
analysis, differentiate AI from existing heritage IT applications (Cheng & Hackett, 2021). These
advantages help HR managers to save time and to make accurate decisions (Borges et al., 2021) in
critical tasks like recruitment and selection, performance management and workforce planning (Black
& van Esch, 2021; Santana & Valle-Cabrera, 2021).
2.2. AI in HR amid COVID-19
The COVID-19 pandemic has created uncertainty in not only firm productivity and operational
terms, but also in HRM efficiency terms. Therefore, significantly increased attention has been paid to
AI during COVID-19. This demand is related to several novel applications with the abilities to
facilitate virtual workplaces. Organizations have leveraged AI in HRM during COVID-19 in several
dimensions. For instance, first AI-powered chatbots have facilitated the role of the HR department in
conducting virtual recruiting and to arrange interviews without candidates having to physically travel to
the workplace (Sowa et al., 2021). Second, AI plays an advanced role in training and development by
analyzing the data collected from employees and test results, and then building individual development
programs based on these results (Boiral et al., 2021). Third, AI also plays a role in monitoring and
analyzing employees’ cultural enrollment and engagement by identifying behavioural patterns (Rao &
Krishan, 2021). To illustrate all this, AI-enabled automation tools can handle and analyze massive
volumes of data, recommend courses of actions and carry out these recommendations (Vahdat, 2021).
As a reflection, HR managers can virtually operate HR departments.
AI applications in HR tasks during COVID-19 have raised employees’ concerns about their
existence (Mohamadou et al., 2020). Many scholars argue that fully adopting AI applications will
reduce aggregate labour hours (Frey & Osborne, 2017), which will imply fewer employees, while
others claim that many AI applications are useless without employees intervening (Dwivedi, et al.,
2021). Employees are required to analyze and confirm AI recommendations, convert recommendations
into courses of action and offer backup if an AI solution fails. As a result, a new trend of an AI
implementation scenario is formulated that depends on boosting employee productivity by AI-enabled
technology rather than replacing them (Dwivedi, et al., 2021).
The opportunities for AI are numerous. To accomplish the potential leverage of these applications,
organizations should maximize "AI-human integration". This integration should be based on detailed
information about the AI implementation process (Wilson & Daugherty, 2018). Therefore, this paper
attempts to bridge the following gap: in what circumstances and to what extent can organizations
transfer knowledge about AI implementation processes to HR managers and employees? It also studies
the impact of COVID-19 on AI implementation.
3.1 Data collection
The study took a qualitative approach to examining the data collected from eight AI consultants
experienced in implementing AI in HR projects in multi-international firms. These professionals were
identified using LinkedIn (see Table 1) to obtain a more holistic overview of the research questions.
LinkedIn has had major implications for recruiters, hiring managers and job seekers. It is used by over
40% of potential job seekers and 85% of hiring managers for the purposes of screening applicants and
other recruitment processes (Collmus et al., 2016; Cubrich et al., 2021).
As the table shows, interviewees’ average work experience is 13.87 years. During the interviews,
we asked questions about how the AI implementation process can be provided to HR managers and
employees, and then about the impact of COVID-19 on AI implementation.
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
Table 1. Interviewees’ profiles
Title / Position
Years of
Area of experience
Senior Artificial
Intelligence and
SAP Consultant
over 16
Technology leader with expertise in
NLP, Deep Learning, Analytics and
More than
(via Zoom)
Co-founder of
over 14
AI-Powered Chatbot Platform for
(via Zoom)
Senior Artificial
(AI) Developer
at Cloud
13 years of
Specialist in:
AI in healthcare
Business Analyst
Full-stack Development (Web &
More than
(via Zoom)
HR & Talent
Tech Start-up
Advisor, AI
Educator and
15 years’
Businesses Adviser for AI and
emergent technology in HRM
(via Zoom)
SAP Success
11 years
SF/SAP solution consultant, business
process analyst, team leader,
integration consultant, trainer, onsite
service coordinator and career
Senior system engineer at IBM
Around 200
(via Zoom)
(Gulf region)
HCM at Oracle
over 16
AI in HRM implementation
Solution orientated for HRM project
(via Zoom)
Hiring Solutions
CyberSec, &
10 years of
Cybersecurity & compliance
Software Engineering (Custom
Applications Development -
Cloud & Infrastructure
Data Science and Advanced analytics
(via Zoom)
Director, AI &
FinTech Leader
in PwC
over 16
years of
Helping PwC and its clients benefit
from global AI insights and expertise
Leading the development of AI-
enabled products and solutions
(via Zoom)
3.2 Data analysis
To evaluate the data collected, we used the following procedure (see Figure 1). In the first step,
interviews were coded into text and turned into a transcript by compiling replies on a single response
sheet for each research question individually. In the next step, the transcript was cleaned of special
characters, numerical values and spaces. Finally, these transcripts were imported to the ATLAS.ti
qualitative data analysis software (Paulus & Lester, 2016). The team working on establishing reliability
and validity consisted of two researchers. Each one has prior research experience and works as an
Associate Professor in a management college. A word cloud was created, which was then utilized to
extract the major content from the analysis. A mixture of text mining and qualitative content analysis
(Using ATLAS.ti) was applied so that thematic convergence was evident from the data collected from
the interview transcripts.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
Figure 1: Data collection and analysis process
Data analysis has been done with the help of statistical tools like Statistical Package for the Social
Sciences (SPSS) and Structural Equation Modelling using AMOS version 21. To ensure the validity of
the Information quality measures (adapted scale), Exploratory Factor Analysis (EFA) was conducted.
The EFA result showed a one-dimensional scale of information quality. The total variance explained
was 67.20%. Further to establish convergent validity, mainly three criteria are used: “Factor loadings
>0.7>, Average Variance Extracted (AVE) >0.50 and Composite Reliability (CR)>0.7” (Sarstedt,
Ringle and Hair, 2017; Hair et al., 2018). The results are under the prescribed threshold limits. Table 2
displays the factor loadings along with Cronbach’s Alpha values. The correlation matrix, composite
reliability (CR), and AVE of the study variables are shown in Table 3.
The analysis of interviewees’ responses revealed three major categories: 1) challenges associated
with AI implementation in the HR process in COVID-19 times; 2) transfer of knowledge about AI
implementation processes to HR managers; 3) HR managers' strategy for smooth AI-employees
interaction (Figure 2). The first two categories addressed the first question, while the third category
dealt with the second question.
Selection of
145 potential AI
consultants and
developers in HRM
were identified from
Initial contact
Official invitations were
sent to the initial sample
through LinkedIn
94.5% did
e in the
Schedule interview
5.5% interviewees
showed an interest and
participated in the study.
Interviews were
conducted via Zoom
recorded and
by the cloud
365 single
cleaning was
done to
numbers and
spaces from
The thematic analysis of the
interview transcripts was browsed,
coded and interpreted by ATLAS.ti
Findings and discussion
Alignment of responses was done
with identified themes and
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
Figure 2: Key elements to transfer knowledge about AI implementation processes for HRM in
COVID-19 times
4.1. Challenges associated with AI implementation in HR processes in COVID-19 times
The word cloud for the first research question indicates that “AI Data” is the word that was
highlighted the most by the interviewees, followed by vision, clear, accept, solution, bypass,
infrastructure, etc. The thematic analysis of the first research question indicated some barriers and
challenges for implementing AI in the HR process (see Table 2). Interviewees highlighted weak
employee datasets, which minimized the efficiency of AI functions. Data and AI are merging into a
synergistic relationship, where AI is useless without HR data sources like personal employee data,
employee performance and financial data. In contrast, mastering these data is difficult without AI
solutions. This idea comes over in the following remarks:
Interviewee 5: “Of the key challenges for AI we find data availability in organizations. If it's
available, it might not be central enough”.
Interviewee 2: “Data collecting and processing speed have enabled the development of AI. This
development leads to an analysis of the data, which help to assess and predict employee performance”.
of the AI
to HR
related to
ng AI in
of the AI
ion process
to HR
strategy for
smooth AI -
Shortage of employee data
Companies have no clear vision and no clear
understanding of the AI decision framework
Weak infrastructure
Understand the client's requirements
AI involvement in decision making and
manager’s desire to bypass AI solutions
Limit integration between departments
Offering trainings program and assigning AI
Create communication channels
Enhance the awareness of the positive impact
of AI on employees’ performance
AI-employees collaboration maintains the
balance among job demand, professional
development and social life
Int. Journal of Business Science and Applied Management / Business-and-Management.org
Interviewee 7: “Massive volumes of data would be worthless unless AI models unlock the
potential of these data and turn them into information”.
Table 2: Themes identified for Research Question 1 (RQ1) (Part 1)
Note: Only themes with a frequency of more than two were included
The next theme was the unclear vision on the areas where AI is used to support organizational
performance (16.6%), which resulted from the ambiguity as to how AI is building its decisions or
predictions. HR managers frequently lack an understanding of the assumptions and decisions made by
AI. They also need standardized assessments of the overall risk and financial metrics of AI like return
on investment (ROI). Additionally, tools that explain how AI systems make decisions could enable
managers to better understand the potential risk associated with AI implementation. Quoted below are
some instances from interviewees’ answers:
Interviewee 3: “One organization manager told me: we are facing problems to build a clear
vision for the future of AI in our company resulting from the ambiguous process of the AI decisions”.
Interviewee 4: “HR managers need a clear conceptual model to analyze the ROI when adopting
AI in their departments; such as potential risks and financial impacts. We know the features of AI, but
managers are unaware of the disadvantages of AI in their departments”.
Another theme is that managers wish to have the authority to bypass AI solutions (11.2%). AI has
wide applications for integrating information, analyzing data and using the insights gained to improve
employee performance. However, interviewees admitted that their organization’s manager wishes to
have the upper hand in the final AI decision related to employees. Some instances from interviewees’
answers are quoted below:
Interviewee 6: “Another challenge that we are facing is: to what extent should AI be involved in
decision making? Some HR managers would like to use AI internally but, at the same time, they want
the authority to bypass AI solutions”.
Interviewee 3: “Some companies are not responding to AI recommendations, which poses
obstacles to machines toward generating correct decisions in the future”.
Finally, another aspect worth highlighting is that all interviewees shared the same impression: AI
is the best solution that organizations should adopt to manage their employees and to minimize the
influence of uncertainty and confusion. Therefore, AI-powered solutions and implementation are in
great demand in COVID-19 times. These responses are reasonable if we analyze the advantages of AI
in the virtual workplace. These advantages include but are not limited to: 1) the role of AI-powered
solutions in effectively achieving employees tasks; 2) helping the HR department to follow up the
analysis of the sentiment of employees and to flag up employees’ concerns; 3) detecting employee
engagement trends across specific employee segments (PwC, 2020)
Along similar lines, all the interviewees also reported that accelerating AI implementation during
pandemics does not have a negative impact on organization performance. However, interviewees voice
a concern about the negative impact of AI bias on organizational performance.
An AI algorithm decision is based on training data, which may either include biased human
decisions or reflect historic or social concerns, even if sensitive variables like gender, race or sexual
orientation are removed. Algorithm biases reduce employees’ ability to participate in developing their
organization by encouraging mistrust, which may produce inadequate results.
Business and organizational leaders need to ensure that the adopted AI system is free from
algorithm bias to maintain the quality of the decision-making process. They have the responsibility to
encourage the progress of research and standards that reduce any bias in AI to eliminate potential
negative impacts from implementing AI. The following remarks made by AI consultants reflect this
S. No
Employee data and data quality
Companies have no clear vision or no clear understanding of the AI
decision framework
Managers’ desire to bypass AI decisions
Weak infrastructure
Limited integration between departments
Understanding the client's requirements
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
Interviewee 3: “if we have an algorithm that is biased toward women because of training data,
this will be reflected in the determinant of women’s future success, and women who have trained will
be subject to an AI algorithm bias because most leading positions have to be for men”.
Interviewee 6: “The role of imbalanced data is vital for introducing bias. Feeding AI with the
correct dataset is vital to maintain the perfect image in the face of our employees. Otherwise AI
implementation will collapse”.
Based on our data, it would appear that while AI is a tempting next step toward creating value for
organizations and employees, shortage of data and the relationship of mistrust between AI and
managers have been considered a barrier for AI implementation. These challenges are boosting the
need for initiatives that eliminate the risk of users being threatened by AI implementation. This
message is similar to a project launched by the European Union called “TRUST-AI” (European, 2020)
to develop trust in AI adequacy to tackle predictable.
4.2. Key factors in transferring knowledge about the AI implementation process to HR
Another word cloud created for the first research question showed that “AI training” and “AI
specialist” were the words highlighted by most interviewees, followed by knowledge, technical,
consultant, person, function, etc. The thematic analysis revealed two main defined themes (see Table
3). According to interview data, interviewees took two different points of view about the best way to
transfer knowledge about AI implementation processes to HR managers.
The most prominent ones were that transferring knowledge about AI implementation depends on
two key factors: offering a training program for HR managers and assigning AI specialists (67%) to
follow AI implementation progress.
Table 3: Responses for Research Question 1 (RQ1) (Part 2)
S. No.
AI provided for HR managers by Internal training and AI
AI provided for HR managers by internal training only
Note: Only the themes with a frequency of more than 2 were included
AI’s contribution to the organization’s performance depends, on the one hand, on enhancing HR
managers’ knowledge by an intensive training program and, on the other hand, appointing an AI
specialist in the organization. Quoted below are some instances from interviewees’ answers:
Interviewee 1: “If I need an AI solution for interviewing candidates, this kind of solution needs the
recruiter to be familiar with the AI system. At the same time, AI technicians should be available in the
organization to maintain data and to cover any problems if the system collapses.”
Interviewee 7: “I believe that internal training is not enough alone. The organization should
assign an AI specialist who is aware of this recently emerged technology to maintain the follow-up of
data in the AI solution”.
To maintain the quality of the first point of view (Theme 2.1), and to explore the potential
negative impact on the organization’s performance, the authors followed interviewees’ answers with
another question: “If AI specialists are hired, could the role of AI specialists overlap that of decision
makers?”. The first point of view believed that AI specialists would have no reflection on HR decisions
for two reasons: 1) AI specialists do not work for the HR department only, but facilitate AI
implementation for other departments within the organization; 2) AI specialists work as consultants
only because HR managers have the qualitative and quantitative skills which are difficult for AI
specialists to acquire. Some instances from interviewees’ answers are quoted below:
Interviewee 7: “AI specialists should do cross-company work, and not just deal with HR. They are
solutions-oriented and AI specialists perform cross-domain functions”.
Interviewee 4: “AI specialists provide and explain why this recommendation is proposed. They
play no role in final management decisions”.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
The second point of view (Theme 2.2) emphasizes that the training program is a sufficient way to
transfer knowledge about the AI implementation process (22%). Here it was argued that personal data
and employee performance data should remain confidential and not be exposed to any third party.
Quoted below are some instances from interviewees’ answers:
Interviewee 2: “We should not accept the AI specialists’ point because [1] of data privacy, and
[2] they could guide us in the wrong direction, which could affect on the functionality of the HR
Interviewee 4: “Training is key for AI implementation. We can’t expose our employee data to a
third party. We can go for some kind of external support, but only at the beginning. But, in the end, you
have to manage the whole process”.
The first point of view is in line with the finding reported by Khabiri et al. (2012). They claimed
that the most important aspect of technology transformation is how to transfer technology and which
mechanism offers the transferee more benefits. Collaboration between the transferer (the AI specialist
in our case) and transferee (HR managers in our case) is essential for the smooth implementation of
new technology. Therefore, in parallel to what is noted above, the authors decided to follow the first
point of view as a key factor for transferring knowledge about the AI implementation process to HR
4.3 HR managers’ strategy for smooth collaboration with AI-employees
A word cloud was generated for the second research question, which indicated that
“communication” is the most frequent word used in the responses, followed by awareness, acceptance,
collaborative, orientation, partners, integrate, help, future, efficiency, etc.
The thematic analysis for the second question revealed some definitive themes related to smooth
AI implementation among employees (see Table 4). Themes were categorized as creating
communication channels (19.4%). This implies that HR managers can gain insights into what things are
threatening employees from implementing AI by expressing their ideas and perspectives without
criticism. These HR manager-employee discussions and communications are the best way to build a
strong relationship, remove ambiguities and increase the efficiency of AI-driven solutions. Aligning the
AI-business goals with the capital strategy in a wide range of attitudes, behaviours and intentions could
strongly impact smooth AI implementation among employees. Quoted below are some instances from
interviewees’ answers:
Interviewee 2: “Communication is essential in this sense because it is a key to solve the problem
of mistrust, preconceived ideas on AI implementation. This kind of communication will promote the
idea that employees are more valuable than AI”.
Interviewee 3: “Open dialogue between managers and employees is the best recipe for integrating
these systems in ways that actually enhance the quality of work”.
Table 4. Themes identified for Research Question 2 (RQ2)
S. No.
3.1 Create communication channels 28 19.4
Enhance the awareness of the positive impact of AI on
employee performance
17 14
Collaboration with AI-employees maintains a balance among
job demand, professional development and social life
9 8.5
Note: Only the themes with a frequency of more than 2 were included
Another dominant theme was enhancing awareness about the positive impact of AI on employee
performance (14%). AI plays a role in accelerating the achievement of employees' daily tedious work
by providing a powerful database and analytical support for their decisions. To rule out alternative
explanations, the dominating idea spread among employees that AI-powered solutions are machine-
oriented and have difficulties in understanding human perceptions. To eliminate negative perceptions,
HR managers should offer practical evidence to convince employees of the abilities of AI-powered
solutions in customized recommendations depending on the tasks demanded. Quoted below are some
instances from the interviewees’ answers:
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
Interviewee 1: “HR managers should make employees aware of the usefulness of AI; it’s here to
support them, not to disturb them, as well as sharing the message that employees could promote
themselves by focusing on developing their skills. On the one hand you improve your performance and,
on the other hand, it could minimize your routine”.
Interviewee 2: “Promote awareness with real examples. For instance, if you would like to speak
with HR staff, obviously you have to talk during working hours. But if you're talking to AI-powered
chatbots you can talk at night once you're back home after work. So this atmosphere should be
dominant among employees. AI minimizes employees’ routine tasks and maintains the organization’s
Therefore, employees collaborating with AI maintains the balance among job demand,
professional development and social life (8.5%). AI is led to constantly change in the jobs conducted
by employees. Combining the rapid speed of AI development with changes in employees’ lifestyles
gives a balanced job-social life a whole new meaning. AI implementation will offer employees new
opportunities to develop their skills and to spend time with their families as a result of achieving
deadlines more quickly. Therefore, sharing the message with employees that AI complements human
productivity and, unlike the normal perception, poses no threats, will facilitate AI implementation
strategies. Quoted below are some instances from interviewees’ answers:
Interviewee 8: “We have to build confidence. What is the benefit of the intention of using new
technology? We should send to employees the notion that AI reduces their workload and helps to strike
a balance between job demands and social life demands. This kind of balance will add value to their
job and family. On the one hand, relieving employees from tedious tasks and providing them with
easier access to information. On the other hand, employees will have time and effort to invest in their
children and family. It’s about these kinds of benefits and how they will impact their lives, and not
about taking their job away but, instead, AI will reduce their work so they can concentrate on
somewhere else”.
Interviewee 2: “HR managers have to share the message that AI is here to support employees and
for them to reach their targets, which will definitely be reflected in social life”.
These results fall in line with the findings reported by Dabbous et al. (2021), who claimed that
employees’ acceptance of new technologies depends on several factors. Individual factors are major
determinants of employee acceptance. They also added that the organization's social environment,
culture and HR strategy are technology acceptance parameters. Therefore, HR managers should
integrate organizational culture, social environment and technology acceptance factors when
implementing AI-powered solutions.
The present study makes some significant contributions with consequences for both research and
practice. This section highlights the areas in which we extend the boundaries of current knowledge and
the ramifications of our findings.
5.1. Research implications
Several studies in the academic literature have dealt with the relation between HR managers and
emerging technology. Previous scholars have identified the crucial role of AI techniques in HR
manager activities, such as supporting complex HR managerial decision making (Reddy et al., 2019)
and assisting HR managers in performing productive big data analyses to achieve the desired outcomes
(Qamar et al., 2021). In contrast, other scholars have advised HR managers to update their technical
skills and enhance their knowledge about data privacy and AI ethical issues (Stahl et al., 2021). These
skills play a role in spreading “AI-enabled-HR services” technology (Qamar et al., 2021).
Scholars have also been advised to identify the keys that facilitate leveraging the AI-powered
applications in HR tasks (Fernandez & Gallardo-Gallardo, 2021). In line with this, the present study
performed an in-depth analysis to understand the key elements for transferring knowledge about the AI
implementation process to HR managers. The findings identified the following challenges associated
with AI implementation: potential risks resulting from data shortage; ambiguous vision of the emerging
technology; mistrusting AI recommendations, which leads to fewer advantages of AI applications.
According to these results, companies that seek to adopt AI solutions in HRM should pay attention
to these elements to ensure the successful knowledge transfer about the AI implementation process. In
order to do so, they should: 1) offer intensive training programs for HR managers that cover the AI
Int. Journal of Business Science and Applied Management / Business-and-Management.org
implementation process, 2) assign AI specialists to follow up implementation progress and to eliminate
any potential collapse in AI-powered solutions.
After the present study performed a detailed analysis of the key elements to transfer knowledge
about AI implementation processes, it provides a comprehensive view of the COVID-19 impact on AI
implementation. There are many advantages from accelerating AI implementation during a pandemic.
However, this usefulness is affected by the potential threat associated with an AI bias to the
performance of both employees and organizations. Prior research on AI implementation during
COVID-19 has demonstrated that making investments in AI can represent a successful approach to
limit disruptive impacts (Acciarini et al., 2021). Accelerating AI implementation may enhance
employee welfare in many ways, such as improving productivity, performance and learning. However,
AI misuse due to an algorithm bias and lack of governance could inhibit employees’ rights and result in
a high turnover, customer dissatisfaction and employee retention (Yang et al., 2021).
Second, this paper presents the steps carried out by HR managers to ensure smooth AIemployee
collaboration. The study proposes a list of factors to ensure positive collaboration, such as: establishing
communication channels; highlighting the positive impact of AI on employee performance; examining
the role of AI in maintaining employees’ job demands, professional development and social life. Prior
research works have indicated that AI-powered technology interventions are seen as being relatively
complex, and this does not pose only a challenge for an organization to implement it, but is also
difficult for employees to accept it (Dabbous et al., 2021). It is HR managers’ responsibility to spread
to employees a sense of trust, morality, transparency and value in relation to AI (Papagiannidis &
Marikyan, 2020). This effort depends on collaboration across several departments because the premise
behind AI implementation is not to replace employees, but to enhance employee performance. A high
transparency level is particularly important when new technology affects the HR process
(Lichtenthaler, 2020).
5.2. Practical implications
AI implementation processes consist of: data input (e.g., software-readable data about employees’
attitude and performance); data analysis (e.g., analysis of the structured and unstructured data); results
generated from data analyses (e.g., information, statistics or predictions) (Meijerink & Bondarouk,
2021). Thus HR managers need to understand the functionality of each stage and improve their
technical skills to obtain utilitarian benefits from AI-powered solutions. For instance, acquiring
essential knowledge on data mining, programming, big data analytics and robotics could be the
backbone of emerging AI technology (Pereira et al., 2021). These skills will help HR managers to
understand the AI implementation process and, at the same time, to protect AI specialists from any
potential employees’ resistance to AI applications affecting and eliminating their decisions. For
instance, one requirement of a new AI system involves making certain modifications to the job
specification and performance appraisal system, which may bring about mistrust in of an organization’s
decision making if it is performed by an AI specialist instead of the HR manager.
The advantages of HR managers acquiring essential knowledge about the AI implementation
process will facilitate the functionality of HR department during crises and pandemics. COVID-19 and
the consequences of lockdown have threatened companies in different ways, such as sacking many
employees, losing financial funds and income, and changing the workplace structure. AI has acted as a
magic wand to solve most of these abnormal circumstances. Companies with a higher AI maturity level
have greater flexibility to adapt to these challenges by protecting employee well-being, having
personalized internal communication, reducing costs and providing differentiated user experience
(Abed, 2021). To leverage AI-powered solutions, organizations have to be aware of managing different
types of AI risks, such as: 1) insufficient data security precautions; 2) insufficient data analytics
processes; 3) AI scripting errors; 4) lapses in data management; 5) misjudgments in the training data
model (Cheatham et al., 2019).
The AI training data bias is considered another type of risk that has an impact on the whole
performance of both employees and the organization. A training data bias may come from algorithms,
data input, or from the interaction between both (Sun et al., 2020). Ignoring potential risks associated
with AI implementation can easily compromise employees’ fairness, privacy, security and compliance.
Second, motivating employees toward using AI should be driven by practical incidents. For
instance, combining the speed of AI to collect and process data and to achieve employees’ tasks and
responsibilities are drivers to minimize employees’ resistance to AI implementation (Jarrahi, 2018).
Demotivated employees toward adopting AI applications could harmed AI functionality in two ways:
through non-cooperation and data obfuscation (Newlands, 2021). Non-cooperation entails ignoring AI
directions and recommendations. For instance, AI is able to boost employee productivity by
recommending various solutions to a single problem. Ignoring these recommendations may affect the
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
overall performance evaluation. Data obfuscation is described as any process that hides sensitive data
while retaining certain aspects of usability. For example, when employees create an alternate version of
data that is not easily identifiable or reverse-engineered they, therefore, seek to manipulate the
generated results using AI-algorithmic control mechanisms (Kellogg et al., 2020). HR managers have
to deploy a sequential AI implementation process by first selecting pilot employees, followed by
analyzing the reflection of AI solutions on the selected sample and, finally, deploying training with a
wide range of employees. The symbiotic interaction between employees and AI solutions is critical for
successful AI implementation (Grover et al., 2022).
The evolving and mutual relation between the implementation of emerging technologies and the
HR process can be lucidly explained by an in-depth qualitative study. This exploratory study builds on
two recommendations made by Chang (2020), namely: 1) key factors in the successful knowledge
transfer of the AI implementation process to HR managers; 2) HR managers’ strategies for smooth AI
implementation among employees. By building further on these earlier contributions, we offer a deep
understanding of the challenges associated with AI implementation in the HR process and the impact of
COVID-19 on AI implementation. These contributions help in building the key elements to transfer
knowledge about AI implementation processes for HRM in COVID 19 times.
Such a study provides a comprehensive understanding in addition to the considerable existing
literature on AI implementation and the AI-HR manager-employee interaction. The study is one that
focuses, on the one hand, on the challenges that affect AI implementation and, on the other hand, on
particular strategies for managers and employees to undertake digital transformation.
The bottom line is that this study’s findings provide valuable details for AI developers and HR
managers when digitalizing the HR process. It calls for a wide range of organizational activities that
come in the form of developing HR managers’ technical and soft skills, such as data analysis,
digitalization trends, basic AI core concepts, communication skills, critical thinking, team building and
leadership skills. All these soft skills are lifesavers in today's technology-driven environment because
they allow employees to accept HR managers’ guidance to implement emerging technologies (Caputo
et al., 2019).
The present study is subject to limitations. Due to the novelty of this study, a qualitative study was
performed and the sample of respondents is relativity small. Using different meta-analyses and
extending the number of interviewees could generate new areas to transfer knowledge about the AI
implementation process.
Different future research work could be carried out in several directions to open up new avenues
for the complex interrelations among AI transformation, management and employees. For instance,
examining if HR managers’ academic background could become the key factor for successfully
transferring the AI implementation process. Second, analyzing if HR managers with IT experience
have enough skills to cover the needs of the AI specialists in their organization. Third, empirically
validating the impact of transparency against employee fears about AI implementation. In addition, it
will be interesting to test moderate factors, such as employees’ age, experience, seniority and academic
background, and their influence on HR managers accepting activities leading toward smooth AI-
employee collaboration.
Bearing in mind the fear of the unknown that has been raised from the AI bias, future research
could investigate the legislation and constraints on AI-trained data, as well as the level of dependence
on AI’s judgment for employee professional development-related issues.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
Abed, S. (2021). A literature review exploring the role of technology in business survival during the
Covid-19 lockdowns. International Journal of Organizational Analysis. doi:10.1108/IJOA-11-
Acciarini, C., Boccardelli, P., & Vitale, M. (2021). Resilient companies in the time of Covid-19
pandemic: a case study approach. Journal of Entrepreneurship and Public Policy, 10(3), 336-351.
Adikaram, A., Naotunna, N., & Priyankara, H. (2021). Battling COVID-19 with human resource
management bundling. Employee Relations, 43(6), 1269-1289. doi:10.1108/ER-08-2020-0390
Ali, D., & Frimpong, S. (2020). Artificial intelligence, machine learning and process automation:
existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review, 53,
60256042. doi:10.1007/s10462-020-09841-6
Allal-Chérif, O., Aránega, A., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select,
and retain talents from around the world using artificial intelligence. Technological Forecasting
and Social Change, 169. doi:10.1016/j.techfore.2021.120822
Alrashedi, A., & Abbod, M. (2021). The effect of using artificial intelligence on performance of
appraisal system: a case study for university of jeddah staff in saudi arabia. In K. Arai, S. Kapoor,
& R. Bhatia, Intelligent systems and applications (Vol. 1250, pp. 145-154). Cham: Springer.
Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2021). Artificial intelligence and human
workers interaction at team level: a conceptual assessment of the challenges and potential HRM
strategies. International Journal of Manpower. doi:10.1108/IJM-01-2021-0052
Black, J., & van Esch, P. (2021). AI-enabled recruiting in the war for talent. Business Horizons, 64(4),
513-524. doi:10.1016/j.bushor.2021.02.015
Boiral, O., Brotherton, M.-C., Rivaud, L., & Guillaumie, L. (2021). Organizations’ management of the
covid-19 pandemic: a scoping review of business articles. Sustainability, 13(7).
Borges, A., Laurindo, F., Spínola, M., Gonçalves, R., & Mattos, C. (2021). The strategic use of
artificial intelligence in the digital era: systematic literature review and future research directions.
International Journal of Information Management, 57. doi:10.1016/j.ijinfomgt.2020.102225
Caputo, F., Cillo, V., Candelo, E., & Liu, Y. (2019). Innovating through digital revolution: the role of
soft skills and Big Data in increasing firm performance. Management Decision, 57(8), 2032-2051.
Caruso, L. (2018). Digital innovation and the fourth industrial revolution: epochalsocial changes? Ai &
Society, 33, 379392. doi:10.1007/s00146-017-0736-1
Chang, K. (2020). Artificial intelligence in personnel management: the development of APM model.
The Bottom Line, 33(4), 377-388. doi:10.1108/BL-08-2020-0055
Cheatham, B., Javanmardian, K., & Samandari, H. (2019, April 26). Confronting the risks of artificial
intelligence. Retrieved February 10, 2022, from McKinsey: https://www.mckinsey.com/business-
Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: definition, theory,
and practice. Human Resource Management Review, 31(1). doi:10.1016/j.hrmr.2019.100698
Collmus, A. B., Armstrong, M. B., & Landers, R. N. (2016). Game-Thinking Within Social Media to
Recruit and Select Job Candidates. In R. N. Landers, & G. B. Schmidt, Social Media in Employee
Selection and Recruitment (pp. 103-124). Springer International Publishing, Cham.
Cubrich, M., King, R., Mracek, D., Strong, J., Hassenkamp, K., Vaughn, D., & Dudley, N. (2021).
Examining the criterion-related validity evidence of LinkedIn profile elements in an applied
sample. Computers in Human Behavior, 120. doi:10.1016/j.chb.2021.106742
Dabbous, A., Barakat, K. A., & Sayegh, M. M. (2021). Enabling organizational use of artificial
intelligence: an employee perspective. Journal of Asia Business Studies. doi:10.1108/JABS-09-
Dahlbom, P., Siikanen, N., Sajasalo, P., & Jarvenpää, M. (2020). Big data and HR analytics in the
digital era. Baltic Journal of Management, 15(1), 120-138. doi:10.1108/BJM-11-2018-0393
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
de Medeiros, M. M., Hoppen, N., & Maçada, A. C. (2020). Data science for business:
benefits,challenges and opportunities. The Bottom Line, 33(2), 149-163. doi:10.1108/BL-12-
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., . . . Kizgin, H. (2021).
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities,
and agenda for research, practice and policy. International Journal of Information Management,
57. doi:10.1016/j.ijinfomgt.2019.08.002
European. (2020, October 28). TRUST-AI improves trustworthiness of artificial intelligence. Retrieved
November 2, 2021, from Horizon 2020:
Fernandez, J. (2019). The ball of wax we call HR analytics. Strategic HR Review, 18(1), 21-25.
Fernandez, V., & Gallardo-Gallardo, E. (2021). Tackling the HR digitalization challenge: key factors
and barriers to HR analytics adoption. Competitiveness Review, 31(1), 162-187. doi:10.1108/CR-
Frey, C., & Osborne, M. (2017). The future of employment: how susceptible are jobs to
computerisation? Technological Forecasting and Social Change, 114, 254-280.
Garg, S., Sinha, S., Kar, A., & Mani, M. (2021). A review of machine learning applications in
humanresource management. International Journal of Productivity and Performance Management.
Grover, P., Kar, A. K., & Dwivedi, Y. (2022). Understanding artificial intelligence adoption in
operations management: insights from the review of academic literature and social media
discussions. Annals of Operations Research, 308, 177213. doi:10.1007/s10479-020-03683-9
Hamouche, S. (2021). Human resource management and the COVID-19 crisis: implications,
challenges, opportunities, and future organizational directions. Journal of Management &
Organization, 1-16. doi:10.1017/jmo.2021.15
Harney, B., & Collings, D. (2021). Navigating the shifting landscapes of HRM. Human Resource
Management Review, 31. doi:10.1016/j.hrmr.2021.100824
He, J., Mao, Y., Morrison, A., & Coca-Stefaniak, J. (2021). On being warm and friendly: the effect of
socially responsible human resource management on employeefears of the threats of COVID-19.
International Journal of Contemporary Hospitality Management, 33(1), 346-366.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in
organizational decision making. Business Horizons, 61(4), 577-586.
Kashyap, A., & Raghuvanshi, J. (2020). A preliminary study on exploring the critical success factors
for developing COVID-19 preventive strategy with an economy centric approach. Management
Research, 18(4), 357-377. doi:10.1108/MRJIAM-06-2020-1046
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested
Terrain of Control. Academy of Management Annals, 14(1). doi:10.5465/annals.2018.0174
Khabiri, N., Rast, S., & Senin, A. A. (2012). Identifying main influential elements in technology
transfer process: a conceptual model. Procedia - Social and Behavioral Sciences, 40, 417 423.
Kolbjørnsrud, V., Richard , A., & Thomas, R. (2017). Partnering with AI: how organizations can win
over skeptical managers. Strategy & Leadership, 45(1), 37-43. doi:10.1108/SL-12-2016-0085
Lichtenthaler, U. (2020). Beyond artificial intelligence: why companies need to go the extra step.
Journal of Business Strategy, 41(1), 19-26. doi:10.1108/JBS-05-2018-0086
Lu, Y. (2021). Examining user acceptance and adoption of the internet of things. Int. Journal of
Business Science and Applied Management, 16(3), 1-17.
Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2021). Impact of artificial intelligence on
employees working in industry 4.0 led organizations. International Journal of Manpower.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
Marin, J. M., Oliveira-Dias, D., Navimipour, N. J., Gardas, B., & Unal, M. (2021). Cloud computing
and human resource management: systematic literature review and future research agenda.
Kybernetes. doi:10.1108/K-05-2021-0420
McKinsey. (2020, November 17). The state of AI in 2020. Retrieved February 8, 2022, from McKinsey
& Company: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-
Meijerink, J., & Bondarouk, T. (2021). The duality of algorithmic management: toward a research
agenda on HRM algorithms, autonomy and value creation. Human Resource Management
Review. doi:10.1016/j.hrmr.2021.100876
Mohamadou, Y., Halidou, A., & Kapen, P. T. (2020). A review of mathematical modeling, artificial
intelligenceand datasets used in the study, prediction and managementof COVID-19. Applied
Intelligence, 50, 39133925. doi:10.1007/s10489-020-01770-9
Newlands, G. (2021). Algorithmic surveillance in the gig economy: the organization of work through
lefebvrian conceived space. Organization Studies, 42(5), 719737.
Nguyen, T., Gosine, R., & Warrian, P. (2021). A review of the role of digitalization in health risk
management in extractive industries a study motivated by COVID-19. Journal of Engineering,
Design and Technology. doi:10.1108/JEDT-03-2021-0129
Nisar, Q. A., Nasir, N., Jamshed, S., Naz, S., Ali, M., & Ali, S. (2021). Big data management
andenvironmental performance: role of big data decision-makingcapabilities and decision-making
quality. Journal of Enterprise Information Management, 34(4), 1061-1096. doi:10.1108/JEIM-04-
Nizamidou, C., & Vouzas, F. (2018). MHR. providing a new perspective in HR in terms of crisis. Int.
Journal of Business Science and Applied Management, 13(1), 1-11.
Oracle. (2022, February 9). What is ERP? Retrieved from Oracle: https://www.oracle.com/erp/what-is-
Papagiannidis, S., & Marikyan, D. (2020). Smart offices: a productivity and well-being perspective.
International Journal of Information Management, 51. doi:10.1016/j.ijinfomgt.2019.10.012
Paulus , T., & Lester, J. (2016). ATLAS.ti for conversation and discourse analysis studies. International
Journal of Social Research Methodology, 405-428. doi:10.1080/13645579.2015.1021949
Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2021). A systematic literature review on the
impact of artificial intelligence on workplace outcomes: a multi-process perspective. Human
Resource Management Review. doi:10.1016/j.hrmr.2021.100857
Pillai, R., & Sivathanu, B. (2021). Measure what matters: descriptive and predictive metrics of HRM-
pathway toward organizational performance. International Journal of Productivity and
Performance Management. doi:10.1108/IJPPM-10-2020-0509
Poba-Nzaou, P., Uwizeyemunugu, S., Gaha, k., & Laberge, M. (2020). Taxonomy of business value
underlying motivations for e-HRM adoption: an empirical investigation based on HR processes.
Business Process Management Journal, 26(6), 1661-1685. doi:10.1108/BPMJ-06-2018-0150
Prikshat, V., Malik, A., & Budhwar, P. (2021). AI-augmented HRM: antecedents, assimilation and
multi level consequences. Human Resource Management Review.
PwC. (2020, October 1). AI Predictions 2021. Retrieved February 8, 2022, from PwC:
Qamar, Y., Agrawal, R. K., Samad, T. A., & Jabbour, C. J. (2021). When technology meets people: the
interplay of artificial intelligence and human resource management. Journal of Enterprise
Information Management, 34(5), 1339-1370. doi:10.1108/JEIM-11-2020-0436
Rao, A., & Krishan, S. (2021, February 22). Jumping onto the right side of the AI divide. Retrieved
February 9, 2022, from PwC: https://www.pwc.com/gx/en/issues/reinventing-the-future/take-on-
Reddy, A., Rani, R., & Chaudhary, V. (2019). Technology for sustainable hrm: an empirical research
of health care sector. International Journal of Innovative Technology and Exploring Engineering,
9(1), 2919-2924. doi:10.35940/ijitee.A9108.119119
Santana, M., & Valle-Cabrera, R. (2021). New Directions in the Future of Work. Emerald Publishing
Limited. doi:10.1108/978-1-80071-298-020211022
Mohand Tuffaha, M Rosario Perello-Marin and Esperanza Suarez-Ruz
SAP. (2022, February 9). The SAP Success Factors HXM Suite. Retrieved from SAP:
Seitz, B., & Roy, R. (2018, March 2). How to build a data-first culture for a digital transformation.
Retrieved February 8, 2022, from McKinsey: https://www.mckinsey.com/business-
Sowa, K., Przegalinska, A., & Ciechanowski, L. (2021). Cobots in knowledge work : human AI
collaboration in managerial professions. Journal of Business Research, 125, 135-142.
Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., & Jiya, T. (2021). Organisational responses to the
ethical issues of artificial intelligence. AI & Society. doi:10.1007/s00146-021-01148-6
Sun, W., Nasraoui, O., & Shafto, P. (2020). Evolution and impact of bias in human and machine
learning algorithm interaction. Plos one, 15(8). doi:10.1371/journal.pone.0235502
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.
Tuffaha, M., & Perello-Marin, M. (2021). Artificial intelligence definition, applications and adoption in
human resource management: a systematic literature review. International Journal of Business
Innovation and Research. doi:10.1504/IJBIR.2021.10040005
Vahdat, S. (2021). The role of IT-based technologies on the management of human resources in the
COVID-19 era. Kybernetes. doi:10.1108/K-04-2021-0333
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2021). Artificial
intelligence, robotics, advanced technologies and human resource management: a systematic
review. The International Journal of Human Resource Management, 1-30.
Wall, S., & Schellmann, H. (2021, July 7). We tested AI interview tools. Here’s what we found.
Retrieved February 8, 2022, from MIT Technology Review:
Wan, W., & Liu, L. (2021). Intrapreneurship in the digital era:driven by big data and humanresource
management? Chinese Management Studies, 15(4), 843-875. doi:10.1108/CMS-07-2020-0282
Wiblen, S., & Marler, J. (2019). The humantechnology interface in talent management and the
implications for HRM. In R. Bissola, & B. Imperatori, HRM 4.0 for human-centered organizations
(Advanced Series in Management (Vol. 23, pp. 99-116). doi:10.1108/S1877-
Wilson , H., & Daugherty, P. (2018, JulyAugust). Collaborative intelligence: humans and AI are
joining forces. Retrieved February 9, 2022, from Harvard Business Review:
Xiong, Y., & Xia, S. (2020). Artificial intelligence and business applications, an introduction.
International Journal of Technology Management, 84(1/2), 1-7. doi:10.1504/IJTM.2020.112615
Yablonsky, S. (2021). AI-driven platform enterprise maturity: from human led to machine governed.
Kybernetes, 50(10), 2753-2789. doi:10.1108/K-06-2020-0384
Yang, S. J., Ogata, H., Matsui, T., & Chen, N.-S. (2021). Human-centered artificial intelligence in
education: Seeing the invisible through the visible. Computers and Education: Artificial
Intelligence, 2. doi:10.1016/j.caeai.2021.100008