Given the swift progress of artificial intelligence in contemporary economies, HR managers are also adopting artificial intelligence tools to perform many HR tasks, from human resource planning to employee quitting. This research employs the TOE (Technology–Organization–Environment) model and incorporates the trust factor to propose a methodology for investigating artificial intelligence (AI) adoption in HRM. A structured questionnaire was used to survey 615 ITeS companies. Data analysis was performed using partial least squares structural equation modelling. The findings of this research indicate that factors such as cost-effectiveness, relative advantage, organization size, top management support, HR readiness, competitive pressure, vendor support, and the trust of HR managers are considered to have a positive impact on AI adoption in managing human resources. The utilization of AI in HRM has been cumbersome by conflicting results stemming from security and privacy concerns and the complexity of the technology involved. It is also found that factors such as reliability and credibility have a positive effect on the trust of HR managers. This article aims to provide valuable insights to senior executives, HR managers, experts, researchers, AI designers, developers, and marketers by strengthening their understanding of the impact of AI in managing human resources.
AgrawalA. (2022). AI adoption by human resource management: A study of its antecedents and impact on HR system effectiveness. Foresight, Emerald Publishing Limited.
2.
AhmadiH., IbrahimO., & NilashiM. (2015). Investigating a new frame work for hospital information system adoption: A case on Malaysia. Journal of Soft Computing and Decision Support Systems, 2(2), 26–33.
3.
AjzenI. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
AlalwanA. A., DwivediY. K., & RanaN. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110.
6.
AlamM. G. R., MasumA. K. M., BehL. S., & HongC. S. (2016). Critical factors influencing decision to adopt human resource information system (HRIS) in hospitals. PloS One, 11(8), 1–22.
7.
AlamS. S., AliM. Y., & JaniM. F. M. (2011). An empirical study of factors affecting electronic commerce adoption among SMEs in Malaysia. Journal of Business Economics and Management, 12(2), 375–399.
8.
AlbertE. T. (2019). AI in talent acquisition: A review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215–221.
9.
Al-QirimN. (2007). The adoption of eCommerce communications and applications technologies insmall businesses in New Zealand. Electronic Commerce Research and Applications, 6(4), 462–473.
10.
AncaraniA., Di MauroC., & MascaliF. (2019). Backshoring strategy and the adoption of Industry 4.0: Evidence from Europe. Journal of World Business, 54(4), 360–371.
11.
AroraR. G., & SiradhanaN. K. (2022). HR transformation through artificial intelligence: An insight through literature survey. International Journal of Information Systems and Social Change, 13(1), 1–16.
12.
AwaH. O., & OjiaboO. U. (2016). A model of adoption determinants of ERP within T-O-E framework. Information Technology and People, 29(4), 901–930.
13.
AwaH. O., UkohaO., & IgweS. R. (2017). Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. Bottom Line, 30(1), 2–22.
14.
AwaH., & KaluS. (2010). Repositioning the non-incremental changes and business strategic windows correlates. International Journal of Business and Management, 5(2), 184–193.
15.
AwaH., BaridamD., & NwibereB. (2015). Demographic determinants of electronic commerce (EC) adoption by SMEs. Journal of Enterprise Information Management, 28(3), 326–345.
16.
BadiF. K. A., AlhosaniK. W., JabeenF., Stachowicz-StanuschA., ShehzadN., & AmannW. (2021). Challenges of AI adoption in the UAE healthcare. Vision: The Journal of Business Perspective, 26(2), 193–207.
17.
BaigM. I., ShuibL., & YadegaridehkordiE. (2019). Big data adoption: State of the art and research challenges. Information Processing and Management, 56(6). 10.1016/j.ipm.2019.102095
18.
BallK. S. (2001). The use of human resource information systems: A survey. Personnel Review, 30(6), 677–693.
19.
BanduraA. (2001). Social cognitive theory of mass communication media psychology. Psychology, 3(3), 265–299.
20.
BeechlerS., & WoodwardI. C. (2009). The global war for talent. Journal of International Management, 15(3), 273−285.
21.
BenlianA., & HessT. (2011). Opportunities and risks of software-as-a-service: Findings from a survey of IT executives. Decision Support Systems, 52(1), 232–246.
22.
BersinJ. (2018). Talent trends technology disruptions for 2018. https://www.isaconnection.org/assets/documents/2018BersinHRTechDisruptionsReport.pdf.
23.
BondaroukT., ParryE., & FurtmuellerE. (2017). Electronic HRM: Four decades of re-search on adoption and consequences. International Journal of Human Resource Management, 28(1), 98–131.
24.
CamA., ChuiM., & HallB. (2019). Global AI survey: AI proves its worth, but few scale impact. McKinsey and Company. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Global%20AI%20Survey%20AI%20proves%20its%20worth%20but%20few%20scale%20impact/Global-AI-Survey-AI-proves-its-worth-but-few-scale-impact.ashx
25.
ChatterjeeS., RanaN. P., DwivediY. K., & BaabdullahA. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting & Social Change, 170, 1–14.
26.
ChenH., LiL., & ChenY. (2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36–68.
27.
ChoiJ. K., & JiY. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human–Computer Interaction, 31(10), 692–702.
28.
ChongA. Y. L., & ChanF. T. S. (2012). Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Systems with Applications, Elsevier Ltd, 39, 8645–8654.
29.
Cody-AllenE., & KishoreR. (2006). An extension of the UTAUT model with e-quality, trust, and satisfaction constructs [Paper presentation]. The 2006 ACM SIGMISCPR Conference 2006, 82–89. 10.1145/1125170.1125196
30.
Cruz-JesusF., OliveiraT., & NaranjoM. (2018). Understanding the adoption of business analytics and intelligence. World Conference on Information Systems and Technologies, Springer, 1094–1103.
31.
DaradkehM. K. (2019). Determinants of visual analytics adoption in organizations: Knowledge discovery through content analysis of online evaluation reviews. Information Technology and People, 32(3), 668–695.
32.
DavisF. D., & DavisF. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(2), 319–340.
33.
DepietroR., WiardaE., & FleischerM. (1990). The context for change: Organization, technology and environment. The processes of technological innovation. The Processes of Technological Innovation, 199, 151–175.
34.
DerousE., & DeFruytF. (2016). Developments in recruitment and selection management essay. International Journal of Selection and Assessment, 24(1). 10.1111/ijsa.12123
35.
DubeyR., LuoZ., GunasekaranA., AkterS., HazenB. T., & DouglasM. A. (2018). Big data and predictive analytics in humanitarian supply chains. The International Journal of Logistics Management, 29(2), 485–512.
36.
DwivediY. K., HughesL., IsmagilovaE., AartsG., CoombsC., CrickT., DuanY., DwivediR., EdwardsJ., EirugA., GalanosV., IlavarasanP. V., JanssenM., JonesP., KarA. P., KizginH., KronemannB., LalB., LuciniB., MedagliaR., Meunier-FitzHughK. L., Meunier-FitzHughL. C. L., MisraS., MogajiE., SharmaS. K., SinghJ. B., RaghavanV., RamanR., RanaN. P., SamothrakisS., SpencerJ., TamilmaniK., TubadjiA., WaltonP., & WilliamsM. D. (2019). Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://bradscholars.brad.ac.uk/handle/10454/17208
37.
EschP. V., BlackJ. W., & FerolieJ. (2018). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215–222.
38.
FornellC., & LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
39.
Fosso WambaS., GunasekaranA., BhattacharyaM., & DubeyR. (2016). Determinants of RFID adoption intention by SMEs: An empirical investigation. Production Planning and Control, 27(12), 979–990.
40.
GangwarH., DateH., & RaootA. D. (2014). Review on IT adoption: Insights from recent technologies. Journal of Enterprise Information Management, 27(4), 488–502.
41.
GefenD. (2002). Reflections on the dimensions of trust and trustworthiness among online consumers. Database for Advances in Information Systems, 3(3), 38–53.
42.
GhobakhlooM., Arias-ArandaD., & Benitez-AmadoJ. (2011). Adoption of e-commerce applications in SMEs. Industrial Management and Data Systems, 111(8), 1238–1269.
43.
GoodhueD. L., & ThompsonR. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.
GopalakrishnanS., & DamanpourF. (1994). Patterns of generation and adoption of innovation inorganizations: Contingency models of innovation attributes. Journal of Engineering and Technology Management, 11(2), 95–116.
46.
GroverV. (1993). An empirically derived model for the adoption of customer-based interorganizational systems. Decision Sciences, 24(3), 603–640.
47.
GuptaR. (2021). Industry 4.0 adaption in Indian banking sector: A review and agenda for future research. Vision: The Journal of Business Perspective, 27(1), 24–32.
48.
HairJ. F.Jr, HultG. T. M., RingleC. M., & SarstedtM. (2017). A primer on partial least squares structural equation modeling, Long Range Planning (2nd ed.). Sage Publication.
49.
HairJ. F.Jr, SarstedtM., HopkinsL., & KuppelwieserV. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121.
50.
HameedM. A., CounsellS., & SwiftS. (2012). Establishing relationships between innovation characteristics and it innovation adoption in organisations: A meta-analysis approach. Information and Management, 49(5), 218–232.
51.
HasanZ., KrischkowskyA., & TscheligiM. (2012). Modelling User-Centered-Trust (UCT) insoftware systems: Interplay of trust, affect and acceptance model. Lecture Notes in Computer Science, 92–109. 10.1007/978-3-642-30921-2_6
52.
HmoudB. (2021). The determinants of HR leaders’ attitude toward the adoption of artificial intelligence in human resource management. [Doctoral Thesis, Károly Ihrig Doctoral School of Management and Business, University Of Debrecen]. https://dea.lib.unideb.hu/server/api/core/bitstreams/4c8c6473-5e1f-40c9-9f0e-5c22db42be7a/content
53.
HmoudB., & VárallyaiL. (2020). Artificial intelligence in human resources information systems: Investigating its trust and adoption determinants. International Journal of Engineering and Management Sciences, 5(1), 749–765.
54.
HossainM. A., StandingC., & ChanC. (2017). The development and validation of a two-staged adoption model of RFID technology in livestock businesses. Information Technology and People, 30(4), 785–808.
55.
HsuC. W., & YehC. C. (2017). Understanding the factors affecting the adoption of the internet of things. Technology Analysis and Strategic Management, 29(9), 1089–1102.
56.
HungundS., & ManiV. (2019). Benchmarking of factors influencing adoption of innovation insoftware product SMEs: An empirical evidence from India. Benchmarking: An International Journal, 26(5), 1451–1468.
57.
HurlburtG. (2017). How much to trust artificial intelligence?IEEE Computer Society, 19(4), 7–11.
58.
IfinedoP. (2011). An empirical analysis of factors influencing internet/e-business technologies adoption by SMEs in Canada. International Journal of Information Technology and Decision Making, 10(4), 731–766.
59.
JiaQ., GuoY., & BarnesS. J. (2016). E2.0 post-adoption: Extending the IS continuance model based on the technology-organization-environment framework [Paper presentation]. The International Conference on Electronic Business (ICEB), 695–707. https://www.semanticscholar.org/paper/E2.0-Post-Adoption%3A-Extending-the-IS-Continuance-IS-Jia/d44f0e7ed26d068e98766b7cd4add21350817e67
60.
KashiK., ZhengC., & MolineuxJ. (2016). Exploring factors driving social recruiting: The case of Australian organizations. Journal of Organizational Computing and Electronic Commerce, 26(3), 203–223.
61.
KaurM., G. RekhaA., & VikasS. (2021). Adoption of artificial intelligence in human resource management: A conceptual model. The Indian Journal of Industrial Relations, 57(2), 331–342.
62.
KhemthongS., & RobertsL. M. (2006). Adoption of internet and web technology for hotel marketing: A study of hotels in Thailand. Journal of Law and Governance, 1(2), 51–70.
63.
KimG., ShinB., & LeeH. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283–311.
64.
KimberlyJ. R., & EvaniskoM. J. (1981). Organizational innovation: The influence of individual, organizational, and contextual factors on hospital adoption of technological and administrative innovations. Academy of Management Journal, 24(4), 689–713.
65.
KumarS. (2019). Artificial intelligence divulges effective tactics of top management institutes of India. Benchmarking: An International Journal, 26(7), 2188–2204.
66.
LaiY., SunH., & RenJ. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676–703.
67.
LippertS. K., & DavisM. (2006). A conceptual model integrating trust into planned change activities to enhance technology adoption behavior. Journal of Information Science, 32(5), 434–448.
68.
LippertS. K., & SwierczP. M. (2005). Human resource information systems (HRIS) and technology trust. Journal of Information Science, 31(5), 340–353.
69.
LowC., ChenY., & WuM. (2011). Understanding the determinants of cloud computing adoption. Industrial Management Data System, 111(7), 1006–1023.
70.
MahmoodS. (2017). 21st-century staffing: The new wave of artificial intelligence in recruitment. Talent Acquisition Excellence Essentials, ProQuest, 1–5.
71.
MasoodT., & EggerJ. (2019). Augmented reality in support of Industry 4.0- implementation challenges and success factors. Robotics and Computer: Integrated Manufacturing, 58, 181–195.
72.
MatsaP., & GullamajjiK. (2019). To study impact of artificial intelligence on human resource management. International Research Journal of Engineering and Technology, 6(8), 1229–1238.
73.
MayerR. C., DavisJ. H., & SchoormanF. D. (1995). An integrative model of organizational trust author. Academy of Management Review, 20(3), 709–734.
74.
McCarthyJ., MinskyM. L., RochesterN., & ShannonC. E. (1955). A proposal for the Dartmouth Summer Research Project on artificial intelligence. AI Magazine, 27(4), 12.
75.
McDonaldK., FisherS., & ConnellyC. E. (2017). E-HRM systems in support of “Smart” workforce management: An exploratory case study of system success. Emerald Publishing.
76.
McknightD. H., & ChervanyN. L. (2002). What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology relationships: Typology an interdisciplinary conceptual. International Journal of Electronic Commerce, 6(2), 35–59.
77.
MoghaddamH. A., RezaeiS., & AminM. (2015). Job seekers’ perception and behavioural intention toward online recruitment: A PLS path modeling approach. Journal for Global Business Advancement, 8(3), 305–325.
78.
MutsuddiI., & SinhaR. (2021). Role of leadership skills for developing digital acumen in information technology employees. Vision: The Journal of Business Perspective, 26(2), 243–253.
79.
NairP. (2017). The rise of the AI recruiter: Is HR tech the next to challenge human intuition. https://www.growthbusiness.co.uk/rise-ai-recruiter-hr-tech-next-challenge-human-intuition-2550350
80.
NunnallyJ. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
81.
OliveiraT., & MartinsM. F. (2010). Understanding e-business adoption across industries in European countries. Industrial Management and Data Systems, 110(9) 1337–1354.
82.
OliveiraT., & MartinsM. F. (2011). Literature review of information technology adoption models at firm level. The Electronic Journal Information Systems Evaluation, 14(1), 110–121.
83.
OliveiraT., MartinsR., SarkerS., ThomasM., & PopovicA. (2019). Understanding SaaS adoption: The moderating impact of the environmental context. International Journal of Information Management, 49, 1–12.
84.
OliveiraT., ThomasM., & EspadanalM. (2014). Assessing the determinants of cloud computing adoption: Analysis of the manufacturing and services sectors. Information and Management, 51(5), 497–510.
85.
PanY., FroeseF., LiuN., HuY., & YeM. (2021). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(7)1–22.
86.
ParasuramanR., SheridanT. B., & WickensC. D. (2008). Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making, 2(2), 140–160.
87.
ParkJ. H., & KimY. B. (2019). Factors activating big data adoption by Korean firms. Journal of Computer Information Systems, 61(3), 1–9.
88.
PereiraV., HadjieliasE., ChristofiM., & VrontisD. (2021). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 1–22.
89.
PerraultR., ShohamY., BrynjolfssonE., ClarkJ., EtchemendyJ., GroszB., LyonsT., ManyikaJ., MishraS., & NieblesJ. C. (2019). Artificial intelligence index 2019 annual report. AI Index Steering Committee, Human-Centered AI Institute, Stanford University. https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf
90.
PhahlaneM. M., & KekwaletsweR. M. (2017). A conceptual research framework for human resource information systems adoption and use in universities [Paper presentation]. Conference on Information Communication Technology and Society, ICTAS 2017–Proceedings. 10.1109/ICTAS.2017.7920654
91.
PillaiR., & SivathanuB. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599–2629.
92.
PodsakoffP. M., MacKenzieS. B., LeeJ. Y., & PodsakoffN. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
93.
PremkumarG., & RobertsM. (1999). Adoption of new information technologies in rural small businesses. Omega, 27, 467–484.
94.
PremnathS. N., & ArunA. (2020). A qualitative study of artificial intelligence application framework in human resource management. Journal of Xi’an University of Architecture & Technology, XI(Xii), 1193–1209.
95.
PrikshatV., IslamM., PatelP., MalikA., BudhwarP., & GuptaS. (2023). AI-augmented HRM: Literature review and a proposed multilevel framework for future research. Technological forecasting and social change, 193, 1–19.
96.
PuklavecB., OliveiraT., & PopovicA. (2018). Understanding the determinants of business intelligence system adoption stages an empirical study of SMEs. Industrial Management and Data Systems, 118(1), 36–261.
97.
QamarY., AgrawalR. K., SamadT. B., & JabbourC. J. C. (2021). When technology meets people: The interplay of artificial intelligence and human resource management. Journal of Enterprise Information Management, 34(5), 0398–1741.
98.
QuW. G., YangZ., & WangZ. (2011). Multi-level framework of open source software adoption. Journal of Business Research, 64(9), 997–1003.
99.
QuaddusM., & HofmeyerG. (2007). An investigation into the factors influencing the adoption of B2B Trading exchanges in small businesses in Western Australia. European Journal of Information Systems, 16, 202–215.
100.
RahayuR., & DayJ. (2015). Determinant factors of E-commerce adoption by SMEs in developing country: Evidence from Indonesia. Procedia–—Social and Behavioral Sciences, 195, 142–150.
101.
RamdaniB., CheversD., & WilliamsD. A. (2013). SMEs’ adoption of enterprise applications: A technology- organisation-environment model. Journal of Small Business and Enterprise Development, 20(4), 735–753.
102.
RogersE. M. (2003). Diffusion of innovations (5th ed.). Free Press.
103.
RogersE. M., & WilliamsD. (1983). Diffusion of innovations. The Free Press.
104.
SabherwalR., JeyarajA., & ChowaC. (2006). Information system success: Individual and organizational determinants. Management Science, 52(12), 1849–1864.
105.
SaraswathiT., KarthikeyanM., BalakrishnanC., NithyaT., MaheswariB., & SubramanianR. S. (2023). Artificial intelligence in human resource management: Advancements, implications and future prospects. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 266–281.
106.
ShihK. H., HungH. F., & LinB. (2010). Assessing user experiences and usage intentions of m-banking service. International Journal of Mobile Communications, 8(3), 257–277.
107.
SivathanuB. (2019a). Adoption of industrial IoT (IIoT) in auto-component manufacturing SMEs in India. Information Resources Management Journal, 32(2), 52–75.
108.
SonyM., & NaikS. (2019). Key ingredients for evaluating Industry 4.0 readiness for organizations: A literature review. Benchmarking: An International Journal, 27(7), 1463–5771.
109.
StrohmeierS., & KabstR. (2009). Organizational adoption of e-HRM in Europe: An empirical exploration of major adoption factors. Journal of Managerial Psychology, 24(6), 482–501.
110.
StrohmeierS., & PiazzaF. (2015). Artificial intelligence techniques in human resource management: A conceptual exploration. Intelligent Techniques in Engineering Management, 87, 149–172.
111.
StuartJ. R., & PeterN. (2010). Artificial intelligence a modern approach (3rd ed.). Pearson Education.
112.
SunJ., & TengJ. T. C. (2017). The construct of information systems use benefits: Theoretical explication of its underlying dimensions and the development of a measurement scale. International Journal of Information Management, 37(5), 400–416.
113.
TavanaM., & HajipourV. (2019). A practical review and taxonomy of fuzzy expert systems: Methods and applications. Benchmarking, 27(1), 81–136.
114.
TeoT. S. H., SrivastavaS. C., & JiangL. I. (2014). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99–132.
115.
ThongJ. (1991). An integrated model of information system adoption in small business. Journal of Management Information System, 15(4), 187–214.
116.
TongD. Y. K. (2009). A study of e-recruitment technology adoption in Malaysia. Industrial Management and Data Systems, 109(2), 281–300.
117.
TornatzkyL., & FleischerM. (1990). The process of technology innovation, Lexington Books.
118.
TroshaniI., JerramC., & GerrardM. (2010). Exploring the organizational adoption of human resources information systems (HRIS) in the Australian public sector [Paper presentation]. 21st Australasian Conference on Information Systems. https://www.researchgate.net/publication/288794677_Exploring_the_organizational_adoption_of_Human_Resources_Information_Systems_HRIS_in_the_Australian_public_sector
119.
TsaiM. C., LeeW., & WuH. C. (2010). Determinants of RFID adoption intention: Evidence from Taiwanese retail chains. Information and Management, 47(5–6), 255–261.
120.
TsouH. T., & HsuS. H. Y. (2015). Performance effects of technology-organization-environment openness, service co-production, and digital-resource readiness: the case of the IT industry. International Journal of Information Management, 35(1), 1–14.
121.
TungL. L., & RieckO. (2005). Adoption of electronic government services among business organizations in Singapore. The Journal of Strategic Information Systems, 14(4), 417–440.
122.
VenkateshV., MorrisM. G., DavisG. B., & DavisF. D. (2009). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
123.
WangY. M., WangY. S., & YangY. F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting & Social Change, 77(5), 803–815.
124.
WangY. S., LiH. T., LiC. R., & ZhangD. Z. (2016). Factors affecting hotels’ adoption of mobile reservation systems: A technology-organization-environment framework. Tourism Management, Elsevier, 53(C), 163–172.
125.
WangY. S., YehC. H., & LiaoY. W. (2013). What drives purchase intention in the context of online content services? The moderating role of ethical self-efficacy for online piracy. International Journal of Information Management, 33(1), 199–208.
126.
WangY. Y., WangY. S., & LinT. C. (2018). Developing and validating a technology upgrade model. International Journal of Information Management, Elsevier, 38(1), 7–26.
127.
WrightJ., & AtkinsonD. (2019). The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting. Charmicael Fisher. https://www.cfsearch.com/wp-content/uploads/2019/10/James-Wright-The-impact-of-artificial-intelligence-within-the-recruitment-industry-Defining-a-new-way-of-recruiting.pdf
128.
WuW. (2011). Expert systems with applications developing an explorative model for SaaS adoption. Expert Systems with Applications, 38(12), 15057–15064.
129.
YoonT. E., & GeorgeJ. F. (2013). Computers in human behavior why aren’t organizations adopting virtual worlds?Computers in Human Behavior, 29(3), 772–790.
130.
ZafarH. (2013). Human resource information systems: Information security concerns for organizations. Human Resource Management Review, 23(1), 105–113.
131.
ZhuK., KraemerK., & XuS. (2003). Electronic business adoption by European firms: A cross-country assessment of the facilitators and inhibitors. European Journal of Information Systems, 12, 251–268.
132.
ZhuK., DongS., XuS. X., & KraemerK. L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601–616.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
