Artificial intelligence adoption among human resource professionals: Does market turbulence play a role?
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[1] Weng Marc Lim. The workforce revolution: Reimagining work, workers, and workplaces for the future , 2023, Global Business and Organizational Excellence.
[2] N. Ahmad,et al. Digital business model innovation among small and medium‐sized enterprises (SMEs) , 2023, Global Business and Organizational Excellence.
[3] Charles Makanyeza,et al. Factors influencing small and medium enterprises’ innovativeness: Evidence from manufacturing companies in Harare, Zimbabwe , 2022, Global Business and Organizational Excellence.
[4] M. Hanafiah,et al. What innovations would enable the tourism and hospitality industry in Malaysia to rebuild? , 2022, Worldwide Hospitality and Tourism Themes.
[5] G. Sahu,et al. Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework , 2022, Research in International Business and Finance.
[6] Mohd Rosli Bin Mohamad,et al. The moderating role of market turbulence beyond the Covid-19 pandemic and Russia-Ukraine crisis on the relationship between intellectual capital and business sustainability , 2022, Technological Forecasting and Social Change.
[7] Weng Marc Lim. The art of writing for premier journals , 2022, Global Business and Organizational Excellence.
[8] A. Gunasekaran,et al. Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions , 2022, Business Strategy and the Environment.
[9] O. Neumann,et al. Exploring artificial intelligence adoption in public organizations: a comparative case study , 2022, Public Management Review.
[10] E. van der Lingen,et al. Determinants of emerging technologies adoption in the South African financial sector , 2022, South African Journal of Business Management.
[11] Rosly Othman,et al. Exploring the factors that hinder Lean improvement initiatives in a job shop environment: A qualitative case study of a Malaysian company , 2022, Global Business and Organizational Excellence.
[12] Jonathan Fu,et al. Fintech in the Time of COVID-19: Trust and Technological Adoption During Crises , 2020, SSRN Electronic Journal.
[13] P. Homami,et al. eAcceptance model of precast concrete components in building construction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework , 2021, Journal of Building Engineering.
[14] Nripendra P. Rana,et al. Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model , 2021 .
[15] Dieu Hack‐Polay,et al. Moderating role of psychological empowerment on the relationship between green HRM practices and millennial employee retention in the hotel industry of Bangladesh , 2021, BUSINESS STRATEGY & DEVELOPMENT.
[16] Weng Marc Lim,et al. The economic impact of a global pandemic on the tourism economy: the case of COVID-19 and Macao’s destination- and gambling-dependent economy , 2021, Current Issues in Tourism.
[17] Weng Marc Lim. Conditional recipes for predicting impacts and prescribing solutions for externalities: the case of COVID-19 and tourism , 2021 .
[18] F. Froese,et al. The adoption of artificial intelligence in employee recruitment: The influence of contextual factors , 2021, The International Journal of Human Resource Management.
[19] Banita Lal,et al. Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life , 2020, Int. J. Inf. Manag..
[20] Nektarios A. Michail,et al. Shipping markets in turmoil: An analysis of the Covid-19 outbreak and its implications , 2020, Transportation Research Interdisciplinary Perspectives.
[21] Garry Wei-Han Tan,et al. Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs , 2020, Int. J. Inf. Manag..
[22] Dragana Radicic,et al. Internationalization as a strategy for small and medium‐sized enterprises and the impact of regulatory environment: An emerging country perspective , 2019, BUSINESS STRATEGY & DEVELOPMENT.
[23] W. Johnston,et al. The Coronavirus crisis in B2B settings: Crisis uniqueness and managerial implications based on social exchange theory , 2020, Industrial Marketing Management.
[24] Shahriar Akter,et al. Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics , 2020, Annals of Operations Research.
[25] Mahfuzur Rahman,et al. Examining economic and technology‐related barriers of small‐ and medium‐sized enterprises internationalisation: An emerging economy context , 2019, BUSINESS STRATEGY & DEVELOPMENT.
[26] Said A. Salloum,et al. Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model , 2019 .
[27] N. T. Ching,et al. Adoption of digital technologies of smart manufacturing in SMEs , 2019, J. Ind. Inf. Integr..
[28] Yogesh Kumar Dwivedi,et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy , 2019, International Journal of Information Management.
[29] Tiago Oliveira,et al. Understanding CRM adoption stages: empirical analysis building on the TOE framework , 2019, Comput. Ind..
[30] Cheng-Liang Yang,et al. Competitive advantage and simultaneous mutual influences between information technology adoption and service innovation: Moderating effects of environmental factors , 2019, Structural Change and Economic Dynamics.
[31] Catherine Tucker,et al. Algorithmic bias? An empirical study into apparent gender-based discrimination in the display of STEM career ads , 2019 .
[32] Fakhar Shahzad,et al. Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change , 2019, Journal of Big Data.
[33] Alice H. Y. Hon,et al. The impacts of social and economic crises on tourist behaviour and expenditure: an evolutionary approach , 2018, Current Issues in Tourism.
[34] Jian Wang,et al. m-Government Security Response System: Predicting Citizens’ Adoption Behavior , 2018, Int. J. Hum. Comput. Interact..
[35] R. Pellerin,et al. The industrial management of SMEs in the era of Industry 4.0 , 2018, Int. J. Prod. Res..
[36] H. Khan,et al. Big data analytics: does organizational factor matters impact technology acceptance? , 2017, Journal of Big Data.
[37] Yogesh Kumar Dwivedi,et al. Factors influencing user acceptance of public sector big open data , 2017 .
[38] Spyros Makridakis,et al. The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms , 2017 .
[39] Michael Chui,et al. Artificial intelligence: the next digital frontier? , 2017 .
[40] Weng Marc Lim. Restoring tourist confidence and travel intentions after disasters: some insights from a rejoinder to a series of unfortunate events in Malaysian tourism , 2017 .
[41] Elfi Furtmueller,et al. Electronic HRM: four decades of research on adoption and consequences , 2017 .
[42] Eric Horvitz,et al. Long-Term Trends in the Public Perception of Artificial Intelligence , 2016, AAAI.
[43] Dong-Hee Shin,et al. Demystifying big data: Anatomy of big data developmental process , 2016 .
[44] John Bessant,et al. Sustainability‐Oriented Innovation: A Systematic Review , 2016 .
[45] Wenyu Dou,et al. The effects of firm capabilities on external collaboration and performance: The moderating role of market turbulence , 2015 .
[46] Mohamed G. Aboelmaged,et al. Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms , 2014, Int. J. Inf. Manag..
[47] A. Tsakalidis,et al. On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV , 2014, Artificial Intelligence Review.
[48] Kuen-Hung Tsai,et al. Firm innovativeness and business performance: The joint moderating effects of market turbulence and competition. , 2013 .
[49] Víctor Jesús García-Morales,et al. The effects of Information Technology on absorptive capacity and organisational performance , 2013, Technol. Anal. Strateg. Manag..
[50] Sung Youl Park,et al. University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model , 2012, Br. J. Educ. Technol..
[51] J. Schibrowsky,et al. Technology adoption by small businesses: An exploratory study of the interrelationships of owner and environmental factors , 2012 .
[52] B. Corbitt,et al. Evaluating the critical determinants for adopting e‐market in Australian small‐and‐medium sized enterprises , 2012 .
[53] Chad W. Autry,et al. The effects of technological turbulence and breadth on supply chain technology acceptance and adoption , 2010 .
[54] Yu Min Wang,et al. Understanding the determinants of RFID adoption in the manufacturing industry , 2010 .
[55] Daniella Ryding,et al. The impact of new technologies on customer satisfaction and business to business customer relationships: Evidence from the soft drinks industry , 2010 .
[56] Yacine Ait-Sahalia,et al. Market Response to Policy Initiatives During the Global Financial Crisis , 2010 .
[57] Viswanath Venkatesh,et al. Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..
[58] Gordon R. Foxall,et al. Technology acceptance: a meta‐analysis of the TAM: Part 1 , 2007 .
[59] Ja-Shen Chen,et al. Information technology adoption for service innovation practices and competitive advantage: the case of financial firms , 2007, Inf. Res..
[60] Miguel R. Olivas-Luján,et al. The diffusion of human‐resource information‐technology innovations in US and non‐US firms , 2006 .
[61] Kenneth L. Kraemer,et al. The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business , 2006, Manag. Sci..
[62] Abhijit Chaudhury,et al. Studying the current status of technology adoption , 2006, CACM.
[63] Kar Yan Tam,et al. Understanding the Adoption of Multipurpose Information Appliances: The Case of Mobile Data Services , 2006, Inf. Syst. Res..
[64] Kenneth A. Kozar,et al. The Technology Acceptance Model: Past, Present, and Future , 2003, Commun. Assoc. Inf. Syst..
[65] Roger J. Calantone,et al. A comparison of three models to explain shop‐bot use on the web , 2002 .
[66] Marios Koufaris,et al. Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior , 2002, Inf. Syst. Res..
[67] Paul Jen-Hwa Hu,et al. Information Technology Acceptance by Individual Professionals: A Model Comparison Approach , 2001, Decis. Sci..
[68] R. Klimoski,et al. Why do ‘great minds’ think alike?: antecedents of team member schema agreement , 2001 .
[69] Detmar W. Straub,et al. The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption , 2000, J. Assoc. Inf. Syst..
[70] Viswanath Venkatesh,et al. Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..
[71] Fred D. Davis,et al. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.
[72] Fred D. Davis. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..
[73] OUP accepted manuscript , 2022, International Data Privacy Law.
[74] Yang Wang,et al. Can fintech improve the efficiency of commercial banks? —An analysis based on big data , 2021 .
[75] M. Sarstedt,et al. A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .
[76] D. A. Kenny,et al. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.