The integration of Artificial Intelligence into Original Brand Manufacturing firms presents both opportunities and challenges for fostering employee innovation. This study investigates how AI adoption influences employees’ innovative behavior, with a particular focus on the mediating role of job security and the moderating role of employee well-being. Drawing on the Technology Acceptance Model and Conservation of Resources theory, we developed a conceptual framework that captures the dual-edged nature of AI in innovation-driven workplaces. This study used a two-wave online survey of 251 OBM professionals from innovation-intensive teams. We used Smart PLS 4 to analyze our data. The results showed that AI adoption has a significant negative impact on employee innovative behavior. Furthermore, job security partially mediates this relationship, indicating that perceived job instability serves as a key mechanism through which AI adoption reduces innovative behavior. Additionally, employee well-being does not moderate the relationship between AI adoption and employee innovative behavior, but it moderates the relationship between AI adoption and job security. This study contributes to the theoretical and practical field of AI in the workplace in OBM firms, and helps to understand the impact of AI on employee innovative behavior.
Cite this paper
Sangar, W. , Ren, H. and Su, Y. (2026). The Impact of Artificial Intelligence on Employees’ Innovative Behavior in the Original Brand Manufacturing Industry. Open Access Library Journal, 13, e15999. doi: http://dx.doi.org/10.4236/oalib.1115999.
Brynjolfsson, E. and McAfee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & Company.
Makridakis, S. (2017) The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms. <i>Futures</i>, 90, 46-60. <br>https://doi.org/10.1016/j.futures.2017.03.006
Melanie, A., Terry, G. and Ulrich, Z. (2016) The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. OECD Social, Employment and Migration Working Papers. 189 p.
Tambe, P., Cappelli, P. and Yakubovich, V. (2019) Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. <i>California Management Review</i>, 61, 15-42. <br>https://doi.org/10.1177/0008125619867910
De Cremer, D., Narayanan, D., Nagpal, M., McGuire, J. and Schweitzer, S. (2023) AI Fairness in Action: A Human-Computer Perspective on AI Fairness in Organizations and Society. <i>International Journal of Human</i>-<i>Computer Interaction</i>, 40, 1-3. <br>https://doi.org/10.1080/10447318.2023.2273673
Ghobakhloo, M., Fathi, M., Iranmanesh, M., Maroufkhani, P. and Morales, M.E. (2021) Industry 4.0 Ten Years On: A Bibliometric and Systematic Review of Concepts, Sustainability Value Drivers, and Success Determinants. <i>Journal of Cleaner Production</i>, 302, Article ID: 127052. <br>https://doi.org/10.1016/j.jclepro.2021.127052
Brynjolfsson, E., Rock, D. and Syverson, C. (2019) Artificial Intelligence and the Modern Productivity Paradox. In: Agrawal, A., Gans, J. and Goldfarb, A., Eds., <i>The Economics of Artificial Intelligence</i>, University of Chicago Press, 23-60. <br>https://doi.org/10.7208/chicago/9780226613475.003.0001
Thomas, A., Duggal, H.K., Khatri, P. and Corvello, V. (2024) ChatGPT Appropriation: A Catalyst for Creative Performance, Innovation Orientation, and Agile Leadership. <i>Technology in Society</i>, 78, Article ID: 102619. <br>https://doi.org/10.1016/j.techsoc.2024.102619
Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M. and Siemens, G. (2023) Opportunities of Artificial Intelligence for Supporting Complex Problem-Solving: Findings from a Scoping Review. <i>Computers and Education</i>:<i> Artificial Intelligence</i>, 4, Article ID: 100138. <br>https://doi.org/10.1016/j.caeai.2023.100138
Janssen, O. (2000) Job Demands, Perceptions of Effort‐Reward Fairness and Innovative Work Behaviour. <i>Journal of Occupational and Organizational Psychology</i>, 73, 287-302. <br>https://doi.org/10.1348/096317900167038
Scott, S.G. and Bruce, R.A. (1994) Determinants of Innovative Behavior: A Path Model of Individual Innovation in the Workplace. <i>Academy of Management Journal</i>, 37, 580-607. <br>https://doi.org/10.2307/256701
De Jong, J. and Den Hartog, D. (2010) Measuring Innovative Work Behaviour. <i>Creativity and Innovation Management</i>, 19, 23-36. <br>https://doi.org/10.1111/j.1467-8691.2010.00547.x
Wu, L., Liu, H. and Bao, Y. (2021) Outside-in Thinking, Value Chain Collaboration and Business Model Innovation in Manufacturing Firms. <i>Journal of Business & Industrial Marketing</i>, 37, 1745-1761. <br>https://doi.org/10.1108/jbim-03-2021-0189
Tsai-Lin, T., Chi, H. and Chang, Y. (2021) The Business Model and Innovation Mix in the Transition of Contract Manufacturers in the Greater China Region. <i>Asia Pacific Business Review</i>, 27, 444-469. <br>https://doi.org/10.1080/13602381.2021.1894844
Raisch, S. and Krakowski, S. (2021) Artificial Intelligence and Management: The Automation-Augmentation Paradox. <i>Academy of Management Review</i>, 46, 192-210. <br>https://doi.org/10.5465/amr.2018.0072
Wang, Y., Kung, L. and Byrd, T.A. (2018) Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations. <i>Technological Forecasting and Social Change</i>, 126, 3-13. <br>https://doi.org/10.1016/j.techfore.2015.12.019
Frey, C.B. and Osborne, M.A. (2017) The Future of Employment: How Susceptible Are Jobs to Computerisation? <i>Technological Forecasting and Social Change</i>, 114, 254-280. <br>https://doi.org/10.1016/j.techfore.2016.08.019
Peng, Y.Y. and Wang, X.Y. (2020) Research on the Influence of Artificial Intelligence on Employment in Manufacturing Industry: Based on the Survey of Total Employment and Structure of Manufacturing Enterprises in Guangdong Province, China. <i>Journal of Beijing University of Technology</i> (<i>Social Sciences Edition</i>), 20, 68-76.
Huang, M. and Rust, R.T. (2020) Engaged to a Robot? The Role of AI in Service. <i>Journal of Service Research</i>, 24, 30-41. <br>https://doi.org/10.1177/1094670520902266
Zhou, Y., Wang, L. and Chen, W. (2023) The Dark Side of Ai-Enabled HRM on Employees Based on AI Algorithmic Features. <i>Journal of Organizational Change Management</i>, 36, 1222-1241. <br>https://doi.org/10.1108/jocm-10-2022-0308
Chatterjee, S., Rana, N.P., Dwivedi, Y.K. and Baabdullah, A.M. (2021) Understanding AI Adoption in Manufacturing and Production Firms Using an Integrated TAM-TOE Model. <i>Technological Forecasting and Social Change</i>, 170, Article ID: 120880. <br>https://doi.org/10.1016/j.techfore.2021.120880
Acemoglu, D. and Restrepo, P. (2020) Robots and Jobs: Evidence from US Labor Markets. <i>Journal of Political Economy</i>, 128, 2188-2244. <br>https://doi.org/10.1086/705716
Autor, D.H. (2015) Why Are There Still So Many Jobs? The History and Future of Workplace Automation. <i>Journal of Economic Perspectives</i>, 29, 3-30. <br>https://doi.org/10.1257/jep.29.3.3
Bhargava, A., Bester, M. and Bolton, L. (2020) Employees’ Perceptions of the Implementation of Robotics, Artificial Intelligence, and Automation (RAIA) on Job Satisfaction, Job Security, and Employability. <i>Journal of Technology in Behavioral Science</i>, 6, 106-113. <br>https://doi.org/10.1007/s41347-020-00153-8
Ghorbanzadeh, D., Kumar, S.P., Mahmood, A.H., Prasad, K.D.V., Alkhayyat, A. and Rahehagh, A. (2026) Examining the Impact of Artificial Intelligence on Physicians’ Performance: Mediating Effects of Innovative Work Behavior and Skills Enhancement. <i>Journal of Health Organization and Management</i>, 1-26. <br>https://doi.org/10.1108/JHOM-03-2025-0150
Tarafdar, M., Cooper, C.L. and Stich, J. (2017) The Technostress Trifecta—Techno Eustress, Techno Distress and Design: Theoretical Directions and an Agenda for Research. <i>Information Systems Journal</i>, 29, 6-42. <br>https://doi.org/10.1111/isj.12169
Wamba-Taguimdje, S., Fosso Wamba, S., Kala Kamdjoug, J.R. and Tchatchouang Wanko, C.E. (2020) Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-Based Transformation Projects. <i>Business Process Management Journal</i>, 26, 1893-1924. <br>https://doi.org/10.1108/bpmj-10-2019-0411
Hobfoll, S.E. (1989) Conservation of Resources: A New Attempt at Conceptualizing Stress. <i>American Psychologist</i>, 44, 513-524. <br>https://doi.org/10.1037/0003-066x.44.3.513
Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. <i>MIS Quarterly</i>, 13, 319-340. <br>https://doi.org/10.2307/249008
Wilkens, U. (2020) Artificial Intelligence in the Workplace—A Double-Edged Sword. <i>The International Journal of Information and Learning Technology</i>, 37, 253-265. <br>https://doi.org/10.1108/ijilt-02-2020-0022
Sah, T., Purwati, D., Reniati, and Valeriani, D. (2024) The Double-Edge Sword Impact of Artificial Intelligence Support on Employee Innovation Behavior. 1<i>st International Conference of Economics</i>,<i> Management</i>,<i> Accounting</i>,<i> and Business Digital</i>, Pangkalpinang, 15 October 2024 133-140. <br>https://doi.org/10.2991/978-94-6463-614-7_18
Venkatesh, V., Thong, J. and Xu, X. (2016) Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. <i>Journal of the Association for Information Systems</i>, 17, 328-376. <br>https://doi.org/10.17705/1jais.00428
Halbesleben, J.R.B., Neveu, J., Paustian-Underdahl, S.C. and Westman, M. (2014) Getting to the “Cor”. <i>Journal of Management</i>, 40, 1334-1364. <br>https://doi.org/10.1177/0149206314527130
Lee, M.K., Kusbit, D., Metsky, E. and Dabbish, L. (2015) Working with Machines. <i>Proceedings of the</i> 33<i>rd Annual ACM Conference on Human Factors in Computing Systems</i>, Seoul, 18-23 April 2015, 1603-1612. <br>https://doi.org/10.1145/2702123.2702548
Chiu, Y., Zhu, Y. and Corbett, J. (2021) In the Hearts and Minds of Employees: A Model of Pre-Adoptive Appraisal toward Artificial Intelligence in Organizations. <i>International Journal of Information Management</i>, 60, Article ID: 102379. <br>https://doi.org/10.1016/j.ijinfomgt.2021.102379
Du, P. and Liu, S. (2024) The Double-Edged Impact of Artificial Intelligence Use on Employee Psychology. <i>Operations Research and </i><i>Fuzziology</i>, 14, 541-547. <br>https://doi.org/10.12677/orf.2024.144422
Brynjolfsson, E., Rock, D. and Syverson, C. (2021) The Productivity J-Curve: How Intangibles Complement General Purpose Technologies. <i>American Economic Journal</i>:<i> Macroeconomics</i>, 13, 333-372. <br>https://doi.org/10.1257/mac.20180386
Viskova-Robertson, A. (2024) Investigating the Impacts of AI Integration on Workplace Well-Being, an Exploratory Case Study. Ph.D. Thesis, Western Michigan University.
Parker, S.K. and Grote, G. (2020) Automation, Algorithms, and Beyond: Why Work Design Matters More than Ever in a Digital World. <i>Applied Psychology</i>, 71, 1171-1204. <br>https://doi.org/10.1111/apps.12241
Espina-Romero, L., Gutiérrez Hurtado, H., Ríos Parra, D., Vilchez Pirela, R.A., Talavera-Aguirre, R. and Ochoa-Díaz, A. (2024) Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis. <i>Sci</i>, 6, Article 60. <br>https://doi.org/10.3390/sci6040060
Yan, H. (2012) Entrepreneurship, Competitive Strategies, and Transforming Firms from OEM to OBM in Taiwan Region. <i>Journal of Asia-Pacific Business</i>, 13, 16-36. <br>https://doi.org/10.1080/10599231.2012.629877
Yang, C.H. and Wang, Y.L. (2014) Determinants Affecting the Transformational Process from OEM to OBM in Creative Design Industry. International Symposium on Business and Social Sciences. NCKU. <br>https://researchoutput.ncku.edu.tw/en/publications/determinants-affecting-the-transformational-process-from-oem-to-o/
Zhang, Q., Liao, G., Ran, X. and Wang, F. (2025) The Impact of AI Usage on Innovation Behavior at Work: The Moderating Role of Openness and Job Complexity. <i>Behavioral Sciences</i>, 15, Article 491. <br>https://doi.org/10.3390/bs15040491
Anantrasirichai, N. and Bull, D. (2021) Artificial Intelligence in the Creative Industries: A Review. <i>Artificial Intelligence Review</i>, 55, 589-656. <br>https://doi.org/10.1007/s10462-021-10039-7
Brynjolfsson, E. (2022) The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence. <i>Daedalus</i>, 151, 272-287. <br>https://doi.org/10.1162/daed_a_01915
Wan, J., Li, X., Dai, H., Kusiak, A., Martinez-Garcia, M. and Li, D. (2021) Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges. <i>Proceedings of the IEEE</i>, 109, 377-398. <br>https://doi.org/10.1109/jproc.2020.3034808
Jackson, I., Ivanov, D., Dolgui, A. and Namdar, J. (2024) Generative Artificial Intelligence in Supply Chain and Operations Management: A Capability-Based Framework for Analysis and Implementation. <i>International Journal of Production Research</i>, 62, 6120-6145. <br>https://doi.org/10.1080/00207543.2024.2309309
Soulami, M., Benchekroun, S. and Galiulina, A. (2024) Exploring How AI Adoption in the Workplace Affects Employees: A Bibliometric and Systematic Review. <i>Frontiers in Artificial Intelligence</i>, 7, Article 1473872. <br>https://doi.org/10.3389/frai.2024.1473872
Zang, J., Shao, Q. and Li, H. (2024) Challenge and Hindrance: Yin and Yang Paths of AI Usage’s Effects on Chinese Employee Innovative Behaviour. <i>Asia Pacific Business Review</i>. <br>https://doi.org/10.1080/13602381.2024.2367526
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989) User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. <i>Management Science</i>, 35, 982-1003. <br>https://doi.org/10.1287/mnsc.35.8.982
Singh, K., Chatterjee, S. and Mariani, M. (2024) Applications of Generative AI and Future Organizational Performance: The Mediating Role of Explorative and Exploitative Innovation and the Moderating Role of Ethical Dilemmas and Environmental Dynamism. <i>Technovation</i>, 133, Article ID: 103021. <br>https://doi.org/10.1016/j.technovation.2024.103021
Khan, S., Mehmood, S. and Khan, S.U. (2024) Navigating Innovation in the Age of AI: How Generative AI and Innovation Influence Organizational Performance in the Manufacturing Sector. <i>Journal of Manufacturing Technology Management</i>, 36, 597-620. <br>https://doi.org/10.1108/jmtm-06-2024-0302
Yin, M., Jiang, S. and Niu, X. (2024) Can AI Really Help? The Double-Edged Sword Effect of AI Assistant on Employees’ Innovation Behavior. <i>Computers in Human Behavior</i>, 150, Article ID: 107987. <br>https://doi.org/10.1016/j.chb.2023.107987
Ahn, S., Park, J. and Ye, S. (2025) How AI Enhances Employee Service Innovation in Retail: Social Exchange Theory Perspectives and the Impact of AI Adaptability. <i>Journal of Retailing and Consumer Services</i>, 84, Article ID: 104207. <br>https://doi.org/10.1016/j.jretconser.2024.104207
Farhan, A. (2023) The Impact of Artificial Intelligence on Human Workers. <i>Journal of Communication Education</i>, 17, 93-104. <br>https://doi.org/10.58217/joce-ip.v17i2.350
Zong, Z. and Guan, Y. (2024) AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency. <i>Journal of the Knowledge Economy</i>, 16, 864-903. <br>https://doi.org/10.1007/s13132-024-02001-z
Bankins, S., Ocampo, A.C., Marrone, M., Restubog, S.L.D. and Woo, S.E. (2023) A Multilevel Review of Artificial Intelligence in Organizations: Implications for Organizational Behavior Research and Practice. <i>Journal of Organizational Behavior</i>, 45, 159-182. <br>https://doi.org/10.1002/job.2735
Brougham, D. and Haar, J. (2017) Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ Perceptions of Our Future Workplace. <i>Journal of Management & Organization</i>, 24, 239-257. <br>https://doi.org/10.1017/jmo.2016.55
Oldham, G.R., Kulik, C.T., Stepina, L.P. and Ambrose, M.L. (1986) Relations between Situational Factors and the Comparative Referents Used by Employees. <i>Academy of Management Journal</i>, 29, 599-608. <br>https://doi.org/10.2307/256226
Nam, T. (2019) Technology Usage, Expected Job Sustainability, and Perceived Job Insecurity. <i>Technological Forecasting and Social Change</i>, 138, 155-165. <br>https://doi.org/10.1016/j.techfore.2018.08.017
Liu, R. and Zhan, Y. (2020) The Impact of Artificial Intelligence on Job Insecurity: A Moderating Role Based on Vocational Learning Capabilities. <i>Journal of Physics</i>:<i> Conference Series</i>, 1629, Article ID: 012034. <br>https://doi.org/10.1088/1742-6596/1629/1/012034
Rodríguez-Espíndola, O., Chowdhury, S., Dey, P.K., Albores, P. and Emrouznejad, A. (2022) Analysis of the Adoption of Emergent Technologies for Risk Management in the Era of Digital Manufacturing. <i>Technological Forecasting and Social Change</i>, 178, Article ID: 121562. <br>https://doi.org/10.1016/j.techfore.2022.121562
Dabbous, A., Aoun Barakat, K. and Merhej Sayegh, M. (2021) Enabling Organizational Use of Artificial Intelligence: An Employee Perspective. <i>Journal of Asia Business Studies</i>, 16, 245-266. <br>https://doi.org/10.1108/jabs-09-2020-0372
Suseno, Y., Chang, C., Hudik, M. and Fang, E.S. (2023) Beliefs, Anxiety and Change Readiness for Artificial Intelligence Adoption among Human Resource Managers: The Moderating Role of High-Performance Work Systems. In: <i>Artificial Intelligence and International HRM</i>, Routledge, 144-171. <br>https://doi.org/10.4324/9781003377085-6
Koo, B., Curtis, C. and Ryan, B. (2021) Examining the Impact of Artificial Intelligence on Hotel Employees through Job Insecurity Perspectives. <i>International Journal of Hospitality Management</i>, 95, Article ID: 102763. <br>https://doi.org/10.1016/j.ijhm.2020.102763
Bos-Nehles, A., Renkema, M. and Janssen, M. (2017) HRM and Innovative Work Behaviour: A Systematic Literature Review. <i>Personnel Review</i>, 46, 1228-1253. <br>https://doi.org/10.1108/pr-09-2016-0257
Sauermann, H. and Cohen, W.M. (2010) What Makes Them Tick? Employee Motives and Firm Innovation. <i>Management Science</i>, 56, 2134-2153. <br>https://doi.org/10.1287/mnsc.1100.1241
Ma, B., Zhou, Y., Lassleben, H., Ma, G. and Yang, R. (2023) Examining the Mediating Effects of Motivation between Job Insecurity and Innovative Behavior Using a Variable-Centered and a Person-Centered Approach. <i>Frontiers in Psychology</i>, 14, Article 1284042. <br>https://doi.org/10.3389/fpsyg.2023.1284042
Durrah, O. and Kahwaji, A. (2022) Chameleon Leadership and Innovative Behavior in the Health Sector: The Mediation Role of Job Security. <i>Employee Responsibilities and Rights Journal</i>, 35, 247-265. <br>https://doi.org/10.1007/s10672-022-09414-5
Chen, C., Chen, Y., Hsu, P. and Podolski, E.J. (2016) Be Nice to Your Innovators: Employee Treatment and Corporate Innovation Performance. <i>Journal of Corporate Finance</i>, 39, 78-98. <br>https://doi.org/10.1016/j.jcorpfin.2016.06.001
Lee, W., Jeon, Y., Kim, J. and Jung, C. (2014) Effects of Job Security and Psychological Ownership on Turnover Intention and Innovative Behavior of Manufacturing Employees. <i>Journal of the Korea Safety Management and Science</i>, 16, 53-68. <br>https://doi.org/10.12812/ksms.2014.16.1.53
Probst, T.M., Stewart, S.M., Gruys, M.L. and Tierney, B.W. (2007) Productivity, Counterproductivity and Creativity: The Ups and Downs of Job Insecurity. <i>Journal of Occupational and Organizational Psychology</i>, 80, 479-497. <br>https://doi.org/10.1348/096317906x159103
Probst, T., Chizh, A., Hu, S., Jiang, L. and Austin, C. (2019) Explaining the Relationship between Job Insecurity and Creativity. <i>Career Development International</i>, 25, 247-270. <br>https://doi.org/10.1108/cdi-04-2018-0118
Cramarenco, R.E., Burcă-Voicu, M.I. and Dabija, D.C. (2023) The Impact of Artificial Intelligence (AI) on Employees’ Skills and Well-Being in Global Labor Markets: A Systematic Review. <i>Oeconomia</i><i> Copernicana</i>, 14, 731-767. <br>https://doi.org/10.24136/oc.2023.022
Ruiz-Vanoye, J.A., Fuentes-Penna, A., Barrera-Cámara, R.A., Díaz-Parra, O., Trejo-Macotela, F.R., Gómez-Pérez, L.J., <i>et al</i>. (2025) Artificial Intelligence and Human Well-Being: A Review of Applications and Effects on Life Satisfaction through Synthetic Happiness. <i>International Journal of Combinatorial Optimization Problems and Informatics</i>, 16, 14-37. <br>https://doi.org/10.61467/2007.1558.2025.v16i1.932
Xavier, D.F. and Korunka, C. (2025) Integrating Artificial Intelligence across Cultural Orientations: A Longitudinal Examination of Creative Self-Efficacy and Employee Autonomy. <i>Computers in </i><i>Human Behavior</i><i> Reports</i>, 18, Article ID: 100623. <br>https://doi.org/10.1016/j.chbr.2025.100623
Dong, X., Tian, Y., He, M. and Wang, T. (2024) When Knowledge Workers Meet AI? the Double-Edged Sword Effects of AI Adoption on Innovative Work Behavior. <i>Journal of Knowledge Management</i>, 29, 113-147. <br>https://doi.org/10.1108/jkm-02-2024-0222
Ali, T., Hussain, I., Hassan, S. and Anwer, S. (2024) Examine How the Rise of AI and Automation Affects Job Security, Stress Levels, and Mental Health in the Workplace. <i>Bulletin of Business and Economics </i>(<i>BBE</i>), 13, 1180-1186. <br>https://doi.org/10.61506/01.00506
Chhatre, R. and Singh, S. (2024) AI and Organizational Change: Dynamics and Management Strategies. <i>Journal of Emerging Trends and Novel Research</i>, 2, a148-a159.
Hobfoll, S.E. (2001) The Influence of Culture, Community, and the Nested-Self in the Stress Process: Advancing Conservation of Resources Theory. <i>Applied Psychology</i>, 50, 337-421. <br>https://doi.org/10.1111/1464-0597.00062
Gupta, A., Pranathy, R.S., Binny, M., Chellasamy, A., Nagarathinam, A., Pachiyappan, S., <i>et al</i>. (2024) Voices of the Future: Generation Z’s Views on AI’s Ethical and Social Impact. In: El Khoury, R., Ed., <i>Technology</i>-<i>Driven Business Innovation</i>, Springer, 367-386. <br>https://doi.org/10.1007/978-3-031-51997-0_31
Hobfoll, S.E., Halbesleben, J., Neveu, J. and Westman, M. (2018) Conservation of Resources in the Organizational Context: The Reality of Resources and Their Consequences. <i>Annual Review of Organizational Psychology and Organizational Behavior</i>, 5, 103-128. <br>https://doi.org/10.1146/annurev-orgpsych-032117-104640
Podsakoff, P.M., MacKenzie, S.B., Lee, J. and Podsakoff, N.P. (2003) Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. <i>Journal of Applied Psychology</i>, 88, 879-903. <br>https://doi.org/10.1037/0021-9010.88.5.879
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012) Sources of Method Bias in Social Science Research and Recommendations on How to Control It. <i>Annual Review of Psychology</i>, 63, 539-569. <br>https://doi.org/10.1146/annurev-psych-120710-100452
Cooper, R.G. (2024) The AI Transformation of Product Innovation. <i>Industrial Marketing Management</i>, 119, 62-74. <br>https://doi.org/10.1016/j.indmarman.2024.03.008
Mehta, A.M., Rauf, A. and Senathirajah, A.R.B.S. (2024) Achieving World Class Manufacturing Excellence: Integrating Human Factors and Technological Innovation. <i>Sustainability</i>, 16, Article 11175. <br>https://doi.org/10.3390/su162411175
Haefner, N., Wincent, J., Parida, V. and Gassmann, O. (2021) Artificial Intelligence and Innovation Management: A Review, Framework, and Research Agenda. <i>Technological Forecasting and Social Change</i>, 162, Article ID: 120392. <br>https://doi.org/10.1016/j.techfore.2020.120392
Füller, J., Hutter, K., Wahl, J., Bilgram, V. and Tekic, Z. (2022) How AI Revolutionizes Innovation Management—Perceptions and Implementation Preferences of Ai-Based Innovators. <i>Technological Forecasting and Social Change</i>, 178, Article ID: 121598. <br>https://doi.org/10.1016/j.techfore.2022.121598
Bouschery, S.G., Blazevic, V. and Piller, F.T. (2023) Augmenting Human Innovation Teams with Artificial Intelligence: Exploring Transformer‐Based Language Models. <i>Journal of Product Innovation Management</i>, 40, 139-153. <br>https://doi.org/10.1111/jpim.12656
Mesa Fernández, J.M., González Moreno, J.J., Vergara-González, E.P. and Alonso Iglesias, G. (2022) Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects. <i>Applied Sciences</i>, 12, Article 11743. <br>https://doi.org/10.3390/app122211743
Sońta-Drączkowska, E. and Mrożewski, M. (2019) Exploring the Role of Project Management in Product Development of New Technology-Based Firms. <i>Project Management Journal</i>, 51, 294-311. <br>https://doi.org/10.1177/8756972819851939
Gosling, S.D. and Mason, W. (2015) Internet Research in Psychology. <i>Annual Review of Psychology</i>, 66, 877-902. <br>https://doi.org/10.1146/annurev-psych-010814-015321
Podsakoff, P.M. and Organ, D.W. (1986) Self-Reports in Organizational Research: Problems and Prospects. <i>Journal of Management</i>, 12, 531-544. <br>https://doi.org/10.1177/014920638601200408
Sarstedt, M., Ringle, C.M. and Hair, J.F. (2021) Partial Least Squares Structural Equation Modeling. In: Homburg, C., Klarmann, M. and Vomberg, A., Eds., <i>Handbook of Market Research</i>, Springer, 587-632. <br>https://doi.org/10.1007/978-3-319-57413-4_15
Babakus, E. and Mangold, W.G. (1992) Adapting the SERVQUAL Scale to Hospital Services: An Empirical Investigation. <i>Health Services Research</i>, 26, 767-786.
Sachdev, S.B. and Verma, H.V. (2004) Relative Importance of Service Quality Dimensions: A Multisectoral Study. <i>Journal of Services Research</i>, 4, 93.
Medcof, J.W. (1996) The Job Characteristics of Computing and Non-Computing Work Activities. <i>Journal of Occupational and Organizational Psychology</i>, 69, 199-212. <br>https://doi.org/10.1111/j.2044-8325.1996.tb00610.x
Kun, Á., Balogh, P. and Krasz, K.G. (2016) Development of the Work-Related Well-Being Questionnaire Based on Seligman’s PERMA Model. <i>Periodica</i> <i>Polytechnica</i><i> Social and Management Sciences</i>, 25, 56-63. <br>https://doi.org/10.3311/ppso.9326
Astrachan, C.B., Patel, V.K. and Wanzenried, G. (2014) A Comparative Study of CB-SEM and PLS-SEM for Theory Development in Family Firm Research. <i>Journal of Family Business Strategy</i>, 5, 116-128. <br>https://doi.org/10.1016/j.jfbs.2013.12.002
Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019) When to Use and How to Report the Results of PLS-SEM. <i>European Business Review</i>, 31, 2-24. <br>https://doi.org/10.1108/ebr-11-2018-0203
Chin, W.W. (1998) The Partial Least Squares Approach to Structural Equation Modeling. In: Marcoulides, G.A., Ed., <i>Modern Methods for Business Research</i>, Psychology Press, 295-336.
Sarstedt, M., Hair, J.F., Cheah, J., Becker, J. and Ringle, C.M. (2019) How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM. <i>Australasian Marketing Journal</i>, 27, 197-211. <br>https://doi.org/10.1016/j.ausmj.2019.05.003
Hair, J.F., Howard, M.C. and Nitzl, C. (2020) Assessing Measurement Model Quality in PLS-SEM Using Confirmatory Composite Analysis. <i>Journal of Business Research</i>, 109, 101-110. <br>https://doi.org/10.1016/j.jbusres.2019.11.069
Fornell, C. and Larcker, D.F. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. <i>Journal of Marketing Research</i>, 18, 39-50. <br>https://doi.org/10.1177/002224378101800104
Henseler, J., Ringle, C.M. and Sarstedt, M. (2014) A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. <i>Journal of the Academy of Marketing Science</i>, 43, 115-135. <br>https://doi.org/10.1007/s11747-014-0403-8
Ab Hamid, M.R., Sami, W. and Mohmad Sidek, M.H. (2017) Discriminant Validity Assessment: Use of Fornell & Larcker Criterion versus HTMT Criterion. <i>Journal of Physics</i>:<i> Conference Series</i>, 890, Article ID: 012163. <br>https://doi.org/10.1088/1742-6596/890/1/012163
Streukens, S. and Leroi-Werelds, S. (2016) Bootstrapping and PLS-SEM: A Step-by-Step Guide to Get More Out of Your Bootstrap Results. <i>European Management Journal</i>, 34, 618-632. <br>https://doi.org/10.1016/j.emj.2016.06.003
Venkatesh, V. and Davis, F.D. (2000) A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. <i>Management Science</i>, 46, 186-204. <br>https://doi.org/10.1287/mnsc.46.2.186.11926
Floridi, L. and Cowls, J. (2022) A Unified Framework of Five Principles for AI in Society. In: Carta, S., Ed., <i>Machine Learning and the City</i>, Wiley-Blackwell, 535-545.
Jobin, A., Ienca, M. and Vayena, E. (2019) The Global Landscape of AI Ethics Guidelines. <i>Nature Machine Intelligence</i>, 1, 389-399. <br>https://doi.org/10.1038/s42256-019-0088-2
Sigfrids, A., Leikas, J., Salo-Pöntinen, H. and Koskimies, E. (2023) Human-Centricity in AI Governance: A Systemic Approach. <i>Frontiers in Artificial Intelligence</i>, 6, Article 976887. <br>https://doi.org/10.3389/frai.2023.976887
