Appraisals Matter: How Artificial Intelligence, Robotics, and Automation Affect Tourism Employees’ Service Performance

Marlena Bednarska

doi:10.58683/sp.2135

Published : 2025-12-20

Vol. 48 No. 2 (2025)

Appraisals Matter: How Artificial Intelligence, Robotics, and Automation Affect Tourism Employees’ Service Performance

Marlena Bednarska

https://orcid.org/0000-0001-9410-115X

DOI: https://doi.org/10.58683/sp.2135

Abstract

Organisations worldwide are undergoing a profound technological transformation driven by advances in artificial intelligence, robotics, and automation (AIRA). AIRA technologies are reshaping not only their structural and operational foundations but also the nature of human work. Drawing on the job demands–resources (JD-R) model, the author examines how tourism employees’ appraisals of AIRA relate to their service performance, which is a key factor of competitive advantage and the long-term success of tourism organisations. The analysis presented in the article is based on quantitative data collected in an online survey involving 303 tourism employees in Poland. Hierarchical multiple regression analysis and the PROCESS macro were used to test the hypotheses. Results reveal the dual nature of AIRA appraisals: demand appraisals undermine service performance by increasing job burnout, whereas resource appraisals enhance performance by fostering greater job engagement. These findings highlight the importance of human-centred strategies in digital transformation in tourism, ensuring that AIRA technologies support rather than replace human contribution.

Keywords:

artificial intelligence, robotics and automation (AIRA), workplace, appraisal, service performance, tourism

Details

References

Statistics

Authors

Download files

PDF

Citation rules

Bednarska, M. (2025). Appraisals Matter: How Artificial Intelligence, Robotics, and Automation Affect Tourism Employees’ Service Performance. Studia Periegetica, 48(2), 2135. https://doi.org/10.58683/sp.2135

Altmetric indicators

Cited by / Share

Licence

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

References

Arias-Pérez, J. & Vélez-Jaramillo, J. (2022). Understanding knowledge hiding under technological turbulence caused by artificial intelligence and robotics. Journal of Knowledge Management, 26(6), 1476–1491. https://doi.org/10.1108/JKM-01-2021-0058

Bakker, A.B., & Demerouti, E. (2017). Job Demands–Resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056

Bankins, S., Ocampo, A.C., Marrone, M., Restubog, S.L.D., & Woo, S.E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735

Bednarska, M.A., & Łuka, P. (2025). Service employees’ appraisals toward artificial intelligence, robotics and automation in the workplace — results of exploratory and confirmatory factor analyses. Scientific Papers of Silesian University of Technology. Organization and Management Series, 219, 69–88. https://doi.org/doi.org/10.29119/1641-3466.2025.219.4

Bélanger, F., Watson-Manheim, M.B., & Swan, B.R. (2013). A multi-level socio-technical systems telecommuting framework. Behaviour & Information Technology, 32(12), 1257–1279. https://doi.org/10.1080/0144929X.2012.705894

Benbya, H., Pachidi, S., & Jarvenpaa, S. (2021). Special issue editorial: Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22(2), 281–303. https://doi.org/10.17705/1jais.00662

Bettencourt, L.A., & Brown, S. W. (2003). Role stressors and customer-oriented boundary-spanning behaviors in service organizations. Journal of the Academy of Marketing Science, 31(4), 394–408. https://doi.org/10.1177/0092070303255636

Bettencourt, L.A., Gwinner, K.P., & Meuter, M.L. (2001). A Comparison of attitude, personality, and knowledge predictors of service-oriented organizational citizenship behaviors. Journal of Applied Psychology, 86(1), 29–41. https://doi.org/10.1037/0021-9010.86.1.29

Borges, A.F.S., Laurindo, F.J.B., Spínola, M.M., Gonçalves, R.F., & Mattos, C.A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225

Bowen, D.E., & Schneider, B. (2014). A service climate synthesis and future research agenda. Journal of Service Research, 17(1), 5–22. https://doi.org/10.1177/1094670513491633

Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management and Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55

Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: Lessons from tourism and hospitality. Journal of Service Management, 30(4), 484–506. https://doi.org/10.1108/JOSM-12-2018-0398

Demerouti, E., Bakker, A.B., Nachreiner, F., & Schaufeli, W.B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499

Ding, L. (2021). Employees’ challenge-hindrance appraisals toward STARA awareness and competitive productivity: A micro-level case. International Journal of Contemporary Hospitality Management, 33(9), 2950–2969. https://doi.org/10.1108/IJCHM-09-2020-1038

Gmyrek, P., Berg, J., & Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper.

Hadžibajramović, E., Schaufeli, W., & De Witte, H. (2024). The ultra-short version of the Burnout Assessment Tool (BAT4) — development, validation, and measurement invariance across countries, age and gender. PLoS ONE, 19(2), e029784. https://doi.org/10.1371/journal.pone.0297843

Hayes, A.F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). The Guilford Press.

Houle, S.A., Rich, B.L., Comeau, C.A., Blais, A.R., & Morin, A.J.S. (2022). The job engagement scale: Development and validation of a short form in English and French. Journal of Business and Psychology, 37(5), 877–896. https://doi.org/10.1007/s10869-021-09782-z

Huang, M.H., & Rust, R.T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459

Huang, M.-H., & Rust, R.T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266

Huang, M.H., Rust, R., & Maksimovic, V. (2019). The Feeling Economy: Managing in the next generation of Artificial Intelligence (AI). California Management Review, 61(4), 43–65. https://doi.org/10.1177/0008125619863436

Huang, Y., & Gursoy, D. (2024). How does AI technology integration affect employees’ proactive service behaviors? A transactional theory of stress perspective. Journal of Retailing and Consumer Services, 77, 103700. https://doi.org/10.1016/j.jretconser.2023.103700

Jia, N., Luo, X., Zheng, F., & Liao, C. (2024). When and how artificial intelligence augments employee creativity. Academy of Management Journal, 67(1), 5–32. https://doi.org/10.5465/amj.2022.0426

Kassa, B.Y., & Worku, E.K. (2025). The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100474. https://doi.org/10.1016/j.joitmc.2025.100474

Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717–734. https://doi.org/10.1108/IJCHM-07-2020-0789

Lazarus, R.S., & Folkman, S. (1984). Stress, appraisal and coping. Springer.

Li, J., Bonn, M.A., & Ye, B.H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172–181. https://doi.org/10.1016/j.tourman.2019.02.006

Liang, X., Guo, G., Shu, L., Gong, Q., & Luo, P. (2022). Investigating the double-edged sword effect of AI awareness on employee’s service innovative behavior. Tourism Management, 92, 104564. https://doi.org/10.1016/j.tourman.2022.104564

Liao, H. & Chuang, A. (2004). A multilevel investigation of factors influencing employee service performance and customer outcomes. Academy of Management Journal, 47(1), 41–58. https://doi.org/10.2307/20159559

Lichtenthaler, U. (2020). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39–45. https://doi.org/10.1108/JBS-12-2018-0204

Liu, Y., Li, Y., Song, K., & Chu, F. (2024). The two faces of artificial intelligence (AI): Analyzing how AI usage shapes employee behaviors in the hospitality industry. International Journal of Hospitality Management, 122, 103875. https://doi.org/10.1016/j.ijhm.2024.103875

Lu, V.N., Wirtz, J., Kunz, W.H., Paluch, S., Gruber, T., Martins, A., & Patterson, P.G. (2020). Service robots, customers and service employees: What can we learn from the academic literature and where are the gaps? Journal of Service Theory and Practice, 30(3), 361–391. https://doi.org/10.1108/JSTP-04-2019-0088

Marinova, D., de Ruyter, K., Huang, M., Meuter, M., & Challagalla, G. (2017). Getting smart: Learning from technology-empowered frontline interactions. Journal of Service Research, 20(1), 29–42. https://doi.org/10.1177/1094670516679273

Noble, S.M., Mende, M., Grewal, D., & Parasuraman, A. (2022). The Fifth Industrial Revolution: How harmonious human–machine collaboration is triggering a retail and service [r]evolution. Journal of Retailing, 98(2), 199–208. https://doi.org/10.1016/j.jretai.2022.04.003

Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A Systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857. https://doi.org/10.1016/j.hrmr.2021.100857

Qiu, H., Li, M., Bai, B., Wang, N., & Li, Y. (2022). The impact of AI-enabled service attributes on service hospitableness: The role of employee physical and psychological workload. International Journal of Contemporary Hospitality Management, 34(4), 1374–1398. https://doi.org/10.1108/IJCHM-08-2021-0960

Quan, J., Li, Y., Zhang, M., & Wu, Z. (2025). Gain or pain: The double-edged sword effect of artificial intelligence adoption on employee service performance. Journal of Organizational Behavior. https://doi.org/10.1002/job.70023

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/AMR.2018.0072

Rich, B.L., LePine, J.A., & Crawford, E.R. (2010). Job engagement: Antecedents and effects on job performance. Academy of Management Journal, 53(3), 617–635. https://doi.org/10.5465/AMJ.

Salanova, M., & Schaufeli, W. B. (2000). Exposure to information technology and its relation to burnout. Behaviour & Information Technology, 19(5), 385–392. https://doi.org/10.1080/014492900750000081

Schaufeli, W.B., Desart, S., & De Witte, H. (2020). Burnout Assessment Tool (BAT) — development, validity, and reliability. International Journal of Environmental Research and Public Health, 17(24), 9495. https://doi.org/10.3390/ijerph17249495

Sharif, M.N., Zhang, L., Asif, M., Alshdaifat, S.M., & Hanaysha, J.R. (2025). Artificial intelligence and employee outcomes: Investigating the role of job insecurity and technostress in the hospitality industry. Acta Psychologica, 253, 104733. https://doi.org/10.1016/j.actpsy.2025.104733

Toscanelli, C., Udayar, S., & Massoudi, K. (2022). Antecedents and consequences of technology appraisal: A person-centered approach. Technology, Mind, and Behavior, 3(3), 1–24. https://doi.org/10.1037/tmb0000081

Trenerry, B., Chng, S., Wang, Y., Suhaila, Z.S., Lim, S.S., Lu, H.Y., & Oh, P.H. (2021). Preparing workplaces for digital transformation: An integrative review and framework of multi-level factors. Frontiers in Psychology, 12, 620766. https://doi.org/10.3389/fpsyg.2021.620766

Tussyadiah, I. (2020). A review of research into automation in tourism: Launching the Annals of Tourism Research curated collection on artificial intelligence and robotics in tourism. Annals of Tourism Research, 81, 102883. https://doi.org/10.1016/j.annals.2020.102883

Vorobeva, D., El Fassi, Y., Costa Pinto, D., Hildebrand, D., Herter, M.M., & Mattila, A.S. (2022). Thinking don’t protect service workers from replacement by artificial intelligence. Journal of Service Research, 25(4), 601–613. https://doi.org/10.1177/10946705221104312

Webster, J.R., Beehr, T.A., & Love, K. (2011). Extending the challenge-hindrance model of occupational stress: The role of appraisal. Journal of Vocational Behavior, 79(2), 505–516. https://doi.org/10.1016/j.jvb.2011.02.001

Yu, X., Xu, S., & Ashton, M. (2023). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: The socio-technical system theory perspective. Information Technology and People, 36(1), 454–474. https://doi.org/10.1108/ITP-04-2021-0254

Zhang, X., & Jin, H. (2023). How does smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) awareness affect hotel employees’ career perceptions? A disruptive innovation theory perspective. Journal of Hospitality Marketing and Management, 32(2), 264–283. https://doi.org/10.1080/19368623.2023.2166186

Zheng, S., Guo, Z., Liao, C., Li, S., Zhan, X., & Feng, X. (2025). Booster or stumbling block? Unpacking the ‘double-edged’ influence of artificial intelligence usage on employee innovative performance. Current Psychology, 44, 7800–7817. https://doi.org/10.1007/s12144-025-07358-1