Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication  Assessment

Feige You; Daisy Mui Hung Kee; Gary Peng Liang Tan

doi:10.47297/wspchrmWSP2040-800502.20251604

PDF
导出
分享
收藏
专辑

Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment

16卷第4期

浏览量: 0 Downloads: 0

Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment
Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment
1.School of Management, Universiti Sains Malaysia, Penang, Malaysia
2.School of Business and Administration, Wawasan Open University, Penang, Malaysia
2025年16卷第4期页码：34-52
纸质出版日期：2025-07-18
DOI：10.47297/wspchrmWSP2040-800502.20251604
Accepted：

Scan QR Code

(2025). Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment. 华夏人力资源管理学报, 16(4), 34-52.

You Feige,Kee Daisy Mui Hung,Tan Gary Peng Liang.Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment[J].Journal of Chinese Human Resources Management,2025,16(04):34-52.
(2025). Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment. 华夏人力资源管理学报, 16(4), 34-52. DOI： 10.47297/wspchrmWSP2040-800502.20251604.

You Feige,Kee Daisy Mui Hung,Tan Gary Peng Liang.Human Resource Analytics Adoption Among Employees in China's New Energy Sector: The Moderating Role of Change Management Communication Assessment[J].Journal of Chinese Human Resources Management,2025,16(04):34-52. DOI： 10.47297/wspchrmWSP2040-800502.20251604.

摘要

Abstract

This research examines the ways China new energy employee mindset and expectancy can enhance human resource analytics adoption (HRAA) based on technology acceptance model (TAM). It also explores the extent of employee change management communication assessment (CMCA) perceptions as moderators in this relationship. Hypotheses are tested in an empirical study involving 656 new energy sector employees using partial least square structural equation modeling technique. The findings confirm the direct effect of growth mindset (GM)

employee performance expectancy (PE)

and effort expectancy (EE) on HRAA. Furthermore

change management communication assessment (CMCA) perceptions significantly moderated the relationships between GM and HRAA. However

CMCA perceptions did not significantly moderate the relationship between PE

EE and HRAA. These results underscore the importance of individual characteristics and organizational support in facilitating HRAA within this rapidly growing industry. This study contributes to technology adoption literature by extending TAM with individual mindset and organizational CMCA perceptions within a specific industrial context to explain HRAA.

关键词

Keywords

references

Andersen, M. K. (2017). Human capital analytics: The winding road. Journal of Organisational Effectiveness: People and Performance, 4 (2), 155–158. https://doi.org/10.1108/JOEPP-03-2017-0024

Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26 (1), 1–11. https://doi.org/10.1111/1748-8583.12090

Arora, M., Prakash, A., Mittal, A., & Singh, S. (2024). Examining the slow acceptance of HR analytics in the Indian engineering and construction industry: A SEM-ANN-based approach. Engineering, Construction and Architectural Management , 31 (5), 1973-1993. https://doi.org/10.1108/E CAM-09-2021-0795

Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M. (2025). Determinants of ChatGPT adoption intention in higher education: Expanding on TAM with the mediating roles of trust and risk. Information , 16 (2), 82. https://doi.org/10.3390/info16020082

Berg, J. M., Wrzesniewski, A., Grant, A. M., Kurkoski, J., & Welle, B. (2023). Getting unstuck: The effects of growth mindsets about the self and job on happiness at work. Journal of Applied Psychology, 108 (1), 152–166. https://doi.org/10.1037/apl0001021

Bohlinger, S., Haake, U., Jørgensen, C. H., Toiviainen, H., & Wallo, A. (2015). Working and learning in times of uncertainty . SensePublishers.

Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education , 49 (5), 831-846. https://doi.org/10.1080/03075079.2024.2333937

Chin, W.W. (2019). How to write up and report PLS analyses. In Handbook of partial least squares: Concepts, methods and applications . Springer.

Chen, F. (2024). Development and evaluation of a machine learning-powered human resource mana gement system utilizing BP neural networks and logistic regression for influencing factor analysis. Informatica , 48 (23), 133-143. https://doi.org/10.31449/inf.v48i23.6791

Cameron, E., & Green, M. (2019). Making sense of change management: A complete guide to the models, tools and techniques of organizational change . Kogan Page Publishers.

Cho, W., Choi, S., & Choi, H. (2023). Human resources analytics for public personnel management: Concepts, cases, and caveats. Administrative Sciences , 13 (2), 41. https://doi.org/10.3390/admsci13020041

Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly , 23 (2), 145-158. https://doi.org/10.2307/249749

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly , 13 (3), 319-340. https://doi.org/10.2307/249008

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies , 38 (3), 475-487. https://doi.org/10.1006/imms.1993.1022

Duxbury, J. (2002). An evaluation of staff and patient views of and strategies employed to manage inpatient aggression and violence on one mental health unit: A pluralistic design. Journal of Psychiatric and Mental Health Nursing , 9 (3), 325-337. https://doi.org/10.1046/j.1365-2850.2002.00497.x

Dweck, C. S. (1999). Caution-praise can be dangerous. American Educator , 23 , 4-9.

Dweck, C. S. (2006). Mindset: The new psychology of success . Random House.

Dweck, C. S. (2012). Mindsets and human nature: Promoting change in the Middle East, the schoolyard, the racial divide, and willpower. American Psychologist , 67 (8), 614. https://doi.org/10.1037/a0029783

Dweck, C. S., & Molden, D. C. (2017). Their impact on competence motivation and acquisition. Handbook of competence and motivation: Theory and application (Vol. 2, pp. 135-154).

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research , 18 (1), 39-50. https://doi.org/10.1177/002224378101800104

French II, R. P. (2016). The f uzziness of mindsets: Divergent conceptualizations and characterizations of mindset theory and praxis. International Journal of Organizational Analysis , 24 (4), 673-691. https://doi.org/10.1108/IJOA-09-2014-0797

Garcia-Varela, F., Bekerman, Z., Nussbaum, M., Mendoza, M., & Montero, J. (2025). Reducing interpretative ambiguity in an educational environment with ChatGPT. Computers & Education , 225 , 105182. https://doi.org/10.1016/j.compedu.2024.105182

Green, D. (2017). The best practices to excel at people analytics. Journal of Organizational Effectiveness: People and Performance , 4 (2), 137-144. https://doi.org/10.1108/JOEPP-03-2017-0027

Hair Jr, F., Gabriel, M. L., & Patel, V. K. (2014). AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. REMark: Revista Brasileira de Marketing , 13 (2). https://doi.org/10.5585/remark.v13i2.2718

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science , 45 , 616-632. https://doi.org/10.1007/s11747-017-0517-x

Halid, H., Kee, D. M. H., & Rahim, N. F. A. ( 2024). Perceived human resource management practices and intention to stay in private higher education institutions in Malaysia: The role of organizational citizenship behavior. Global Business Review , 25(1), 162-179. https://doi.org/10.1177/0972150920950906

Han, X., Zeng, H., Bartocci, P., Fantozzi, F., & Yan, Y. (2018). Phytohormones and effects on growth and metabolites of microalgae: A review. Fermentation , 4 (2), 25. https://doi.org/10.3390/fermentation4020025

Haslinda, A. (2009). Evolving terms of human resource management and development. The Journal of International Social Research , 2 (9), 180-186.

He, Y., Chen, Q., & Kitkuakul, S. (2018). Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy. Cogent Business & Management , 5 (1), 1459006. https://doi.org/10.1080/23311975.2018.1459006

Holloway, S. (2025). Examining the impact of human resource management on supply chain efficiency. Available at SSRN 5122764 . http://dx.doi.org/10.2139/ssrn.5122764

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal , 20 (2), 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2 < 195::AID-SMJ13 > 3.0.CO ;2-7

Huselid, M. A. (2023). Integrating utility analysis and workforce strategy research: Suggestions for future work. The International Journal of Human Resource Management , 34 (13), 2620-2635. https://doi.org/10.1080/09585192.2023.2225281

International Energy Agency. (2024). World energy investment 2024 . https://www.iea.org/reports/world-energy-investment-2024

Jacobs, R. L., & Park, Y. (2009). A proposed conceptual framework of workplace learning: Implications for theory development and research in human resource development. Human Resource Development Review , 8 (2), 133-150. https://doi.org/10.1177/1534484309334269

Koloski, D., Porter, C., Almand-Hunter, B., Gatchell, S., & Logan, V. (2025). Data literacy in industry: High time to focus on operationalization through middle managers. Harvard Data Science Review , 7 (1). https://doi.org/10.1162/99608f92.6f5dfc6f

Kulikowski, K. (2024). Defining analytical skills for human resources analytics: A call for standardization. Journal of Entrepreneurship, Management and Innovation , 20 (4), 88-103. https://doi.org/10.7341/20242045

Kurikala, V., & Parvathi, V. (2023). Identifying the charact eristics that lead to effective data analytics deployment in human resource management—A paradigm for consequences. Journal of Chinese Human Resources Management , 14 (1), 60-89. https://doi.org/ 10.47297/wspchrmWSP2040-800503.20231401

Kyndt, E., Govaerts, N., Verbeek, E., & Dochy, F. (2014). Development and validation of a questionnaire on informal workplace learning outcomes: A study among socio-educational care workers. The British Journal of Social Work , 44 (8), 2391-2410. https://doi.org/10.1093/bjsw/bct056

Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025, January). Understanding psychosocial barriers to healthcare technology adoption: A review of TAM technology acceptance model and unified theory of acceptance and use of technology and UTAUT frameworks. Healthcare, 13 (3), 250. https://doi.org/10.3390/healthcare13030250

Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior , 75 , 935-948. https://doi.org/10.1016/j.chb.2017.06.013

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management , 28 (1), 3-26. https://doi.org/10.1080/09585192.2016.1244699

McCartney, S., Murphy, C., & Mccarthy, J. (2021). 21st century HR: A competency model for the emerging role of HR Analysts. Personnel Review , 50 (6), 1495-1513. https://doi.org/10.1108/PR-12-2019-0670

Myllyvirta, L., Qin, Q., & Qiu, C. (2025). Analysis: Clean energy contributed 10% of China's GDP growth in 2024 . Carbon Brief. https://www.carbonbrief.org/analysis-clean-energy-contributed-a-record-10-of-chinas-gdp-in-2024/

Mushtaq, N., Manjiang, X., Bakhtawar, A., & Mufti, M. A. (2025). Elevating the influence of HR analytics on organizational performance: An empirical investigation in Hi-Tech manufacturing industry of a developing economy. Journal of Chinese Human Resources Management , 15 (2), 3-40. https://doi.org/10.47297/wspchrmWSP2040-800501.20241502

Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human–Computer Interaction , 41 (2), 1431-1445. https://doi.org/10.1080/10447318.2024.2314358

Oxford Institute for Energy Studies. (2024). Key themes for the global energy economy in 2024 . https://www.oxfordenergy.org/wpcms/wp-content/uploads/2024/01/2024-Key-Themes-Global-Energy-Economy-in-2024.pdf

Okatta, C. G., Ajayi, F. A., & Olawale, O. (2024). Leveraging HR analytics for strategic decision making: Opportunities and challenges. International Journal of Management & Entrepreneurship Research , 6 (4), 1304-1325. https://doi.org/10.51594/ijmer.v6i4.1060

Patwa, N., & Kee, D. M. H. (2024). Sustainability starts with HR: Exploring green HRM practices in the UAE. In Yusliza, M.Y., Renwick, D. (eds) Green human resource management . Springer. https://doi.org/10.1007/978-981-99-7104-6_12

Phillips, J., & Klein, J. D. (2023). Change management: From theory to practice. TechTrends , 67 (1), 189-197. https://doi.org/10.1007/s11528-022-00775-0

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology , 88 (5), 879. https://doi.org/10.1037/0021-9010.88.5.879

Praba, K., Vijay, B., Abuya, J. O. O., Poreddy, D., Mulakala, S., & Manoharan, S. (2025). Human resource management practices in the age of industrial automation: Impact, adoption, and future workforce. In Advancements in intelligent process automation (pp. 79-102). IGI Global. https://doi.org/10.4018/979-8-3693-5380-6.ch004

Rasheed, M. H., Khalid, J., Ali, A., Rasheed, M. S., & Ali, K. (2024). Human resource analytics in the era of artificial intelligence: Leveraging knowledge towards organizational success in Pakistan. Journal of Chinese Human Resources Management , 15 (3), 3-20. https://doi.org/10.47297/wspchrmWSP2040-800501.20241503

Ratnam, D. S., & Devi, V. R. (2024). Addressing impediments to HR analytics adoption: Guide to HRD professionals. Human Resource Development International, 27 (1), 142-151. https://doi.org/10.1080/13678868.2023.2195986

Redifer, J. L., Bae, C. L., & DeBusk-Lane, M. (2019). Implicit theories, working memory, and cognitive load: Impacts on creative thinking . SAGE Open, 9 (1), 2158244019835919. https://doi.org/10.1177/2158244019835919

Richman, N. (2015). Human resource management and human resource development: Evolution and contributions. Creighton Journal of Interdisciplinary Leadership, 1 (2), 120–129. http://dx.doi.org/10.17062:CJIL.v1i2.19

Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR spectrum , 36 , 251-276. https://doi.org/10.1007/s00291-013-0320-0

Rubel, M. R. B., Kee, D. M. H., & Rimi, N. N. (2021). High commitment human resource management practices and hotel employees' work outcomes in Bangladesh. Global Business and Organizational Excellence, 40 (5), 37–52. https://doi.org/10.1002/joe.22089

Sancak, I. E. (2023). Change management in sustainability transformation: A model for business organizations. Journal of Environmental Management , 330 , 117165. https://doi.org/10.1016/j.jenvman.2022.117165

Sekaran, U. (2016). Research methods for business: A skill building approach . John Wiley & Sons.

Sousa, B. J., & Clark, A. M. (2025). Growth mindsets in academics and academia: A review of influence and interventions. Journal of Higher Education Policy and Management , 47 (1), 38-56. https://doi.org/10.1080/1360080X.2024.2384003

Sigmundsson, H., & Haga, M. (2024). Growth mindset scale: Aspects of reliability and validity of a new 8-item scale assessing growth mindset. New Ideas In Psychology , 75 , 101111. https://doi.org/10.1016/j.newideapsych.2024.101111

Solberg, E., Adamska, K., Wong, S. I., & Traavik, L. E. (2024). When managers believe technological ability is fixed. Human Resource Management Journal , 34 (2), 437-454. https://doi.org/10.1111/1748-8583.12478

Song, L., Zhang, D., Lyu, B., & Chen, Y. (2024). Chinese leadership. In Oxford research encyclopedia of business and management . Oxford University Press. https://doi.org/10.1093/acrefore/9780190224851.013.376

Santos, G., Dutra, E., Mattos, C. S., Fortuna, A., dos Santos, R. P., Ramos, L. F., & da Cruz Andrade, Á. J. (2025). What leads to the success of agile transformation initiatives? A study on critical success factors. Journal of Software Engineering Research and Development , 13 (1), 13-1. https://doi.org/10.5753/jserd.2025.4502

Thakral, P., Srivastava, P. R., Dash, S. S., Jasimuddin, S. M., & Zhang, Z. (2023). Trends in the thematic landscape of HR analytics research: A structural topic modeling approach. Management Decision , 61 (12), 3665-3690. https://doi.org/10.1108/MD-01-2023-0080

Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management , 43 , 224-247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002

Tynjälä, P. (2013). Toward a 3-P model of workplace learning: A literature review. Vocations and Learning , 6 (1), 11-36. https://doi.org/10.1007/s12186-012-9091-z

van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness: People and Performance , 4 (2), 127-132. https://doi.org/10.1108/JOEPP-02-2017-0013

Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: A fine grained view of the early stages leading to adoption. The International Journal of Human Resource Management , 29 (22), 3046-3067. https://doi.org/10.1080/09585192.2018.1446181

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly , 425-478. https://doi.org/10.2307/30036540

Wallo, A., Kock, H., Lundqvist, D., & Coetzer, A. (2020). Understanding factors that enable and inhibit assessment of outcomes of competence development. Human Resource Development Review , 19 (4), 384-421. https://doi.org/10.1177/1534484320943332

Wang, J., Zhang, S., & Zhang, L. (2023). Intelligent hog farming adoption choices using the unified theory of acceptance and use of technology model: Perspectives from China's new agricultural managers. Agriculture , 13 (11), 2067. https://doi.org/10.3390/agriculture13112067

Wang, L., Zhou, Y., Sanders, K., Marler, J. H., & Zou, Y. (2024). Determinants of effective HR analytics Implementation: An In-Depth review and a dynamic framework for future research . Journal of Business Research , 170 , 114312. https://doi.org/10.1016/j.jbusres.2023.114312

Wang, Y., & Sun, X. (2025). Growth mindset in Chinese culture: A meta-analysis. Social Psychology of Education , 28 (1), 24. https://doi.org/10.1007/s11218-024-09955-7

Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist , 47 (4), 302-314. https://doi.org/10.1080/00461520.2012.722805

You, F., & Kee, D. M. H. (2024). Green HRM, green creativity, and sustainability: evidence from Chinese private enterprises in China. In Yusliza, M.Y., Renwick, D. (eds), Green human resource management . Springer. https://doi.org/10.1007/978-981-99-7104-6_18

文章被引用时，请邮件提醒。

Submit

Green Human Resource Management, Green Transformational Leadership, and Green Behavior in Manufacturing Industry in China: The Role of Green Creativity

The evolution of maternity leave benefits and child care policy in China: A study of institutional factors of fertility desires

Related Author

Haiming Li Law School, Central University of Finance and Economics

Chen Zhang Law School, Central University of Finance and Economics

Jiayue Liu Law School, Central University of Finance and Economics

Wenjie Fan Law School, Xiamen University

Related Institution

Law School, Central University of Finance and Economics

Law School, Xiamen University

⁰