The differential impacts of top management support and transformational supervisory leadership on employees’ digital performance

ABSTRACT

How do leaders across different hierarchies motivate employees’ job performance in the new digital age? In order to answer this under-investigated question, we first conceptualise digital performance as employees’ job performance that is attained through using the new generation of digital technologies and then propose a research model that integrates and differentiates the influential mechanisms of the dual leadership factors – top management support and transformational supervisory leadership – regarding employees’ digital-enabled task performance and innovative performance. Specifically, we tested the research model with two different samples, including 230 sales personnel from a large automobile manufacturing company and 206 employees from multiple joint ventures across different industries. We find that top management support exhibits a stronger influence on digital-enabled task performance than on innovative performance through the mediation of data-driven culture, while transformational supervisory leadership nurtures a stronger effect on digital-enabled innovative performance than on task performance through the mediation of digital self-efficacy. Our study consolidates and extends the technology use literature on management support and advances IS leadership theory to the digital context. Our findings also offer practical insights into the effective use of the new generation of digital technologies in organisations.

KEYWORDS:

• Digital performance
• top management support
• transformational supervisory leadership
• digital self-efficacy
• data-driven culture

Acknowledgments

We want to thank for the research sponsorship received by the National Natural Science Foundation of China (72271069, 71771064, 71701110, 71490721), the Ministry of Education of Humanities and Social Science Project (22YJA630070), the Science and Technology Think Tank Young Talent Program (20220615ZZ07110112), the Government of Andalusia and the European Regional Development Fund (European Union) (Research Project B-SEJ74-UGR20), the Government of Spain (Research Project PID2021.124725NB.I00), and the Slovenian Research Agency (Research Core Funding No. P5-0410).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Digital transformation refers to the improvements that the new generation of digital technologies can bring into a company’s business model, which usually involve changes in product development, service delivery, management structures, or business processes (Hess et al., 2016; Vial, 2019).

2. Note that management support is a construct conceptualised and examined at both individual and organisational levels in the previous studies. In Section 2.2., we reviewed about 30 studies that investigated the influence of management support in technology use contexts and found about half of them theorised and measured management support at the individual level (i.e., Choi & Chang, 2009; Essex et al., 1998; Igbaria et al., 1995, 1997; Karahanna et al., 1999; Klein et al., 2001; Lewis et al., 2003; Peng & Guo, 2019; Petter et al., 2013; Sharma & Yetton, 2003; Wixom & Watson, 2001; Yoon & Guimaraes, 1995; Yoon et al., 1995). Therefore, we follow these studies and conceive top management support as a construct at the individual level, which is aligned with the level of conceptualisation, measurement, and theorising of transformational supervisory leadership.

3. For Sample 2, we selected one of the three items with the highest loading with Sample 1 for each dimension and used five items to model transformational supervisory leadership as a first-order reflective construct (Tepper et al., 2018) (see Appendix A).

4. For Sample 1, we only asked for demographic information of gender, age, education, and work experience. For Sample 2, in addition to the four demographic factors, we also asked information about (1) the top three digital technologies they used to support daily work, (2) the location of the foreign headquarters, (3) the employee number of their organisations in China, and (4) the industry to which their organizations belonged (see the last paragraph of Section 4.1.).

5. We calculated the effect sizes of all verified relationships in hypotheses tests and found that all these relationships showed medium-to-large (0.15–0.35) or even large (>0.35) effect sizes that were independent of sample size (Benitez et al., 2020; Cohen, 1988). (Sample 1: f²_TMS→_DDC(H1) >0.35, f²_TSL→_DSE(H2) >0.35, f²_DDC→_DTP(H3a) = 0.181, f²_DSE→_DIP(H5b) = 0.182; Sample 2: f²_TMS→_DDC(H1) >0.35, f²_TSL→_DSE(H2) >0.35, f²_DDC→_DTP(H3a) >0.35, f²_DSE→_DIP(H5b) = 0.202).

6. We also tested the structural model by adding seven dummy variables to capture the eight industry types that Sample 2 belonged to (see the last paragraph of Section 4.1.). The hypotheses tests remained unchanged and we did not detect any significant influences of industry type on DTP or DIP.

Additional information

Notes on contributors

Zhen Shao

Zhen Shao is an Associate Professor of IS in the Department of Management Science and Engineering at the School of Economics and Management, Harbin Institute of Technology, Harbin, China. Her research primarily focuses on enterprise information systems assimilation, digital innovation, and sharing economy. Her work has been published in leading IS journals, including the European Journal of Information Systems, Information Systems Journal, Information & Management, Decision Support Systems, International Journal of Information Management, Journal of Enterprise Information Management, Electronic Commerce and Research Applications, Internet Research, Behaviour & Information Technology, Computers in Human Behavior, and Industrial Management & Data Systems, and presented at leading conferences including the INFORMS Annual Conference, International Conference on Information Systems, Americas Conference on Information Systems, the Hawaii International Conference on System Sciences, and the Pacific Asia Conference on Information Systems.

Xixi Li

Xixi Li is an Associate Professor in the Department of Management Science and Engineering at the School of Economics and Management, University of Science and Technology Beijing, Beijing, China. She worked as a Post-Doctoral Research Fellow at the Robinson College of Business, Georgia State University. She received her Ph.D. in Management Information Systems and B.S. (Hons.) in Management from the Hong Kong Polytechnic University. Her research focuses on developing and testing behavioral theories on the individual, group, and societal use of digital technologies. Her work has been published in the Information Systems Journal, Information Systems Research, Journal of the Association for Information Systems, and others. She currently serves as Senior Editor of IT and People and Associate Editor of Information Systems Journal.

Yumei Luo

Yumei Luo is an Associate Professor in the Department of Management Science at the School of Business and Tourism Management, Yunnan University, Kunming, China. She received her Ph.D. in Management Science and Engineering at the School of Management, from Fudan University, Shanghai, China. Her research interests include IT business value, digital innovation, and healthcare digital transformation. Her work has been published in Information Technology & People, Computers in Human Behavior, Journal of Systems Science and Systems Engineering, and Journal of Global Information Technology Management.

Jose Benitez

Jose Benitez is a Full Professor of Management Information Systems at EDHEC Business School, France. His research interests cover the impact of digitalization on companies and individuals, and the development of theory and quantitative research methods in Information Systems (IS) research. His research has been published in leading journals such as MIS Quarterly, Journal of Operations Management, Journal of Management Information Systems, Journal of the Association for Information Systems, European Journal of Information Systems, Journal of Information Technology, Information & Management, Decision Support Systems, Decision Sciences, and Journal of Business Research. Jose's engagement with the IS discipline has been recognized, designating him as an Association for Information Systems Distinguished Member Cum Laude. He currently serves as Senior Editor of the European Journal of Information Systems, Information & Management, and Decision Support Systems and as Associate Editor for the Journal of the Association for Information Systems. In addition, Jose has served as Guest Editor of Decision Sciences. His teaching interests and instructional expertise cover managing digital business transformation, digital innovation, the business value of digital technologies, IT management, IT strategy, and theory development and quantitative research methods in IS research at Ph.D., Executive MBA, Global MBA, MIM, and BBA. Jose is a passionate speaker who enjoys working with students, colleagues, and executives to positively impact the business world and society. He has also provided consulting services and worked on IT development and digital transformation projects with many leading companies worldwide.