数字化人力资源管理：整合框架与研究展望

doi:10.6049/kjjbydc.2021070405

科技进步与对策

2021, Vol. 38

Issue (23): 151-160 DOI: 10.6049/kjjbydc.2021070405

综述

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数字化人力资源管理：整合框架与研究展望

李燕萍¹,李乐¹,胡翔²

（1.武汉大学经济与管理学院，湖北武汉 430072；2.湖北大学商学院，湖北武汉 430062）

Digital Human Resource Management: Integration Framework and Research Prospects

Li Yanping¹,Li Le¹,Hu Xiang²

（1.School of Economics and Management, Wuhan University, Wuhan 430072, China；2.School of Business, Hubei University, Wuhan 430062, China）

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摘要数字经济的快速发展促使企业数字化转型，人力资源管理作为管理的重要部分，正经历数字化转型带来的变革，出现数字化人力资源管理。采用文献研究方法，从技术与人力资源结合的历史视角分析数字化人力资源管理的产生，从技术、功能及综合视角梳理数字化人力资源管理相关概念，界定数字化人力资源管理内涵，并结合数字技术特征提炼数字化人力资源管理的五大特征，将I-P-O范式引入数字化人力资源管理领域，构建研究框架，系统分析数字化人力资源管理过程及其影响因素和结果产出。最后，提出研究展望及管理建议。

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	李燕萍
	李乐
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关键词 ：数字化人力资源管理, 数字化转型, I-P-O框架

收稿日期: 2021-08-19 修回日期: 2021-10-28

ZTFLH:

F279

基金资助:国家社会科学基金重大项目(15ZDC014)；国家社会科学基金重点项目（21AGL018）

作者简介: 李燕萍（1965—），女，湖南常宁人，博士，武汉大学经济与管理学院教授、博士生导师，研究方向为战略性人力资源管理、组织与领导力、创新创业人才与产学研合作、科技人才及政策；李乐（1998—），女，四川广元人，武汉大学经济与管理学院硕士研究生，研究方向为组织与人力资源管理；胡翔（1981—），男，湖北武汉人，博士，楚天学子，湖北大学商学院副教授，研究方向为组织与人力资源管理。本文通讯作者：李燕萍。

引用本文:

李燕萍,李乐,胡翔. 数字化人力资源管理：整合框架与研究展望[J]. 科技进步与对策, 2021, 38(23): 151-160.
Li Yanping,Li Le,Hu Xiang. Digital Human Resource Management: Integration Framework and Research Prospects. SCIENCE & TECHNOLOGY PROGRESS AND POLICY, 2021, 38(23): 151-160.

链接本文:

http://www.kjjb.org/CN/10.6049/kjjbydc.2021070405 或 http://www.kjjb.org/CN/Y2021/V38/I23/151

[1] SIVATHANU B, PILLAI R. Smart HR 4.0-how industry 4.0 is disrupting HR [J]. Human Resource Management International Digest, 2018, 26(4): 7-11.
[2] 谢小云, 左玉涵, 胡琼晶. 数字化时代的人力资源管理：基于人与技术交互的视角 [J]. 管理世界, 2021, 37(1): 200-216.
[3] DESANCTIS G. Human resource information systems: a current assessment [J]. MIS Q, 1986, 10: 15-27.
[4] STROHMEIER S. Research in e-HRM: review and implications [J]. Human Resource Management Review, 2007, 17(1): 19-37.
[5] OREG S, VAKOLA M, ARMENAKIS A. Change recipients' reactions to organizational change: a 60 year review of quantitative studies [J]. Journal of Applied Behavioral Science A Publication of the Ntl Institute, 2011, 47(4): 461-524.
[6] DANIE P, SKARLICKI, et al. HRMR special issue: fairness and human resources management [J]. Human Resource Management Review, 2003, 13(1): 1-5.
[7] 宋立丰, 郭海, 杨主恩. 数字化情景下的传统管理理论变革——数据基础观话语体系的构建[J]. 科技管理研究, 2020, 40(8): 228-36.
[8] ZHOU Y, LIU G, CHANG X, et al. The impact of HRM digitalization on firm performance: investigating three-way interactions [J]. Asia Pacific Journal of Human Resources, 2021, 59(1): 20-43.
[9] VERMA S, SINGH V, BHATTACHARYYA S. Do big data-driven HR practices improve HR service quality and innovation competency of SMEs [J]. International Journal of Organizational Analysis, 2021, 29(4): 950-973.
[10] YOO Y, HENFRIDSSON O, LYYTINEN K. Research commentary:the new organizing logic of digital innovation: an agenda for information systems research [J]. Information Systems Research, 2010, 21(4): 724-735.
[11] 刘飞. 大数据背景下组织人力资源管理流程创新研究 [J]. 吉首大学学报(社会科学版), 2017, 38(S2): 33-35.
[12] ANGRAVE D, CHARLWOOD A, KIRKPATRICK I, et al. HR and analytics: why HR is set to fail the big data challenge [J]. Human Resource Management Journal, 2016, 26(1): 1-11.
[13] 姚凯, 桂弘诣. 大数据人力资源管理:变革与挑战 [J]. 复旦学报(社会科学版), 2018, 60(3): 146-155.
[14] THOMAS S, KURESHI S, SUGGALA S, et al. HRM 4.0 and the shifting landscape of employer branding [M]. Human & Technological Resource Management (HTRM): New Insights into Revolution 40. Emerald Publishing Limited, 2020.
[15] DUGGAN J, SHERMAN U, CARBERY R, et al. Algorithmic management and app-work in the gig economy: a research agenda for employment relations and HRM [J]. Human Resource Management Journal, 2020, 30(1): 114-132.
[16] TAMBE P, CAPPELLI P, YAKUBOVICH V. Artificial intelligence in human resources management: challenges and a path forward [J]. California Management Review, 2019, 61(4): 15-42.
[17] ABDELDAYEM M M, ALDULAIMI S H. Trends and opportunities of artificial intelligence in human resource management: aspirations for public sector in Bahrain [J]. International Journal of Scientific and Technology Research, 2020, 9(1): 3867-3871.
[18] HACIOGLU U. Digital business strategies in blockchain ecosystems: transformational design and future of global business [M]. Springer Nature, 2019. DOI:10.1007/978-3-030-29739-8.
[19] ZANG S, YE M. Human resource management in the era of big data [J]. Journal of Human Resource and Sustainability Studies, 2015, 3(1): 41-45.
[20] MAJUMDER S, MONDAL A. Are chatbots really useful for human resource management[J]. International Journal of Speech Technology, 2021（5）: 1-9.
[21] MYLLYMAKI D. Beyond the 'e-' in e-HRM: integrating a sociomaterial perspective [J]. International Journal of Human Resource Management, 2021, 32(12): 2563-2591.
[22] STROHMEIER S. Smart HRM-a delphi study on the application and consequences of the internet of things in human resource management [J]. International Journal of Human Resource Management, 2020, 31(18): 2289-2318.
[23] ORLIKOWSKI W J, SCOTT S V. Sociomateriality: challenging the separation of technology, work and organization [J]. Academy of Management Annals, 2008, 2(1): 433-474.
[24] MOSCA M. Digitalization of HRM: a study of success factors and consequences in the last decade [D]. University of Twente, 2020.
[25] 魏丹霞, 赵宜萱, 赵曙明. 人力资本视角下的中国企业人力资源管理的未来发展趋势 [J]. 管理学报, 2021, 18(2): 171-9.
[26] STROHMEIER S, KABST R. Organizational adoption of e-HRM in Europe: an empirical exploration of major adoption factors [J]. Journal of Managerial Psychology, 2009, 24(6): 482-501.
[27] 赵宜萱, 赵曙明, 栾佳锐. 基于人工智能的人力资源管理:理论模型与研究展望 [J]. 南京社会科学, 2020，31(2): 36-43.
[28] MIKALEF P, BOURA M, LEKAKOS G, et al. Big data analytics and firm performance: findings from a mixed-method approach [J]. Journal of Business Research, 2019, 98(MAY): 261-276.
[29] RAGOWSKY A, LICKER P S, GEFEN D. Organizational IT maturity (OITM): a measure of organizational readiness and effectiveness to obtain value from its information technology [J]. Journal of Information Systems Management, 2012, 29(2): 148-160.
[30] MORRIS M G, VENKATESH V, ACKERMAN P L. Gender and age differences in employee decisions about new technology: an extension to the theory of planned behavior [J]. IEEE Transactions on Engineering Management, 2005, 52(1):69-84.
[31] HAO S B, ZHANG H L, SONG M. Big data, big data analytics capability, and sustainable innovation performance [J]. Sustainability, 2019, 11(24): 7145.
[32] FAY D, SHIPTON H, WEST M A, et al. Teamwork and organizational innovation: the moderating role of the HRM context [J]. Creativity & Innovation Management, 2015, 24(2): 261-277.
[33] ARNAUD S, CHANDON J-L. Will monitoring systems kill intrinsic motivation： an empirical study [J]. Revuede Gestiondes Ressources Humaines, 2013(4): 35-53.
[34] MASLACH C, LEITER M. Early predictors of job burnout and engagement [J]. The Journal of Applied Psychology, 2008, 93（3）: 498-512.
[35] SONNENTAG S, BINNEWIES C, MOJZA E J.Did you have a nice evening：a day-level study on recovery experiences, sleep, and affect [J]. Journal of Applied Psychology, 2008, 93(3): 674-684.
[36] SHRESTHA Y R, BEN-MENAHEM S M, KROGH G V. Organizational decision-making structures in the age of artificial intelligence [J]. California Management Review, 2019, 61(4): 66-83.
[37] SULLIVAN S E, CARDEN W A, MARTIN D F. Careers in the next millennium: directions for future research [J]. Human Resource Management Review, 1998, 8(2): 165-185.
[38] SULLIVAN S E, ARTHUR M B. The evolution of the boundaryless career concept: examining physical and psychological mobility [J]. Journal of Vocational Behavior, 2006, 69(1): 19-29.