人工智能背景下制造业知识型员工技术空心化: 内涵、生成及影响机制

doi:10.3724/SP.J.1042.2024.02005

摘要/Abstract

摘要： 智能化浪潮为我国由制造大国迈向制造强国以及制造业智能转型注入新的源动力, 但与此同时制造业知识型员工也面临着劳动过程被人工智能重塑的挑战。本文在人工智能背景下创新性地提出技术空心化这一动态性概念来反映人工智能技术的发展和应用对制造业知识型员工劳动过程的影响。本文有三个研究目的: 第一, 基于意义构建理论时间动态视角探讨技术空心化的内涵、维度结构以及其测量量表; 第二, 以“认知-行为”互动链为基础构建技术空心化的“执行技能空心化”和“概念技能空心化”两阶段过程模型, 并进一步探讨企业和员工层情境因素在其中的催化作用; 第三, 基于能力构建视角探讨技术空心化对知识型员工双元创新行为和可持续职业生涯发展的影响。研究结论不仅能丰富人工智能背景下技术空心化的理论研究, 还能在中国制造业智能转型之际为和谐稳定的劳动关系的建立、实现企业与员工的长期发展及共同繁荣提供实践启示。

关键词: 技术空心化, 人工智能技术, 知识型员工, 意义构建理论

Abstract:

The wave of intelligence has injected new impetus for China to transform from a manufacturing power to a manufacturing powerhouse and for the intelligent transformation of enterprises. However, at the same time, knowledge workers in the manufacturing industry face the challenge of reshaping the labor process with artificial intelligence. Previous studies mainly used labor process theory to analyze the impact of technological progress on the labor of “blue-collar workers” in manufacturing, while related research on knowledge workers in manufacturing is still in the conceptual discussion stage. Therefore, this study innovatively proposes the dynamic concept of technical hollowing out under the background of artificial intelligence to reflect the impact of the development and application of artificial intelligence technology on the labor process of knowledge workers in the manufacturing industry.

This study constructs a theoretical study on the technical hollowing out of knowledge workers from three perspectives of sensemaking: cognition, behavior, and ability. This study has three research purposes: First, to explore the definition and dimensional structure of technical hollowing out from the perspective of “cognition-behavior-ability” sensemaking, and intends to extract two dimensions: executive skill hollowing and conceptual skill hollowing, technical hollowing out measurement scale was developed based on; second, based on the “cognition-behavior” interaction chain, we construct a two-stage model of “executive skill hollowing out” and “conceptual skill hollowing out” for the technical hollowing out of knowledge workers, and further explore the catalytic role of situational factors at the enterprise and employee levels; third, based on the capability-building perspective, the impact of technical hollowing out on knowledge workers’ dual innovation behavior and sustainable career development is explored. We intend to use case study methods to explore the definition, dimensional structure, measurement scale, and generation process of technological hollowing out. In addition, we use empirical research methods to analyze the impact mechanism of technological hollowing out on the multi-dimensional development of employees. Based on the above conception, this study attempts to construct a relatively systematic and complete theoretical framework of technological hollowing out through three closely related and hierarchical parts.

This study takes knowledge workers in the manufacturing industry as the research object, expands the subject boundaries of existing AI in reshaping the labor process of workers, and grasps the research frontier of technological hollowing out of knowledge workers. The dynamic concept of technological hollowing out was innovatively proposed, and its dimensional structure and measurement scale were analyzed, deepening the dynamic research of technological hollowing out under the background of AI. At the same time, combining the sensemaking and labor process perspectives, the “double separation” model of employee skills and core science and technology is integrated based on the AI background, forming a theoretical model for the generation of technological hollowing out, which provides a new theoretical perspective for revealing the mechanism of AI’s reshaping of the labor process of knowledge workers. It makes up for the deficiency of labor process theory that focuses on the labor control of “blue-collar workers” from a static perspective. From the perspective of ability building in sensemaking, this study uses empirical analysis methods to reveal the impact path and boundary conditions of technical hollowing out on employees’ dual innovation behavior and sustainable career development by acting on their technological absorptive ability. It provides more evidence for a deeper understanding of the process of employees’ dual innovation behavior and sustainable career development. Also, it provides a new theoretical perspective for the cultivation and motivation of innovative and sustainable talents in the background of intelligent manufacturing. Moreover, the research conclusions can also provide practical inspiration for establishing harmonious and stable labor relations, as well as realizing long-term development and shared prosperity of enterprises and employees during the intelligent transformation of China’s manufacturing industry.

Key words: technical hollowing out, artificial intelligence, knowledge workers, sensemaking theory

中图分类号:

B849: C93

王永跃, 黄飘飘, 金杨华, 白新文, 岳峰凯, 张范颖, 郭子浩. (2024). 人工智能背景下制造业知识型员工技术空心化: 内涵、生成及影响机制. 心理科学进展 , 32(12), 2005-2017.

WANG Yongyue, HUANG Piaopiao, JIN Yanghua, BAI Xinwen, YUE Fengkai, ZHANG Fanying, GUO Zihao. (2024). Technical hollowing out of knowledge workers in the manufacturing industry in artificial intelligence context: The definition, formation and influence mechanism. Advances in Psychological Science, 32(12), 2005-2017.

参考文献

[1] 蔡莉, 杨亚倩, 詹天悦, 盛乃珩. (2022). 数字经济下创新驱动创业过程中认知、行为和能力的跨层面作用机制—基于三一集团的案例研究.南开管理评论, 1-24.
[2] 曹昕怡, 王前. (2021). 人工智能对人类思维能力的双重影响.长沙理工大学学报(社会科学版), (3), 67-74.
[3] 高中华, 徐燕. (2023). 智能制造师徒关系理论构建及对团队双元创新的驱动机制.心理科学进展, 31(8), 1411-1428.
[4] 胡双钰, 吴和成. (2023). 技术多元化、吸收能力与创新绩效.系统工程, 41(6), 30-40.
[5] 李敏, 黄海艳. (2022). 工业机器人应用与制造业企业创新绩效——基于研发投入和员工知识能力的中介效应.中国人事科学, (3), 73-84.
[6] 梁萌. (2015). 知识劳动中的文化资本重塑—以E互联网公司为例. 社会发展研究,(1), 64-85+244.
[7] 马克思. (1985). 马克思 1844 年经济学哲学手稿 (中共中央马克思恩格斯列宁斯大林著作编译局译). 人民出版社. (原著发表于1932年)
[8] 邱子童, 吴清军, 杨伟国. (2019). 人工智能背景下劳动者技能需求的转型: 从去技能化到再技能化.电子政务, 6, 23-30.
[9] 束超慧, 王海军, 金姝彤, 贺子桐. (2022). 人工智能赋能企业颠覆性创新的路径分析.科学学研究, (10), 1884-1894.
[10] 孙强. (2020). 马克思主义视野下的人工智能与人权问题探析.湖北社会科学, (2), 5-11.
[11] 王林辉, 姜昊, 董直庆. (2022). 工业智能化会重塑企业地理格局吗.中国工业经济, 2, 137-155.
[12] 汪前元, 魏守道, 金山, 陈辉. (2022). 工业智能化的就业效应研究—基于劳动者技能和性别的空间计量分析.管理世界, 38(10), 110-126.
[13] 王琼. (2022). 职业可续视角下的工作重塑行为研究: 动力, 路径及干预机制.心理科学进展, 30(3), 499-510.
[14] 王潇. (2019). 技术空心化: 人工智能对知识型员工劳动过程的重塑—以企业电子研发工程师为例.社会发展研究, 6(3), 84-102.
[15] 吴海民. (2012). 资产价格波动、通货膨胀与产业“空心化”—基于我国沿海地区民营工业面板数据的实证研究.中国工业经济, (1), 46-56.
[16] 谢小云, 左玉涵, 胡琼晶. (2021). 数字化时代的人力资源管理: 基于人与技术交互的视角.管理世界, 37(1), 200-216.
[17] 杨倩, 焦特, 雷亚萍. (2022). 团队心理资本对个体双元创新行为的影响研究.科技管理研究, 42(15), 139-147.
[18] 杨艳玲, 郑雁玲, 田宇. (2022). 渐进还是突破? 双元创新对新企业绩效的影响.科技管理研究, 42(9), 76-81.
[19] 尹萌, 牛雄鹰. (2024). 与 AI “共舞”: 系统化视角下的AI-员工协作.心理科学进展, 32(1), 162-176.
[20] 于文轩, 魏炜. (2023). 数据开放中的算法依赖: 发展模式与驱动路径.理论与改革, (2), 109-122.
[21] Acemoglu D., Autor D., Hazell J., & Restrepo P. (2022). Artificial intelligence and jobs: Evidence from online vacancies.Journal of Labor Economics, 40(S1), S293-S340.
[22] AlQershi N., Saufi R. B. A., Yaziz M. F. B. A., Permarupan P. Y., Muhammad N. M. N., Yusoff M. N. H. B., & Ramayah T. (2023). The threat of robots to career sustainability, and the pivotal role of knowledge management and human capital.Journal of Innovation & Knowledge, 8(3), 100386.
[23] Aminizadeh S., Heidari A., Toumaj S., Darbandi M., Navimipour N. J., Rezaei M., .. Unal, M.(2023). The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer Methods and Programs in Biomedicine, 241, https://doi.org/10.1016/j.cmpb.2023. 107745.
[24] Baruch, Y., & Sullivan, S. E. (2022). The why, what and how of career research: A review and recommendations for future study.Career Development International, 27(1), 135-159.
[25] Braverman, H. (1998). Labor and monopoly capital: The degradation of work in the twentieth century. NYU Press.
[26] Bright, J. R. (1958). Automation and management. Boston: Division of Research, Graduate School of Business Administration, Harvard University.
[27] Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace.Journal of Management & Organization, 24(2), 239-257.
[28] Cain, S. (2023-1-17). ‘This song sucks’: Nick Cave responds to ChatGPT song written in style of Nick Cave.The Guardian.
[29] Cardon M. S., Stevens C. E., & Potter D. R. (2011). Misfortunes or mistakes?: Cultural sensemaking of entrepreneurial failure.Journal of Business Venturing, 26(1), 79-92.
[30] Cazzaniga M., Jaumotte M. F., Li L., Melina M. G., Panton A. J., Pizzinelli C., .. Tavares, M. M. M. (2024). Gen-AI: Artificial intelligence and the future of work. International Monetary Fund.
[31] Cha J., Kim Y., & Kim T. Y. (2009). Person-career fit and employee outcomes among research and development professionals.Human Relations, 62(12), 1857-1886.
[32] Chatterjee S., Chaudhuri R., & Vrontis D. (2022). Knowledge sharing in international markets for product and process innovation: Moderating role of firm’s absorptive capacity.International Marketing Review, 39(3), 706-733.
[33] Danneels, E. (2002). The dynamics of product innovation and firm competences.Strategic Management Journal, 23(12), 1095-1121.
[34] Daugherty P. R.,& Wilson, H. J. (2018). Human+ machine: Reimagining work in the age of AI. Harvard Business Press.
[35] De Rond M., Holeman I., & Howard-Grenville J. (2019). Sensemaking from the body: An enactive ethnography of rowing the Amazon.Academy of Management Journal, 62(6), 1961-1988.
[36] Dervin, B. (1999). Chaos, order and sense-making: A proposed theory for information design. Cambridge: MIT Press.
[37] Dervin, B., & Frenette, M. (2001). Sense-making methodology: Communicating communicatively with campaign audiences.Public Communication Campaigns, 3, 69-87.
[38] De Vos A., Van Der Heijden, B. I. J. M., & Akkermans J. (2020). Sustainable careers: Towards a conceptual model.Journal of Vocational Behavior, 117, 103196.
[39] Dwivedi Y. K., Kshetri N., Hughes L., Slade E. L., Jeyaraj A., Kar A. K., .. Wright R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy.International Journal of Information Management, 71, 102642.
[40] Farhan, A. (2023). The impact of artificial intelligence on human workers.Journal of Communication Education, 17(2), 93-104.
[41] Fayad, Y., & El Ebrashi, R. (2022). Social capital and corporate entrepreneurship: The role of absorptive capacity in emerging markets.Management Decision, 60(9), 2503-2531.
[42] Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?Technological Forecasting and Social Change, 114, 254-280.
[43] Fui-Hoon Nah F., Zheng R., Cai J., Siau K., & Chen L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration.Journal of Information Technology Case and Application Research, 25(3), 277-304.
[44] Gallie D., Felstead A., Green F., & Inanc H. (2017). The hidden face of job insecurity.Work, Employment and Society, 31(1), 36-53.
[45] Haipeter, T. (2020). Digitalisation, unions and participation: The German case of ‘industry 4.0’.Industrial Relations Journal, 51(3), 242-260.
[46] Haug A., Adsbøll Wickstrøm K., Stentoft J., & Philipsen K. (2023). The impact of information technology on product innovation in SMEs: The role of technological orientation.Journal of Small Business Management, 61(2), 384-410.
[47] Ibrahim, S., & Fallah, M. H. (2005). Drivers of innovation and influence of technological clusters.Engineering Management Journal, 17(3), 33-41.
[48] Iskender, A. (2023). Holy or unholy? Interview with open AI’s ChatGPT.European Journal of Tourism Research, 34, 3414-3414.
[49] Jaiswal A., Arun C. J., & Varma A. (2022). Rebooting employees: Upskilling for artificial intelligence in multinational corporations.The International Journal of Human Resource Management, 33(6), 1179-1208.
[50] Kellogg K. C., Valentine M. A., & Christin A. (2020). Algorithms at work: The new contested terrain of control.Academy of Management Annals, 14(1), 366-410.
[51] Kok H., Faems D., & de Faria P. (2020). Ties that matter: The impact of alliance partner knowledge recombination novelty on knowledge utilization in R&D alliances.Research Policy, 49(7), 104011.
[52] Kong H. Y., 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.
[53] Kreps, D., & Rowe, F. (2021 December). Free will in philosophy and in IS research: Where do we stand and where do we want to be? Forty-Second International Conference on Information Systems, Austin, Texas, USA.
[54] 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.
[55] Lingmont, D. N., & Alexiou, A. (2020). The contingent effect of job automating technology awareness on perceived job insecurity: Exploring the moderating role of organizational culture.Technological Forecasting and Social Change, 161, 120302.
[56] Mabungela, M. (2023). Artificial intelligence (AI) and automation in the world of work: A threat to employees?Research in Social Sciences and Technology, 8(4), 135-146.
[57] Maitlis, S., & Christianson, M. (2014). Sensemaking in organizations: Taking stock and moving forward.Academy of Management Annals, 8(1), 57-125.
[58] Malik N., Tripathi S. N., Kar A. K., & Gupta S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations.International Journal of Manpower, 43(2), 334-354.
[59] Martin S. L., Javalgi R. G., & Cavusgil E. (2017). Marketing capabilities, positional advantage, and performance of born global firms: Contingent effect of ambidextrous innovation.International Business Review, 26(3), 527-543.
[60] McArdle S., Waters L., Briscoe J. P., & Hall, D. T. T. (2007). Employability during unemployment: Adaptability, career identity and human and social capital.Journal of Vocational Behavior, 71(2), 247-264.
[61] McDonald K. S., Hite L. M., & O’Connor K. W. (2022). Developing sustainable careers for remote workers.Human Resource Development International, 25(2), 182-198.
[62] Meister D.,& Chandrasekhar, R. (2021). McCormick & Co: Deploying artificial intelligence in new product development (Case No. W25509). Ivey Publishing.
[63] Morais-Storz M., Nguyen N., & Sætre A. S. (2020). Post- failure success: Sensemaking in problem representation reformulation.Journal of Product Innovation Management, 37(6), 483-505.
[64] Ng T. W., Eby L. T., Sorensen K. L., & Feldman D. C. (2005). Predictors of objective and subjective career success: A meta-analysis.Personnel Psychology, 58(2), 367-408.
[65] O’ Connor, S. (2022). Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?Nurse Education in Practice, 66, 103537-103537.
[66] Petropoulos, G. (2018). The impact of artificial intelligence on employment.Praise for Work in the Digital Age, 119, 121.
[67] Piasna, A. (2024). Algorithms of time: How algorithmic management changes the temporalities of work and prospects for working time reduction.Cambridge Journal of Economics, 48(1), 115-132.
[68] Piszczek, M. M. (2017). Boundary control and controlled boundaries: Organizational expectations for technology use at the work-family interface.Journal of Organizational Behavior, 38(4), 592-611.
[69] Retkowsky J., Nijs S., Akkermans J., Jansen P., & Khapova S. N. (2023). Toward a sustainable career perspective on contingent work: A critical review and a research agenda.Career Development International, 28(1), 1-18.
[70] Sandberg, J., & Tsoukas, H. (2015). Making sense of the sensemaking perspective: Its constituents, limitations, and opportunities for further development.Journal of Organizational Behavior, 36(S1), S6-S32.
[71] Scott, S. G., & Bruce, R. A. (1994). Determinants of innovative behavior: A path model of individual innovation in the workplace.Academy of Management Journal, 37(3), 580-607.
[72] Smith, C. (2006). The double indeterminacy of labour power: Labour effort and labour mobility.Work, Employment and Society, 20(2), 389-402.
[73] Srisathan W. A., Ketkaew C., & Naruetharadhol P. (2023). Assessing the effectiveness of open innovation implementation strategies in the promotion of ambidextrous innovation in Thai small and medium-sized enterprises.Journal of Innovation & Knowledge, 8(4), 100418.
[74] Tang, C. S. (2022). Innovative technology and operations for alleviating poverty through women's economic empowerment.Production and Operations Management, 31(1), 32-45.
[75] Thomas J. B., Clark S. M., & Gioia D. A. (1993). Strategic sensemaking and organizational performance: Linkages among scanning, interpretation, action, and outcomes.Academy of Management Journal, 36(2), 239-270.
[76] Thompson, P. (1983). The nature of work: An introduction to debates on the labour process. Macmillan International Higher Education.
[77] 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.
[78] Van der Heijden, B. I., & De Vos, A. (2015). Sustainable careers: Introductory chapter. In A. De Vos, Antwerp Management School and University of Antwerp, Belgium, B. I. Van der Heijden, Radboud University Institute for Management Research, Nijmegen, the Open University of the Netherlands, Heerlen, the Netherlands and Kingston University, London, UK (Eds.), Handbook of research on sustainable careers (pp. 1-19). Edward Elgar Publishing.
[79] Van der Heijden B., De Vos A., Akkermans J., Spurk D., Semeijn J., Van der Velde M., & Fugate M. (2020). Sustainable careers across the lifespan: Moving the field forward.Journal of Vocational Behavior, 117, 103344.
[80] Xu G., Xue M., & Zhao J. (2023). The relationship of artificial intelligence opportunity perception and employee workplace well-being: A moderated mediation model.International Journal of Environmental Research and Public Health, 20(3), 1974.
[81] Yilmaz E. D., Naumovska I., & Aggarwal V. A. (2023). AI-driven labor substitution: Evidence from Google translate and ChatGPT.SSRN Electronic Journal, 60.
[82] Yu, S. H., & Chen, H. C. (2020). External knowledge, intraorganisational networks and exploratory innovation: An empirical examination.Innovation, 22(3), 250-269.
[83] Zarifhonarvar, A. (2024). Economics of ChatGPT: A labor market view on the occupational impact of artificial intelligence.Journal of Electronic Business & Digital Economics, 3(2), 100-116.