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| The Empowerment Path of Regional Traditional Industrial Enterprise Innovation by Digital Intelligence Based on the TOEP Theoretical Framework |
| Chen Xusheng,Wang Pengfei,Zhang Xudong |
| (School of Management, Harbin University of Science and Technology, Harbin 150040, China) |
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Abstract As a fundamental industry of the national economy, traditional manufacturing can drive the development of upstream and downstream enterprises through the industrial chain, reduce import dependence through export-oriented products, improve resource allocation efficiency through technological accumulation and scale operation, and mitigate the impact of external environmental changes to some extent. However, traditional industrial enterprises are generally faced with the dilemma of product homogeneity and rising costs. The rapid development of digital intelligence technology brings new transformations to enterprise operations, driving traditional industrial enterprises towards knowledge-intensive and technology-intensive transformation. Big data analysis enables enterprises to accurately understand customer needs, while artificial intelligence provides efficient and precise autonomous solutions, accelerating the innovation process of traditional industrial enterprises. Therefore, it is of great significance to explore the empowerment of digital intelligence on the innovation of traditional industrial enterprises.#br#Most existing research on innovation in traditional industrial enterprises focuses on how to improve production efficiency, and there is little exploration of the driving rules of innovation from the perspective of digital intelligence technology. At the same time, existing studies often analyze data intelligence technology and services as independent factors when examining the intelligent process, with less consideration of their synergy, which makes it difficult to clarify the overall impact of data intelligence on innovation. In addition, current strategies for data intelligence empowerment often emphasize industry characteristics, while differences in regional resource endowments result in resource mismatches.#br#In this study, the "technology-organization-environment-process" (TOEP) framework is constructed to identify the influencing factors driving innovation activities, and it is used to analyze the innovation paths of traditional industrial enterprises empowered by fuzzy set qualitative comparative analysis (fsQCA). It is found that the digital intelligence empowerment process does not have a single necessary condition or key dimension, and the promotion of enterprise innovation performance improvement needs to be achieved through configuration paths. The "technology-organization" dimension focuses on technological research and development promotion, while the "environment-process" dimension is driven by demand and forms a complementary empowerment pattern. By comparing the resource advantages of 12 typical regional samples from the configuration paths, the inherent rules of innovation performance differences in the configuration paths are analyzed. There are multiple sets of alternative relationships between the antecedent conditions of the paths, and there is a convergence trend for different paths , while regions that have strong innovation capabilities and high performance mostly adopt the whole-factor path, while other regions can choose paths that match their own resource conditions, and gradually improve the synergy between factors to achieve overall optimization of digital intelligence empowerment effects.#br#This research has three contributions. Firstly, it proposes intelligence empowerment paths for traditional industrial enterprises and reveals the differences in influencing factors in the empowerment processes of different regions. Secondly, the complementarity between digital intelligence empowerment dimensions is clarified, and the empowerment process can be achieved through different approaches. The regional resource advantages are the basis for deciding the empowerment paths, and the introduction of other antecedent factors can enhance the synergy of resources in the empowerment paths based on the asymmetry of dimension combinations. Finally, it is found that the dimension of the "process" plays a significant role in the paths, and the application of digital intelligence expands the regional market demand, overcoming the entry barriers of the digital transformation in traditional industries.#br#The reference for regional empowerment policy formulation is presented. Firstly, empowerment policies should be selected according to regional resources so as to realize market potential and industrial restructure by gradually increasing inputs. Secondly, the governments in the central and western regions should choose complementary development paths with their counterparts in the eastern regions, combining regional human resources and industrial advantages to promote the digital transformation of traditional industrial enterprises through preferential policies and different regional empowerment advantages. Thirdly, the new business models are expected to promote the application of digital intelligence technology, combining mass production capabilities with customized design to break away from the "low-end lock-in" dilemmas and promote technological breakthroughs and business model innovation.#br#
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Received: 21 May 2023
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[1] NAMBISAN S,LYYTINEN K,MAJCHRZAK A,et al.Digital innovation management[J].MIS Quarterly,2017,41(1):223-238.
[2] MARIANI M M,MACHADO I,MAGRELLI V,et al.Artificial intelligence in innovation research:a systematic review,conceptual framework,and future research directions[J].Technovation,2022,3(122):102623.
[3] 刘庆龄,曾立.数字技术推动高质量发展的机理与策略研究[J].科技进步与对策,2023,40(12):12-22.
[4] 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.
[5] ANDERSON N,DE DREU C K W,NIJSTAD B A.The routinization of innovation research:a constructively critical review of the state-of-the-science[J].Journal of organizational Behavior,2004,25(2):147-173.
[6] WIMELIUS H,MATHIASSEN L,HOLMSTRM J,et al.A paradoxical perspective on technology renewal in digital transformation[J].Information Systems Journal,2021,31(1):198-225.
[7] CLASSEN M,FRIEDLI T.Eight organizational enablers of digital service-sales ambidexterity in industrial firms[J].Journal of Business&Industrial Marketing,2022,37(11):2142-2155.
[8] 武建龙,鲍萌萌,陈劲,等.产业联盟创新生态系统升级路径研究[J].科研管理,2022,43(9):20-31.
[9] DEMIL B,LECOCQ X,RICART J E,et al.Introduction to the SEJ special issue on business models:business models within the domain of strategic entrepreneurship[J].Strategic Entrepreneurship Journal,2015,9(1):1-11.
[10] 江积海,王烽权.O2O商业模式的创新路径及其演进机理——品胜公司平台化转型案例研究[J].管理评论,2017,29(9):249-261.
[11] KOTTEMANN J E,BOYER-WRIGHT K M.Human resource development,domains of information technology use,and levels of economic prosperity[J].Information Technology for Development,2009,15(1):32-42.
[12] MCLEOD L,DOOLIN B.Information systems development as situated socio-technical change:a process approach[J].European Journal of Information Systems,2012,21(2):176-191.
[13] ALI AHANI,NOR ZAIRAH A B RAHIM,MEHRBAKHSH NILASHI.Forecasting social CRM adoption in SMEs:a combined SEM-neural network method[J].Computers in Human Behavior,2017,75:560-578.
[14] PORTER M E,HEPPELMANN J E.How smart,connected products are transforming competition[J].Harvard business Review,2014,92(11):64-88.
[15] HENDERSON R M,CLARK K B.Architectural innovation:the reconfiguration of existing product technologies and the failure of established firms[J].Administrative Science Quarterly,1990,34(1):9-30.
[16] GHOBAKHLOO M.Industry 4.0,digitization,and opportunities for sustainability[J].Journal of Cleaner Production,2020,10(252):119869.
[17] 张邦辉,万秋兰,吴健.在线政务服务的营商环境优化效应探析——“数字红利”与“数字鸿沟”[J].中国行政管理,2021,37(4):70-75.
[18] SRINIVASAN A,VENKATRAMAN N.Entrepreneurship in digital platforms:a network-centric view[J].Strategic Entrepreneurship Journal,2018,12(1):54-71.
[19] 王梦菲,张昕蔚.数字经济时代技术变革对生产过程的影响机制研究[J].经济学家,2020,32(1):52-58.
[20] ADNER R.Match your innovation strategy to your innovation ecosystem[J].Harvard Business Review,2006,84(4):98.
[21] WOODSIDE A G.Responding to the severe limitations of cross-sectional surveys:commenting on Rong and Wilkinson's perspectives[J].Australasian Marketing Journal,2011,19(3):153-156.
[22] GRECKHAMER T,FURNARI S,FISS P C,et al.Studying configurations with qualitative comparative analysis:best practices in strategy and organization research[J].Strategic Organization,2018,16(4):482-495.
[23] 张明,蓝海林,陈伟宏,等.殊途同归不同效:战略变革前因组态及其绩效研究[J].管理世界,2020,36(9):168-186.
[24] 刘军,曹雅茹,鲍怡发,等.制造业智能化对收入差距的影响研究[J].中国软科学,2021,36(3):43-52.
[25] NAMBISAN S.Digital entrepreneurship:toward a digital technology perspective of entrepreneurship[J].Entrepreneurship Theory and Practice,2017,41(6):1029-1055.
[26] VON BRIEL F,DAVIDSSON P,RECKER J.Digital technologies as external enablers of new venture creation in the IT hardware sector[J].Entrepreneurship Theory and Practice,2018,42(1):47-69.
[27] 张永安,鲁明明.高技术制造业创新效率评价及要素投入差异性研究[J].科技进步与对策,2019,36(16):51-57.
[28] 曾卓骐,王跃.战略性新兴产业上市公司动态创新效率测度及其影响因素研究——基于两阶段DSBM模型与Tobit模型[J].科技进步与对策,2022,39(21):65-74.
[29] 林剑铬,夏丽丽,蔡润林,等.中国高新技术产业开发区的知识基础及其创新效应——基于国家级高新区上市企业的研究[J].地理研究,2021,40(2):387-401.
[30] 杜传忠,王鑫,刘忠京.制造业与生产性服务业耦合协同能提高经济圈竞争力吗——基于京津冀与长三角两大经济圈的比较[J].产业经济研究,2013,67(6):19-28.
[31] 周晓辉,刘莹莹,彭留英.数字经济发展与绿色全要素生产率提高[J].上海经济研究,2021,38(12):51-63.
[32] 戴翔,马皓巍.数字化转型、出口增长与低加成率陷阱[J].中国工业经济,2023,40(5):61-79.
[33] 温珺,阎志军,程愚.数字经济驱动创新效应研究——基于省际面板数据的回归[J].经济体制改革,2020,38(3):31-38.
[34] 程广斌,李莹.基于技术—经济范式的数字经济发展水平测度与区域差异研究[J].工业技术经济,2022,41(6):35-43.
[35] 简兆权,刘念,黄如意.动态能力、企业规模与双元创新关系研究——基于fsQCA方法的实证分析[J].科技进步与对策,2020,37(19):77-86.
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