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The Influence Mechanism of Big Data Capability on the Entrepreneurial Performance of New Ventures from the Perspective of Knowledge Coupling |
Yuan Yongzhi1,Zhuge Kai1,Zhang Yong2,Lin Weiwei1 |
(1.School of Business, Soochow University, Suzhou 215021, China;2.Institute of Quality Economics, China Jiliang University, Hangzhou 310018, China) |
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Abstract The importance of big data in innovation and entrepreneurship activities is well-acknowledged.The big data revolution has further refined the information access channels of entrepreneurs, fundamentally changed the social demand diagnosis and network resource access methods of new ventures, and promoted the innovation and catch-up of latecomer entrepreneurs in knowledge, products and business models.However, it should be pointed out that the emergence of massive data has also exacerbated the complexity and uncertainty of the economic environment, and new ventures are facing many challenges, such as data compression, knowledge iteration and other issues.Therefore, in the big data-driven context, how to seize entrepreneurial opportunities, and improve the efficiency of interactive integration of knowledge resources has become an urgent issue to be solved in its survival and growth process.On the one hand, faced with the practical difficulties of resource constraints and insufficient knowledge base, entrepreneurs need to establish efficient knowledge integration processing procedures to fill the knowledge gaps, and combine new technologies through knowledge coupling to reduce resource redundancy and entrepreneurial difficulty.Big data capability, as a low-order capability for carrying out data perception, integration and analysis, is regarded as the basis for forming high-order dynamic capabilities.The entrepreneurs rely on big data technology to accurately identify value information in massive data, optimize the process of knowledge transformation, and realize the integration and reconstruction of knowledge, showing a theoretical picture from low-order big data capability to dynamic knowledge coupling.On the other hand, enterprises need to improve the efficiency of resource integration through the coordination and alignment of IT and business strategies, so that IT personnel and business managers can reach a strategic consensus.The IT and business strategy alignment is an important boundary condition to be included.#br#This paper explores the mechanism of big data capability on the entrepreneurial performance of new ventures based on the dynamic capability theory and knowledge-based view theory.New ventures founded more than 1 year but less than 8 years ago are chosen as the research object with 212 valid survey data.By constructing a theoretical model with knowledge coupling as the mediator variable and IT—business strategy alignment as the moderator variable, it analyzes the mediating mechanism and boundary conditions of different knowledge coupling modes on the survival and growth of new ventures.The results show that big data capability has a positive impact on knowledge coupling and entrepreneurial performance of new ventures, and it can also affect the entrepreneurial performance of new ventures through the mediating role of knowledge coupling.Complementary knowledge coupling is inversely U-shaped with entrepreneurial survival performance, and alternative knowledge coupling is positively correlated with entrepreneurial growth performance.The IT—business strategy alignment positively moderates the relationship between big data capability and knowledge coupling.#br#The theoretical contributions of this paper lie in three aspects.First, it introduces big data capability to the survival and growth of new ventures, and confirms that big data capability has a positive impact on entrepreneurial performance.This conclusion enriches the relevant research on the mechanism of big data capability on entrepreneurial performance, and also provides positive theoretical support for further solving the "IT paradox".Second, this paper reveals the mediating mechanism of knowledge coupling in different configurations between big data capabilities and entrepreneurial performance of new ventures.According to the dynamic capability theory, for complementary knowledge, big data capability improves the opportunity identification and business capture of knowledge resources for new enterprises; for alternative knowledge, acquiring knowledge in similar fields within the existing product niche can help improve the level of specialization and innovation depth of knowledge.While complementary knowledge coupling and entrepreneurial survival performance have an inverted U-shaped path relationship, alternative knowledge coupling is conducive to the continuous improvement of enterprise competitiveness.This conclusion enriches the research on relevant mechanisms of knowledge coupling.Finally, this paper clarifies the moderating role of IT—business strategy alignment between big data capability and knowledge coupling, and finds that IT—business strategy alignment can positively moderate the relationship between big data capability and knowledge coupling, which further expands the theoretical boundary conditions of the relationship between big data capability and knowledge coupling.#br#
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Received: 24 October 2022
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[1] 杨艳,王理,廖祖君.数据要素市场化配置与区域经济发展——基于数据交易平台的视角[J].社会科学研究,2021,43(6):38-52. [2] GHASEMAGHAEI M, CALIC G. Assessing the impact of big data on firm innovation performance: big data is not always better data [J].Journal of Business Research, 2020,108: 147-162. [3] 李文博.大数据驱动情景下数字创业商业模式创新关键影响因素——话语分析方法的一项探索性研究[J].科技进步与对策,2022,39(16):67-76. [4] BRANDS K C M A. Big data and business intelligence for management accountants [J].Strategic Finance, 2014,95: 64-65. [5] PASCHOU T, RAPACCINI M, ADRODEGARI F, et al. Digital servitization in manufacturing: a systematic literature review and research agenda [J].Industrial Marketing Management, 2020,89: 278-292. [6] REIBENSPIESS V, DRECHSLER K, ECKHARDT A, et al. Tapping into the wealth of employees' ideas: design principles for a digital intrapreneurship platform [J].Information & Management, 2022, 59(3): 103287. [7] YANG C, HUANG Q, LI Z, et al. Big Data and cloud computing: innovation opportunities and challenges [J].International Journal of Digital Earth, 2017, 10(1): 13-53. [8] 唐彬,卢艳秋,叶英平.大数据能力视角下平台企业知识创造模型研究[J].情报理论与实践,2020,43(7):123-129. [9] 张吉昌,龙静,陈锋.大数据能力、知识动态能力与商业模式创新——创新合法性的调节效应[J].经济与管理,2022,36(5):19-28. [10] YAYAVARAM S, CHEN W R. Changes in firm knowledge couplings and firm innovation performance: the moderating role of technological complexity [J].Strategic Management Journal, 2015, 36(3): 377-396. [11] CANER T, TYLER B B. The effects of knowledge depth and scope on the relationship between R & D alliances and new product development [J].Journal of Product Innovation Management, 2015, 32(5): 808-824. [12] GKYPALI A, FILIOU D, TSEKOURAS K. R&D collaborations: is diversity enhancing innovation performance[J].Technological Forecasting and Social Change, 2017,118: 143-152. [13] CARLO J L, LYYTINEN K, ROSE G M. A knowledge-based model of radical innovation in small software firms [J].MIS Quarterly, 2012,36: 865-895. [14] MIKALEF P, BOURA M, LEKAKOS G, et al. Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment [J].British Journal of Management, 2019, 30(2): 272-298. [15] TEECE D J, PISANO G, SHUEN A. Dynamic capabilities and strategic management[J].Strategic Management Journal, 1997, 18(7): 509-533. [16] VIDGEN R, SHAW S, GRANT D B. Management challenges in creating value from business analytics [J].European Journal of Operational Research, 2017, 261(2): 626-639. [17] 李新春,梁强,宋丽红.外部关系—内部能力平衡与新创企业成长——基于创业者行为视角的实证研究[J].中国工业经济,2010,27(12):97-107. [18] CHRISMAN J J, BAUERSCHMIDT A, HOFERC W. The determinants of new venture performance: an extended model[J].Entrepreneurship Theory and Practice, 1998, 23(1): 5-29. [19] 申佳,李雪灵,马文杰.不同成长阶段下新企业关系强度与绩效研究[J].科研管理,2013,34(8):115-122. [20] 何会涛,袁勇志.海外人才创业双重网络嵌入及其交互对创业绩效的影响研究[J].管理学报,2018,15(1):66-73. [21] WAMBA S F, GUNASEKARAN A, AKTER S, et al. Big data analytics and firm performance: effects of dynamic capabilities [J].Journal of Business Research, 2017,70: 356-365. [22] HAGSTROM M. High-performance analytics fuels innovation and inclusive growth: Use big data, hyperconnectivity and speed to intelligence to get true value in the digital economy[J]. Journal of Advanced Analytics, 2012, 2(3): 31-44. [23] MENGUC B, AUH S, YANNOPOULOS P. Customer and supplier involvement in design: the moderating role of incremental and radical innovation capability [J].Journal of Product Innovation Management, 2014, 31(2): 313-328. [24] COLOMBELLI A, KRAFFT J, QUATRARO F. Properties of knowledge base and firm survival: evidence from a sample of French manufacturing firms [J].Technological Forecasting and Social Change, 2013, 80(8): 1469-1483. [25] CHEN D Q, PRESTON D S, SWINK M. How the use of big data analytics affects value creation in supply chain management[J].Journal of Management Information Systems, 2015, 32(4): 4-39. [26] AGOSTINO M, DONATI C, TRIVIERI F. External knowledge flows and innovation capacity: the Italian service industries [J].International Review of Applied Economics, 2020, 34(3): 342-360. [27] RYOO S Y, KIM K K. The impact of knowledge complementarities on supply chain performance through knowledge exchange [J].Expert Systems with Applications, 2015, 42(6): 3029-3040. [28] XU S. Balancing the two knowledge dimensions in innovation efforts: an empirical examination among pharmaceutical firms [J].Journal of Product Innovation Management, 2015, 32(4): 610-621. [29] TANRIVERDI H, VENKATRAMAN N. Knowledge relatedness and the performance of multibusiness firms [J].Strategic Management Journal, 2005, 26(2): 97-119. [30] 姚艳虹,张翠平.知识域耦合、知识创新能力与企业创新绩效——环境不确定性和战略柔性的调节作用[J].科技进步与对策,2019,36(23):76-84. [31] 于飞,刘明霞,王凌峰,等.知识耦合对制造企业绿色创新的影响机理——冗余资源的调节作用[J].南开管理评论,2019,22(3):54-65,76. [32] 易法敏,朱洁.ICT赋能的扶贫平台商业模式创新[J].管理评论,2019,31(7):123-132. [33] REICH B H, BENBASAT I. Factors that influence the social dimension of alignment between business and information technology objectives [J].MIS Quarterly, 2000,24: 81-113. [34] KROH J, LUETJEN H, GLOBOCNIK D, et al. Use and efficacy of information technology in innovation processes: the specific role of servitization [J].Journal of Product Innovation Management, 2018, 35(5): 720-741. [35] LIANG H, WANG N, XUE Y, et al. Unraveling the alignment paradox: how does business-IT alignment shape organizational agility[J].Information Systems Research, 2017, 28(4): 863-879. [36] 何会涛,袁勇志.双维市场导向、本地网络嵌入与海外人才在华创业绩效研究[J].科技进步与对策,2019,36(1):105-114. [37] GERSCHEWSKI S, XIAO S S. Beyond financial indicators: an assessment of the measurement of performance for international new ventures [J].International Business Review, 2015, 24(4): 615-629. [38] KARAHANNA E, PRESTON D S. The effect of social capital of the relationship between the CIO and top management team on firm performance [J].Journal of Management Information Systems, 2013, 30(1): 15-56. [39] PODSAKOFF P M, MACKENZIE S B, LEE J Y, et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies [J].Journal of Applied Psychology, 2003, 88(5): 879. [40] STEININGER D M. Linking information systems and entrepreneurship: a review and agenda for IT-associated and digital entrepreneurship research [J].Information Systems Journal, 2019, 29(2): 363-407.
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