Improving high-tech enterprise innovation in big data environment: A combinative view of internal and external governance
Introduction
The rapid development of information technology promotes the data to be generated at unprecedented rates in recent years. According to the report from the renowned IT company Industrial Development Corporation, the total amounts of data in the world has increased nine times within five years (Gantz & Reinsel, 2011), and this figure is expected to double at least every two years in the future (Chen, Mao, & Liu, 2014). The advent of big data era provides great opportunities for enterprises to improve competitive advantages (Yaqoob, Chang et al., 2016; Yaqoob, Hashem, & Gani, 2016) and makes significant impacts on value creation in the process of production, R&D, operational management and service (Raguseo, 2018). But the enterprise in big data environment has to suffer more challenges and risks than before due to severe competition, especially for the high-tech enterprise. How to improve high-tech enterprise’s innovation performance and core competence in big data environment has been a key issue and attracts much attention.
Most of literatures discussing enterprise innovation generally focused on innovation capability, organizational learning and the use of advanced technology (Gallos, Minou, Routsis, & Mantas, 2017; Jiménezjiménez & Sanzvalle, 2011; Romijn & Albaladejo, 2002). Corporate governance, however, is also an important factor that influences high-tech enterprise innovation significantly. Corporate governance aims at decreasing manager’s opportunistic behavior, enhancing accuracy and effectiveness of innovation decision-making, and improving firm’s ability to cope with external uncertainties. The company with strong corporate governance typically performs better and gains higher returns especially in a more complex environment (Balkin, Markman, & Gomez-Mejia, 2000; Boyd, 1994). In this article, we concentrate on the impact of the corporate governance on high-tech enterprise innovation in big data environment. Specifically, we explore how the managerial power (internal governance) and enterprise’s network centrality (external governance) affects enterprise innovation, and systematically analyze their influence mechanism.
Managerial power is one of the corporate governance structures and makes important influence on firm’s strategic choice and performance. Similar governance structure in high-tech enterprises often leads to different innovation performance, which lies in the difference of managerial power. For example, the team headed by Jack Ma in Alibaba Group has great control over the corporate decision-making and resources configuration, and helps the corporation make great achievements in technologic innovation. In the development of big data environment, it’s necessary to examine the role of managerial power in enterprise innovation. On the other hand, the enterprises close to central network position are more likely to accumulate valuable resources and gain the competitive advantages (Pan, Zhang, Song, & Ai, 2017). The managers with higher managerial power are often inspired to make connections with other companies, which helps to enhance enterprise’s network centrality, and promote the enterprise innovation by acquiring more resources from others (Mazzola, Perrone, & Kamuriwo, 2016). But previous researches paid less attention to the role of corporate networks which formed by interlocking executives in innovation practices.
In this research, we mainly address following questions: (1) how does managerial power (internal governance) and network centrality (external governance) influence high-tech enterprise innovation in big data environment; (2) how does network centrality mediating the relationship of managerial power and innovation performance; (3) considering about the regional big data environment, how will above relationships make differences. Overall, this article makes several contributions. First, different from prior researches, we pay attention to the role of corporate governance in the high-tech enterprise under big data environment, which provides a new insight into the research in this field. Second, this paper fills in the extant literatures which focused on either internal-based governance (Sena, Duygun, Lubrano, Marra, & Shaban, 2018) or network-based governance, and combines with both governance perspectives to explore the high-tech enterprise innovation in big data environment for the first time. Third, this paper expands the research of interlocking directors (Silva, Majluf, & Paredes, 2006) and focuses on the interlocking executives including board of directors, CEO and other TMT members. Because it’s a common phenomenon in Chinese capital market that most of executives hold management positions in at least two companies. Forth, we examine the differences under distinct regional big data environment, which enriches the research of enterprise innovation on the regional-level.
The following research are structured as: the second part reviews the literatures related to the research; the third part theoretically analyzes the influence mechanism among the relationships of managerial power, network centrality and enterprise’s innovation performance, and proposes the hypotheses; the forth part describes the methodology; the fifth part reports the results of empirical research; the sixth part makes the discussions of key findings and the implications for both research and practice, and the final part makes conclusion and discusses our study’s limitations and future research directions.
Section snippets
Literature review
Big data is a new term and has received considerable attention from academia in recent years (Gandomi & Haider, 2015; Kshetri, 2016; Mamonov & Triantoro, 2018; Ohbyung, Namyeon, & Bongsik, 2014). Rehman et al. (2016) described the big data characteristics as 3 V, namely volume, velocity, and value. The term volume represents the size of data while the velocity refers to the speed of incoming and outgoing data, and value is defined as the usefulness to take actionable decisions after big data
Research model and hypotheses development
In this section, several hypotheses are developed, which elaborate on the relationship of managerial power, network centrality and enterprise’s innovation performance in big data environment. Our research model is illustrated in Fig. 1.
Sample and data collection
We take Chinese listed high-tech enterprises during 2010–2016 as the research sample. The Chinese context is appropriate and advantageous for three reasons. First, Chinese high-tech enterprises developed rapidly and made a great achievement in the past few years. Most of the high-tech enterprises have attached great importance to the application of big data technology into innovation practices. Second, according to our research on Chinese listed companies, there are 2518 enterprises have
Regression results
Table 2 reports descriptive statistics of the variables in our models. We check Variance Inflation Factors (VIF) to test the presence of multicollinearity. A value below 10 is generally accepted as an indication that no significant impact of multicollinearity exists. The result shows VIF values for all variables range from 1.01 to 2.53. Additionally, the correlation coefficients for each of variables are under 0.5. Thus, there is no serious multicollinearity problem in our model. We use
Key findings of research
The emerging of big data brings much opportunities and challenges for the development of enterprises in the past few years. How to improve the innovation of high-tech enterprises in big data environment has been a key issue and attracts much public attentions. Combining with the perspectives of internal and external governance, this paper empirically analyzes the influence of managerial power and enterprise network centrality on enterprise innovation performance in big data environment, and
Conclusion
The development of big data promotes the high-tech enterprise’s transformation from factor-driven to innovation-driven. Increasing higher competitive and complex environment incent the enterprises to improve their innovation capability to achieve sustainable development. This paper aims to combine with the perspectives of internal and external governance, and explores how the managerial power and network centrality affects firm’s innovation performance in big data environment. The results
Acknowledgments
We are grateful for funding support from the National Natural Science Foundation of China (Project No. 71772096, 71732005), the Research Project of Ministry of Education, Humanities and Social Sciences project, China (Project No. 18YJC630233), and Zhejiang Social Science Fund, China (Project No. 15JDZS01Z). We also appreciate the support from Zhejiang Natural Science Foundation (Project No. Y16G020007).
Runhui Lin is a professor at Business School of Nankai University (China). He is also a researcher at the China Academy of Corporate Governance of Nankai University. His research interests include strategy management, network organizations and governance, technology and innovation management.
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Runhui Lin is a professor at Business School of Nankai University (China). He is also a researcher at the China Academy of Corporate Governance of Nankai University. His research interests include strategy management, network organizations and governance, technology and innovation management.
Zaiyang Xie is currently working toward the pH.D. degree at Business School of Nankai University (China). Her research interests include strategy management, corporate governance, network organization and innovation.
Yunhong Hao is a professor at School of Management, Zhejiang Gongshang University (China). His research interests include corporate governance and strategy management.
Jie Wang is currently working toward the pH.D. degree at School of Management, Zhejiang Gongshang University (China). His research interests include information management and strategy management.