Improving high-tech enterprise innovation in big data environment: A combinative view of internal and external governance

https://doi.org/10.1016/j.ijinfomgt.2018.11.009Get rights and content

Highlights

  • Combine with the insights of internal and external governance, explore how the managerial power and network centrality affect high-tech enterprise’s innovation performance in big data environment.

  • Enterprise's managerial power and network centrality have significant positive impacts on innovation performance.

  • Network centrality plays a partial mediating role through the effects of resource acquisition, reputation incentive and R&D competition.

  • The relationships among managerial power, network centrality and innovation performance are more significant in the strong big data environment.

Abstract

The emergence of big data brings both opportunities and challenges to high-tech enterprises. How to keep competitive advantages and improve innovation performance is important for enterprises in big data environment. Except from organizational learning ability and the use of advanced technology, the corporate governance also plays an important role in the process of enterprise’s innovation practice. This article creatively combines with the insights of internal and external governance, and explores how the managerial power and network centrality affects enterprise’s innovation performance in big data environment. Considering about the differences among distinct regional big data environment (strong/weak), this paper also takes classification research on it. The research findings show that managerial power has a significant positive impact on innovation performance, managerial power could enhance enterprise’s centrality in network, and the enterprise which located in network central position has more advantages in obtaining resources and significantly improves firm’s innovation performance. Network centrality plays a mediating role on managerial power and innovation performance. Further research finds that the positive effects of managerial power and network centrality are more significantly in the strong big data environment. These findings enrich the research of high-tech enterprise innovation from a combinative governance view, and contribute to the literatures on enterprise innovation in big data environment.

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.

References (103)

  • M. Jensen et al.

    Theory of the firm: Managerial behavior, agency costs and ownership structure

    Journal of Financial Economic

    (1976)
  • S.M. Jasimuddin

    Case study: Exploring knowledge transfer mechanisms: The case of a UK-based group within a high-tech global corporation

    International Journal of Information Management

    (2007)
  • D. Jiménezjiménez et al.

    Innovation, organizational learning, and performance

    Journal of Business Research

    (2011)
  • N. Kshetri

    Big data’s role in expanding access to financial services in China

    International Journal of Information Management

    (2016)
  • D.F. Larcker et al.

    Boardroom centrality and firm performance

    Journal of Accounting and Economics

    (2013)
  • V.M. Lefebvre et al.

    Social capital and knowledge sharing performance of learning networks

    International Journal of Information Management

    (2016)
  • F. Malerba et al.

    User–producer relations, innovation and the evolution of market structures under alternative contractual regimes

    Structural Change and Economic Dynamics

    (2010)
  • S. Mamonov et al.

    The strategic value of data resources in emergent industries

    International Journal of Information Management

    (2018)
  • E. Mazzola et al.

    The interaction between inter-firm and interlocking directorate networks on firm’s new product development outcomes

    Journal of Business Research

    (2016)
  • R. Minetti et al.

    Ownership structure, governance, and innovation

    European Economic Review

    (2015)
  • R. Morck et al.

    Management ownership and market valuation: An empirical analysis

    Journal of Financial Economics

    (1988)
  • E. Raguseo

    Big data technologies: An empirical investigation on their adoption, benefits and risks for companies

    International Journal of Information Management

    (2018)
  • M.H.U. Rehman et al.

    Big data reduction framework for value creation in sustainable enterprises

    International Journal of Information Management

    (2016)
  • H. Romijn et al.

    Determinants of innovation capability in small electronics and software firms in southeast England

    Research Policy

    (2002)
  • J. Sarkar et al.

    Multiple board appointments and firm performance in emerging economies: Evidence from India

    Pacific-Basin Finance Journal

    (2009)
  • V. Sena et al.

    Board independence, corruption and innovation. Some evidence on UK subsidiaries

    Journal of Corporate Finance

    (2018)
  • S. Shim et al.

    Rival precedence and open platform adoption: An empirical analysis

    International Journal of Information Management

    (2018)
  • F. Silva et al.

    Family ties, interlocking directors and performance of business groups in emerging countries: The case of Chile

    Journal of Business Research

    (2006)
  • K. Talke et al.

    How top management team diversity affects innovativeness and performance via the strategic choice to focus on innovation fields

    Research Policy

    (2010)
  • E.T.G. Wang et al.

    Improving enterprise resource planning (ERP) fit to organizational process through knowledge transfer

    International Journal of Information Management

    (2007)
  • N. Xu et al.

    Excess perks and stock price crash risk: Evidence from China

    Journal of Corporate Finance

    (2014)
  • I. Yaqoob et al.

    Big data: From beginning to future

    International Journal of Information Management

    (2016)
  • G. Ahuja

    Collaboration networks, structural holes, and innovation: A longitudinal study

    Administrative Science Quarterly

    (2000)
  • S. Aktamov et al.

    Impact of network centrality positions on innovation performance of the firm: Evidence from China Automobile Industry

    Business Management and Strategy

    (2014)
  • A. Alexiev et al.

    Top management team advice seeking and exploratory innovation: The moderating role of TMT heterogeneity

    Journal of Management Studies

    (2010)
  • P.G. Audia et al.

    Less likely to fail: Low performance, firm size, and factory expansion in the Shipbuilding industry

    Management Science

    (2006)
  • D.B. Balkin et al.

    Is CEO pay in high-technology firms related to innovation?

    The Academy of Management Journal

    (2000)
  • K.A. Bantel et al.

    Top management and innovations in banking: does the composition of the top team make a difference?

    Strategic Management Journal

    (1989)
  • R.M. Baron et al.

    The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations

    Journal of Personality and Social Psychology

    (1986)
  • C. Beaudry et al.

    Are firms in clusters really more innovative?

    Economics of Innovation and New Technology

    (2003)
  • L.A. Bebchuk et al.

    Managerial power and rent extraction in the design of executive compensation

    The University of Chicago Law Review

    (2002)
  • L.A. Bebchuk et al.

    Executive compensation as an agency problem

    The Journal of Economic Perspectives

    (2003)
  • M. Bogers et al.

    A functional perspective on learning and innovation: Investigating the organization of absorptive capacity

    Industry and Innovation

    (2011)
  • B.K. Boyd

    Board control and CEO compensation

    Strategic Management Journal

    (1994)
  • T. Brink

    Governance of innovation and growth in SME networks

    International Journal of Innovation Management

    (2016)
  • J.S. Brown et al.

    Organizational learning and communities of practice: Toward a unified view of working, learning, and innovation

    Organization Science

    (1991)
  • A. Capaldo

    Network structure and innovation: The leveraging of a dual network as a distinctive relational capability

    Strategic Management Journal

    (2010)
  • M. Chen et al.

    Big data: A survey

    Mobile Networks and Applications

    (2014)
  • R.A. Connolly et al.

    Firm size and the effect of R&D on Tobin's q

    R&D Management

    (2005)
  • W.M. Cohen et al.

    Absorptive capacity: A new perspective on learning and innovation

    Administrative Science Quarterly

    (1990)
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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.

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