Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms

Saqib Shamim, Jing Zeng, Zaheer Khan and Najam Ul Zia

Technological Forecasting and Social Change, 2020, vol. 161, issue C

Abstract: This study examines the role of big data contractual and relational governance in big data decision-making performance of firms based in China. It investigates the mediation of big data analytics (BDA) capability in the association of contractual and relational governance with decision-making performance. Furthermore, moderating role of data-driven culture in the relationship of BDA capability and decision-making performance is examined. Data are collected from 108 Chinese firms engaged in big data-related activities. Structural equation modeling is employed to test the hypotheses. This study contributes towards the literature on big data management and governance mechanisms, by establishing the relationship of decision-making performance with big data contractual and relational governance directly and through the mediation of BDA capabilities. It also contributes towards knowledge based dynamic capabilities (KBDCs) view of firms, arguing that dynamic capabilities such as BDA capabilities can be influenced through knowledge sources and activities. We add to the discussions on whether contractual and relational governance are alternatives or they complement each other, by establishing the moderating role of big data relational governance in the relationship of contractual governance and decision-making performance. Finally, we argue that social capital can enhance KBDCs through contractual and relational governance in big data context.

Keywords: Big data; Contractual governance; Relational governance; Big data analytics capability; Culture; Decision-making performance; Emerging markets (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:161:y:2020:i:c:s0040162520311410

DOI: 10.1016/j.techfore.2020.120315

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