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Big data and emerging market firms’ innovation in an open economy: The diversification strategy perspective

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  • Xie, Zaiyang
  • Wang, Jie
  • Miao, Ling

Abstract

Big data development encourages emerging market firms (EMFs) to diversify strategies and increase competitive advantages by acquiring resources embedded in different markets and industries. This study draws on the composition-based view (CBV) and empirically examines how EMFs integrate resources through international and business diversification to improve innovation performance in open economies. Based on the data collected from Chinese listed companies, our findings show that international diversification and related business diversification positively improve firms’ innovation performance, whereas overall business diversification negatively impacts firms' innovation. International and business diversification substitute for each other to affect firms’ innovation outcomes. Further research found that these results are more significant in a higher big data development environment. Moreover, we examine the moderating role of organizational slack in the relationship between diversification strategies and firms’ innovation performance. This study contributes to the EMF innovation literature by highlighting the effects of diversification strategy from a CBV perspective. Firms creatively use and combine open resources to promote innovation in open economies. This study also contributes to diversification research in big data environments, arguing that EMFs that lack strong capabilities may suffer lower innovation performance if they concurrently apply international and business diversification at a high level.

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  • Xie, Zaiyang & Wang, Jie & Miao, Ling, 2021. "Big data and emerging market firms’ innovation in an open economy: The diversification strategy perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005242
    DOI: 10.1016/j.techfore.2021.121091
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