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Big Data Analytics and Firm Performance: A Systematic Review

1
Azman Hashim International Business School, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
2
DMCC-Dialog Marketing Competence Center, University of Kassel, 34125 Kassel, Germany
3
Sulaiman AlRajhi School of Business, Sulaiman AlRajhi Colleges, Al Bukayriyah 51941, Saudi Arabia
4
School of Management, Universiti Sains Malaysia, Penang 11800, Malaysia
*
Authors to whom correspondence should be addressed.
Information 2019, 10(7), 226; https://doi.org/10.3390/info10070226
Received: 25 May 2019 / Revised: 22 June 2019 / Accepted: 27 June 2019 / Published: 1 July 2019
(This article belongs to the Section Information Applications)
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Abstract

The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance. View Full-Text
Keywords: big data analytics; business analytics; firm performance; technology adoption; systematic review big data analytics; business analytics; firm performance; technology adoption; systematic review
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MDPI and ACS Style

Maroufkhani, P.; Wagner, R.; Wan Ismail, W.K.; Baroto, M.B.; Nourani, M. Big Data Analytics and Firm Performance: A Systematic Review. Information 2019, 10, 226.

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