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Does Implementation of Big Data Analytics Improve Firms’ Market Value? Investors’ Reaction in Stock Market

Hansol Lee (), Eunkyung Kweon (), Minkyun Kim () and Sangmi Chai ()
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Hansol Lee: School of Business, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea
Eunkyung Kweon: School of Business, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea
Minkyun Kim: Sogang Business School, Sogang University, 35 Baekbum-ro, Mapo-gu, Seoul 04107, Korea
Sangmi Chai: School of Business, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea

Sustainability, 2017, vol. 9, issue 6, 1-17

Abstract: Recently, due to the development of social media, multimedia, and the Internet of Things (IoT), various types of data have increased. As the existing data analytics tools cannot cover this huge volume of data, big data analytics becomes one of the emerging technologies for business today. Considering that big data analytics is an up-to-date term, in the present study, we investigated the impact of implementing big data analytics in the short-term perspective. We used an event study methodology to investigate the changes in stock price caused by announcements on big data analytics solution investment. A total of 54 investment announcements of firms publicly traded in NASDAQ and NYSE from 2010 to 2015 were collected. Our results empirically demonstrate that announcement of firms’ investment on big data solution leads to positive stock market reactions. In addition, we also found that investments on small vendors’ solution with industry-oriented functions tend to result in higher abnormal returns than those on big vendors’ solution with general functions. Finally, our results also suggest that stock market investors highly evaluate big data analytics investments of big firms as compared to those of small firms.

Keywords: big data; data analytics; measuring stock market value of investment; event study methodology (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2017
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