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Session Overview
Session
03-10: Heli Hallikainen
Time:
Friday, 19/Jul/2019:
11:30am - 11:55am

Seminar Room 3-4

Chair: Paul Richard Messinger


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Abstract

How the Level of Servitization Influences on Big Data Use in Organizations

Authors: Heli Hallikainen (University of Eastern Finland, Finland), Tommi Laukkanen (University of Eastern Finland)

In 2015, Ostrom et al. (2015) nominated the use of big data to advance service as one of the top research priorities. Three years later, it seems that a paucity still exists regarding the use of big data in the service context, with only few academic articles addressing the issue from the viewpoint of the service industry (e.g. Cohen, 2018; Lehrer et al., 2018). In recent years, several success stories have evolve based on data-driven applications to enhance service, and several companies have embraced service as an engine for the company’s growth (Barrett et al., 2015). Real-time personalization, targeted promotions and campaigns, identification of patterns and trends and predictive modeling represent some examples of the mechanisms through which big data can be leveraged to advance service (Cohen, 2018). Further, with the utilization of big data, companies can enhance their dynamic and adaptive capabilities, which should eventually lead to a sustainable competitive advantage (Erevelles, Fukawa & Swayne, 2016).

The study attempts to understand differences in the extent, and the way in which companies utilize big data in their operations, and as a driver of company’s financial performance. We test if the level of servitization in organizations (i.e. the share of turnover generated by services) influences on the organizations’ big data use and, further, how the big data use influences on sales. Using a sample of 551 responses from company CEOs in Finland, we find that, overall, the greater the proportion of turnover consists of services, the less organizations utilize big data in their operations (β=-0.156, p<=0.001). With a detailed look at specific operations, we find that this appears to be the case especially in procurement, manufacturing, distribution, pricing and yield management, merchandising and store operations, while the effect is statistically not significant in product development, marketing and sales. These results are alarming for the service companies, as we further find that big data use generates growth in sales among the participating companies (β=0.167, p<0.001). This is supported by, for example, Wamba et al., 2017 and Ren et al., 2017, who argue that big data use operates as a driver for the company’s financial performance. For this reason, we encourage service companies to focus on the opportunities that big data and data analytics can provide.



 
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