The Missing Variable in Big Data for Social Sciences: The Decision-Maker

Abstract

The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. A new big data cycle has been proposed in this paper, where the decision-maker is placed at the centre of the process. The proposed cycle is tested through two cases and, as a result of the suggested approach, two operations—filtering and framing—which are routinely carried out independently by scientists and end users in an unconscious manner, become clear and transparent. The result is a new cycle where four dimensions guide the interactions for creating value.

Big Data and Their Social Impact: Preliminary Study

“…The connection of big data research to social sciences as well as the big impact of data-intensive applications and processing methods to societal challenges provides a very interesting research challenge. From the one side we have the social actors, humans, decision makers that both provide and consume data available in diverse, interconnected information systems [5]. The quest for impact on big data platforms and big data [6] requires a detailed study of different factors and accordingly new metrics like analytics or KPIs (key performance indicators) [6].…”

“…Advanced user profiling [1,5] is also critical for the launch and management of social sensitive applications powered by big data research. The standardization of profiles is the first step toward interoperability of applications and social services.…”

“…A great discussion on the real impact of big data research has been initiated. An interesting study [5] sets the significance of the human decision maker at the center of any type of big data information processing cycle. There is an agreement between different academics that big data can make a big impact [1,6].…”

Big Data Analytics and Firm Performance: A Systematic Review

“…The latter term is used in almost all the articles reviewed. Two of the three papers that did not use the term big data analytics are those of Ghasemaghaei, Hassanein and Turel [5] and Arnaboldi [55], who use the keyword data analytics. Another is that of Ashrafi and Zare Ravasan [6], who use the keyword business analytics.…”

“…Its results are intended to support scholars in precisely framing their studies by taking advantage of our stringent assurance procedure. The publications selected for review comprise those of (Mikalef, Pappas, Krogstie and Giannakos [1], Ghasemaghaei, Hassanein and Turel [5], Ashrafi and Zare Ravasan [6], Ardito, Scuotto, Del Giudice and Petruzzelli [8], Wamba and Mishra [10], Ji-fan Ren, Wamba, Akter, Dubey and Childe [16], Chen, Preston and Swink [31], Akter, Wamba, Gunasekaran, Dubey and Childe [32], Raguseo and Vitari [35], Mandal [36], Zhan, et al [37], Côrte-Real, et al [38], Gravili, et al [39], Gupta and George [40], Hughes [41], Vera-Baquero, et al [42], Popovič, et al [43], Kwon, et al [44], Wamba, et al [45], Wang and Hajli [46], Wang and Byrd [47], Wang, et al [48], Wang, et al [49], Müller, et al [50], Lai, et al [51], Rialti, et al [52], Mikalef, et al [53,54], Arnaboldi [55], Vidgen, et al [56], Cillo, et al [57], Saggi and Jain [58], Wamba, et al [59]). Figure 1 shows the distribution of the papers based on the WoS category.…”