Big Data Analytics: A Threat or an Opportunity for Knowledge Management?

Crane, Lesley; Self, Richard

doi:10.1007/978-3-319-08618-7_3

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…Therefore, further research should be oriented to explore how to investigate properly semantic, patterns and variety of human-generated content in BSD collections. This would reveal sub-sets of data, described in their context of digital relationships, temporal location, physical location and interests/preferences category, which would reveal core knowledge about what people consider important and relevant (Crane and Self, 2014). At this purpose, a set of research propositions can be proposed to investigate the technical groundwork enabling the "intelligence" of BSD (Abu-salih et al, 2019; García-S anchez et al, 2020; Camastra et al, 2020).…”

Section: Mechanisms For Quality Assurance Of Bsdmentioning

confidence: 99%

“…In this sense, state that "there is a timely need to describe, review and reflect on BSD literature to bring clarity to the concept and understanding about its beneficial opportunities for the practitioners of computational social science and other related research fields". This need becomes even more relevant within the KM field that shares synergies and mutual lessons with Big Data (Crane and Self, 2014). Actually, both KM and big data were conceptualized as agents that would provide deeper insights and promote better decisionmaking, improve productivity, generate higher revenue and provide a competitive edge to organizations (Elgendy and Elragal, 2014;Pauleen, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Defining the big social data paradigm through a systematic literature review approach

Solazzo

Elia

Passiante

2021

JKM

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Purpose This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources. Design/methodology/approach The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques. Findings The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective. Research limitations/implications The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field. Practical implications Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories. Originality/value The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

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Section: Mechanisms For Quality Assurance Of Bsdmentioning

confidence: 99%