One decade of big data for firms' competitiveness: insights and a conceptual model from bibliometrics

Abstract

PurposeUsing analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.Design/methodology/approachIn this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.FindingsBased on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.Research limitations/implicationsThe findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.Practical implicationsThis study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.Social implicationsThis study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.Originality/valueThe authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

Do knowledge sharing and big data analytics capabilities matter for green absorptive capacity and green entrepreneurship orientation? Implications for green innovation

“…Applying underpinning theory, this study aims to address the effect of BDA capabilities on GAC and GEO. Based on the literature by Garmaki et al (2023), Ardito et al (2019) and Tchuente and El Haddadi (2023), this research is among fewer studies that introduce to test the BDAC-GAC and BDAC-GEO relationships, which it question whether BDA could develop GAC to the level of GEO green business agenda. However, how BDA capabilities boost the GEO eco-business agenda is still ambiguous, and the relationship between BDA and GAC is unclear.…”

Big data analytics and market performance: the roles of customization and personalization strategies and competitive intensity

“…These two issues have not been empirically discussed so far in prior literature. Thirdly, the relationship between BD-enabled customization and personalization strategies and firm performance has been operationalized in existent literature based on conceptual and theoretical outlines by focusing on systematic literature reviews (Varadarajan, 2020;Saidali et al, 2019;Anshari et al, 2019) or through frameworks and case studies (Gupta and Ramachandran, 2021;Kastouni and Lahcen, 2020;Kunz et al, 2017) or depending alternatively on some sub-dimensions such as CRM performance and CRM capabilities (Tchuente and El Haddadi, 2023). Hence, this study as far as we know is among the earliest empirical studies to scrutinize this relationship in one moderated-mediation model.…”

“…, 2019; Raguseo and Vitari, 2018; Huang et al. , 2018), competitive (Tchuente and El Haddadi, 2023), innovation (Ghasemaghaei and Calic, 2020), decision-making (Shamim et al. , 2020) and marketing performance (Gupta et al.…”

“…, 2019) or through frameworks and case studies (Gupta and Ramachandran, 2021; Kastouni and Lahcen, 2020; Kunz et al. , 2017) or depending alternatively on some sub-dimensions such as CRM performance and CRM capabilities (Tchuente and El Haddadi, 2023). Hence, this study as far as we know is among the earliest empirical studies to scrutinize this relationship in one moderated-mediation model.…”

“…Additionally, managers and practitioners still lack tools and sufficient knowledge of how to employ and benefit from Big Data (BD) in attaining a market advantage (Hallikainen et al, 2020). Management researchers have argued that achieving business value depends on the extent to which BDA is actually utilized in business activities along with the organization's ability to use it to raise their promising benefits in improving multiple types of performance like operational (Gupta et al, 2019;Gunasekaran et al, 2017;Wamba et al, 2017;Xu et al, 2016), financial (Maroufkhani et al, 2020;Mikalef et al, 2019;Raguseo and Vitari, 2018;Huang et al, 2018), competitive (Tchuente and El Haddadi, 2023), innovation (Ghasemaghaei and Calic, 2020), decision-making (Shamim et al, 2020) and marketing performance . Other studies have considered BDA as a determinant of market performance by enabling organizations to identify potential market opportunities, identify appropriate and profitable market segments and develop new products/services (Olabode et al, 2022;Xu et al, 2016;Wamba et al, 2015).…”