Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory

Big Data Capability and Sustainable Competitive Advantage: The Mediating Role of Ambidextrous Innovation Strategy

After the rapid expansion of data variety, velocity and volume, human civilization experienced rapid changes in the period between the “IT” and “Data” ages. Researchers view big data and data capability as new sustainable competitive advantages that enhance the sustainability of organizational development. This paper aims to develop and empirically test a framework that will investigate how big data capability is achieved through exploitative, explorative and ambidextrous modes of innovation strategies, and will also explore how they can, in turn, build firms’ sustainable competitive advantage. Using data from surveys of 229 respondents working in Chinese manufacturing firms, we test the framework using regression and bootstrapped mediation analyses. It also shows how big data capability will make firms more inclined to implement exploitative innovation strategy and construct sustainable competitive advantage, as opposed to explorative innovation strategy; when viewed from an ambidextrous perspective, combined dimension of ambidextrous innovation strategy is found to partially mediate between big data capability and sustainable competitive advantage while balanced dimension of ambidextrous innovation strategy does not. The conclusions are of great significance because they will help firms to deal with challenges that arise in big data applications and digital transformation. The findings offer new insight into the strategic choices of organizational innovations.

Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector

The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.

Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance

This study intends to provide scholars and practitioners with an understanding of human resource challenges in the context of Big Data Analytics (BDA). This paper provides a holistic framework of human-related capabilities that organizations must consider when implementing BDA to facilitate decision-making. For this purpose, the authors conducted a systematic literature review adapted from Tranfield et al. (BJM 14:207–222, 2003) to identify relevant studies. The 75 publications reviewed provided the sample for an inductive, and systematic data evaluation following the well-known and accepted approach introduced by Gioia et al. (ORM 16:15–31, 2012). The comprehensive review uncovered 33 first-order concepts linked to human-related capabilities, which were distilled into 15 s-order themes and then merged into five aggregated dimensions: Personnel Capability, Management Capability, Organizational Capability, Culture and Governance Capability, and Strategy and Planning Capability. The study is, to the best of the authors’ knowledge, the first to categorize all relevant human-related capabilities for successful BDA application. As such, it not only provides the scientific basis for further research, but also serves as a useful overview of the critical factors for BDA use in decision-making processes.