• Big Data Analytics-enabled Sensing Capability and Organizational Outcomes: Assessing the Mediating Effects of Business Analytics Culture

      Fosso Wamba, S.; Queiroz, M.M.; Wu, L.; Sivarajah, Uthayasankar (2020-10-07)
      With the emergence of information and communication technologies, organizations worldwide have been putting in meaningful efforts towards developing and gaining business insights by combining technology capability, management capability and personnel capability to explore data potential, which is known as big data analytics (BDA) capability. In this context, variables such as sensing capability—which is related to the organization’s ability to explore the market and develop opportunities—and analytics culture—which refers to the organization’s practices and behavior patterns of its analytical principles—play a fundamental role in BDA initiatives. However, there is a considerable literature gap concerning the effects of BDA-enabled sensing capability and analytics culture on organizational outcomes (i.e., customer linking capability, financial performance, market performance, and strategic business value) and on how important the organization’s analytics culture is as a mediator in the relationship between BDA-enabled sensing capability and organizational outcomes. Therefore, this study aims to investigate these relationships. And to attain this goal, we developed a conceptual model supported by dynamics capabilities, BDA, and analytics culture. We then validated our model by applying partial least squares structural equation modeling. The findings showed not only the positive effect of the BDA-enabled sensing capability and analytics culture on organizational outcomes but also the mediation effect of the analytics culture. Such results bring valuable theoretical implications and contributions to managers and practitioners.
    • What does Big Data has in-store for organisations: An Executive Management Perspective

      Hussain, Zahid I.; Asad, M.; Alketbi, R. (2017)
      With a cornucopia of literature on Big Data and Data Analytics it has become a recent buzzword. The literature is full of hymns of praise for big data, and its potential applications. However, some of the latest published material exposes the challenges involved in implementing Big Data (BD) approach, where the uncertainty surrounding its applications is rendering it ineffective. The paper looks at the mind-sets and perspective of executives and their plans for using Big Data for decision making. Our data collection involved interviewing senior executives from a number of world class organisations in order to determine their understanding of big data, its limitations and applications. By using the information gathered by this is used to analyse how well executives understand big data and how well organisations are ready to use it effectively for decision making. The aim is to provide a realistic outlook on the usefulness of this technology and help organisations to make suitable and realistic decisions on its investment. Professionals and academics are becoming increasingly interested in the field of big data (BD) and data analytics. Companies invest heavily into acquiring data, and analysing it. More recently the focus has switched towards data available through the internet which appears to have brought about new data collection opportunities. As the smartphone market developed further, data sources extended to include those from mobile and sensor networks. Consequently, organisations started using the data and analysing it. Thus, the field of business intelligence emerged, which deals with gathering data, and analysing it to gain insights and use them to make decisions (Chen, et al., 2012). BD is seem to have a huge immense potential to provide powerful information businesses. Accenture claims (2015) that organisations are extremely satisfied with their BD projects concerned with enhancing their customer reach. Davenport (2006) has presented applications in which companies are using the power of data analytics to consistently predict behaviours and develop applications that enable them to unearth important yet difficult to see customer preferences, and evolve rapidly to generate revenues.