Identifying patterns in Big Data Analytics to enhance value co-creation.

Purpose – Recognizing the key role of Big Data Analytics in influencing business, the research aims to elaborate main phases and subsequently linked challenges through which companies can approach, develop, and manage Big Data Analytics. Hence, this research provides insights for increasing opportunities of collaboration and value co-creation in the emerging digital and hyperconnected economy. Design/Methodology/approach – Following a case study approach (Bensabat et al.1987; Darke et al. 1998) leading enterprises in different sectors have been examined and a conceptual framework has been designed. The conceptual framework is then discussed by means of the SD-Logic to evaluate its validity and applicability. Thanks to the interpretative lens provided by the Service Dominant Logic insights how companies should ‘live’ for approaching to Big Data Analytics in a suitable and value generative way are provided. Findings – After analyzing the collected data and materials as provided in the Grounded Theory Approach (Strauss & Corbin 1994; Glaser & Strauss 2017), the study provides a comprehensive framework with the three main phases and 16 associated patterns typically emerge and evolve at specific states in Big Data Analytics projects. Subsequently, main risks and opportunities are underlined in the light of value co-creation. Research implications/limitations – The paper recalls the attention in defining clear patterns for digitization and technology-based projects. The proposed conceptual model derives from multiple case studies, its generalizability requires discussion and analysis despite its validity is discussed and questioned by SDL. In particular, a quantitative evaluation of the patterns and challenges is desirable to weight the importance of the aspects. Practical implications (if applicable) – The framework offers practitioners awareness about the critical tasks at different phases of Big Data Analytics Projects. Thus, in advance the possibility is given for developing performance measurement tools able to cope with the challenges and evaluate in which ways each phase can contribute to the value co-creation processes. Originality/value – The paper contributes to the ongoing debate about the potential benefits and risks in data-driven digitization projects. Providing a conceptual model researchers and practitioners are supported in understanding how Big Data Analytic can be planned and managed for enhancing value co-creation processes. Key words (max 5) – Big Data; Big Data Consumer Analytics; data-driven digitization; Service Dominant Logic; Value co-creation.