BIG DATA-DRIVEN MARKETING: AN ABSTRACT

Customer information plays a key role in managing successful relationships with valuable customers. Big data customer analytics use (BD use), i.e., the extent to which customer information derived from big data analytics guides marketing decisions, helps firms better meet customer needs for competitive advantage. This study addresses three research questions: What are the key antecedents of big data customer analytics use? How, and to what extent, does big data customer analytics use influence firm performance? Is competitive advantage, if any, achieved through big data customer analytics use contingent upon its prevalence within an industry? Drawing primarily from market information use theory, we advance a theoretical framework to examine how informational and organizational factors act to enhance big data customer analytics use, which in turn influences customer relationship and financial performance. More specifically, we identify and show how information quality (IQ), big data analytics culture, and customer orientation act as key antecedents of big data customer analytics use, which in turn is the critical mechanism to achieve superior CRM outcomes. Finally, we investigate whether the performance implications of big data customer analytics use vary depending on the prevalence of big data customer analytics use in the firm’s industry. Empirical findings from a survey of 301 senior marketing executives, representing large US-based firms in B2C industries, support our conceptualization of the performance outcomes and antecedents of BD use. First, the results highlight that the characteristics of the customer information (IQ) and the characteristics of the user organization (customer orientation and big data analytics culture) strongly predict BD use. The findings also reveal the relative importance of different customer information characteristics to marketing decision-makers. Second, the results confirm BD use as a key predictor of firm performance, and more specifically, that big data customer analytics use primarily influences financial performance indirectly via customer relationship performance. Third, this study suggests that the performance impacts of BD use are highly contingent on its prevalence among industry rivals. References Available Upon Request

Value creation through Big Data in Emerging Economies: the role of Resource Orchestration and Entrepreneurial Orientation

Purpose – The purpose of this paper is to examine how managers orchestrate, bundle, and leverage resources from big data for value creation in emerging economies. Design/methodology/approach – The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation. The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data. Findings – The findings suggest that entrepreneurial orientation is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance. Originality/value – This is one of the first studies to have integrated resource orchestration theory and entrepreneurial orientation in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies

Industry 4.0 and the future of manufacturing. Theoretical base and empirical analyses

A new industrial revolution \u2013 also called \u201cIndustry 4.0\u201d \u2013 is unfolding fueled by the introduction of broadly interconnected digital technologies, including the Internet of Things, cloud computing, artificial intelligence and additive manufacturing. Many industries are witnessing the entrance of new players integrating new technologies into disruptive business models; incumbents are also urged to rethink how they operate against trends that are expected to further accelerate in the current pandemic situation. The overarching aim of the research presented in this doctoral dissertation is to investigate to what extent Industry 4.0 represents a fundamental challenge to existing paradigms and requires researchers to modify their theoretical frameworks to approach emerging issues. With this in mind, each chapter can be seen as a step forward in journey whereby some core issues come progressively into focus. The starting point is a conceptual work analyzing the phenomenon \u2013 \u201cIndustry 4.0\u201d and similar labels \u2013 and its underlying technological and non-technological components. As a second step \u2013 under the assumption of Industry 4.0 having paradigmatic properties comparable to previous industrial revolutions \u2013 potential new configurations of manufacturing value chains are investigated. Through a future-oriented expert study, eight scenarios are conceived identifying critical drivers to value chain configurations. Finally, one of these critical drivers \u2013 data sharing in inter-organizational relationships \uac\u2013 is investigated through the development of a multiple case study analysis in the automotive sector. The contribution of this dissertation to the academic debate is at least twofold. On the one hand, the research highlights the cornerstones of the phenomenon to make sense of its overarching features and building elements. This contributes to lay solid theoretical foundations needed to advance the understanding in the field. On the other hand, my empirical investigations suggest that several barriers counterbalance the technological drivers for change, posing significant questions as for when and how the future of manufacturing will materialize. Overall, an approach focused on understanding how technologies influence the assumptions behind the current reasoning might lead at a synthesis between \u201cold\u201d and \u201cnew\u201d elements in the Industry 4.0 phenomenon

Critical analysis of the impact of big data analytics on supply chain operations

Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example of that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of ‘improving with data’, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO

Customer analytics and new product performance: The role of contingencies

Drawing from the Knowledge Based View (KBV) of the firm and Contingency Theory, this paper examines the extent to which the relationship between Customer Analytics (CA) and new product performance is contingent on the strategic fit of CA with certain internal and external contingencies. The paper first conducts a multiple case study based on secondary data analysis. It then undertakes an empirical analysis based on a survey data of 249 high and medium tech firms based in China. We find that while some internal contingencies (such as exploitative learning strategy and market knowledge breadth) negatively moderate the effect of CA on new product performance, others (such as internal capability and knowledge integration mechanisms) mediate its effect on performance. Technological turbulence, as an external contingency, was found to reduce the positive impact of CA deployment on new product performance. This study contributed to the literature by focusing on how several internal and external contingencies of a firm may affect the relationship between CA and new product performance

Customer Analytics and New Product Performance: The Role of Contingencies

Drawing from the Knowledge Based View (KBV) of the firm and Contingency Theory, this paper examines the extent to which the relationship between Customer Analytics (CA) and new product performance is contingent on the strategic fit of CA with certain internal and external contingencies. The paper first conducts a multiple case study based on secondary data analysis. It then undertakes an empirical analysis based on a survey data of 249 high and medium tech firms based in China. We find that while some internal contingencies (such as exploitative learning strategy and market knowledge breadth) negatively moderate the effect of CA on new product performance, others (such as internal capability and knowledge integration mechanisms) mediate its effect on performance. Technological turbulence, as an external contingency, was found to reduce the positive impact of CA deployment on new product performance. This study contributed to the literature by focusing on how several internal and external contingencies of a firm may affect the relationship between CA and new product performance