The concept of Artificial Intelligence (AI) as a business-disruptive technology has developed in academic and professional literature in a chaotic and unstructured manner. This study aims to provide clarity over the phenomenon of business activation of AI by means of a comprehensive and systematic literature review, aimed at suggesting a clear description of what Artificial Intelligence is today. The study analyses a corpus of 3780 contributions through an original combination of two established machine learning algorithms (LDA and hierarchical clustering). The review produced a structured classification of the various streams of current research and a list of promising emerging trends. Results have shed light on six topics attributable to three different themes, namely Implications, Applications and Methods (IAM model). Our analysis could provide researchers and practitioners with a meaningful overview of the body of knowledge and research agenda, to exploit AI as an effective enabler to drive business value.

The emergence of consumer-generated data and the growing availability of Machine Learning (ML) techniques are revolutionizing marketing practices. Marketers and researchers are far from having a thorough understanding of the broad range of opportunities ML applications offer in creating and maintaining a competitive business advantage. In this paper, we propose a taxonomy of ML use cases in marketing based on a systematic review of academic and business literature. We have identified 11 recurring use cases, organized in 4 homogeneous families which correspond to the fundamentals leverage areas of ML in marketing, namely: shopper fundamentals, consumption experience, decision making, and financial impact. We discuss the recurring patterns identified in the taxonomy and provide a conceptual framework for its interpretation and extension, highlighting practical implications for marketers and researchers.

Purpose: The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.Design/methodology/approach: The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.Findings: The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.Practical implications: Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.Social implications: Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.Originality/value: This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.