مقاله انگلیسی هوش مصنوعی به عنوان توانمندکننده بازاریابی B2B
Abstract
Keywords
Introduction
Theoretical background
Research approach
Findings
Discussion
Conclusions
References
ABSTRACT
Over the past few decades research has predominantly focused on the technical aspects and theoretical challenges of Artificial Intelligence (AI). With the deluge of data and the increase in processing power, businesses are now facing the challenge of how to deploy AI that generates business value. In this direction, there is still nascent research on how AI can be leveraged in for B2B operations, and particularly marketing. To address this gap, this study draws on the dynamic capabilities view of the firm and specifically on the micro-foundations approach and builds on three selected case studies of large organizations in Norway that use AI for B2B marketing purposes. The study identifies a number of AI-specific micro-foundations of dynamic capabilities, essentially highlighting how organizations can use AI to manage B2B marketing operations in dynamic and uncertain environments. This study also identified several key cross-cutting elements emerging from the data, demonstrating how some key concepts are inter-related and how they affect overall business value.
Introduction
The value of Artificial Intelligence (AI) in augmenting organizational operations has started to attract the interest of practitioners over the past few years (Davenport & Ronanki, 2018). A growing number of firms have begun deploying AI initiatives with the aim of automating or augmenting key business processes, with the ultimate goal of gaining a competitive edge (Duan, Edwards, & Dwivedi, 2019). Some practitioners and researchers have associated AI with the next frontier for competition and productivity (Dwivedi et al., 2021), while others have even claimed that it is a revolution that will radically transform how business is conducted (Ågerfalk, 2020). Following the deluge of data, significant developments have been documented in terms of techniques and technologies for data storage and processing (Ransbotham, Kiron, Gerbert, & Reeves, 2017). Yet, empirical research on the value of AI is still at a rudimentary state with a general lack of understanding concerning the mechanisms through which such investments can generate business value (Duan et al., 2019).