Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing

Abstract

In this digital era, marketing theory and practice are being transformed by increasing complexity due to information availability, higher reach and interactions, and faster speeds of transactions. These have led to the adoption of intelligent agent technologies (IATs) by many companies. As IATs are relatively new and technologically complex, several definitions are evolving, and the theory in this area is not yet fully developed. There is a need to provide structure and guidance to marketers to further this emerging stream of research. As a first step, this paper proposes a marketing-centric definition and a systematic taxonomy and framework. The authors, using a grounded theory approach, conduct an extensive literature review and a qualitative study in which interviews with managers from 50 companies in 22 industries reveal the importance of understanding IAT applications and adopting them. Further, the authors propose an integrated conceptual framework with several propositions regarding IAT adoption. This research identifies the gaps in the literature and the need for adoption of IATs in the future of marketing given changing consumer behavior and product and industry characteristics. Keywords Intelligent agent technologies. Marketing strategy. Grounded theory The growth of online marketing and increasingly tough competitive realities have led to a paradigm shift in marketing, where understanding needs and demands of each individual customer is becoming increasingly important, and it has become critical for companies to respond to market dynamics accurately and quickly. The revenue for the intelligent technology-based predictive analytics market is expected to grow by 22 % each year to $5.25B by 2018 (August 2013, www.marketsandmarkets.com). 1 Companies such as Amazon, eBay, and Netflix are embracing intelligent agent technologies (henceforth referred to as IATs, or BAgents^) for collaborative filtering, personalization, recommendation systems, and price-comparison engines to facilitate exchanges in the marketplace (Iacobucci et al. 2000). Recent literature (e.g., Bodapati 2008; Chen and Sudhir 2004; Clemons 2009; Diehl et al. 2003; Iyer and Pazgal 2003) has taken important initial steps and discussed complex marketing applications of IATs that were developed in different contexts and time-frames. For example, Iyer and Pazgal 2003 show how IATs affect market competition in general. Similarly, Chen and Sudhir 2004 and Diehl et al. 2003 examine how IATs such as shopbots and smart agents affect price competition and sensitivity in e-commerce settings. Bodapati 2008 shows how sensitivity of intelligent recommendation systems affects consumer behavior. In their recent empirical paper focusing on the banking industry, Köhler et al. 2011 find 1 See http://www.marketsandmarkets.com/Market-Reports/predictiveanalytics-market-1181.html.

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