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Artificial Intelligence as a Market-Facing Technology

Special issue call for papers from European Journal of Marketing

Artificial intelligence (AI) is rapidly transforming how consumers, businesses, government agencies and other institutions interact. AI involves the use of smart technologies to perform and collaborate on tasks requiring human intelligence, including learning, action and flexibly adapting to fast-paced marketplaces. According to PwC(1), artificial intelligence will make $15 trillion potential contribution to global economy by 2030. Moreover, the role of AI is changing, from a primary focus on augmenting and automating tasks and roles, to deeper involvement in core business and marketing functions; for example, 45% of predicted value gains are estimated to come from product enhancement, stimulating customer demand(2). In order to deliver on such potential, there is a pertinent need for an in-depth understanding of AI as a market-facing technology.

The aim of this special issue is to examine the current and future impact of AI and related technologies (including machine learning, AR and VR) in marketing. AI, along with big data, can help to more clearly understand markets, and create, capture and sustain value along the customer journey(3). Notwithstanding, AI also presents potential risks for society, consumer autonomy and individual well-being, the implications of which are not yet well understood(4). Questions are emerging concerning the ethics around the use of dominant algorithms by marketers and consumers, the threat of superintelligence, and in upholding appropriate international customer data protection and privacy.

This special issue will showcase novel, high quality, up-to-date research into the application of AI in marketing. We wish to bridge the gap between managerial and technical perspectives to identify papers that make a substantial research contribution to AI in marketing by taking a strategic perspective on AI as a market-facing technology. It is anticipated that submitted papers are likely to be multidisciplinary, including multiple points of view and collaborations between marketing and information systems, data science, and other disciplines to advance our understanding of AI on the organizational frontline. We welcome a variety of types of submissions, including theory development, state-of-the-art practical case study applications, and thought-leading conceptual papers. In addition to standard-length papers, we also encourage contributors to submit shorter (approx. 6000 word) papers as conceptual or research notes.

We will also invite a forum of practitioners to reflect on the scholarly articles. All submitted papers will be thoroughly peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. Papers invited for revision will be invited to present their research at a workshop held at King’s College London in May 2020.

The list of possible topics for this special issue includes, but is not limited to:
•    AI for customer prediction. AI and deep learning can be used to predict customer churn, value and sales demand, among others, based on analytics of customer engagement data, and user-generated text, images, video, and audio. For example, AI and social media and often used to successfully predict box office movies sales.
•    AI for co-created product design and manufacture. AI offers the possibility for product designs to be data-driven (e.g. through health or fitness devices), and customized co-creatively, interactively and on demand, either open source or through digital design IP. AI mockups can potentially be sold online and made in small batches using automated production, e.g. 3D printing, as is used by Adidas.
•    AI in product selection and evaluation. AI is widely used by customers to evaluate and select products, including AI-enabled search results, recommendations, and AI-assisted product selection. For example, Sephora Virtual Artist combines AI with augmented reality to allow customers to try different makeup looks.
•    AI for pricing. AI algorithms enable automated price calculation to maximize potential profits, such as price prediction, bundling, and surge pricing, e.g. Uber.
•    AI for real-time, behavioral content provision. AI-enabled behavioral prediction, with data from images, text, speech, and video, enable the adjustment of advertisements and content to individual customer needs, preferences, and/or behaviors in real-time. For example, AI can recognize emotions and how consumers feel about a store and its products, or predict reactions to advertisements, allowing optimal provision of specific ads or online content. For example, ASOS predict real-time product relevancy based on online customer interactions.
•    AI in physical retail environments. AI offers the potential for real-time physical store behavior analysis, e.g. using facial recognition, voice and body language, to analyze patterns of buying behavior, recognize problems, and offer assistance. AI may be used further to enhance instore retailing and the customer experience through analyzing and optimizing store layouts and product interaction.
•    AI for inventory management. Shelf-scanning robots may be linked to sales data and AI-inventory control to predict and order required products and avoid stockouts, while in the home, combining AI with the Internet of Things (IoT), e.g. through smart fridges and devices such as the Google Echo, allows automatic decisions regarding reordering of products.
•    AI in product distribution. AI enables autonomous product delivery via drones, e.g. in rural China, and optimal logistical planning for deliveries.
•    AI in customer service. AI underpins automation of customer service and automated assistance, including via the use of virtual agents, text analytics, natural language processing, and natural language generation to provide engaging experiences.
•    The ethics of AI in marketing. AI presents numerous ethical issues for marketers, including its impacts on consumer equality, diversity, autonomy, wellbeing, over-reliance on AI and consumer protection, among others.
•    AI for sustainable consumption. AI provides the potential for improving consumption paradigms, including further enabling sustainable consumption and the circular economy.

Special Issue Co-Editors:

Professor Ko de Ruyter
Chair in Marketing
Head of the Marketing Group.
King's Business School
King’s College London
Bush House
30 Aldwych
London WC2B 4BG
United Kingdom
E: [email protected]

Professor Stuart J. Barnes
Chair in Marketing
Director of the Consumer and Organisational Digital Analytics (CODA) Research Centre
King's Business School
King’s College London
Bush House
30 Aldwych
London WC2B 4BG
United Kingdom
E: [email protected]

Important Dates

•    Submissions can be made from 1 December 2019 until 31 December 2019.
•    Invitation to Revise Paper for Special Issue: 30th April 2020.
•    Invitation to Workshop: to be held in May 2020
•    Deadline for Revised Paper: 31st July 2020.
•    Final Decision on Acceptance: 30th October 2020.
•    Publication: Volume 55 2021.

Submission Information

Submission can be made during December 2019. During this period submitting authors should upload their article to the journal’s Scholar One system available through the author guidelines at During submission the authors should select this special issue from the drop down menu available. Any queries should be addressed initially to the Guest Editors.

(1)  PwC (2017). Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise? London: PwC.
(2)  Op. cit.
(3)  Kietzman, J., Paschen, J., and Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the customer journey. Journal of Advertising Research, 58(3), 263-267.
(4) André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., van Boven, L., Weber, B., and Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Consumer Needs and Solutions, 5(1-2), 28-37.