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What if artificial intelligence (AI) itself were used to investigate the current
literature on AI in marketing? That is what we do in this study!
Having received more than US$5 billion in venture capital investments in just the
past two years, artificial intelligence (AI) is poised to exert transformative effects on
markets and marketing around the world (PricewaterhouseCoopers, 2017;
Rangaswamy et al., 2020; Smart Insights, 2018). Marketing increasingly relies on its
algorithms, which mimic human cognitive functions and exhibit aspects of human
intelligence (Huang & Rust, 2018; Rangaswamy et al., 2020; Russell & Norvig, 2016;
Sterne, 2017), such that 72% of marketers cite AI as a business advantage. Consumers
benefit from these applications, in the form of decreased costs, more diverse service
channels, innovative breakthroughs, and opportunities for expanded human creativity
and ingenuity when tedious, repetitive tasks are performed by AI (Haenlein & Kaplan,
2019; PricewaterhouseCoopers, 2017; Smart Insights, 2018). This revolution of AI usage
in marketing, and its potential for producing superior value outcomes, has sparked
substantial research attention (Davenport et al.; 2020; Haenlein & Kaplan, 2019; Huang
& Rust, 2018), prompting, for example, applications of intelligent technology (Marinova
et al., 2017); descriptions of services enabled, facilitated, and delivered by various
technologies (Rust & Huang, 2012); investigations of AI-powered robotics (Lu et al.,
2020; Wirtz et al., 2018); explorations of AI-led marketing and sales strategies
(Davenport et al., 2020); considerations of how AI-enabled delivery can lead to cost
effective service excellence (Wirtz, 2020; Wirtz & Zeithaml, 2018); proposals of AI
enabled platform business models (Wirtz et al., 2019); investigations of the impact of AI
chatbot disclosures on customer purchases (Luo et al., 2019); considerations of effects
on workforces (Davenport & Kirby, 2015) and redefinitions of AI-enabled workplaces
(Chui et al., 2015); and discussions of digital technologies as driving forces of work and
life (McAfee & Brynjolfsson, 2016).
Despite this extensive list, marketing still lacks a cohesive understanding of how
AI technologies have been applied thus far and how they should be in the future
(Haenlein & Kaplan, 2019; Paschen et al., 2019). That is, it needs literature analyses to
scrutinize and synthesize the use of AI in marketing and pave a concrete path for
future-focused academic research. Objective, reflective analyses are crucial to
evaluate any extant knowledge base, identify knowledge gaps, and evaluate research
effectiveness and productivity (Huang & Rust, 2018; Russell & Norvig, 2016), for both
researchers and publication outlets or journals (Lowry et al., 2004, 2013). Indeed,
where and how scholars publish represent essential aspects of the identity of the
marketing discipline, reflecting its value systems, aspirations, paradigms, reward
systems, cultural conduct, and political hierarchy (Garfield, 2006; Lowry et al., 2004,
2013). Therefore, with this study, we investigate dominant research topics related to AI
in marketing, outlining key themes, influential publications, and networks among
authors and journals, to provide a clear view of the extant knowledge base.3
We apply two complementary analytical approaches to examine the evolution
and structure of the research field. First, to identify dominant, salient topics, we
undertake topic modeling and apply natural language processing, machine learning,
and statistical algorithms. Second, using scientometric techniques, we generate further
insights about authors and research networks, as well as establishing keyword co
occurrence rates, identifying landmark publications, and depicting the evolution of the
field over time (Lowry et al., 2013; Nikolenko et al., 2017; Zhao et al., 2019).
In turn, this study makes four contributions. First, the combination of advanced
topic modeling algorithms, established through text analytics techniques, and of
scientometric analysis, enables a highly objective, robust, structured, and
comprehensive review of this rapidly expanding research domain than traditional
literature reviews and analyses can provide (Vanhala et al., 2020). Second, our
systematic, data-driven approach reveals 10 dominant AI research topics in marketing,
which we can classify as consumer research or organization and strategy–related
research. Third, the scientometric analysis produces information maps of co-citation
clusters, landmark articles, conceptual and theoretical foundations, and reciprocal
interconnections of concepts on the basis of their paired presence in extant literature.
Such a comprehensive approach establishes a broad understanding and in-depth
insights, without restricting the analysis to any predesigned aspects. Fourth, we offer
an extensive discussion of research gaps that in turn produces a robust agenda for
increasing the depth and breadth of AI research in marketing.
After we establish the conceptual underpinning of this study in Section 2, we
explicate the methodological details in Section 3. Section 4 contains descriptive details
of the reviewed publications. In Section 5, we review the salient topics of research on
AI in marketing, and then in Section 6, we report the scientometric analysis. Finally, we
discuss the results and conclude with concrete recommendations for examining
multiple overlooked facets of AI in marketing in Section 7.
What if artificial intelligence (AI) itself were used to investigate the current
literature on AI in marketing? That is what we do in this study!
Having received more than US$5 billion in venture capital investments in just the
past two years, artificial intelligence (AI) is poised to exert transformative effects on
markets and marketing around the world (PricewaterhouseCoopers, 2017;
Rangaswamy et al., 2020; Smart Insights, 2018). Marketing increasingly relies on its
algorithms, which mimic human cognitive functions and exhibit aspects of human
intelligence (Huang & Rust, 2018; Rangaswamy et al., 2020; Russell & Norvig, 2016;
Sterne, 2017), such that 72% of marketers cite AI as a business advantage. Consumers
benefit from these applications, in the form of decreased costs, more diverse service
channels, innovative breakthroughs, and opportunities for expanded human creativity
and ingenuity when tedious, repetitive tasks are performed by AI (Haenlein & Kaplan,
2019; PricewaterhouseCoopers, 2017; Smart Insights, 2018). This revolution of AI usage
in marketing, and its potential for producing superior value outcomes, has sparked
substantial research attention (Davenport et al.; 2020; Haenlein & Kaplan, 2019; Huang
& Rust, 2018), prompting, for example, applications of intelligent technology (Marinova
et al., 2017); descriptions of services enabled, facilitated, and delivered by various
technologies (Rust & Huang, 2012); investigations of AI-powered robotics (Lu et al.,
2020; Wirtz et al., 2018); explorations of AI-led marketing and sales strategies
(Davenport et al., 2020); considerations of how AI-enabled delivery can lead to cost
effective service excellence (Wirtz, 2020; Wirtz & Zeithaml, 2018); proposals of AI
enabled platform business models (Wirtz et al., 2019); investigations of the impact of AI
chatbot disclosures on customer purchases (Luo et al., 2019); considerations of effects
on workforces (Davenport & Kirby, 2015) and redefinitions of AI-enabled workplaces
(Chui et al., 2015); and discussions of digital technologies as driving forces of work and
life (McAfee & Brynjolfsson, 2016).
Despite this extensive list, marketing still lacks a cohesive understanding of how
AI technologies have been applied thus far and how they should be in the future
(Haenlein & Kaplan, 2019; Paschen et al., 2019). That is, it needs literature analyses to
scrutinize and synthesize the use of AI in marketing and pave a concrete path for
future-focused academic research. Objective, reflective analyses are crucial to
evaluate any extant knowledge base, identify knowledge gaps, and evaluate research
effectiveness and productivity (Huang & Rust, 2018; Russell & Norvig, 2016), for both
researchers and publication outlets or journals (Lowry et al., 2004, 2013). Indeed,
where and how scholars publish represent essential aspects of the identity of the
marketing discipline, reflecting its value systems, aspirations, paradigms, reward
systems, cultural conduct, and political hierarchy (Garfield, 2006; Lowry et al., 2004,
2013). Therefore, with this study, we investigate dominant research topics related to AI
in marketing, outlining key themes, influential publications, and networks among
authors and journals, to provide a clear view of the extant knowledge base.3
We apply two complementary analytical approaches to examine the evolution
and structure of the research field. First, to identify dominant, salient topics, we
undertake topic modeling and apply natural language processing, machine learning,
and statistical algorithms. Second, using scientometric techniques, we generate further
insights about authors and research networks, as well as establishing keyword co
occurrence rates, identifying landmark publications, and depicting the evolution of the
field over time (Lowry et al., 2013; Nikolenko et al., 2017; Zhao et al., 2019).
In turn, this study makes four contributions. First, the combination of advanced
topic modeling algorithms, established through text analytics techniques, and of
scientometric analysis, enables a highly objective, robust, structured, and
comprehensive review of this rapidly expanding research domain than traditional
literature reviews and analyses can provide (Vanhala et al., 2020). Second, our
systematic, data-driven approach reveals 10 dominant AI research topics in marketing,
which we can classify as consumer research or organization and strategy–related
research. Third, the scientometric analysis produces information maps of co-citation
clusters, landmark articles, conceptual and theoretical foundations, and reciprocal
interconnections of concepts on the basis of their paired presence in extant literature.
Such a comprehensive approach establishes a broad understanding and in-depth
insights, without restricting the analysis to any predesigned aspects. Fourth, we offer
an extensive discussion of research gaps that in turn produces a robust agenda for
increasing the depth and breadth of AI research in marketing.
After we establish the conceptual underpinning of this study in Section 2, we
explicate the methodological details in Section 3. Section 4 contains descriptive details
of the reviewed publications. In Section 5, we review the salient topics of research on
AI in marketing, and then in Section 6, we report the scientometric analysis. Finally, we
discuss the results and conclude with concrete recommendations for examining
multiple overlooked facets of AI in marketing in Section 7.