How artificial intelligence will change the future of marketing

Davenport, Thomas H.; Guha, Ayan; Grewal, Dhruv; Breßgott, Timna

doi:10.1007/s11747-019-00696-0

Cited by 1,192 publications

(804 citation statements)

References 64 publications

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“…Since the early 2010s however, there has been a resurgence of interest in AI (Brock & Von Wangenheim, 2019). Rapid advances in statistical machine learning techniques have broadened the scope for AI applications, and commercial uses now span diverse areas such as marketing (T. Davenport, Guha, Grewal, & Bressgott, 2019), molecule discovery (Gawehn, Hiss, & Schneider, 2016), automotive manufacturing (Luckow et al, 2018) A subset of machine learning called deep learning has also advanced rapidly (LeCun, Bengio, & Hinton, 2015). Influenced by human biology, deep learning employs the concept of deep neural networks (DNNs) to create hierarchical layers of synthetic neurons that each extract different patterns from an input (e.g.…”

Section: Contemporary Aimentioning

confidence: 99%

Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution

Chalmers

MacKenzie

Carter

2020

Entrepreneurship Theory and Practice

218

130

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This article explores the ways artificial intelligence (AI) may impact new venture processes, practices and outcomes. We examine how such technology will augment and replace tasks associated with idea production, selling, and scaling. These changes entail new ways of working, and we consider implications for the organizational design of entrepreneurial ventures. While AI can enhance entrepreneurial activities, liabilities stem from this technological leverage. We advance a research agenda that draws attention towards negative social and economic implications of AI, particularly for more traditional small firms at risk of disintermediation in an AI economy.

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Section: Contemporary Aimentioning

confidence: 99%

Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution

Chalmers

MacKenzie

Carter

2020

Entrepreneurship Theory and Practice

218

130

View full text Add to dashboard Cite

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“…The interactions between advertising/advertiser industries and consumers are two-way and they can be enacted by either party, and the moderation of their relationships through technology is changing the manner in which business is conducted (Yadav and Pavlou 2020). Leading the change in these relationships is the role of AI (Davenport et al 2020). For example, consumers will be able to interact with data bots in the shopping environment that are designed not only to respond to consumers' queries but also to collect information, and these interactions will provide marketers with new ways to communicate with consumers on a personal level.…”

Section: Issues Involving Environmental Factorsmentioning

confidence: 99%

Macro and Exogenous Factors in Computational Advertising: Key Issues and New Research Directions

Helberger

Huh

Milne

et al. 2020

Journal of Advertising

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To advance the emerging research field of computational advertising this article describes the new computational advertising ecosystem, identifies key actors within it and interactions among them, and discusses future research agendas. Specifically, we propose systematic conceptualization for the redefined advertising industry, consumers, government, and technology environmental factors, and discuss emerging and anticipated tensions that arise in the macro and exogenous factors surrounding the new computational advertising industry, leading to suggestions for future research directions. From multidisciplinary angles, areas of tension and related research questions are explored from advertising, business, computer science, and legal perspectives. The proposed research agendas include exploring transparency of computational advertising practice and consumer education; understanding the trade-off between explainability and performance of algorithms; exploring the issue of new consumers as free data laborers, data as commodity, and related consumer agency challenges; understanding the relationship between algorithmic transparency and consumers' literacy; evaluating the trade-off between algorithmic fairness and privacy protection; examining legal and regulatory issues regarding power imbalance between actors in the computational advertising ecosystem; and studying the trade-off between technological innovation and consumer protection and empowerment.

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“…fundamental properties of human-to-human conversations (such as turn-taking or the presence of social cues throughout a conversation; Davenport et al 2020;Thomaz et al 2020). The key hypothesis of the current research is that conversational robo advisors can provide an unexplored alternative to address and compensate for the lack of "human touch" during the advisory process compared to traditional, nonconversational robo advisors.…”

Section: Introductionmentioning

confidence: 99%

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

Hildebrand

Bergner

2020

J. of the Acad. Mark. Sci.

128

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The current research demonstrates how conversational robo advisors as opposed to static, non-conversational robo advisors alter perceptions of trust, the evaluation of a financial services firm, and consumer financial decision making. We develop and empirically test a novel conceptualization of conversational robo advisors building on prior work in human-to-human communication and interpersonal psychology, showing that conversational robo advisors cause greater levels of affective trust compared to non-conversational robo advisors and evoke a more benevolent evaluation of a financial services firm. We demonstrate that this increase in affective trust not only affects firm perception (in terms of benevolence attributions or a more positively-valenced onboarding experience), but has important implications for investor behavior, such as greater recommendation acceptance and an increase in asset allocation toward conversational robo advisors. These findings have important implications for research on trust formation between humans and machines, the effective design of conversational robo advisors, and public policy in the digital economy.

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How artificial intelligence will change the future of marketing

Cited by 1,192 publications

References 64 publications

Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution

Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution

Macro and Exogenous Factors in Computational Advertising: Key Issues and New Research Directions

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

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