BUS 6523 Module 1 Review of Frameworks for AI-Mediated Consumer Behavior Example

When the Algorithm Shops Too: A Review of Frameworks for AI-Mediated Consumer Behavior

Student Name

American College of Education

BUS6523: Global Marketing in the Digital Economy

Module 1 Assignment

Instructor Name

April 9, 2029

Introduction

Consumers increasingly meet products through software that searches, ranks, recommends and sometimes buys on their behalf. Streaming services choose what plays next, marketplaces rank sellers, voice assistants reorder household goods and chat-based assistants summarize options before a person visits any brand's site. Marketing theory developed for a world in which consumers searched and chose for themselves. This review asks how well existing and emerging frameworks explain consumer behavior when artificial intelligence mediates the relationship between brand and buyer. It organizes the research by stage of the decision, compares what each framework explains and builds an integrated model for the global cases in later modules.

Scope and Method

The review draws on peer-reviewed marketing, consumer psychology and information systems research published mainly since 2015, together with foundational work on brand equity and decision making. Sources were identified through searches of business databases using terms such as artificial intelligence, recommendation, algorithm and consumer decision, and through citation tracking from widely cited reviews. The review is narrative rather than systematic; it aims to compare frameworks and identify gaps rather than estimate effect sizes.

The Customer Journey as an Organizing Frame

Lemon and Verhoef (2016) described customer experience as a journey across prepurchase, purchase and postpurchase stages, made up of touchpoints that are brand-owned, partner-owned, customer-owned and social or external. Their framework is useful here because AI is entering every stage and because many AI touchpoints are partner-owned or external, such as a marketplace's ranking algorithm or a third-party assistant. The journey frame also highlights a shift in control: the share of touchpoints a brand designs is shrinking, while the share designed by platforms is growing. The remaining frameworks can be read as explanations of what happens at particular stages of this journey.

Mechanical, Thinking and Feeling AI

Huang and Rust (2021) proposed a strategic framework that classifies marketing AI by the intelligence it applies. Mechanical AI automates repetitive tasks, such as dynamic pricing; thinking AI analyzes data to reach decisions, such as recommendations and segmentation; and feeling AI analyzes and responds to emotions, such as conversational agents. They map these onto marketing research, strategy and action. Davenport et al. (2020) offered a complementary framework classifying AI by its level of intelligence, the task it performs and whether it is embedded in a robot or platform, and they predicted that AI would most change how consumers search and decide. Both frameworks describe what firms can do with AI; neither centers on how consumers experience it.

When Consumers Trust or Resist Algorithms

Consumer psychology offers conflicting evidence on how people respond to algorithmic advice. Dietvorst et al. (2015) found that people were less willing to rely on an algorithm after seeing it make errors, even when it outperformed humans, which they called algorithm aversion. Logg et al. (2019) reported the reverse across several experiments: participants adjusted their estimates more toward advice labeled as an algorithm's output than toward identical advice attributed to another person. The two findings can be reconciled by context. Appreciation appears in tasks seen as objective and when people have not watched the algorithm err; aversion appears after visible mistakes and in domains people regard as personal.

Resistance in High-Stakes Domains

Longoni et al. (2019) showed that consumers resisted AI in medical decisions because they believed their own health situation was unique and that algorithms could not account for it. This uniqueness neglect has implications beyond medicine. In categories where consumers see themselves as distinctive, such as fashion, food and financial planning, AI recommendations may be discounted unless they appear tailored and the system's reasoning is visible. For global marketers, the boundary between objective and personal categories may also vary across cultures, which the research has examined less.

Brand Equity Under Mediation

Keller (1993) proposed that a brand holds value with customers when what they know about it, how readily it comes to mind and the associations attached to it, makes them react more favorably to its marketing than they would to an unnamed version of the same offer. The framework assumes consumers encounter the brand and form associations from it. When an algorithm ranks options and a consumer accepts the top recommendation, brand awareness and associations may matter less at the moment of choice, while the platform's ranking criteria matter more. Brand equity may then work through a different path: a strong brand may influence the algorithm through ratings, return rates and engagement, rather than directly influencing the consumer.

Comparing the Frameworks

Compared on the stage they explain, the frameworks divide labor. The customer journey covers the whole process and identifies who controls each touchpoint. Huang and Rust and Davenport et al. explain what AI can do for firms at each stage. Research on aversion, appreciation and uniqueness neglect explains when consumers accept algorithmic input at the moment of decision. Brand equity theory explains how prior knowledge shapes response, but it was built for unmediated choice. Compared on level, the firm-side frameworks are strategic and descriptive, while the consumer research is experimental and narrow, which makes it rigorous but difficult to generalize.

Gaps in the Research

The literature leaves three questions open. First, most experimental studies involve single decisions in Western samples, so little is known about repeated delegation to AI over time or about cross-cultural differences in trust. Second, research seldom examines the platform as an actor with its own goals, although recommendation systems are designed by firms whose interests differ from those of brands and consumers. Third, brand equity research has not yet tested how brand strength operates when consumers delegate choices to assistants. These gaps matter for global marketers and shape the questions this course takes up next.

An Integrated Model

Combining the frameworks yields a model with three actors and three paths. The actors are the consumer, the platform's algorithm and the brand. On the first path, the brand influences the consumer directly through familiar brand knowledge, as Keller described. On the second, the brand influences the algorithm through signals the platform uses, such as ratings and engagement. On the third, the algorithm influences the consumer, with acceptance shaped by perceived task objectivity, visible errors and uniqueness. The model predicts that the second and third paths grow in importance as AI mediation increases and that the balance differs by category and culture.

Implications for Global Marketers

Even before testing, the review suggests practical implications. Marketers entering a new country should identify which platforms and assistants control the main touchpoints there, since these differ widely between markets. They should treat platform signals such as ratings and return rates as brand assets to be managed. And they should expect acceptance of AI recommendations to vary by category and culture, testing rather than assuming how much consumers will delegate.

Conclusion

No single framework explains consumer behavior in AI-mediated markets. The customer journey shows where AI enters and who controls it, firm-side frameworks describe what AI can do, consumer research explains when people accept algorithmic advice and brand equity theory explains the value of prior knowledge but assumes unmediated choice. Integrated, they suggest a three-actor model in which brands increasingly reach consumers through algorithms. The next module will test this model against a case of digital disruption in a global market.

References

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114-126. https://doi.org/10.1037/xge0000033

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00749-9

Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22. https://doi.org/10.1177/002224299305700101

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420

Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103. https://doi.org/10.1016/j.obhdp.2018.12.005

Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. https://doi.org/10.1093/jcr/ucz013