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Enterprise AI Analysis: The impact of artificial intelligence on consumer behavior towards brands: a systematic review

Enterprise AI Analysis

The Impact of Artificial Intelligence on Consumer Behavior Towards Brands: A Systematic Review

Authors: Ana Ribeiro, Alfonso José López Rivero, José Luís Abrantes

Abstract: Artificial Intelligence is increasingly integrated into consumers' daily lives, making it essential to understand the future impact of this relationship. This systematic literature review aims to understand the influence of AI on consumer behavior, identifying future trends for brands. Using Web of Science research, following the PRISMA method, and employing VOSviewer software for cluster and keyword analysis, the results demonstrate that AI impacts consumer behavior and brands', presenting five clusters that highlight: consumer experience, relationship, online challenges, functionality, and autonomous purchasing. The study contributes to current knowledge by verifying that the AI-consumer relationship is complex, focusing on five key aspects: convenience, effectiveness, trust, security, and personalization, which collectively influence purchase decisions and loyalty. This article also demonstrates that AI can facilitate strategic marketing decisions through segmentation and prediction capabilities, enabling personalization in an increasingly digital and e-commerce world. Marketers must consider AI as a consumer and expand their capabilities.

Key Performance Indicators & Research Scope

Snapshot of the research landscape and its relevance to enterprise AI strategy, highlighting publication trends and focus areas.

0 Total Articles (WOS)
0 Total Citations (WOS)
0 Articles Analyzed (VOSviewer)
0 Top Publication Year
0 Top VOSviewer Publication Year
Business Economics Primary Research Area

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Consumer Experience
AI Relationship
Online Challenges
AI Functionality
Autonomous Purchasing

Cluster 1: Consumer Experience—The Potential of AI

This cluster highlights how AI, particularly voice assistants, shapes consumer interactions by offering personalized recommendations, convenience, and speed. Humanization of AI is crucial for natural communication and fostering emotional bonds, influencing purchase intentions and loyalty. However, factors like perceived intelligence and context of use significantly impact adoption.

Enterprise Process Flow: PRISMA Methodology

Identification
Screening
Included

Cluster 2: The Relationship with AI

AI's evolution in service robots, including humanized appearance and interaction, influences consumer attitudes. Trust, social presence, and the perception of competence are key. While AI enhances convenience, the absence of emotion and empathy in sensitive sectors can undermine interactions. A balanced approach, combining human and AI employees, may yield the best results for customer satisfaction and loyalty.

Trust & Humanization Pillars of Consumer-AI Relationships

Cluster 3: Online Challenges

AI-driven predictive analytics and data analysis are transforming online marketing, enabling deeper understanding of consumer needs, personalized advertising, and effective segmentation. While AI enhances efficiency and engagement, concerns around data security, privacy, and ethical implications persist. Building trust through transparent and secure practices is paramount for success in e-commerce.

Personalization Driving Engagement & Purchase Intent

Cluster 4: The Functionality of AI

AI's functional capabilities in marketing are vast, from enhancing personalized campaigns and data analysis to improving decision-making and forecasting. It allows companies to better understand customers, optimize pricing, and streamline customer service through virtual assistants. The key is to leverage AI to complement human capabilities, driving efficiency and customer satisfaction while aligning with consumer values.

Predictive Capacity AI's Core Marketing Advantage

Cluster 5: Autonomous and Automated Purchasing

Autonomous purchasing systems, empowered by AI, offer convenience and speed, influencing consumer brand recall and loyalty. The impact of AI's human likeness varies across product types and consumer ideologies. While these systems can simplify decisions, concerns about loss of control, privacy, and ethical considerations must be addressed. Brands must balance automation with consumer trust and perceived value.

Convenience & Control Key Drivers & Barriers in Automated Purchases

Calculate Your Potential AI Impact

Estimate the operational savings and reclaimed hours your enterprise could achieve by strategically implementing AI.

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Future Research & Strategic Roadmap

Leveraging the systematic review, these are the critical pathways for future research and strategic AI implementation in consumer behavior, based on identified gaps and recommendations.

Emotional Attachment & Behavior

Understand consumers' emotional attachment to humanized AI assistants and their impact on behavior. This is crucial as stronger bonds can lead to increased loyalty and positive brand interactions.

Personal Assistant Personality & Engagement

Explore how the personality of personal AI assistants influences brand engagement. Different personalities can shape consumer perception of brand value and interaction quality.

AI Loyalty & Convenience Addiction

Investigate consumer loyalty to AI and the potential limits of convenience addiction. Differentiating loyalty to the tool versus the brand can inform strategies for leveraging AI-consumer relationships.

AI Communication Styles & Product Involvement

Examine the impact of diverse AI communication styles on consumer behavior, especially for low-involvement products. Tailoring communication can optimize interactions and influence purchasing decisions.

Consumer Motivation, Engagement & Anthropomorphism

Perceiving consumer motivation is vital as it influences engagement and the perception of AI anthropomorphism. Understanding these motivations will help develop more personalized and effective AI strategies.

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