Recommendation Agents: Elevate Your Online Retail Strategy

Prof. Markus Blut, Dr. Arezou Ghiassaleh, and Dr. Cheng Wang have conducted a comprehensive meta-analysis, to guide retailers in choosing the most effective electronic recommendation agents for their business.

With a staggering 350 million goods available on platforms like Amazon, consumers are faced with an overwhelming array of choices. To enhance the customer experience, online retailers turn to RAs. These digital assistants analyse consumer preferences and deliver tailored product recommendations. However, not all RAs are created equal, making the selection of the right one paramount.

This research examines the strengths and weaknesses of various RA algorithms, recommendation presentations, and data sources, as well as how different RA types leverage perceived recommendation quality's impact on decision-making satisfaction, RA satisfaction, and consumers' intentions for future RA use. This insight empowers retailers to make informed choices.

Key Findings in a Nutshell:

• Collaborative-filtering RAs excel in enhancing perceived recommendation quality.
• Self-serving recommendation RAs, which benefit retailers, possess unique advantages.
• Cutting-edge AI-powered Interactive RAs perform well.
• Solicited recommendation-based RA presentations stand out.
• Location and social media data amplify the impact of perceived recommendation quality.

Stay Ahead in the Digital Marketplace:

This research not only advances the theory of recommendation agents but also provides practical guidance for managers seeking the ideal RA. Elevate your online retail strategy with data-backed insights and remain at the forefront of the ever-evolving digital landscape.

View the full article in Journal of Retailing: https://www.sciencedirect.com/science/article/pii/S0022435923000349#bib0078