Incomodando consumidores: quando agentes de recomendação não facilitam a nossa vida realmente

Fábio Verruck,Walter Meucci Nique

doi:10.4013/base.2017.144.01

Abstract

Intelligent systems have been used in different types of websites with the intention of creating personalized messages and understanding consumers’ needs more deeply. They are supposed to facilitate the decision-making process, make internet browsing easier and give users a sense of social feeling and personalization. So far, research in the field has focused attention on the positive aspects of using these systems. Little effort has been made, however, to try to recognize and correct situations in which they do not perform so well. This work is the result of an exploratory research destined to understand the broadness of the concept of failure in personalized environments as well as its antecedents and consequents. Based on the critical incident technique, we collected the opinion of 86 subjects in a multicultural environment and used their responses to elaborate a comprehensive framework of recommendation failure considering the different motivations for Internet use. Keywords: personalization, online shopping, recommendation agents, recommendation failure.

Highlights

The development of online technologies has brought to Internet-based companies a whole new set of possibilities for data collection and personalization
A great amount of this information is computed by intelligent systems for personalization purposes
Commercial websites try to increase sales performance either by making the decision process easier, playing the role of decision aids, or by making personalized offers according to the inferred consumers preferences and needs

Summary

INTRODUCTION

The development of online technologies has brought to Internet-based companies a whole new set of possibilities for data collection and personalization. Commercial websites try to increase sales performance either by making the decision process easier, playing the role of decision aids, or by making personalized offers according to the inferred consumers preferences and needs It is, paradoxical the fact that consumers who browse products online often leave the website without buying and do not return (Lambrecht and Tucker, 2013). Netflix never bothered to implement the winning algorithm, because, according to them, the additional accuracy gains did not justify the engineering effort needed to bring them into a production environment (Holiday, 2016) These findings suggest that some other factors related to the consumer behavior need a better examination in order to shed light over this apparent contradiction. The results of this study could be used to understand how to identify a recommendation failure and what can be made to alleviate the possible negative consequences such an event can cause in the online purchasing behavior

LITERATURE REVIEW

RESEARCH METHOD

RESULTS

Findings

CONCLUSIONS