Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality

Pierre-Majorique Léger,Sylvain Sénécal,Émilie Bigras

doi:10.3390/app9204244

Abstract

The purpose of this paper is to report the results of a laboratory experiment that investigated how assortment planners’ perceptions, usage behavior, and decision quality are influenced by the way recommendations of an artificial intelligence (AI)-based recommendation agent (RA) are presented. A within-subject laboratory experiment was conducted with twenty subjects. Participants perceptions and usage behavior toward an RA while making decisions were assessed using validated measurement scales and eye-tracking technology. The results of this study show the importance of a transparent RA demanding less cognitive effort to understand and access the explanations of a transparent RA on assortment planners’ perceptions (i.e., source credibility, sense of control, decision quality, and satisfaction), usage behavior, and decision quality. Results from this study suggest that designing RAs with more transparency for the users bring perceptual and attitudinal benefits that influence both the adoption and continuous use of those systems by employees. This study contributes to filling the literature gap on RAs in organizational contexts, thus advancing knowledge in the human–computer interaction literature. The findings of this study provide guidelines for RA developers and user experience (UX) designers on how to best create and present an AI-based RA to employees.

Highlights

Optimizing the composition and size of an inventory is critical for retailers to maximize their sales or gross margin [1]
H1 suggested that a transparent recommendation agent (RA) together with low cognitive effort (T2) will have a greater positive impact on participants’ perception regarding source credibility (H1a), control (H1b), decision quality (H1c), and satisfaction (H1d) than other conditions (T1 and task 33 (T3))
The results showed that exposing the logical reasoning behind the RA had a significant effect on the assortment planners’ perception towards the RA regarding source credibility (H1a), decision quality (H1c), and satisfaction (H1d) (T2 greater than T1, respectively 0.7055, p ≤ 0.0001; 0.4706, p = 0.0474; 0.6068, p = 0.0027)

Summary

Introduction

Optimizing the composition and size of an inventory is critical for retailers to maximize their sales or gross margin [1]. In order to create an optimal assortment of products, both consumers’ needs and retailers’ constraints must be respected [2,3]. In order to create the most optimal assortment of products, assortment planners must examine these variables thoroughly and compare them based on their level of importance which relies on the consumers’. Needs and retailers’ constraints that must be respected This important amount of information that needs to be considered by the assortment planners could negatively impact their decision quality [5], negatively affecting the current and future sales of retailers [1]. In an organizational context, understanding the logical reasoning behind the recommendations of an intelligent agent is crucial for employees to justify their decisions to their superiors [12]. Exposing a part of the algorithm responsible for the recommendations of an RA is usually done to protect the details of an algorithm or to diminish its complexity [14]

Objectives

Methods

Results

Discussion

Conclusion