UBC Theses and Dissertations
Investigating consumers' use of product recommendation agents: understanding the influence of product type and in-store context Lee, Young Eun
Product Recommendation Agents (RAs) are web-based software systems that advise consumers about what to buy based on the needs expressed by those consumers. Most research on RAs has focused on evaluating the different algorithms that generate recommendations, while the effectiveness of RAs is determined by many factors beyond design of algorithms. This dissertation focuses on two important but understudied factors, namely, the influences of product type and in-store context. These factors were investigated in two separate laboratory experiments: Study 1 and Study 2. Study 1 examined the influence products with high emotional contents on consumers' use of RAs. Such products are characterized by attribute conflicts which involve the correlation of the favourable values of some attributes with the unfavourable values of others. Two RAs - one that highlights attribute conflicts and one that presents them implicitly - were developed and compared. The experiment results show that the RA highlighting attribute conflicts negatively influences consumers' perceptions and acceptance of RAs, as compared to the RA obscuring the conflicts. In addition, task emotionality - the degree to which a decision task is perceived to bring severe negative consequences - moderates such relationships. Study 2 examined the influence of in-store contexts on consumers' use of RAs. The in-store context has become important with the advent of mobile RAs operated on handheld devices, such as Personal Digital Assistants. In the in-store context, compatibility between the way the store displays the products and the way RAs guides consumers' decision making is an important predictor of decision performance. Two RAs which guide consumers' decisions in two different manners - alternative-driven and attribute-driven ways - were contrasted. Due to a limit to displaying complex products sorted by every attribute on product shelves, products are displayed randomly by alternative in a store. Therefore, the alternative-driven RA is more compatible with the store's product displays than attribute-driven RA. The experiment results show that compatibility increases both accuracy of decisions and perceived control. In addition, mobile RA use, as compared to RA non-use, decreases consumers' perceived effort and increases their intentions to return to the store.
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