Ronald Timothy Cenfetelli
Relevant Degree Programs
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - May 2019)
An online recommendation agent (RA) provides users assistance by eliciting from users theirproduct preferences and then recommending products that satisfy these preferences. While the importance of the RA has been emphasized by practitioners and scholars, precisely how toimplement the RA, and what an RA’s effectiveness is relative to other recommendation sources,are not well understood. Through three empirical studies conducted utilizing the experimentalmethod, this dissertation evaluates and improves the input, process, and output interfaces of an RA to facilitate the communication between consumers and RAs in order to reduce decisioneffort and enhance the quality of their purchasing decisions. Regarding the input component of an RA, Study 1 finds that an RA that interactively demonstrates trade-offs among product attributes improves consumers’ perceived enjoyment and perceived product diagnosticity. It also finds that a medium level of trade-off transparency should be revealed to the user, as it leads to the best perceived enjoyment and product diagnosticity. Further, Study 1 augments the Effort-Accuracy Framework by proposing perceived enjoyment and perceived product diagnosticity as two antecedents for decision quality and decision effort. With respect to the process component of an RA, Study 2 evaluates the efficacy of three types of user feedback (attribute-based feedback, alternative-based feedback, and integrated feedback) in an e-commerce setting and shows that they are better than the absence of feedback in terms of perceived decision effort. Additionally, Study 2 demonstrates that the recommendation source (RA, consumers, or experts) moderates the effects of the three types of user feedback on perceived decision quality. Regarding the output component, Study 3 shows that users are more likely to select a product that is commonly recommended by multiple sources. This also results in higher perceived decision quality. Study 3 also reveals that users with high product knowledge or task involvement are more likely to adhere to the recommendation from the RA as compared to recommendations from experts or consumers. Further, users who rely on the RA’s recommendations will perceive a higher level of decision quality as compared to those who rely on consumer or expert recommendations.
Master's Student Supervision (2010 - 2018)
This study examines the role of technology in motivating online consumers to purchase green products. The cause-and-effect simulation proposed by Fogg (2002) and the construal-level theory (CLT, Liberman and Trope 1998) are employed to develop two website designs: 1) the low-level and 2) the high-level cause-and-effect simulation. Both simulation designs show the relationship between consumers’ decision-making on the product attributes, the cause, and its impact on resources (e.g., energy) the product consumes, the effect. A recommendation agent (RA) is used to reflect the cause part of both designs. The main difference between the two designs is the effect part developed based on CLT. Specifically, the low-level simulation presents a more concrete, short-term effect, the utility cost per load, and the high-level simulation provides a more abstract, long-term effect, the 10-year utility cost. We compare these two designs against two control conditions—1) the no simulation which does not provide the RA and the utility cost and 2) the partial simulation which has only the RA. An online experiment with 79 participants was conducted to evaluate the effectiveness of the simulation design on the green selection. Specifically, we assess whether the simulation design could persuade people to pay more for green products. The experimental results show both low-level and high-level simulations successfully motivate participants to choose greener products than the no simulation and the partial simulation. Moreover, the results suggest both full designs persuade participants to go green by enhancing the desirability consideration associated with the outcome resulting from the green purchase. This consideration was found to influence self-efficacy which leads to greener choices. Self-efficacy was also found to have a greater impact on the green product selection than participants’ attitudes. This is evident by the fact that participants generally have a pre-existing positive intention to buy green products. Thus, the role of the cause-and-effect simulation design is not so much to change people’s attitudes, but rather to reinforce those positive attitudes and thus help participants to abide by their good intentions. In other words, it helps increase self-efficacy that would be the key to promote the green purchase.