Ronald Timothy Cenfetelli
Relevant Thesis-Based Degree Programs
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Graduate Student Supervision
Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
With the continued growth of technologies, persuasion practices in online settings are on the rise. However, the use of technologies is a double-edged sword. Technologies can influence users without their awareness of being persuaded, making users more vulnerable to such influence. As technologies have been embedded throughout online platforms and provided more insights about their users, there is a major possibility of persuading users via technology design. Thus, the likelihood of being persuaded without awareness will increase. However, extant literature posits perceived persuasion beliefs can also promote careful evaluations and decisions (Friestad and Wright 1994). Despite its importance, persuasion awareness has received little attention in IS research. To this end, I attempt to address the following questions: What are the key features of persuasive design which influence online users’ persuasion awareness? How do persuasive design features affect users’ persuasion awareness and behavioral responses? What are the mechanisms which improve users’ persuasion awareness?To answer these questions, I propose a theoretical model of persuasion awareness in online settings and empirically investigate it in an e-commerce context in empirical study 1 and 2. Relying on the Decision Support System literature and Toulmin (2003), I identify two forms of persuasive design features (PDF)—suggestive and supportive—and analyze the suggestive form in terms of its content, mode, and invocation style. I apply the Persuasion Knowledge Model to outline how users perceive and respond to persuasion attempts triggered by online entities, and identify transparency mechanisms, specific ways in which entities can be designed to influence users’ persuasion awareness. An integrated model and a typology of PDF are discussed in Chapter 3. Study 1 (Chapter 4) reveals that suggestive content affects perceived persuasion and assistance beliefs, which, in turn, shape users’ responses. Also, only perceived persuasion increases careful evaluations of targeted products. Study 2 (Chapter 5) adds persuasion transparency information disclosing persuasion tactics online entities use. Results demonstrate that persuasion transparency enhances perceived persuasion and dampens perceived assistance. Thus, persuasion transparency improves users’ persuasion awareness. Overall, this thesis serves as an initial step toward understanding online persuasion awareness that promotes users’ informed evaluations and decisions.
Inadvertent and Irrational human errors (e.g., clicking on phishing emails) have been the primary cause of security breaches in recent years. It has been estimated that these errors are a source of approximately 84% of all breaches in 2017 (Sher-Jan, 2018). To understand the root cause of these errors and examine practical solutions for personal users, I applied the theory of bounded rationality (Simon, 1972, 2000). In the second chapter, I examined the role of several factors (i.e., objective knowledge, subjective knowledge, and default security level) on how secure a decision made by a personal user is (i.e., security level of user’s decision). I discovered that the default security level has the most significant influence on the security level of a user’s decision. Furthermore, the results illustrated that subjective security knowledge mediates the impact of objective security knowledge on security decisions. In Chapter 3, I explored the role of heuristics (i.e., short mental processes) in security decision making. Interviews conducted reveal that users rely on various heuristics to simplify their decision making. Specifically, users rely on experts’ comments (i.e., expertise heuristic), information at hand, such as recent events (i.e., availability heuristic), and security-representative visual cues (i.e., representativeness heuristic). Findings also showed the use of other heuristics, including affect, brand, and anchoring, to a lesser degree. In Chapter 4, I examined the impact of several nudging strategies by using the most prevalent heuristic cues discovered in Chapter 3 and the construal level (i.e., level of abstraction) of messages on users’ security decisions. Using the security level of settings and password entropy as measures of the overall degree of security, users made more secure decisions in the presence of any of the heuristic cues irrespective of the construal level compared to the baseline group (i.e., no-message group). Additionally, with respect to the security level of settings, low-level construal availability, low-level construal representativeness, and high-level construal expertise had the highest impact. For password entropy, low-level construal availability and low-level construal representativeness were also the most effective combination. However, there was no significant difference between high-level and low-level construal expertise conditions.
Over two billion people use Facebook, the world’s largest social network site (SNS). Most SNS communication research to date focuses on primary communication (disclosure) within a SNS (e.g. Dinev and Hart 2006; Krasnova 2010, 2012; Norberg 2009; Pavlou 2011; Smith et al. 2011). I extend SNS communication research by examining secondary communication (asking, reacting, and responding). I extend the APCO (antecedents, privacy, outcomes) model (Smith et al. 2011) to ABCO (antecedents, costs, benefits, outcomes). I develop and test three models to explain, and gain insights about, secondary communication in SNSs. In study 1, I examine how feedback (likes and views) affects a SNS users intentions to disclose information in the future. My findings show that when a user receives positive feedback (likes) it affects his benefits more, whereas neutral feedback (views) affects his privacy concerns more; however, these effects are moderated significantly by audience closeness and information sensitivity. I proposed that a viewership feedback tool could increase transparency and help users to make more informed disclosure decisions, and reify the invisible audience (Bernstein et al. 2013).In study 2, I develop and test a model of SNS solicitation (asking for information or support) as social capital conversion. My results confirm that larger networks lead to larger perceived social capital but perceptions of social capital are not always predictive of information and support seeking intentions. Predicting mobilization requires a deeper understanding of how user traits and preferences lead to network formation and how user perceptions of privacy and impression management enable or disrupt conversion of social capital through mobilization intentions. I identity four solicitation archetypes based on network size/value, and solicitation intentions: the concerned user, the power user, the occasional user, and the habituated user.In study 3, I adopt social exchange theory (Emerson 1976) to show that when providing feedback to others, SNS users’ impression-management benefits are more important than privacy concerns, and—surprisingly—that providing anonymity inhibits providing feedback by lowering the impression-management benefits.
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
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
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.