Yuhua (Jake) Liang

Students’ Reactions to Teachers’ Management of Compulsive Communicators

Using Expectancy Violations Theory as a framework, this study examined students’ perceptions of how teachers manage compulsive communicators (CCs). College students (n=265) were given one of three scenarios describing a teacher managing a compulsive communicator. After reading the scenario, students were asked to rate the expectedness of the teachers’ management behavior, along with their perceptions of the teachers credibility and their affect toward the teacher. Results indicated that students have specific expectations for how teachers should manage CCs in the classroom. Moreover, the type of management strategy employed influenced students’ ratings of credibility and their affect toward the teacher. Pedagogical implications, limitations, and directions for future research are discussed.

Mindlessness and gaining compliance in Computer-Human Interaction

The computers are social actors (CASA) effect refers to the application of social rules when individuals interact with computers. Although the most plausible mechanism for CASA involves mindlessness, according to Langer, Blank, and Chanowitzs (1978) original theorization, mindlessness stems from a motivational deficit during a compliance gaining interaction. Mindlessness occurs when individuals view their behavior as relatively unimportant or inconsequential. However, researchers often employ a cognitive deficit approach and examine the CASA effect as a perceptual rather than behavioral outcome. Moreover, previous findings suggest that computer agents may activate the norm of reciprocity to induce human compliance (Fogg & Nass, 1997). To test the mechanism of mindlessness and address possible methodological artifacts from past work on Computer-Human reciprocity, an experiment employing a 3 (request type: direct, placebic, or sufficient)x3 (request size: large, medium, or small) design tested alternative hypotheses based on the cognitive and motivational explanations. The data are consistent the motivational hypothesis. In contrast to previous findings from Fogg and Nass, neither obligation, liking, nor mood correlated with compliance. The findings offer several directions for future work.

Reading to make a decision or to reduce cognitive dissonance? The effect of selecting and reading online reviews from a post-decision context

This research challenges the presumption that reading online reviews solely affect reader attitudes toward a prospective decision. Instead, readers may strategically select and read reviews after a decision. This research advances and tests hypotheses proposing that: (1) post-decision readers select decision-supportive reviews earlier and more frequently; and (2) the reviews they read affect the subsequent cognitive dissonance they experienced. Two studies employed an original post-decision experiment and demonstrated that readers expressed biased review selection. Interestingly, the reviews they read affected and increased their cognitive dissonance. The findings contributed to understanding the complex dynamic of online reviews in a post-decision context.

Message Characteristics in Online Product Reviews and Consumer Ratings of Helpfulness

Consumers often rate online product reviews in terms of helpfulness. To explore the linguistic features that may contribute to helpfulness ratings, a linguistic inquiry and word count analysis compared 377 helpful and unhelpful Amazon.com product reviews, showing that helpful and unhelpful reviews differed across 23 of 67 linguistic categories. Results also suggested a consumer preference for messages that contain objective characteristics and high-quality arguments. A follow-up study tested hypothesized relationships among helpfulness ratings and review relevance, descriptiveness, and evaluation discrepancy. The data showed that descriptive reviews were rated as helpful. Implications, limitations, and directions for future research are discussed.

The Role of Reciprocity in Verbally Persuasive Robots

The current research examines the persuasive effects of reciprocity in the context of human-robot interaction. This is an important theoretical and practical extension of persuasive robotics by testing (1) if robots can utilize verbal requests and (2) if robots can utilize persuasive mechanisms (e.g., reciprocity) to gain human compliance. Participants played a trivia game with a robot teammate. The ostensibly autonomous robot helped (or failed to help) the participants by providing the correct (vs. incorrect) trivia answers. Then, the robot directly asked participants to complete a 15-minute task for pattern recognition. Compared to no help, results showed that a robots prior helping behavior significantly increased the likelihood of compliance (60 percent vs. 33 percent). Interestingly, participants evaluations toward the robot (i.e., competence, warmth, and trustworthiness) did not predict compliance. These results also provided an insightful comparison showing that participants complied at similar rates with the robot and with computer agents. This result documents a clear empirically powerful potential for the role of verbal messages in persuasive robotics.

Responses to Negative Student Evaluations on RateMyProfessors.com: The Effect of Instructor Statement of Credibility on Student Lower-Level Cognitive Learning and State Motivation to Learn

Instructors have the ability to respond to student evaluations on RateMyProfessors.com (RMP). The current research conceptualized the juxtaposition of student evaluations and instructor responses using communication processes on participatory websites. In an original experiment, the results demonstrated that when faced with multiple negative student evaluations on RMP, an instructors statement of trustworthiness robustly increased students’ lower-level cognitive learning. A supplemental analysis revealed that an instructors statement of competence, caring, and trustworthiness additively increased the likelihood of student enrollment toward course content. This research established the empirical efficacy of instructor responses to student evaluations on RMP.

Reciprocity in Computer–Human Interaction: Source-Based, Norm-Based, and Affect-Based Explanations

Individuals often apply social rules when they interact with computers, and this is known as the Computers Are Social Actors (CASA) effect. Following previous work, one approach to understand the mechanism responsible for CASA is to utilize computer agents and have the agents attempt to gain human compliance (e.g., completing a pattern recognition task). The current study focuses on three key factors frequently cited to influence traditional notions of compliance: evaluations toward the source (competence and warmth), normative influence (reciprocity), and affective influence (mood). Structural equation modeling assessed the effects of these factors on human compliance with computer request. The final model shows that norm-based influence (reciprocity) increased the likelihood of compliance, while evaluations toward the computer agent did not significantly influence compliance.

The Effect of Peer and Online Sources on Student Course Selections and Impressions of Prospective Teachers

Students may learn about prospective teachers and obtain information about them by communicating with peers and/or conferring with online teacher rating systems such as RateMyProfessors.com. Drawing on media richness theory, artificial scenarios (Study 1) and recall prompts (Study 2) compared the effects of these information sources on how students select courses and form teacher impressions. The results showed that the valence of the information students received affected their course selection decisions and impressions about the prospective teacher. However, the two sources did not differ in their effects. Study 3 found that using multiple sources of information affected students above and beyond any single source alone. The results highlight and draw implications regarding the effect of these information sources in the higher education environment.

Robotic foot-in-the-door: Using sequential-request persuasive strategies in human-robot interaction

Abstract The current study investigates the effectiveness of sequential-request strategies that robots may employ to persuade humans. Specifically, this study focuses on the foot-in-the-door technique, whereby a small request is made first and is then followed up with a larger, actual target request. Participants played a trivia game with an ostensibly autonomous robot teammate. At the end of the game, the robot asked participants to complete a series of pattern recognition tasks, either by requesting directly or by starting with a small request, then following with a larger request. The results demonstrated a strong foot-in-the-door effect, suggesting a robots potential to persuade humans using verbal message strategies. The robots performance or perceived credibility did not influence compliance. This robotic foot-in-the-door effect provides some important practical implications for designers and developers who aim to enhance the persuasive outcomes of human-robot interaction.

Fear of Autonomous Robots and Artificial Intelligence: Evidence from National Representative Data with Probability Sampling

People vary in the extent to which they report fear toward robots, especially when they perceive that the robot is autonomous or has artificial intelligence. This research examines a specific form of sociological fear, which we name as fear of autonomous robots and artificial intelligence (FARAI). This fear may serve to affect how people will respond to and interact with robots. Applying data from a nationally representative dataset with probability sampling (Nxa0=xa01541), research questions examine (1) the extent and frequency of FARAI, (2) demographic and media exposure predictors, and (3) correlates with other types of fear (i.e., loneliness, drones, and unemployment). A latent class analysis reveals that approximately 26% of participants reported experiencing a heightened level of FARAI. Demographic analyses show that FARAI is connected to participant sex, age, education, and household income; albeit these effects were small. Media exposure to science fiction predicts FARAI above and beyond the demographic variables. Correlational results indicate that FARAI is associated with other types of fear, including loneliness, becoming unemployed, and drone use. In sum, these findings render a much needed glimpse and update regarding how much individuals fear robots and artificial intelligence.