The Effect of Online Review Portal Design: The Moderating Role of Explanations for Review Filtering

The flood of non-constructive and fake online consumer reviews erects a considerable barrier to consumers making efficient decisions. Various review filtering algorithms have been developed to address this challenge, but the design of post-development review portals continues to lack a consensus. In review portals, disclosing more transparent reviews is efficient for enhancing users’ trust. However, it will cause users’ diminished focus on recommended reviews, leading to sub-optimal decisions. A research model is then developed to investigate users’ cognitive processes in their responses to three review exhibition designs (i.e., informed silent display design, filtered review display design, and composite display design) regarding trust in the review portal and perceived decision quality. We also suggest that explanations for review filtering play a moderating role in users’ perceptions, which appears to be a viable resolution to this dilemma. This paper provides significant theoretical and practical insights for the review portal design and implementation

Are Online Consumer Reviews Credible? A Predictive Model based on Deep Learning

As the importance of online consumer reviews has grown, the concerns about their credibility being damaged by the presence of fake reviews have also grown. Extant literature reveals the importance of online reviews for consumers. Yet, there is a lack of research in the literature that considers consumer perception while developing a predictive model for the credibility of online reviews. This research aims to fill this gap by combining two different streams in the literature namely human-driven and data-driven approaches. To do so, we use two datasets with different labelling approaches to develop a predictive model, the first one is labelled based on the Yelp filtering algorithm and the second one is labelled based on the crowd’s perception towards credibility. Results from our predictive model reveal that it can predict credibility with a performance of 82% AUC, using reviews’ attributes namely, length, subjectivity, readability, extremity, external and internal consistency

Text Versus Paratext: Understanding Individuals’ Accuracy in Assessing Online Information

Fake news has emerged as a significant problem for society. Recent research has shown that shifting attention to accuracy improves the quality of content shared by individuals, thereby helping us mitigate the harmful effects of fake news. However, the parts of a news story that can influence individuals’ ability to discern the true state of information presented to them are understudied. We conducted an online experiment (N=408) to determine how different elements (text and paratext) of a news story influence individuals’ ability to detect the true state of the information presented. The participants were presented with the headline (control), main text, graphs/images, and sharing statistics of true and fake news stories and asked to evaluate the story's accuracy based on each of these elements separately. Our findings indicate that individuals were less accurate when identifying fake news from headlines, text, and graphs/images. When asked to evaluate the story based on sharing statistics, they were able to distinguish fake stories from real news with greater accuracy. Our findings also indicate that heuristics that apply to true news are ineffective for detecting the veracity of fake news

FAKE REVIEWS AND MANIPULATION: DO CUSTOMER REVIEWS MATTER?

With the prevalence of fake reviews across web and e-commerce platforms it has become difficult for the customers to make an informed purchase decision. Considering this we examine the influence of review manipulation on customer’s purchase decision. A qualitative approach employing interviews with frequent online shoppers was employed to explore the phenomenon. The results of the study suggest that customers accord recommendations from their social network more weightage than the reviews available on an e-commerce platform. Further, we found that customers apply either or both interactive and extractive strategies to deal with review manipulation. Keywords: information processing, review manipulation, fake reviews, grounded theory

Healthy Deliciousness': Discovering the Secret to Healthy Eating via Social Media.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Implicit Partner Evaluations: How They Form and Affect Close Relationships

For decades, research on couples has attempted to understand the source of relationship decay by explicitly asking people how they evaluate their relationships. Ironically, however, relationship science also indicates that people seem largely indisposed to acknowledge some aspects of their relationships in self-report questionnaires, particularly when those are undesirable. To circumvent these limitations, a growing body of work has started to employ more indirect measurement tools (the so-called ‘implicit measures’) to capture people’s spontaneous evaluative associations, or gut-feeling reactions, toward their partner: their implicit partner evaluations. Recent evidence suggests that implicit partner evaluations, as assessed by implicit measures, differ quite sharply from self-reported explicit evaluations and predict later relationship quality and stability, even when explicit evaluations do not. To date, however, little is known about the sources of implicit partner evaluations and the reasons why they have such powerful predictive power. The present dissertation contributes to this growing field of research in many ways by examining how implicit partner evaluations form and affect close relationships in everyday life. First, using a combination of longitudinal and observational methods, Chapter 2 provides evidence that, compared to their explicit counterparts, implicit partner evaluations remain more stable over time, are more resistant to day-to-day relationship experiences, and update gradually as relationship experiences accumulate in time. Second, Chapters 3 and 4 capitalize on diary and experimental designs to show that one of the reasons why implicit partner implicit partner evaluations have important implications for relationship maintenance is because, under specific yet prevalent conditions (i.e., when opportunities to deliberate are limited), they determine daily behaviors that are critical for long-term relational well-being, such as nonverbal communication in problem-solving conversations and forgiveness toward the partner’s offense. Third, drawing on a large dyadic sample of newlyweds, Chapter 5 further extends these findings by showing that having ambivalent implicit partner evaluations can also affect relationship functioning over time by motivating spouses to make behavioral efforts that may improve their marital problems. Last, Chapter 6 describes how studying implicit evaluations in close relationship contexts can also invigorate basic implicit social cognition research on how attitudes change and affect behavior in the real world, and inform interventions for society. Taken together, the findings from the present dissertation provide novel insights about the key role of implicit partner evaluations in relational contexts, and further illustrate the scientific and practical value of integrating research in relationship science and implicit social cognition

Who Gets the Job? Synthesis of Literature Findings on Provider Success in Crowdsourcing Marketplaces

Background: Over the past decade, crowdsourcing marketplaces — online exchange platforms which facilitate commercial outsourcing of services — have witnessed a dramatic growth in the number of participants (service providers and customers) and the value of outsourced services. Deciding about the most appropriate provider is a key challenge for customers in crowdsourcing marketplaces because available information about providers may be incomplete and sometimes irrelevant for customer decisions. Ineffective information impedes many service providers to develop long-term relationships with customers, obtain projects on a regular basis and survive on crowdsourcing marketplaces. Previous studies have investigated the impact of a range of factors on customers’ choice decisions and providers’ success, given the important role of customer–provider relationship development for long-term success on crowdsourcing marketplaces. Method: This paper reviews the literature of crowdsourcing marketplaces with the aim of developing a comprehensive list of factors that influence customers’ choice decisions and providers’ success. Results: We found 31 conceptually distinct profile information components/factors that determine customers’ choices and providers’ business outcomes on crowdsourcing marketplaces. Conclusion: We classified these 31 factors into five major categories: 1) prior relationship between a customer and a provider or a customer’s invitation, 2) providers’ bidding behavior, 3) crowdsourcing marketplace or auction characteristics, 4) providers’ profile information, and 5) customer characteristics. The main factors in each category, associated considerations, related literature gaps and avenues for future research are discussed in detail

Human-Machine Communication: Complete Volume. Volume 3. Diffusion of Human-Machine Communication During and After the COVID-19 Pandemic

This is the complete volume of HMC Volume 3. Diffusion of Human-Machine Communication During and After the COVID-19 Pandemi

Journal of Communication Pedagogy, Complete Volume 4, 2021

This is the complete volume 4 of the Journal of Communication Pedagogy

Attribution and Attribution Error in Relationship to False Confessions

False confessions are the most difficult type of confession to detect. Because the Reid interview and interrogation technique is the global gold standard for interviews, interrogations, and confessions, it is used to obtain confessions from suspects. However, the Reid method has been untested in regard to if it can detect false confessions to potentially eliminate wrongful convictions. The purpose of this qualitative study was to perform a content analysis of videos of confessions using several models that make up the Reid interview and interrogation technique. Utilizing attribution theory as a framework, these models were qualitatively assessed for their ability to detect false confessions in comparison with the legal casebook analysis and linguistic analysis. The key research questions addressed how interviewers attribute identification of false confessions through the assessment of the various models and the complete Reid interview and interrogation technique. An additional research question concerned how interviewers identify attribution error in false confessions through the assessment of the various models and the complete Reid interview and interrogation technique. Data were collected from 6 videos and subjected to content analysis, triangulated with discourse analysis and conversation analysis. The results of this study showed that the models applied to the confessions could distinguish between true and false confessions. A social change could occur if some or all of these models are applied to all interrogations to detect false confessions, which would provide law enforcement and the intelligence professions the tools to assess confessions in order to potentially stop wrongful convictions and intelligence failures in interviews and interrogations