Examining the effect of reviewer expertise and personality on reviewer satisfaction: An empirical study of TripAdvisor

Is negative e-WOM more powerful? Multimodal data analysis on air passengers’ perception of COVID-19 safety measures

During the normalization stage of the COVID-19 epidemic prevention and control, the safety threats caused by improper epidemic prevention measures of airlines have become the primary concern for air passengers. Negative e-WOM related to safety perception obtained based on online multimodal reviews of travel websites has become an important decision-making basis for potential air passengers when making airline choices. This study aims to examine the relationship between potential air passengers’ negative safety perception and the usefulness of online reviews, as well as to test the moderating effect of review modality and airline type. It also further explores the effectiveness and feasibility of applying big data sentiment analysis to e-WOM management. To this end, the theoretical model of negative safety perception, review modality, and airline type affecting review usefulness was constructed. Then we select 10 low-cost airlines and 10 full-service airlines, respectively, according to the number of reviews sorted by the TripAdvisor website, and use crawling techniques to obtain 10,485 reviews related to COVID-19 safety of the above companies from December 2019 to date, and conduct safety perception sentiment analysis based on Python’s Textblob library. Finally, to avoid data overdispersion, the model is empirically analyzed by negative binomial regression using R software. The results indicate that (1) Negative safety perception significantly and negatively affects review usefulness, that is, extreme negative safety perception can provide higher review usefulness for potential air passengers. (2) Review modality and airline type have a significant moderating effect on the relationship between negative safety perception and review usefulness, in which multimodal reviews and full-service airlines both weakened the negative impact of negative safety perception on review usefulness. The theoretical model in this paper is both an extension of the application of big data sentiment analysis techniques and a beneficial supplement to current research findings of e-WOM, providing an important reference for potential air passengers to identify useful reviews accurately and thus reduce safety risks in online decision-making.

How Do Review Party's Identity Cues Influence Consumers' Online Review Adoption Intention?

Professionalism and popularity are two important external identity cues of the review party. Previous studies have mostly focused on the content of the reviewers' comments. However, few studies have explored the potential impact of the review party's cues on consumers' adoption willingness and consumption behavior. This study mainly examined the neural mechanisms of how the differences in the two identity cues of the review party affect consumers while adopting the comments. The current study employed an event-related potential (ERP) experiment, in which the participants were asked to make a personal choice quickly based on the review party's identity cues after seeing the target product. A 2-level professionalism (low vs. high) x 2-level popularity (low vs. high) experiment design was used to test the impact of the review party's professionalism and popularity on consumers' intentions to adopt the review. The behavioral data reveal that the two identity cues of the review party impact the adoption rate, and the review party's popularity has an impact on the reaction time. The ERP data indicate that the review party's popularity affects the perceived risk (the N2 component, which is a high-risk signal) and the two identity cues of the review party affect the evaluation and classification process [the later positive potential (LPP) component]. These results indicate that when the review party has a high degree of professionalism, its popularity has less influence on consumers' review adoption intention. On the contrary, when the level of professionalism is low, high popularity will promote consumers' review adoption intention. Compared to professionalism, popularity is a higher risk cue for consumers.

Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?

With a surging number of online courses on MOOC (Massive Open Online Course) platforms, online learners face increasing difficulties in choosing which courses to take. Online course reviews posted by previous learners provide valuable information for prospective learners to make informed course selections. This research investigates the effects of reviewer experience and expertise on reviewer competence in contributing high-quality and helpful reviews for online courses. The empirical study of 39,114 online reviews from 3276 online courses on a leading MOOC platform in China reveals that both reviewer experience and expertise positively affect reviewer competence in contributing helpful reviews. In particular, the effect of reviewer expertise on reviewer competence in contributing helpful reviews is much more prominent than that of reviewer experience. Reviewer experience and expertise do not interact in enhancing reviewer competence. The analysis also reveals distinct groups of reviewers. Specifically, reviewers with low expertise and low experience contribute the majority of the reviews; reviewers with high expertise and high experience are rare, accounting for a small portion of the reviews; the rest of the reviews are from reviewers with high expertise, but low experience, or those with low expertise, but high experience. Our work offers a new analytical approach to online learning and online review literature by considering reviewer experience and expertise as reviewer competence dimensions. The results suggest the necessity of focusing on reviewer expertise, instead of reviewer experience, in choosing and recommending reviewers for online courses.

Exploring the Association Between the "Big Five" Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis

BACKGROUND

Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction-the heart of this problem-ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction.

OBJECTIVE

The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner.

METHODS

We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level "Big Five" psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county.

RESULTS

After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, P<.001), neuroticism (β=.248, P<.001), and conscientiousness (β=.229, P<.001).

CONCLUSIONS

Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.