The influence of the language style of the anchor on consumers' purchase intention

Remarkably, e-commerce anchors have become one of the hot careers in the new media era. As a link between goods and consumers, anchors affect the willingness of consumers to purchase, which eventually impacts the sales volume of commodities in the live broadcast. Therefore, the language style of anchors is of vital significance. However, local and foreign research rarely investigates the interaction between the language style of anchors and different product types and the influential mechanism on consumers' purchase willingness. In light of the SOR theory's logic and from the viewpoint of consumer perceived value, this research study scrutinizes the interaction between the language styles of different authors (appealing to emotion and appealing to logic) and different types of products (hedonic products and practical products), as well as the effect mechanism on the consumers' willingness to purchase. Using questionnaire surveys and empirical analysis, this paper intends to analyze the inherent correlation between study variables, in order to extend valuable suggestions for enterprise practice.

Exploring the Impact of Online Medical Team Engagement on Patient Satisfaction: A Semantic Features Perspective

Online medical teams (OMTs), a new mode of online healthcare service, have emerged in online health communities (OHCs) in China. This study attempts to explore the underlying mechanism of how OMTs' engagement influences patient satisfaction through the lens of semantic features. This study also scrutinizes the moderating effect of multiple specializations on the link between OMTs' engagement and semantic features. We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5000 samples was employed to test the mediation effects. The findings reveal that OMTs' engagement significantly improves language concreteness in online team consultations, which subsequently enhances patient satisfaction. OMT engagement has a negative impact on emotional intensity, ultimately decreasing patient satisfaction. Multiple specializations strengthen the impact of OMT engagement on both language concreteness and emotional intensity. This study contributes to the literature on OMTs and patient satisfaction, providing insights into patients' perceptions of OMTs' engagement during online team consultation. This study also generates several implications for the practice of OHCs and OMTs.

Effect of Prosocial Behaviors on e-Consultations in a Web-Based Health Care Community: Panel Data Analysis

BACKGROUND

Patients using web-based health care communities for e-consultation services have the option to choose their service providers from an extensive digital market. To stand out in this crowded field, doctors in web-based health care communities often engage in prosocial behaviors, such as proactive and reactive actions, to attract more users. However, the effect of these behaviors on the volume of e-consultations remains unclear and warrants further exploration.

OBJECTIVE

This study investigates the impact of various prosocial behaviors on doctors' e-consultation volume in web-based health care communities and the moderating effects of doctors' digital and offline reputations.

METHODS

A panel data set containing information on 2880 doctors over a 22-month period was obtained from one of the largest web-based health care communities in China. Data analysis was conducted using a 2-way fixed effects model with robust clustered SEs. A series of robustness checks were also performed, including alternative measurements of independent variables and estimation methods.

RESULTS

Results indicated that both types of doctors' prosocial behaviors, namely, proactive and reactive actions, positively impacted their e-consultation volume. In terms of the moderating effects of external reputation, doctors' offline professional titles were found to negatively moderate the relationship between their proactive behaviors and their e-consultation volume. However, these titles did not significantly affect the relationship between doctors' reactive behaviors and their e-consultation volume (P=.45). Additionally, doctors' digital recommendations from patients negatively moderated both the relationship between doctors' proactive behaviors and e-consultation volume and the relationship between doctors' reactive behaviors and e-consultation volume.

CONCLUSIONS

Drawing upon functional motives theory and social exchange theory, this study categorizes doctors' prosocial behaviors into proactive and reactive actions. It provides empirical evidence that prosocial behaviors can lead to an increase in e-consultation volume. This study also illuminates the moderating roles doctors' digital and offline reputations play in the relationships between prosocial behaviors and e-consultation volume.

No time to lie: Examining the identity of pro-vaccination and anti-vaccination supporters through user-generated content

OBJECTIVE

This study delves into the social identity of pro-vaccination and anti-vaccination supporters, emphasizing an understanding of the values that shape these distinct identities. Furthermore, the research highlights that user-generated content pertaining to vaccines offers valuable insights into the underlying personal values of both pro-vaccination and anti-vaccination groups.

METHOD

We constructed a textual dataset based on 142,596 tweets. This data was analyzed in three steps. First, the linguistic characteristics of the textual data, together with the underlying personal values of the text creators, were identified using LIWC software. Second, the identified personal values were used as an input for the moderation analysis, which examined the relationship between personal values and social identity for pro- and anti-vaccination groups. Finally, an automated, in-depth text analysis was conducted in Mathematica to understand the narratives created by both groups.

RESULTS

The study findings indicate that both pro-vaccination and anti-vaccination supporters display characteristics of subcultures with distinct group identities. Consequently, based on the results, there is a need for more tailored public health communication strategies that address these two groups separately.

CONCLUSIONS

Understanding how users create health-related content based on their personal values is crucial. Acknowledging and appreciating the diverse personal values and identities within different groups in the vaccination discourse can inform health communication efforts, aligning these efforts with the specific values of each group. This targeted communication is vital for effectively conveying relevant peer-reviewed health information amid the abundance of health-related user-generated content.

Continuous idea contribution in open innovation communities: The role of verbal persuasion from peers

Motivating continuous idea contributions from members is especially challenging for open innovation communities (OIC). Scholars have thus explored a range of incentives, among which peer feedback has received much attention. However, existing research on peer feedback tends to focus on the structural features, ignoring the richness of the text. To fill this research gap, this study investigates the influence of feedback language use from peers, including emotional support and constructive feedback, on individuals' continuous idea contributions, based on the creative self-efficacy theory. The results show that emotional support, especially emotional approval, positively affects members' continuous contributions, and that the effect is stronger when the provider is of a higher status. However, individualized consideration does not seem to work. In addition, in terms of the effect of constructive feedback, we also find support from cognitive stimulation, while intellectual stimulation exerts an effect only when the provider's status is high. Overall, these findings extend the current research on peer feedback and offer practical guidelines to the open innovation community.

Steele L, de Vreede GJ. Decide Now or Later: Making Sense of Incoherence Across Online Reviews

Consumers read online reviews to decide whether to buy a product. Extensive research examines what makes a single review helpful or credible, yet there is very limited understanding of how a collection of reviews facilitates purchase decisions. Such understanding is critical because consumers rarely consult all reviews or a single review. They often start by reading the “top reviews” that a website highlights, then deciding whether to read additional reviews and how many. This paper investigates how inconsistency among top reviews affects a consumer’s purchase deferral—the likelihood to decide immediately or defer the decision until after obtaining more information. We found that, if different reviewers disagree on their opinions about the same feature of a product, consumers are more likely to defer the purchase decision and consult more reviews. Further, this effect is weaker when reviewers provide specific details about their needs or use of the product along with their opinions. This work provides guidance to review platforms on how to select and present a set of top reviews. Our findings also inform retailers and product manufacturers on how to focus their attention in dealing with reviews and when a focus only on the top reviews is not sufficient.

sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics

This study proposes a novel supervised deep topic modeling approach for effective text analysis. This approach leverages the auxiliary data associated with text, such as ratings in consumer reviews or categories of posts in online forums, to enhance the discovery of latent topics in text. The proposed approach can effectively improve topic modeling performance in several ways. First, the learned latent topics are more meaningful and distinguishable, which helps text data exploration. Second, the latent topics discovered by the novel supervised deep topic model are more accurate, which improves the performance of downstream econometrics and predictive analytics that utilize latent topics as inputs. Given the prevalence of auxiliary data in real-world text analysis tasks and the wide adoption of topic modeling in business research and practice, the study offers an effective solution for extracting insights from text data.

The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model

Background

Messages on one's stance toward vaccination on microblogging sites may affect the reader's decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited.

Objective

This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message.

Methods

English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user.

Results

Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001).

Conclusions

The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics.

What Questions Are You Inclined to Answer? Effects of Hierarchy in Corporate Q&A Communities

Are employees willing to voluntarily share knowledge with their higher-ups? The existing studies show that the answer is no—employees are less likely to share knowledge with their higher-ups in the offline setting, corporate wikis, and online discussion groups. We answer the same question in a corporate question-and-answer (Q&A) community and argue that the answer can be yes. A potential-dyads approach and a quasi-natural experiment jointly demonstrate that employees are inclined to answer a question from their higher-ups and even exert more effort in those answers. Using an instrumental-variable design, we show that users who post more answers to higher-ranked individuals and who display greater effort in those answers are more likely to get promoted in subsequent years, meaning that employees do not need to worry about their careers when sharing knowledge with their higher-ups in corporate Q&A communities. Our research, together with research on other contexts, are useful for companies to take the role of the managers into account when considering which type of online community to adopt. Community designers can use our findings to better motivate knowledge sharing by considering users’ different job ranks.