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The purpose of this paper is to examine the driving factors of user innovation behaviors using the case of smartphone applications to estimate the indirect and direct effects of consumers’ attitudes toward user-generated content (UGC).

This study proposes a structural model to examine the relationship between user innovation behaviors toward UGC and three attitude factors: involvement, consumer knowledge and customer orientation. The empirical analysis is based on a consumer survey that examines the commonalities and differences between Japan and China. In each country, two social media services are chosen as representative cases of the UGC business model to measure user innovation behaviors toward the quality and quantity aspects.

Customer orientation is the most significant driving factor of user innovation behaviors toward UGC. It positively affects both the number of followers and the frequency of information transmissions. In particular, for the quality dimension of user innovation, customer orientation has a more significant effect on the number of followers than does familiarity.

This study emphasizes the quality aspect of user innovation. Previous research has focused on the quantity of user innovation behaviors by measuring the amount of information. However, this research measures both the quality and the quantity aspects with the number of followers and the frequency of uploading content. The findings of this study suggest that companies should maintain relationships with highly customer-oriented users to manage content quality.

E-commerce content platforms are a typical type of multi-sided platform that combines transactions with social media platforms. To solve the managerial dilemma of balancing the tension between control and autonomy of influencers' output performance, this study aims to investigate how exercising output controls through performance rewards and performance punishments impact the quantity and quality of influencers' content generation.

Choosing JD WeChat Shopping Circle as the research context and leveraging the introduction of a double commission subsidy policy and a removal policy as quasi-natural experiments, this study applied the difference-in-differences (DID) method to empirically test hypotheses.

Performance rewards incentivize influencers to generate high-quality content, but such incentivizing effects attenuate over time. Performance punishment drives influencers to generate expected pieces of high-quality content, and such safeguarding effects accentuate over time.

This study proposes output controls as an important form of governance mechanism in multi-sided platforms and substantiates how rewards and punishments as two facets of incentives affect complementors' behaviors. In addition, by distinguishing performance-contingent rewards from completion-contingent rewards, this study helps resolve the mixed findings on the effects of monetary rewards in the user-generated content (UGC) literature.

The purpose of this paper is to quantify how mobile app usage relates to the unique characteristics of behavioral orientations and content types, focussing on the interrelationship among content usage in the context of in-app purchase.

Using a large-scale data set of individual content usage in a particular music mobile app, the author builds a simultaneous equation panel data model to examine dynamic interdependent usage of mobile app.

The paper finds a positive temporal effect of self-oriented content usage (download) on other-oriented content usage (gift), based on behavioral orientation, and also a temporal interdependence between external (ringtone) and internal usage (mp3) based on types of content. The paper also finds that the fourth generation communications standard increases content usage in this mobile app.

These findings provide useful insights for mobile app developers, mobile network operators, content providers, and mobile device manufacturers.

This paper is one of the first to consider and empirically test the interrelationship between various kinds of content usage in music apps.

A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting.

Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations.

There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience.

Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively.

Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.

Over the past decade, the rapid evolution of social media has impacted the field of human resource management in numerous ways. In response, scholars and practitioners have sought to begin an investigation of the myriad of ways that social media impacts organizations. To date, research evidence on a range of HR-related topics are just beginning to emerge, but are scattered across a range of diverse literatures. The principal aim of this chapter is to review the current literature on the study of social media in HRM and to integrate these disparate emerging literatures. During our review, we discuss the existent research, describe the theoretical foundations of such work, and summarize key research findings and themes into a coherent social media framework relevant to HRM. Finally, we offer recommendations for future work that can enhance knowledge of social media’s impact in organizations.

Short-form videos have gradually become important marketing tools for tourist destinations. However, chaotic sources and homogenized content have led to poor user experiences. Taking Kulangsu and Xi'an City Wall in China as examples, this study explored the influence of matching short-form video sources with destination types on user engagement and visit intention.

This study selected three short-form videos from different sources for each destination on TikTok and conducted an empirical research using a 3 × 2 experimental design to examine the proposed research model.

The results showed that the matching effect between short-form video sources and destination types will positively affect user engagement and visit intention. (1) the short-form videos with user-generated content (UGC) or professional user-generated content (PUGC) in hedonic destinations can obtain higher user engagement and visit intention; (2) the short-form videos with professionally generated content (PGC) or PUGC in utilitarian destinations can obtain higher user engagement and visit intention and (3) perceived credibility and perceived usefulness played mediating roles in these interactions.

This study considers short-form video sources as antecedent variables influencing user engagement and visit intention and confirms the matching effect between short-form video sources and tourism destination types. The findings will help researchers and marketers better understand the impact of short-form video on destination marketing.

With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.

The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.

The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).

This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.

This study aimed to empirically examine the effectiveness of disclosing user comment history without disclosing personal identity as a nudge policy to refrain users from posting malicious content online.

The authors collected the number of comments and posters from the leading portal website in South Korea, Naver.com. To causally investigate the impacts of the new nudge policy on the number of comments and posters, the authors used the regression discontinuity design (RDD) approach.

The authors found that the new policy reduced all types of comments, including the number of malicious comments, self-deleted comments and current comments. This resulted in an overall decrease in the total number of posted comments, which is considered a side effect. In addition, the authors found that the effect of the nudge policy, which disclosed user comment history, has a stronger effect on older female users than their counterparts.

The study findings extend the current knowledge on a nudge policy being implemented by a website as a means to reduce malicious online content and how it impacts user content posting behaviors.

Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.

The authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.

The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.

This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

The broad goal of the study is to determine how travelers communicate in the cyberspace in relation to their positive and negative experiences they had when staying in a particular hotel. Further goals of this study include identifying the main themes that motivate consumers to evaluate hotel experiences in online environments and categorize the most frequently mentioned areas in the online hotel reviews.

Content analysis techniques were applied by using the software tool NVivo 8 in order to analyze comments extracted using an automated web spider. The spider extracted qualitative data in the form of reviews and comments and quantitative data in the form of demographic information and ratings. The reviews were considered as a primary data for analysis, these reviews portrayed both positive and negative experiences. During this process, the spider collected data on 3,124 hotels and 17,357 traveler reviews from the TripAdvisor site.

By reviewing and understanding traveler comments of their hotel experiences, managers could gain knowledge concerning which element influence to form a positive brand image. Cleanliness of the hotel generally is a common concern in traveler's expectations. Words about deficiency of cleanliness (dirty) appeared more regularly when travelers write negative reviews about the hotel. Travelers showed to be more likely to write positive reviews of hotels with convenient location to good areas such as attractions, shopping, airports, and restaurants. The data in this research shows that travelers can be positively influenced by quality of service received a friendly and well trained staff. When travelers are pleased with the quality of human contact offerings of a well‐trained employee, they tend to feel more satisfied and to form a positive brand image that it is translated into a positive review.

Limitations could be listed using a relatively small sample size, and a relatively limited geographical capacity. Future studies are advised to include bigger sample sizes and also advised to explore a diverse pool of geographical.

The study identifies the possible areas that hoteliers need to pay close attention to improve service. Further, it is one of the first studies in hospitality that highlights strategies to create and reinforce brand image by using online reviews.