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Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This study seeks to provide a greater understanding of the variables that influence travellers’ intention to participate in social media, paying special attention to (1) the direct impact of perceived usefulness (PU) of social media and (2) the moderating impact of tourists’ altruism and self-interest.

The proposed conceptual model was empirically tested using an online questionnaire distributed to a sample of 394 tourists visiting a World Heritage city.

The findings show that perceived social media usefulness has a significant effect on users’ intention to share experiences. Additionally, self-interest appears to moderate the relationship between perceived social media usefulness and users’ sharing intention, but the results do not support the moderating effect of altruism.

Despite scholars’ growing interest in social networks as sources of tourist information, little is known about the aspects that encourage users’ participation in these platforms. This paper offers key contributions to the relevant literature in this field and offers compelling recommendations for tour operators' management of social networks.

本研究擬讓我們更清楚了解驅使旅行人士參與社交媒體上的交流活動的變數;為求達至這研究目的，研究人員特別對以下兩方面加以注意和研究：(一) 、旅行人士對社交媒體的感知效用所帶來的直接影響;(二) 、旅行人士的利他主義，以及其對個人利益的考慮所帶來的緩和影響。

研究人員對其提出之概念模型進行實證測試，方法乃透過收集一個包含394名曾參觀世界遺產城市的旅行人士的樣本所回應的網上問卷數據，並進行數據分析。

研究結果顯示，旅行人士若覺得社交媒體是有用的話，則他們會更願意在那裡分享旅行經歷;而且，他們對自己個人利益的考慮，似會緩和他們對社交媒體的感知效用與其分享經歷的願意程度之間的關係;唯研究結果沒有證實利他主義會帶來緩和的影響。

雖然學者對社交網絡作為提供資訊的來源感到興趣，而且這興趣不斷增加，但我們對促進旅行人士參與社交網絡平台活動之因素的了解仍然淺薄，就此而言，本研究於有關的文獻提供了重要的貢獻;研究亦為旅遊經營者就應如何管理社交網絡提供了具說服力的建議。

Social media is widely used to capture citizens' opinions and topics deemed important. The importance or interest social media users attribute to a topic is traditionally measured by tweet frequency. This approach is practical but overlooks other user engagement tools such as retweets, likes, quotes, and replies. As a result, it may lead to a misinterpretation of social media signals. This paper aims to propose a method that considers all user engagement indicators and ranks the topics based on the interest attributed by social media users.

A multi-criteria decision-making framework was proposed, which calculates the relative importance of user engagement tools using objective (information entropy) and subjective (Bayesian Best-Worst Method) methods. The results of the two methods are aggregated with a combinative method. Then, topics are ranked based on their user engagement levels using Multi-Objective Optimization by Ratio Analysis.

The proposed approach was used to determine citizens' priorities in transport policy, and the findings are compared with those obtained solely based on tweet frequency. The results revealed that the proposed multi-criteria decision-making framework generated more comprehensive and robust results.

The proposed method provides a systematic way to interpret social media signals and guide institutions in making better policies, hence ensuring that the demands of users/society are properly addressed.

This study presents a systematic method to prioritize user preferences in social media. It is the first in the literature to discuss the necessity of considering all user engagement indicators and proposes a reliable method that calculates their relative importance.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.

The purpose of this study was to empirically investigate the role of e-service quality factors in predicting e-satisfaction. The study context was spirituality and well-being over-the-top services. The e-service quality factors consisted of perceived functional completeness, perceived performance, perceived quality of interface and interaction, perceived quality of content and information and perceived quality of customer support. The study goal was to ascertain over-the-top services customers’ behavioral intention toward upgrading to premium subscription and the spread of electronic word of mouth.

This study was based upon the integrated stimulus-organism-response framework where e-service quality represented the stimulus, e-satisfaction the organism, behavioral intention and electronic word of mouth as the response. The study used a moderated-mediation approach with e-satisfaction as the mediator and the price value of a premium subscription as the moderator. To empirically test the model, the authors collected data from 312 spirituality and well-being over-the-top services users in India. Partial least squares-structured equation modeling was used to analyze the collected data.

The findings of the study supported the association between e-service quality factors and e-satisfaction while using spirituality and well-being over-the-top service. The results furthermore indicated that satisfied spirituality and well-being over-the-top customers were willing to upgrade to the premium subscription and spread favorable electronic word of mouth. The moderated-mediation study results revealed that the price value of premium subscriptions moderated the relationship between e-service quality and e-satisfaction but did not moderate the relationship between e-satisfaction and behavioral intention, and e-satisfaction and electronic word of mouth.

This study offered a comprehensive stimulus-organism-response theoretical model by using the five e-service quality measurement factors as “stimuli” for motivating the internal state of spirituality and well-being over-the-top subscribers. This was toward sustained usage in over-the-top services subsequent to the end of the freemium period. Furthermore, in this study, both e-service quality theory and user satisfaction theory were integrated into the stimulus-organism-response model. This helped to better comprehend the impact of e-service quality factors in driving e-satisfaction among spirituality and well-being over-the-top service users.

This study revealed the significance of differentiating premium over-the-top subscriptions based on price value. To ensure a high level of e-satisfaction from a premium subscription, a greater emphasis on the e-service quality dimensions was required. This study provided insights to managers regarding the role of favorable electronic word of mouth in fostering effective customer acquisition.

This was one of the first studies which concurrently integrated perceived value of the premium subscription and e-satisfaction with customers’ behavioral intention and electronic word of mouth through the theoretical lens of stimulus-organism-response.

User discontinuance on short-video platform has become increasingly prevalent in recent years. Short-video discontinuance refers to reduced use, controlled use or suspended use of the short-video platform. In this study, we examined factors associated with discontinuance behavior on short-video platform.

From the perspective of stressor–strain–outcome (SSO), we put forward a theoretical model integrating perceived information overload and perceived system feature overload (stressors), dissatisfaction (psychological strain), flow experience and regret to explain discontinuance behavior on short-video platform (behavioral outcome). We collected 482 survey data from Douyin users in China, and empirically examined the proposed research model via Partial least squares structural equation modeling (PLS-SEM) technique.

Our results demonstrated that perceived system feature overload exerts a positive effect on perceived information overload. Perceived system feature overload has a stronger influence on dissatisfaction than perceived information overload. Regret increases user dissatisfaction, while flow experience decreases user dissatisfaction. We also discovered that dissatisfaction and regret have significant positive effects on discontinuance behavior. Interestingly, flow exerts no significant influence on discontinuance behavior.

This study enriches the body of knowledge on social media discontinuance by revealing the interaction and effects of flow experience, dissatisfaction and regret on discontinuance. This study also extends the understanding on the complex role of flow experience in leading to social media discontinuance. Additionally, this study deepens the research on the interaction between perceived system feature overload and perceived information overload as well as their different influences on negative emotion.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

This study aims to fill the lack of quantitative studies of user preferences in quasi-public spaces to observe the use of quasi-public spaces by questioning the contemporary needs of urban communities and to develop design strategies accordingly.

Within the scope of this study, public space design elements affecting users' preferences in the quasi-public spaces of the Podium Park shopping center in Bursa, Turkey were evaluated. By considering the spatial characteristics of the study area, 4 main and 15 subcriteria were determined and utilized by analytic hierarchy process (AHP). These criteria were evaluated by experts and locals with a participatory approach.

According to the obtained results, “events” (S2), “sun/shade” (C2), “safety” (P3) and “planting” (U4) subcriteria were determined as the vital elements for quasi-public spaces.

Although the concept of quasi-public space has been discussed for nearly 30 years, it has been observed that there are no quantitative studies to determine the criteria of user preferences in these open spaces in the literature. This study is the first quantitative research for user preferences in quasi-public spaces and there is no previous study on this subject and study area in Turkey.

In this study, the authors seek to introduce ways that show that in the age of artificial intelligence (AI), selective dissemination of information (SDI) performance can be greatly enhanced by leveraging AI technologies and algorithms.

AI holds significant potential for the SDI. In the age of AI, SDI can be greatly enhanced by leveraging AI technologies and algorithms. The authors discuss SDI technique used to filter and distribute relevant information to stakeholders based on the pertinent modern literature.

The following conceptual indicators of AI can be utilized for obtaining a better performance measure of SDI: intelligent recommendation systems, natural language processing, automated content classification, contextual understanding, intelligent alert systems, real-time information updates, intelligent alert systems, real-time information updates, adaptive learning, content summarization and synthesis.

The authors propose the general framework in which AI can greatly enhance the performance of SDI but also emphasize that there are challenges to consider. These include ensuring data privacy, avoiding algorithmic biases, ensuring transparency and accountability of AI systems and addressing concerns related to information overload.

This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies.

This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes.

The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged.

This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.