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As fertile soil for product promotion, online interest communities have gradually come into brands' view. However, existing research does not clarify whether brand engagement in consumer interaction is beneficial to the development of online interest communities. This study attempts to investigate the effects of brand engagement on the online interest community operation.

The authors propose a model that delineated the influence of brand engagement on consumers' citizenship behavior in the online interest community from the commitment-trust perspective. Scenario-based experiments were conducted and 536 data were collected by simple random sampling.

Results shows that a stronger perception of brand engagement has a positive influence on the relationship (trust and commitment) between the community and its users, which further influences online community citizenship behavior (feedback, advocacy and tolerance) of both posters and lurkers, especially for the posters. Although relationships are more complex, brand engagement activates the development of online interest communities to some extent.

This original study contributes to the commitment-trust theory by examining the impact of brand engagement on citizenship behavior via community commitment and trust in the online interest community context. In addition, this study compares the moderating effect of posters vs lurkers on the relationship between brand engagement and citizenship behavior in the online interest community.

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.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

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。

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Media users daily exchange personal data for “free” personalised media. Is this a fair trade, or user “exploitation”? Do personalisation benefits outweigh privacy risks?

This study surveyed experts in three consecutive online rounds (e-Delphi). The authors explored personal data processing value for media, personalisation relevance, benefits and risks for users. The authors scrutinised the value-exchange between media and users and determined whether media communicate transparently, or use “dark patterns” to obtain more personal data.

Communication to users must be clear, correct and concise (prevent user deception). Experts disagree on “payment” with personal data for “free” personalised media. This study discerned obstacles and solutions to substantially balance the interests of media and users (fair value exchange). Personal data processing must be transparent, profitable to media and users. Media can agree “sector-wide” on personalisation transparency. Fair, secure and transparent information disclosure to media is possible through shared responsibility and effort.

This study’s innovative contribution is threefold: Firstly, focus on professional stakeholders’ opinion in the value network. Secondly, recommendations to clearly communicate personalised media value, benefits and risks to users. This allows media to create codes of conduct that increase user trust. Thirdly, expanding literature explaining how media realise personal data value, deal with stakeholder interests and position themselves in the data processing debate. This research improves understanding of personal data value, processing benefits and potential risks in a regional context and European regulatory framework.

Digital Libraries are complex, and this complexity is a motive to study user success on the behalf of appropriate user success models. These models comprise the factors which play a part between people, technology and organizations. The purpose of this study was to specify and examine an integrated digital library user success (IDLUS) model within the context of digital library settings, Higher Education Commission National Digital Library (HEC-NDL) of Pakistan, by adopting and reusing the existing digital library and Web success models.

Stratified random sampling technique was used to choose the sample from the University of the Punjab, a highly ranked public sector university in Pakistan. Participants were asked to complete an adapted survey questionnaire. A total of 355 completed and usable questionnaires were obtained. Data analyses through confirmatory factor analyses and structural equation modeling produced the results that have supported the proposed IDLUS model. The proposed IDLUS model was tested and supported through model fit statistics in the academic computing environment of the HEC-NDL of Pakistan.

Findings revealed that relationships between the latent variables hypothesized in the model were confirmed.

The study has both theoretical and practical ramifications for academicians and information system designers and developers.

The IDLUS model is recommended first time in the history of librarianship in Pakistan as an overall user success model in the digital library information system computing environment. That made numerous recommendations for future research in the field of information management, particularly for digital library development at national and international levels.

This study aims to explore the switching behaviour of short video (SV) users and its influencing factors and promote the sustainable development of SV platforms (SVPs) and the marketing strategy formulation of library and information institutions.

Using the qualitative research method of semi-structured interviews and grounded theory, this study conducts an exploratory study on the user switching phenomenon of an SVP. The authors encoded the interview text at three levels, extracted the factors influencing user switching behaviour on an SVP and constructed the corresponding theoretical model.

This study identifies the following major internal and external factors influencing user switching behaviour of SVP: platform quality, social environment, individual characteristics and use needs. It also elaborates on the impact of these internal and external factors on user switching behaviour.

This study explored the factors influencing SV user switching behaviour and constructed corresponding theoretical models, enriching research in information technology and social media switching. In practice, this study helped the existing SVPs and library and information institutions establish a corresponding early warning mechanism to prevent the loss of existing users and attract new users.

The International Accounting Standards Board (IASB) has justified the simplification of International Financial Reporting Standards (IFRS) for small- and medium-sized enterprises (SMEs) in several ways, but no effective justification for this simplification has been made based on the information needs of users. This study aims to provide empirical evidence of the decision usefulness of IFRS for SMEs from a prominent user group of SME financial statements – the banks.

This study uses a mixed-method approach. First, a survey was conducted on commercial bank lending officers to assess the usefulness of different disclosure items included in the SME financial statements. Second, semi-structured interviews were conducted with commercial bank lending officers to gain an in-depth insight into the appropriateness and economic consequences of the requirements of IFRS for SMEs on their lending decisions.

The findings show that commercial bank lending officers did not consider all the disclosure requirements presented to them to be equally important. Hence, to facilitate the actual needs of the users’ decision usefulness, it is imperative that when given the opportunity, users participate in the development of accounting standards.

The findings of this study will be of interest to accounting regulators for evaluating the successful implementation of IFRS for SMEs and planning the next review of IFRS for SMEs. The IASB and SME Implementation Group are presently considering ways to increase user involvement for the next review of IFRS for SMEs, and the findings of this study signify the need for user involvement in the standard setting process.

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.