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The basic purpose of this study is to increase the installation rate of mobile games and their life cycle. Little research has been conducted into the competitive market of mobile games and word-of-mouth (WOM), advertisement and quality simultaneously. In this paper, the authors aim to assist developers with a better understanding of how to invest in the game industry.

The primitive modeling has been made based on the Bass diffusion model and its extensions using features related to the game industry. Modeling and simulation have been performed using the system dynamics method.

The results show that if game developers only focus on advertising at the introduction of the game to the market, and neglect quality features, uninstall rate of adopters increases and the game life cycle decreases. And if developers only focus on the quality improvement of the game, and ignore advertisement, they couldn't create enough WOM and thus game installation wouldn't grow enough to create revenue. Furthermore, as long as the pleasure of the game is high for its adopters, developers shouldn't give up on investing in advertising to create even higher WOM. And also in communities with higher levels of variety-seeking among game players, quality is important at the start and advertisement is vital in the following steps of the game life cycle. And if the contact rate among people is high, developers could focus on quality more than the advertisement.

Even though WOM in mobile game installation is crucial, fewer studies have been done in this area and they only address the problem using qualitative or statistical methods. The quantitative system dynamics approach based on the Bass diffusion model, used in this research, fixes the shortcomings of the previous studies. Moreover, due to serious competition in the mobile game industry, only those developers can remain in the market who can predict the future trend of the game market. Furthermore, game development is a creative process and its key factors include engaging the gamers and monetizing.

Given the importance of teacher leadership in influencing, motivating and inspiring student learning engagement and associated learning outcomes, a robust instrument to assess this construct is critical. Although there are some teacher leadership instruments available in existing literature, efforts to adapt robust psychometric instruments to measure teachers' leadership practices in Ethiopian higher education institutions have been limited. Therefore, this study attempted to address this gap by adapting the Teacher Leadership Scale (TLS) based on the Multifactor Leadership Questionnaire (MLQ-5X) and validating its psychometric properties for use in higher education settings.

Using a cross-sectional design, the study involved 409 undergraduate university students who were randomly selected from public universities. Factor analytic methodologies, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), were used to analyze the data collected.

The result confirmed a set of 36 items arranged in nine factors, which have a theoretically supported factor structure, excellent model fit and robust evidence for validity, and reliability and measurement invariance. These results demonstrate that the scale is a strong psychometric tool for measuring the leadership profile and practice of higher education teachers.

It can be concluded that the TLS can assist stakeholders in several ways. Researchers can benefit from the scale to measure teachers' leadership practices and predict their influence on student learning outcomes. In addition, the scale can help practitioners and policymakers collect relevant data to rethink teacher professional development initiatives, leadership training programs and other practices aimed at improving teacher leadership effectiveness.

This paper examines the impact of leadership behaviors on startup's entrepreneurship in Vietnamese southern areas in the time of Covid-19.

The paper uses OLS regression model to evaluate the impact of leadership behaviors on startup's entrepreneurship in Vietnamese southern areas. Besides, the paper also uses AHP method to identify the factors that influence leadership behaviors in startups in the context of economic shock like the Covid-19 pandemic.

Results reveal that the transformational leadership behavior has a positive correlation with startup's entrepreneurship while the transactional and laissez-faire leadership behaviors are negatively related to startup's entrepreneurship. In addition, using AHP method, certain determinants of leadership behaviors in startups have been specified; among which, the most profound influencing factors are respectively hi-tech use (0.081), training policy (0.079) and check (0.78). In contrast, the factors with negligible impacts include recruitment policy (0.004) and culture (0.037) in startups in the context of economic shock like the Covid-19 pandemic.

This research only evaluates this impact in the Covid-19 times. In the future, comparisons between the time after the Covid-19 at traditional enterprises and state enterprises are to be done to clarify the difference of this impact.

By using OLS model and AHP model with the data collected from 209 start-ups, the paper examines the impact of leadership behaviors on start-up's entrepreneurship in the time context of economic shock like the Covid-19 pandemic and propose some recommendations to enhance entrepreneurship in startups in the developing countries.

This study is a step forward in the entrepreneurship research branch that investigates the entrepreneurship issue from the perspectives of social and interpersonal processes. With the identification of the leadership behaviors’ role in shaping start-up's entrepreneurship come highly applicable implications towards the emergence of more effective entrepreneurial startups in the time of Covid-19 and this prevalent digital context.

With the identification of the leadership behaviors' role in shaping startup's entrepreneurship come highly applicable implications toward the emergence of more effective entrepreneurial startups in the time of Covid-19 and this prevalent digital context. This study is conducted to analyze the impact of leadership behaviors on startup's entrepreneurship in the southern areas of Viet Nam having the most startups but affected the most seriously by the Covid-19 pandemic, which has not research on this topic for startups in a developing country like Vietnam in the context of an economic shock.

Successful adoption of building information modelling (BIM) by early adopters is crucial for its effective diffusion. The purpose of this study is to develop a framework of understanding that supports contextualized understanding of BIM adoption decisions in a BIM infant industry. The framework bridges the gap in current knowledge in terms of the absence of such a framework, which has hindered the structured understanding of the BIM decision of an adopter, curtailing the appropriate strategizing of their BIM adoption.

The study focuses on a BIM infant industry, where early adopters begin using BIM, allowing insights into this crucial initial stage of adoption. Identifying affordances as a versatile concept that could effectively represent not only what an adopter perceives and expects from BIM implementation, but also, what the adopter, in fact, can achieve from it, an affordance-led framework of understanding (AFU) was developed to comprehensively capture varying dynamics of BIM decision process. The study took a qualitative retroductive approach to theory with semi-structured interviews to gather necessary data from a sample of BIM adopters purposively selected to maximize the breadth and depth of data.

The study concludes by identifying and defining pertinent affordances as a new concept and a compulsory state for BIM adoption. Findings further demonstrate that existing theories can be linked to the AFU to strategically direct the affordances dynamics towards the pertinent state.

The AFU enables a deeper contextualizable view of innovation adoption that was absent in existing innovation studies. It significantly enhances the precision of strategizing BIM adoption compared to previous approaches, enabling adopters to plan and implement BIM in a manner that aligns well with their expectations and specific conditions.

We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.

Online interaction and the usage of social media have dramatically altered both consumers’ behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers’ behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers’ different assessments of newly introduced products.

By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established.

From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities.

The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products.

This study aims to investigate the factors influencing the adoption intention of artificial intelligence (AI) by micro, small and medium enterprises (MSMEs) in Jordan.

The study adopts the technology–organization–environment (TOE) model. It examines the moderating effects of innovation culture, employee digital skill level and market competition on the relationships between the independent and dependent variables. A survey was utilized to collect data from 537 MSME owners or managers in Jordan and employed partial least squares structural equation modeling to test the hypotheses.

The results of the study support seven out of eight hypotheses. Business innovativeness, management support, perceived benefits and technological infrastructure have positive and significant effects on AI adoption intention, while perceived costs have no significant effect. However, the innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables.

The study provides valuable insights and recommendations for MSME owners, managers, employees, policymakers, educators and researchers interested in promoting and facilitating AI adoption by MSMEs in Jordan.

The current attempt extends the TOE framework by adding significant constructs representing the three contexts. Moreover, it is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan, which are significant to the Jordanian economy and represent 99.5% of enterprises.

With the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.

We conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.

Our results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.

Few studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

This study aims to explore and conceptualize metaverse adoption using a systematic literature review (SLR). It also aims to propose a conceptual model that identifies significant factors affecting metaverse adoption in the entertainment, education, tourism and health sectors.

A SLR was conducted using the “preferred reporting items for systematic reviews and meta-analyses” report protocol and the “theory, context, characteristics, methods” framework to include all relevant articles published up to March 2023, which were sourced from the Scopus and Web of Science databases.

The reviewed literature revealed that the countries with the highest publications in the field of metaverse were China and the USA. It was also found that the technology acceptance model was the most used theoretical framework. Survey-based research using purposive and convenience sampling techniques emerged as the predominant method for data collection, and partial least square-structural equation modeling was the most used analytical technique. The review also identified the top six journals and the variables that help to develop a proposed model.

This review presents a novel contribution to the literature on metaverse adoption by forming a conceptual model that incorporates the most used variables in the entertainment, education, tourism and health sectors. The possible directions for future research with identified research gaps were also discussed.

This study aims to investigate the user’s behavior and intention to continue using e-health services by analyzing the factors influencing their decision. The stimuli–organism–behavior–consequence (SOBC) framework was used to assess how the risks and benefits of e-health influence both behavioral intention (BI) and continued use.

The hypotheses proposed for this study were tested with partial least square-based structural equation modeling. This study relied on the primary data collected from 572 respondents using convenient sampling.

This study examined the SOBC model’s four phases’ with the help of eight constructs. The conceptual model allowed the authors to examine 10 relationships to measure e-health adoption behavior in emerging nations like India. This study found that convenience, perceived risk and facilitating conditions are positively related to the effort and performance expectancy (PE) associated with e-health consultation. Only social influence is unrelated. This study also found a positive and substantial relationship between BI, effort and PE.

The study uses the unified theory of acceptance and use of technology and the SOBC paradigm to determine the factors influencing users’ intentions to use e-health. In addition to bringing new aspects to the existing literature on technology adoption and e-health consultation, the results of this study will aid e-health service providers in formulating strategies to promote the usage of e-health in emerging economies like India.

By adopting learning theory and a guanxi perspective, this study aims to investigate the effects of interpersonal guanxi (interpersonal networks or connections) and relationship learning on companies’ business performance when operating in a large emerging market.

Using a sample of 294 sales managers and salespeople in the Chinese hotel sector, the authors empirically test the authors' arguments through a structural equation modeling (SEM) approach.

The authors' findings indicate that strong interpersonal guanxi tends to generate more positive business performance. Furthermore, the authors find that relationship learning plays a mediating role in the association between interpersonal guanxi and hotel companies’ business performance in a Chinese context. Finally, the authors empirically explore the moderating effect of inter-firm dependence on the contribution of interpersonal guanxi to relationship learning. Findings demonstrate that this effect varies significantly based on inter-firm dependence, with interpersonal guanxi exhibiting a greater positive impact if such dependence is high.

This study enriches our understanding of interpersonal guanxi and of how companies can enhance the companies' business performance in an emerging market context.