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Active participation is critical to the survival and development of the multiplayer online battle arena (MOBA) game community. However, this issue has not received much attention in the information systems literature. To address this issue, we develop a tripartite model that accounts for the roles of behavioral dedication, constraint, obligation mechanisms on active participation in the MOBA community.

The research model is empirically validated by online survey data among 971 users of a popular MOBA community.

The results show that perceived enjoyment, perceived escapism, and affective commitment are key behavioral dedication factors, which further promote active participation in the MOBA community. In addition, past investment, self-efficacy for change, and calculative commitment are important behavioral constraint factors, which ultimately influence active participation in the MOBA community. Finally, subjective norm, group norm, social identity, and normative commitment are influential behavioral obligation factors, which in turn facilitate active participation in the MOBA community.

This study contributes to the theoretical understanding of active participation in the MOBA community and offers practical guidance for promoting active participation in the community.

The health and survival of massively multiplayer online games (MMOGs) are of paramount concern to stakeholders. It is essential to understand the usage behaviors of exploitative and exploratory strategies. By combining the typical user experience with psychological mechanisms in MMOGs, this study is devoted to clarifying how technology affordance and digital perfectionistic intention influence reinforcement and variety-seeking orientations of MMOGs use.

This study adopted a sequential triangulation mixed-methods design to explore how diverse usage behaviors of reinforced and varied use in MMOGs are formed. After proposing the theoretical framework from MMOGs affordance, perfectionistic intentions, and diverse use, empirical evidence was initially collected from representative samples through a survey. Qualitative interviews from players in MMOGs and game industry practitioners are conducted to confirm the results, supplement understanding, and gather insights from diverse backgrounds. The quantitative and qualitative inferences are discussed to validate the research focus.

Findings from various perspectives suggest that perfectionistic intentions are critical antecedents of different usage behaviors influenced by affordances provided in MMOGs. Goal-driven affordance with reward and competition, interaction affordance, and identity affordance are key MMOGs affordances and could affect perfectionistic intentions differently. People with different perfectionistic intentions, which are the psychological outcome of MMOGs affordances, possess diverse usage behaviors.

This study is the first to consider diverse usage behaviors in virtual worlds such as MMOGs by combining lenses of perfectionistic intentions and technology affordance. Findings from mixed-methods analysis significantly enrich the research on online game usage behavior, offering valuable theoretical and practical implications for studying usage behaviors within the virtual world.

Recent emerging information technologies like digital twin (DT) provide new concepts and transform information management processes in the architecture, engineering and construction (AEC) industry. Although numerous articles are pertinent to DT applications, existing research areas and potential future directions related to the state-of-the-art DT in project operation and maintenance (O&M) are yet to be studied. Therefore, this paper aims to review the state-of-the-art research on DT applications in project O&M.

The current review adopted four methodological steps, including literature search, literature selection, science mapping analysis and qualitative discussion to gain a deeper understanding of DT in project O&M. The impact and contribution of keywords and documents were examined from a total of 444 journal articles retrieved from the Scopus database.

Five mainstream research topics were identified, including (1) DT-based artificial intelligence technology for project O&M, (2) DT-enabled smart city and sustainability, (3) DT applications for project asset management, (4) Blockchain-integrated DT for project O&M and (5) DT for advanced project management. Subsequently, research gaps and future research directions were proposed.

This study intends to raise awareness of future research by summarizing the current DT development phases and their impact on DT implementation in project O&M among researchers and practitioners.

The purpose of this paper is to explore user engagement (UE) within the Metaverse (MV) environment, emphasising the crucial role of immersive experiences (IEs). This study aims to understand how IEs influence UE and the mediating effects of hedonic value (HV) and utilitarian value (UV) on this relationship. Additionally, the authors examine the moderating impacts of user perceptions (UPs) such as headset comfort, simulation sickness, prior knowledge and ease of use on the utilisation of the MV. This study seeks to elucidate the dynamics of virtual travel at a pre-experience stage, enhancing the comprehension of how digital platforms can revolutionise UE in travel and tourism.

This study used a triangulation methodology to provide a thorough investigation into the factors influencing UE in the MV. A systematic literature review (SLR) was conducted to frame the research context and identify relevant variables. To gather empirical data, 25 interviews were performed with active MV users, supplemented by a survey distributed to 118 participants. The data collected was analysed using structural equation modelling (SEM) to test the hypothesised relationships between IEs, UPs, HV and UV and their combined effect on UE within the MV.

The findings from the SEM indicate that engaging in the MV leads to a positive IE, which significantly enhances UE. Additionally, it was discovered that HV and UV play a mediating role in strengthening the link between IEs and UE. Furthermore, UPs, including headset comfort, simulation sickness, prior knowledge and ease of use, are significant moderators in the relationship between IEs and MV usage. These insights provide a nuanced understanding of the variables that contribute to and enhance UE in virtual environments.

This research contributes original insights into the burgeoning field of digital tourism by focusing on the MV, a rapidly evolving platform. It addresses the gap in the existing literature by delineating the complex interplay between IEs, UPs and value constructs within the MV. By using a mixed-method approach and advanced statistical analysis, this study provides a comprehensive model of UE specific to virtual travel platforms. The findings are particularly valuable for developers and marketers in the hospitality and tourism sectors seeking to capitalise on digital transformation and enhance UE through immersive technologies.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

This study uses data from the National Financial Capability Study to examine the financial vulnerability of Asian American and Pacific Islander (AAPI) adults relative to that of other major racial/ethnic groups in the United States across the past decade and within the AAPI population, examining how vulnerability varied across AAPI adults of East Asian, South Asian, Southeast Asian, and Pacific Islander heritage.

The study uses four waves (2012, 2015, 2018 and 2021) of the State-by-State National Financial Capability Study (NFCS) and the 2021 NFCS AAPI Oversample dataset. Financial vulnerability was estimated using five binary indicators: (1) An inability to come up with $2,000, (2) An experience of overdraw, (3) A lack of emergency fund savings, (4) Difficulty paying bills and expenses, and (5) Credit card revolving. A financial vulnerability index was also created using the binary indicators. Logistic regression analyses were conducted on binary indicators and an OLS regression was additionally conducted on the aggregated financial vulnerability index.

Results show that, overall, AAPI respondents reported the lowest levels of financial vulnerability relative to White respondents, Black respondents, Hispanic respondents, and those of another race or ethnicity. However, using the 2021 datasets, we found that within the AAPI population, financial vulnerability varied widely by heritage, with those of East Asian heritage reporting less vulnerability than AAPI adults of other studied heritage groups.

These results provide insights into the financial well-being of AAPI households, particularly amidst the COVID-19 pandemic, and present initial evidence of the significant disparities that exist within this heterogenous community. This study provides valuable insights for researchers, educators, policymakers, and financial practitioners.

The authors contribute to the literature on leadership by investigating how characteristics of principal investigators (PIs) affect innovation performance, and how collaborative and non-collaborative projects moderate this relationship within the context of inter-organisational research projects.

The authors analysed panel data from the National Science and Technology Information Service on 171 research projects within a biomedical and regenerative medicines programme overseen by the Korea Health Industry Development Institute. The authors used a hierarchical regression model, based on the ordinary least squares method, to examine the relationship between PI characteristics and performance, considering both quantity and quality.

The results show that the characteristics of PIs have diverse effects on the quantity and quality of innovation performance. Gender diversity within PIs negatively affects the quality of innovation performance, while the capacity of PIs positively influences it. Moreover, the degree of PI’s engagement is positively associated with the quantity of innovation performance but does not have a significant relationship with the quality of performance. In terms of moderating effects, collaborative projects with multiple leaders seem less reliant on PI capacity than non-collaborative projects led by a single leader, in terms of innovation performance.

The results contribute significantly to the literature on innovation management by examining the role of leadership in collaborative environments to enhance innovation performance, addressing the need for empirical evidence in this area. Analyses of PI characteristics in government R&D management can lead to improved team performance, more efficient processes and effective resource allocation, ultimately fostering innovation.

Sustainable practices are the modern-day necessities for organisations as the world is becoming highly dynamic. The purpose of this study is to examine the influence of sustainable training and creativity practices (STP and SCP) on organisational citizenship behaviour (OCB-individual and OCB-organisation) via the mediating role of psychological contract fulfilment (PCF).

A sample of 326 white-collar Indian service industry employees was collected. The data are analysed using structural equation modelling and random forest regression supervised learning (RFRSL).

The findings indicate that sustainable training practices (STP) had an indirect impact on organisational citizenship behaviour (OCB-I, OCB-O) via the mediating effect of transactional (T-PCF) and relational psychological contract fulfilment (R-PCF). In terms of sustainable creative practices (SCP), the impact on OCB-I was indirect due to T-PCF. In addition, R-PCF acts as a mediator between SCP and OCB-O. In the latter portion of the analysis, the RFRSL approach created a prediction model for T-PCF, R-PCF, OCB-I and OCB-O, with demographic characteristics such as industry experience, gender, age, etc. playing a constructive role.

The study conducts a combination of both traditional and newer technology (machine learning), resulting in highlighting the uniqueness of the relationship between variables and the role of demographic variables.

The construction of megaprojects often involves substantial risks. While insurance plays an important role as a traditional risk transfer means, owners and insurance companies may still suffer huge losses during the risk management process. Therefore, considering the strong motivation of insurance companies to participate in the on-site risk management of megaprojects, this study aims to propose a collaborative incentive mechanism involving insurance companies, to optimize the risk management effect and reduce the risk of accidents in megaprojects.

Based on principal-agent theory, the research develops the static and dynamic incentive models for risk management in megaprojects, involving both the owner and insurance company. The study examines the primary factors influencing incentive efficiency. The results are numerically simulated with a validation case. Finally, the impact of parameter changes on the stakeholders' benefits is analyzed.

The results indicate that the dynamic incentive model is available to the achievement of a flexible mechanism to ensure the benefits of contractors while protecting the benefits of the owner and insurance company. Adjusting the incentive coefficients for owners and insurance companies within a specified range promotes the growth of benefits for all parties involved. The management cost and economic benefit allocation coefficients have a positive effect on the adjustment range of the incentive coefficient, which helps implement a more flexible dynamic incentive mechanism to motivate contractors to carry out risk management to reduce risk losses.

This study makes up for the absence of important stakeholders in risk management. Different from traditional megaproject risk management, this model uses insurance companies as bridges to break the island effect of risk management among multiple megaprojects. This study contributes to the body of knowledge by designing appropriate dynamic incentive mechanisms in megaproject risk management through insurance company participation, and provides practical implications to both owner and insurance company on incentive contract making, thus achieving better risk governance of megaprojects.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.