With the development and proliferation of online games, understanding how to seize these players has become an essential issue for academic scholars and practitioners. Based on virtual experience in online gaming communities and consumer engagement perspectives, this research developed and verified a multi-dimensional framework for assessing how to retain and encourage players continuous dedication and engagement behaviors. The proposed framework illustrates the interrelationships among six constructs in online gaming communities: social presence, telepresence, cognitive social identity, affective social identity, psychological engagement, and behavioral engagement. Data collected from 338 players who have experience in engagement behaviors in online gaming communities. The collected data were examined against the research framework using structural equation modeling (SEM). The research findings offer sufficient evidence that behavioral engagement is influenced by psychological engagement, which consecutively, are determined by cognitive social identity, affective social identity, social presence, and telepresence. More importantly, the mediating analysis indicated that psychological engagement is a crucial mediator, meaning that consumer engagement is a sequential process, and behavioral engagement cannot independently exist without psychological engagement. Taken together, the research results of this study have several critical theoretical and practical implications for future academic researchers and practitioners to have better understanding of consumer engagement. By doing so, the game companies can have sustainable competitive advantage and support sustainable development.

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

A review of the literature shows that much academic effort has been expended studying information system usage and information technology adoption. However, these theories/models based on psychological research are not specific to the virtual reality context and may not fully capture the nature of virtual reality-based learning system (VR-BLS) usage behavior in the virtual environment. Given the limited utility of existing theories/models, this study aims to build a theoretical model and to validate the psychological and physiological effects of using VR-BLSs. To explore students’ usage intentions, this study integrates the extended unified theory of acceptance and use of technology (UTAUT2), representing the psychological effects, and the perceived physiological vulnerability to IT usage (PPVITU), representing the physiological effects. We empirically examine the research model using a cross-sectional survey, collecting data from a sample of 352 students, and utilizing partial least squares (PLS) for the analysis. Our findings show that the behavioral intention to use VR-BLSs is determined by performance expectancy, social influence, and facilitating conditions. Moreover, PPVITU negatively moderates the association between social influence and the behavioral intention to use VR-BLSs. The results of this study enhance our understanding of the relationship between students’ VR-BLS usage behavior and VR-based education.