This study examines the role of information and communication technologies (ICT) in promoting organizational inclusion, especially in globally dispersed firms addressing discrimination. Drawing on the resource-based view and contingency theory, it explores ICT-enabled practices in job access, orientation, and enculturation, their impact on knowledge management capability (KMC) and firm performance, and the moderating effects of managers’ initiating structure and considerations. Survey data from 329 managers and employees across diverse industries were analyzed using structural equation modeling. The results reveal that ICT-enabled inclusion practices enhance KMC, which in turn improves firm performance. KMC is found to mediate this relationship. However, the study uncovers that managers’ inclusive initiating structure unexpectedly weakens the link between ICT practices and KMC. These findings offer new insights into how ICT supports workplace inclusion and highlight the critical role of managerial leadership—particularly initiating structure and consideration—in shaping the effectiveness of ICT-enabled inclusive initiatives.

This study investigates the impact of social interaction anxiety on compulsive chat with a social chatbot named Xiaoice. To provide insights into the limited literature, the authors explore the role of fear of negative evaluation (FONE) and fear of rejection (FOR) as mediators in this relationship. By applying a variance-based structural equation modeling on a non-clinical sample of 366 Chinese university students who have interacted with Xiaoice, the authors find that social interaction anxiety increases compulsive chat with a social chatbot both directly and indirectly through fear of negative evaluation and rejection, with a more substantial effect of the former. The mediating effect of fear of negative evaluation transfers through fear of rejection, which establishes a serial link between social interaction anxiety and compulsive chat with a social chatbot. Further, frustration about unavailability (FAU) strengthens the relationship between FOR and compulsive chat with a social chatbot (CCSC). These findings offer theoretical and practical insights into our understanding of the process by which social interaction anxiety influences chat behavior with a social chatbot.

Although social commerce induces consumers’ impulse purchase behavior, no work has been done to examine the impacts of different uses of social commerce on online impulse purchase and their influencing mechanisms. Grounded on the stimulus-organism-response theory, this study explored the relationships between cognitive use (CU), hedonic use (HU), and social use (SU) of social commerce (SC) and online impulse purchase (OIP) and technostress (TS), with mediating effect of social capital and information overload (IO). Non-probability purposive samples of 693 respondents were collected via a questionnaire survey in different provinces of China, and the data were scrutinized with Smart Partial Least Squares (SmartPLS). The results reveal that SU, CU, and HU positively affect social bridging (SBR), social bonding (SBO), and IO. Furthermore, SBR and SBO positively influence OIP, but SBO negatively influences TS. Moreover, IO positively influences TS but negatively influences OIP. Additionally, TS hurts OIP. This is the first study that incorporated different uses of social commerce (i.e., SU, CU, and HU) and investigated their positive and adverse effects on OIP. The study helps understand SC uses’ influencing mechanisms on customers’ OIP. The results show the positive role of social capital (i.e., SBR and SBO) in enhancing OIP. It also shows that improper and uncontrolled use of SC could lead to stress or switching behavior. The research also offers guidelines for managers to grow more constructive tactics for promoting consumers’ impulse purchases.

This research examines the impact of contingent self-esteem on compulsive usage of social media application WeChat in China. The authors attempt to identify the channels based on self-verification and self-determination theory and propose that fear of negative evaluation (FNE) and fear of missing out (FoMO) transfer the impact of contingent self-esteem (CSE) to compulsive WeChat usage (CWU). Additionally, these relationships were tested with frustration about unavailability (FaU) as a moderator in the framework to explain the phenomenon. By employing a convenience method, 396 samples of Chinese students were analyzed. The analyses indicate that CSE contributes to CWU directly and indirectly through FNE and FoMO. Furthermore, FNE mediates the link between CSE and FoMO, ultimately transferring the effect of CSE to CWU in series. These results can enhance our knowledge of how CSE affects CWU, a growing problem among young people today. Our results may guide psychologists to prepare a counselling programme for compulsive social media users and help them overcome social interaction fears in the real world.

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

The aim of multimodal optimization is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. The results of seven variants of particle swarm optimization (PSO) algorithms on IEEE Congress on Evolutionary Computation (CEC) 2015 single objective multi-niche optimization problems are reported in this paper. The PSO algorithms include PSO with star structure, PSO with ring structure, PSO with four clusters structure, PSO with Von Neumann structure, social-only PSO with star structure, social-only PSO with ring structure, and cognition-only PSO. The experimental tests are conducted on fifteen benchmark functions. Based on the experimental results, the conclusions could be made that the PSO with ring structure performs better than the other PSO variants on multimodal optimization. To obtain good performance on the multimodal optimization problems, an algorithm needs to converge the candidate solutions to the global optima while keep the population diversity during whole search process.