Purpose: The purpose of this paper is to examine the relationship between entrepreneurial orientation (EO) and intrapreneurial intentions among knowledge workers in the Iranian R&D sector using transformational leadership (TL) as a moderator and to also investigate the extent of the knowledge workers’ intrapreneurial intentions.Design/methodology/approach: The research was tested as a cross-sectional survey of knowledge workers involved in the R&D centers in Iran under the Society of the Centers for Research. The sampling method in this study was based on simple random sampling. Finally, 380 valid questionnaires were collected from 48 R&D centers of different industries.Findings: The findings of this study reveal that except for the “need for achievement (NFA),” other variables such as “innovativeness,” “proactiveness,” “risk taking,” and “networking” had a significant and positive relationship toward “intrapreneurial intention.” Moreover, it was found that “TL” had a moderating role in the relations between innovativeness, proactiveness, and risk taking with intrapreneurial intention; however, TL did not have a moderating role in the relations between NFA and networking with intrapreneurial intention.Research limitations/implications: Reliance on cross-sectional questionnaires instead of an experimental design imposes caution about the relationships between the predictors and intrapreneurial intention.Originality/value: This paper is one of the few studies on the EO at the individual level with new dimensions on the intrapreneurial intention, which examined moderating role of TL at this relationship, which took place in Iranian R&D sector.

Social entrepreneurship had been acknowledged as an important solution to highlight various social issues, which many are compounded by the COVID-19 pandemic. Locally, the Malaysian government launched the first Social Entrepreneurship policy framework in 2015 with an allocation of RM 20 million to produce 1000 social enterprises by 2018. However, as of June 2022, the reported number of social enterprises in Malaysia was only 414. This raised questions on the viability of the agenda. Nevertheless, in April 2022, the government expressed continued political will by launching the Malaysian Social Entrepreneurship Action Plan 2030 (SEMy2030). To ensure success, it is important to understand the determinants of social entrepreneurship intention, especially among youth, the leaders of tomorrow. A previous study on another emerging economy observed changes to the social entrepreneurship dynamics due to the COVID-19 pandemic. Thus, the analysis for this study was performed following a longitudinal design on a sample of 486 respondents before and after the COVID-19 pandemic. The findings found Social Awareness, Self-Efficacy, Prior Entrepreneurship Experience, and Cosmopolitanism consistently to be significant determinants of Social Entrepreneurship Intention both pre- and post-pandemic. Meanwhile, Perceived Social Support became significant post-pandemic. Furthermore, gender was found to have a moderating role in several relationships. These insights can lead to the formulation of effective policies and programs to encourage, as well as enable, new generations of social entrepreneurs.

Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines to understand human emotions. This paper aims to review previous studies related to PSO findings for ERS and identify modalities that can be used to achieve better results through PSO. To achieve a comprehensive understanding of previous studies, this paper will adopt a Systematic Literature Review (SLR) process to filter related studies and examine papers that contribute to the field of PSO in ERS. The paper’s primary objective is to provide better insights into previous studies on PSO algorithms and techniques, which can help future researchers develop more accurate and sustainable ERS technologies. By analysing previous studies over the past decade, the paper aims to identify gaps and limitations in the current research and suggest potential areas for future research. Overall, this paper’s contribution is twofold: first, it provides an overview of the use of PSO in ERS and its potential applications. Second, it offers insights into the contributions and limitations of previous studies and suggests avenues for future research. This can lead to the development of more effective and sustainable ERS technologies, with potential applications in a wide range of fields, including healthcare, gaming, and customer service.

The harmonic motion of pendulum swinging centered at a pivot point is mimicked in this work. The harmonic motion’s amplitude at both side of the pivot are equal, damped, and decreased with time. This behavior is mimicked by the agents of the pendulum search algorithm (PSA) to move and look for an optimization solution within a search area. The high amplitude at the beginning encourages exploration and expands the search area while the small amplitude towards the end encourages fine-tuning and exploitation. PSA is applied for a vaccine distribution problem. The extended SEIR model of Hong Kong’s 2009 H1N1 influenza epidemic is adopted here. The results show that PSA is able to generate a good solution that is able to minimize the total infection better than several other methods. PSA is also tested using 13 multimodal functions from the CEC2014 benchmark function. To optimize multimodal functions, an algorithm must be able to avoid premature convergence and escape from local optima traps. Hence, the functions are chosen to validate the algorithm as a robust metaheuristic optimizer. PSA is found to be able to provide low error values. PSA is then benchmarked with the state-of-the-art particle swarm optimization (PSO) and sine cosine algorithm (SCA). PSA is better than PSO and SCA in a greater number of test functions; these positive results show the potential of PSA.