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This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

This paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration.

The prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value.

The predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner.

In this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.

A lesson has been learned from the pandemic experience that less damages to the environment and realizing more social responsibilities would be the direction of the post-pandemic period globally. The purpose of this study is to focus on identifying the appropriate determinants of the proposed urban travel behavior model to develop Smart Mobility in Smart Cities to protect the environment. Potential to realize Smart Cities with infrastructure development has been explored in this study if road users are keen to combat climatic change which is clear from the challenges of flattening the infection rate through the enforcement of rules and regulations by the various government.

The proposed urban travel behavior model includes sub-drivers for each of the main drivers in the theory of interpersonal behavior (TIB). These sub-drivers emphasize in forming intentions to perform the behavioral changes while driving on urban roads during COVID-19 and post-pandemic periods. A primary online survey was conducted among road commuters in the most crowded place in Malaysia, the Greater Kuala Lumpur. A total of 383 respondents who frequently drive on road during the past one year were surveyed for this study. This data analysis of this quantitative study applied a partial least squares-structural equation modeling approach to determine the significant findings and results.

The significant findings of the study reveal that environmental consciousness and timely deviation in driving during traffic congestion are positively and significantly influencing the travel behavior performance (TBP) of commuters on urban roads. On the other hand, wet conditions due to weather, narrow road infrastructure and habits of road commuters are negatively influencing TBP. Social responsibility is positively and significantly influencing TBP through the mediating effect of the intention of road commuters’ behavior.

The current environmental concerns and societal adherence efforts in breaking the chain of the infectious COVID-19 among people can be manifested to develop Smart Cities with less air and noise pollution in the future. In this context, the present study proposes an urban travel behavior model and tests for its suitability of a greener and cleaner environment for the benefit of future generations. The limitation of the present study is that travel hazards are not included in the framework, as it is a topic of its own volume.

It is timely to implement Smart Mobility on road business models for Smart Cities as the consequences of the pandemic make us to realize the importance of environmental concerns and the social responsibilities of everyone. TIB considers four drivers, namely, attitude, subjective norm, affect and habit which induce intention to perform behavioral decisions. The novelty of the present study is the development of sub-drivers for these four drivers in the context of the urban travel behavior model.

Founded on the concept of organizational ambidexterity and the competing value model, the purpose of this paper is to develop an information technology (IT) ambidexterity framework to underscore the importance of a balanced and harmonious IT environment in enterprise cloud adoption.

With survey responses from 165 IT executives in a managerial position who are in charge of cloud computing implementation, partial least square method is used to test the research model.

Cloud absorptive capacity plays an important role for firms to secure a competitive advantage. The synergy of the two capabilities (flexibility and control), which have conflicting characteristics, contributes to the enhancement of cloud absorptive capacity and leads to a firm’s knowledge accumulation and performance.

This study is the very first attempt that empirically establishes the relationship between a firm’s competitiveness and cloud computing absorptive capacity. This study provides a comprehensive framework that integrates ambidexterity theory with the competing value framework (CVF) with extending the concept of absorptive capacity that is bounded within an organizational perspective into a cloud computing context.

Firms should treat cloud computing as a strategic consideration to secure a competitive advantage in the contemporary business environment. For a firm’s performance, a dual governance structure, that encompasses flexibility and control, is required to achieve competitive advantage from cloud computing adoption.

To facilitate organizational effort in achieving a harmonious cloud environment, the authors propose a comprehensive ambidexterity framework integrating the CVF approach. This framework maps IT ambidexterity onto the CVF. As CVF considers internal and external factors that ambidexterity theory does not cover, integrating two theories can provide more comprehensive implications and discussions regarding cloud computing adoption.

The purpose of this paper is to investigate how employer brand experience (EBE) impacts organizational citizenship behavior (OCB). In addition, it aims to identify the mediating role of employee engagement (EE) in relationship between EBE and OCB.

To test the research hypotheses, a web questionnaire was developed and data were collected from 426 respondents working in the Indian banking sector. Hypotheses were tested using structural equational modeling.

EBE was positively related to OCB. The predicted mediating role of EE in the relationship between EBE and OCB was also supported.

The study is confined to the banking sector only, which limits the generalization of the findings.

The results imply that firms should leverage on various dimensions of employer brand (EB) i.e. compensation, work–life balance, working environment, training and corporate social responsibility to enhance EE and OCB.

The research is among the very few to confirm the role of EBE vis-à-vis current employees especially in a collectivist society like India. The study also confirmed the mediating role of EE between EBE and OCB which have not been studied previously.

This paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.

The first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.

By using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.

The framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

The purpose of this paper is to investigate whether market conditions have an effect on investors’ propensity to herd in an emerging economy’s stock market. Additionally, given the lack of research on Islamic behavioral finance, the authors further investigate if the herding phenomenon is distinct in Islamic versus conventional stocks.

The authors used daily data for the period of 1995–2016 according to the herding behavior model of Chang et al. (2000), which relies on cross-sectional absolute deviation of returns.

Findings reveal the herding behavior of investors among Shariah-compliant during up and down market exits with non-linear relationship to the market return, while for conventional stocks herding behavior does not exist with linear nor nonlinear relationships during the up and down market. Furthermore, for the whole market, herding behavior only exists during upmarket with a nonlinear relationship to the market return. However, this relationship is not significant. Moreover, the results of this study are robust with respect to the effect of the Asian and global financial crisis.

The findings are useful for investors to identify which market conditions are associated with rational and irrational behavior of investors.

Most of the theoretical and empirical studies on herding behavior have focused on developed countries. Only a few studies have paid attention to the herding behavior in Islamic financial markets, particularly in the context of an emerging market such as Malaysia. This study fills this void.

This study aims to investigate the evolving travel intentions of tourists in the aftermath of the relaxation of international mobility restrictions in 2022. It aims to understand how the concept of “revenge travel” – travelling with the intent to make up for lost time during crisis periods – impacts tourists' travel intentions in the post-crisis era.

Employing a quantitative approach, the study uses Structural Equation Modelling (SEM). Data were collected through a survey of 320 respondents in 2022. This methodology enables a comprehensive understanding of travel intentions, including motivations, perceptions of destination safety and the influence of revenge travel.

The findings reveal that revenge travel is a significant predictor of travel intentions post-crisis. The results indicate that tourists are less concerned with health and safety and are more driven by the desire to compensate for lost travel opportunities. This shift suggests that travel intentions in the post-crisis era are not predominantly fear-induced. The study also offers insights into how these intentions can inform the recovery and sustainable planning of the tourism industry.

This paper recognises the impact of revenge travel on travel intentions following the lifting of crisis-related mobility restrictions. It provides novel insights into tourists' post-crisis travel behaviour, extending the understanding of travel motivations in extraordinary circumstances. The findings are valuable for tourism practitioners and researchers, offering guidance for future tourism development and marketing strategies within a post-crisis context.

This study aims to examine the effect of green human capital readiness on business performance in the green economy era. This study also focuses on investigating whether the relationship is mediated by green market orientation and green supply chain management.

This is a quantitative study using the data of 182 manufacturing small and medium-sized enterprises in East Java, Indonesia. Data are collected using an online survey. A multiple mediation research framework is employed, and partial least squares structural equation modeling is used to test the hypotheses.

The findings demonstrate the following important results. First, green human capital readiness affects business performance. Second, green market orientation partially mediates the effect of green human capital readiness on business performance. Third, green supply chain management partially mediates the effect of green human capital readiness on business performance. Fourth, green market orientation and green supply chain management sequentially mediate the green human capital readiness-business performance relationship.

This study limits its sample to the small and medium-sized enterprises in East Java, Indonesia. Thus, caution must be applied when generalizing to other types of organizations and different regions. The results confirm the resource-based view and sustainability theory in explaining the antecedents of business performance in the era of the green economy which are useful for future researchers and students who are interested in studying human capital, market orientation, supply chain management and business performance.

This study has the following practical implications. First, it implies that the owners/managers of small and medium-sized enterprises need to properly develop their green human capital readiness because they play a strategic role in driving green market orientation, improving green supply chain management and enhancing business performance. Second, it provides useful information to policymakers to make better decision-making in developing environmentally friendly companies.

This study is a response to calls for studies on the antecedents of business performance in the green economy era. It provides empirical evidence for the development of resource-based view and sustainability theory by employing the new construct of green human capital readiness, which has been rarely investigated in previous studies. It also employs a multiple mediation research framework that provides a more comprehensive understanding by including green market orientation and green supply chain management. It also provides empirical evidence in the research setting of small and medium-sized enterprises in Indonesia as an emerging market.

The focus of this research is to identify the optimum commercial grade sewing thread and stitch density to be used with woven linen shirting fabric used in making men’s formal shirt. Maximum seam efficiency and interaction between the process parameters were assessed.

The classical method of optimisation involves varying one variable at a time and keeping the others constant. This is often useful, but it does not explain the effect of interaction between the variables under consideration. In this study, the response surface methodology was used for securing a more accurate optimisation of seam quality (seam efficiency) of woven linen shirting fabric. The response surface method is an empirical statistical technique used for multiple regression analysis of quantitative data obtained from statistically designed experiments by solving the multivariate equations simultaneously. Through this system, the input level of each process parameter, i.e. variable and the level of the selected response (seam efficiency), can be quantified. The central composite, Box–Behnken, is the common design used here.

The maximum seam efficiency is 79.62 per cent and 83.13 per cent in warp and weft direction, respectively, with optimum areal density (G) of 110 g/m² of woven linen shirting fabric. The most suitable stitch density and ticket number of commercial grade sewing thread for woven linen shirting fabric are 13-13.5 and 40, respectively.

This study could help apparel manufacturers to evaluate seam quality, i.e. seam efficiency of woven linen fabric for men’s shirting, more effectively from the proposed regression model. The optimisation of the commercial grade sewing thread size and stitch density used in this study for woven linen shirting fabric within the range of 110-150 g/m² will facilitate apparel engineers in production planning and quality control.

There is dearth of research on seam quality for woven linen shirting fabric using commercial grade sewing thread and engineering of prediction regression model for the estimation of seam efficiency by using process parameters, namely, fabric G, thread size and thread density and their interaction.