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This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.

This study explores the factors influencing the investment intentions of potential home buyers among paraprofessionals in sustainable housing within urban construction. By combining the technology acceptance model (TAM) and the extended model of goal-directed behavior (EMGB), the research seeks to understand how technological perceptions, personal motivations and behavioral intentions intersect to shape these investment decisions.

A quantitative, cross-sectional and descriptive research design was employed in this study. The study surveyed 641 paraprofessionals as potential home buyers in various Indian cities using a multi-stage stratified sampling technique. It incorporated variables from the TAM and EMGB, modifying some aspects to include financial self-efficacy, hedonic motivation and anticipated regret, alongside adding location as a new factor to examine its impact. For data analysis, partial least squares structural equation modeling was utilized. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess these relationships’ significance and magnitude.

The study found that perceived usefulness and ease of use significantly enhance attitudes toward sustainable homes, while subjective norms have a minimal effect on such investments in India, emphasizing personal rather than societal influences. Financial self-efficacy, anticipated regret and hedonic motivation are key drivers, indicating that economic capacity and the pursuit of a satisfying lifestyle are crucial for investment intentions. Additionally, the importance of location is highlighted, with infrastructural aspects notably affecting sustainable housing appeal. These insights reveal unique dynamics in India’s sustainable housing sector, diverging from trends in developed countries.

The study lies in its unique fusion of the TAM and EMGB specifically tailored to the Indian urban construction context. It introduces financial self-efficacy, hedonic motivation and anticipated regret as novel variables within these frameworks, alongside emphasizing the significant role of location in sustainable housing decisions. This approach offers new insights into the psychological and socioeconomic factors driving sustainable housing investments in developing countries.

The purpose of this paper is to examine mediating role of individual performance between the association of organizational politics and organizational performance in Jordanian public sector.

The partial least squares–structural equation modeling was conducted to test the suggested hypotheses.

The findings revealed that organizational performance is significantly and negatively influenced by organizational politics and positively influenced by individual performance. In addition, the results also revealed that individual performance is significantly and negatively influenced by organizational politics. Finally, the results show that individual performance has mediated the association between organizational politics and organizational performance, and hence last hypothesis was accepted.

The current study provides several recommendations to the decision-makers in the Jordanian public sector, including enhancing transparency and publishing policies and regulations in a general and easily accessible manner.

The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor.

A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these relationships, respectively.

One of the key findings of this study was the establishment of significant positive relationships between awareness of consequences, ascription of responsibility and personal norms with behavioral intentions. This underscored the importance of personal ethical considerations in shaping intentions. Perceived usefulness and ease of use were found to significantly influence attitudes positively, highlighting the relevance of these factors in forming favorable attitudes toward behaviors. Attitude, subjective norms, financial self-efficacy and location played significant positive roles. However, negative anticipated emotions decreased desire. This illustrated the complex role emotions play in motivational processes. The study also revealed that subjective norms did not significantly contribute to shaping personal norms. This indicated a potential decoupling of societal expectations from personal ethical obligations in the decision-making process.

This study offers actionable insights for both policymakers and real estate developers. For policymakers, the findings highlight the need to craft initiatives that go beyond mere awareness, instead fostering a deep sense of personal responsibility and environmental stewardship among potential homebuyers. For real estate developers, the emphasis on financial self-efficacy and location suggests a strategy shift toward designing sustainable homes that not only meet environmental standards but also align with buyers’ financial confidence and geographic preferences. Together, these strategies can drive a more widespread adoption of sustainable housing, making sustainability a tangible and appealing choice for millennials.

To the best of the authors’ knowledge, this empirical research study was one of the first studies that contributed to the literature by integrating the model of goal-directed behavior, technology acceptance model and norm activation model. This study thus offered a nuanced understanding of the interplay between normative influences, usability perceptions, ethical considerations and emotions in the context of behavioral intentions.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

The purpose of this study was to investigate the connection between green human resource management (GHRM) and corporate social responsibility (CSR). In addition, this study also investigates how the impact of GHRM varies depending on the extent of transformational leadership.

Adopting a quantitative approach, the sample consists of 376 employees who hold leadership positions in the academic body and those who work in human resources units at universities who won the Web Metric Award.

Results have shown that all GHRM practices were found to have a significant and positive effect on CSR. In addition, the findings revealed that transformational leadership positively moderates the relationship between GHRM and CSR.

The findings of this study contribute to the existing body of knowledge by providing empirical evidence of the positive relationship between GHRM practices, transformational leadership and CSR performance. In addition, the study highlights the moderating influence of transformational leadership on the relationship between GHRM and CSR, suggesting that transformational leadership can increase the efficacy of GHRM practices in promoting CSR outcomes.

The main purpose of this study is to determine the antecedent factors of smart government service apps intention and its impact on actual use by extending the unified theory of acceptance and use of technology (UTAUT) in the Jordanian context.

To achieve the main purpose, a quantitative method was used to test collected data, and hypotheses testing through using statistical package for social sciences (SPSS) and smart partial least squares software.

The findings indicate that social media advertising has a positive effect on both social influence and peer influence. Furthermore, it demonstrated a significant effect of social influence on performance expectancy. In addition, there is a direct correlation between the government capacity, effort expectancy, facilitating conditions and the intention to use e-government services. Lastly, the results mainly show that the actual use of e-government services is significantly and positively influenced by intention and self-isolation. Next, as expected, self-isolation moderated the relationship between intention to use and actual use of e-government services via the Sanad application, and hence the related hypothesis was supported.

This study provides practical recommendations for the policy-makers in the Jordanian e-government and Ministry of Digital Economy and Entrepreneurship (MoDEE) in Jordan.

The purpose of the article is to shed light on how COVID-19 affects employee involvement in environmental responsibility and innovative performance in the banking industry, and whether employee engagement mediates the relationship between the variables. Thus, this study tries to understand bank employees’ perspectives in relation to the variables.

The study was collected during Time lag (1) and Time lag (2) from 156 to 216 bank employees, respectively. The study applied two types of analysis, to comprehend the impact of COVID-19 on employees, descriptive analysis and the partial least squares (PLS) are used.

The study's findings focused mainly on the influence of COVID-19 in Jordanian banks on employee innovative performance (EIP) due to pandemic, in addition to its effect on environmental responsibility engagement (ERE). The findings indicated a positive significant relationship between the variables. Meanwhile, employee engagement (EE) mediated the effect between the exogenous and endogenous variables.

The current research provide light on the value of employees' innovative performance and banks' commitment to environmental responsibility for those working in the banking industry, particularly during a pandemic. The findings have significant ramifications for the banking industry and in raising employee engagement.

This paper aims to systematically review the managerial literature on Industry 4.0 (I4.0) in emerging markets (EMs) through bibliometric analyses to identify incipient research streams and literature gaps and recommend avenues for future research.

This research uses bibliographic coupling analysis (BCA) to obtain a comprehensive view of the intellectual contours within the addressed literature. The primary source utilised is the Web of Science database. A total of 345 peer-reviewed journal articles were retrieved. Complementing BCA, we use social network analysis and the content analysis of articles to study the resulting literature clusters.

The results reveal four thematic clusters: (1) Adoption of I4.0 in EMs; (2) impact of I4.0 on organisational aspects and financial performance of companies and supply chains in EMs; (3) I4.0, lean management and operational performance in EMs and (4) I4.0 and the development of sustainable practices in EMs. We supplement these results with the proposal of different future avenues of research, both general and specific, for each identified cluster.

The current study has certain limitations arising from using the bibliometric method and techniques employed in the analyses.

To the best of the authors’ knowledge, there is no comprehensive literature review article on this subject. This research is deemed valuable for future scholars as it facilitates the identification of research fronts that define the forefront of knowledge, reveals current trends and sets the stage for further exploration of key issues in the field. This, in turn, can offer valuable insights to academics, policymakers and practitioners.

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.