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In response to the escalating global CO2 emissions and the urgent need to reduce dependence on fossil fuels, this study diverges from prior research that predominantly focuses on intentions or attitudes towards renewable energy. It investigates the actual uptake of residential solar photovoltaic (PV) systems in regions rich in solar radiation, where, despite the potential, renewables remain a minor part of the energy mix. Incorporating psychological and functional factors and employing the innovation resistance theory (IRT), the study comprehensively examines solar PV technology’s resistance aspects. Utilizing a robust methodological framework that uses partial least squares-structural equation modeling (PLS-SEM) with fuzzy set qualitative comparative analysis (fsQCA), the research evaluates responses from a comprehensive questionnaire survey of 758 households. The advantages of this method lie in its ability to capture both symmetric and asymmetric relationships, thereby offering a richer and more detailed analysis compared to traditional single-method approaches. PLS-SEM results identify significant barriers: image barriers (β = −0.131, t = 3.418, p

The Metaverse, underpinned by its technical infrastructure, heavily relies on user engagement and behavior for successful integration into educational settings. Understanding its driving factors is essential for such a platform to transition from theory to practice, especially in educational settings. However, these factors remain elusive due to inconsistencies in infrastructure and environments. Therefore, this systematic review aims to fill this void by presenting an integrative view on Metaverse adoption in education. This is achieved via three primary dimensions: establishing a taxonomy of the factors influencing Metaverse adoption in education, proposing a framework for Metaverse adoption, and suggesting future research trajectories in this domain. The review systematically classifies the influential factors into four distinct categories: psychological and motivational factors, quality factors, social factors, and inhibiting factors. The proposed framework provides a structured approach for future studies investigating the Metaverse adoption in educational settings. The proposed framework also emphasizes that educational institutions should not only consider the technical prerequisites but also the social, psychological, and motivational aspects of the Metaverse. The study also pinpoints several critical research agendas to enhance our understanding of Metaverse adoption in education. The insights from this review are invaluable for educational institutions, policymakers, developers, and researchers, significantly enriching the emerging field of Metaverse adoption.

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs’ environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

Construction is a critical sector of any economy in terms of value production, labor, and contributing to the gross national product. Managing risk is a relatively young area in Yemen’s construction sector, but it is gaining traction as building activity and competition rise. Construction firms mitigate risk by using a variety of risk management methods. Therefore, there is a need to assess these procedures in order to detect shortcomings. This research aims to establish the existing risk management strategies used in Yemeni building projects. Survey questionnaires were used to collect data. Respondents were drawn from Yemeni construction businesses. Risk management is not executed systematically, intentionally, or continuously, and most firms’ risk management procedures are reactive, semipermanent, informal, and unstructured, with no or few dedicated resources to address risks. This strategy is inconsistent with generally accepted risk management principles. Nonetheless, the findings suggest a general understanding of risk management and a willingness to learn from previous errors. The study of the findings suggests that risk identification approaches such as judgment and historical data are employed for risk analysis, and that the industry typically attempts to avoid or transfer risks in Yemeni building projects. The results shed light on the shortcomings of Yemen’s project management practices. To guarantee that construction projects obtain maximum value for money, project managers of big construction businesses in Yemen need a strong understanding of and training in globally accepted systematic risk management procedures. Finally, this study can help future stakeholders determine how to work together to manage risk.

Innovation has become an important source of growth and survival for the telecom industry, especially with the highly competitive, dynamic business environment of this sector. The telecom market attributes of liberalization, privatization and globalization result in fierce competition, which aggravates telecom firms’ challenges to expand their market share, and hence, increase customer base, sales volume, and ultimately profit. A new strategy to succeed is innovation differentiation, which leads to breaking out the competition and creating uncongested market space. The aim of this paper is to highlight the logic of value innovation in the wireless telecom sector. A qualitative research approach was used to analyze the actions related to value innovation in the mobile service sector in Malaysia. In particular, this study focuses on the motivation, and hence the changed strategies, of telecom service providers to embrace value competition rather than traditional tariff competition in order to enhance customer satisfaction and encourage customer loyalty, which will ultimately result in higher profitability and growth. The services and loyalty packages provided by the telecom firms were reviewed through employing analytical case study. Also, this paper highlights the drawbacks and limitations of the services that did not live up to the expectations of customers and failed to earn their loyalty. Moreover, this study gives suggestions to service providers regarding ways to achieve long-run profitable growth.

The construction industry has been experiencing a rapid increase in complex projects for the last two decades. Simultaneously, project complexity has received more attention from academics and practitioners worldwide. Many studies suggest that perceiving complexity is critical for successful construction project management. This study investigates the current status and future trends in construction project complexity (CPC) literature from the Scopus database. This review systematically uses bibliometric and scientometric methods through co-occurrence and co-citation analysis. First, 644 academic documents were retrieved from the Scopus database. Then, co-occurrence and co-citation analysis were performed along with network visualization to examine research interconnections’ patterns. As a result, relevant keywords, productive authors, and important journals have been highlighted. The prominent research topics within the literature on construction project complexity focus on the following topics: identifying and measuring project complexity, schedule performance and cost estimation, system integration and dynamic capabilities, and risk assessment and uncertainty. Finally, the potential research directions are developing towards safety performance, organizational resilience, and integrated project delivery (IPD). The study still has a limitation. The review focuses only on the academic documents retrieved from the Scopus database, thus restricting the coverage of the reviewed literature relating to construction project complexity. To the best of the author’s knowledge, this study is the first study that provides a systematic review of the literature from the Scopus database on construction project complexity.

Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to understand and predict the key determinants that drive managerial decision-makers’ perspectives for adopting this technology. The data were gathered from 134 institutional managers, involved in the decision making of the institutions. This study applied two analytical approaches, namely variance-based structural equation modeling (i.e., PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that seven factors were identified as significant in influencing decision-makers’ intention towards adopting CC. Second, the normalized importance among those seven significant predictors was ranked utilizing the ANN. The results of the ANN approach showed that technology readiness is the most important predictor for CC adoption, followed by security and competitive pressure. Finally, this study presented a new and innovative approach for comprehending CC adoption, and the results can be used by decision-makers to develop strategies for adopting CC services in their institutions.

Artificial intelligence (AI)-based chatbots have received considerable attention during the last few years. However, little is known concerning what affects their use for educational purposes. This research, therefore, develops a theoretical model based on extracting constructs from the expectation confirmation model (ECM) (expectation confirmation, perceived usefulness, and satisfaction), combined with the knowledge management (KM) factors (knowledge sharing, knowledge acquisition, and knowledge application) to understand the sustainable use of chatbots. The developed model was then tested based on data collected through an online survey from 448 university students who used chatbots for learning purposes. Contrary to the prior literature that mainly relied on structural equation modeling (SEM) techniques, the empirical data were analyzed using a hybrid SEM-artificial neural network (SEM-ANN) approach. The hypotheses testing results reinforced all the suggested hypotheses in the developed model. The sensitivity analysis results revealed that knowledge application has the most considerable effect on the sustainable use of chatbots with 96.9% normalized importance, followed by perceived usefulness (70.7%), knowledge acquisition (69.3%), satisfaction (61%), and knowledge sharing (19.6%). Deriving from these results, the study highlighted a number of practical implications that benefit developers, designers, service providers, and instructors.