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The construction industry is suffering from incomplete knowledge flow that results in time and cost over-runs. Many researchers believe that a large part of this problem is related to the design phase being a tacit knowledge-dominated area. Since tacitness of knowledge contributes to incomplete flow of knowledge, we posit that facilitating the conversion of tacit knowledge to explicit knowledge can improve the overall knowledge flow. Within this context, this paper analyses recognised knowledge-capturing techniques and compares them in view of the operating characteristics of the conceptual design phase. This paper proposes a new tacit knowledge-capturing technique for this phase. The outcomes include a new knowledge-capturing technique, a method for validation of the proposed technique, and recommendations on how to deal with the challenges concomitant with the application and utilisation of the proposed technique in the building industry. By proposing a combinational tacit knowledge-capturing technique, this study attempts to mitigate the impact of the potential incomplete knowledge flow during the conceptual design phase in building projects. [ABSTRACT FROM AUTHOR]

Ontology is an important concept in Computer Science to formally represent knowledge. The software engineering ontology assists in defining information for the exchange of semantic project information framework. Research into ontological issues has been widely active in various areas. This paper presents the origin of ontology research and gives the different definitions of ontology. The paper gives an overview of ontology and its types including the building and design for an enterprise system. This review tries to study articles in which adaption model and there properties were discussed in order to get a clear review about the ontology. This study is showed a Systematic Literature Review which was used to identify important characteristic about the ontology. The research identified more than 70 paper on this study but only 46 of them was precisely relevant in the field of Ontology development processs. In this paper we analyse the ontology based on types, design, building, model as a systematic review of the subject. And choose the best way according to that. [ABSTRACT FROM AUTHOR]

The tremendous growth of the cloud computing environments requires new architecture for security services. In addition, these computing environments are open, and users may be connected or disconnected at any time. Cloud Data Storage, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. To verify the correctness, integrity, confidentially and availability of users' data in the cloud, we propose a security framework. This security framework consists of two main layers as agent layer and cloud data storage layer. The propose MAS architecture includes five types of agents: User Interface Agent (UIA), User Agent (UA), DER Agent (DERA), Data Retrieval Agent (DRA) and Data Distribution Preparation Agent (DDPA). The main goal of this paper is to formulate our secure framework and its architecture. [ABSTRACT FROM AUTHOR]

Higher Education Institutions (HEIs) consider resource optimization as an essential concern. Cloud computing (CC) in the fourth industrial revolution became the de-facto standard for delivering IT resources and services. CC is now a mainstream technology, andHEIs across the globe are rapidly transitioning to this model; hence, maintaining the retention of the customers of such technologies is challenging for cloud service providers. Current research concerning CC focused on adoption and acceptance. However, there is still a scarcity of research concerning such technology's continued use in an organizational setting. Drawing on the prior literature in organizational-level continuance, this paper established a positivist quantitative-empirical study to bridge the research gap and assess the precursors for a continuance of cloud technology in HEIs. Subsequently, this study developed a conceptual framework by integrating the IS success model and the IS discontinuance model through the lens of the TOE framework. The data were collected from the decision-makers of Malaysian HEIs that have adopted CC services, and analyzed using Structural equation Modelling (SEM) based on Partial Least Squares (PLS). The results indicate that the continuance intention can be predicted by technology, organizational, environmental, and other contextualized factors, explaining 85.2% of the dependent variables' variance. The paper closes with a discussion of the research limitations, contribution, and future directions. [ABSTRACT FROM AUTHOR]

Global research is being directed towards addressing sustainable development topics. Organizations face an increasing pressure for practicing environment-friendly behaviour. Green Information Technology (IT) is an example of such practices that aims to enhance the organizational performance and sustainability. Regardless of the significance of this technology, inadequate number of studies have focused on its adoption in Malaysian organizations. Therefore, the aim of this study is to investigate the factors that influence the Green IT adoption practices and its impact on organizational performance through the lens of Resource-Based View (RBV) theory. Based on previous literature and the Resource-Based View (RBV) theory, six factors were identified for adopting Green IT in Malaysian organization context. The research model hypothesizes that institutional pressure, organizational strategy, openness, environmental performance, economic performance, and competitive advantage can affect the individual perception of Green IT adoption. The proposed hypotheses in this study will be tested in the upcoming future. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill. [ABSTRACT FROM AUTHOR]

Green information technology (IT) adoption has helped enhance the overall organization's environmental sustainability. Developing the strategies for effective adoption of Green IT is one of the essential goals of decision-makers. This study purposes to investigate the factors that influence decision-makers' intention to use Green IT and the proposed green IT adoption model in Malaysian manufacturing firms. The 183 valid data were obtained using survey questionnaires from Malaysia's manufacturing industries' industrial managers and examine collect data through two analytical techniques. Two-staged structural equation modeling and artificial neural network applied for hypotheses evaluation and finding the significance level of every factor in the model. The outcomes of hypotheses evaluation through structural equation modeling revealed that managerial interpretation and ascription of responsibility have a significant role in predicting the adoption of green information technology in manufacturing companies. Besides, the Artificial Neural Network (ANN) results showed that the managerial interpretation and ascription of responsibility are considered as the most significant factors of green information technology adoption. This study will help the decision-makers and policymakers develop policies and programs for the effective employment of green information technology in manufacturing industries. • A novel adoption model of green IT in manufacturing industries in Malaysia is proposed. • Structural equation modeling and neural network as an effective approach to for green IT adoption. • Managerial interpretation is the most significant factor for green IT adoption. [ABSTRACT FROM AUTHOR]

Electric vehicles have significantly contributed to the sustainability of world economic growth. It is of critical importance to understand and examine the factors impacting the consumers' attitude toward the adoption of electric vehicles. However, limited study investigated the effect of altruism on electric vehicles adoption from a pro-environmental behaviour perspective. Therefore, the present work aimed at identifying the influencing factors on consumers' intention to use electric vehicles. To this end, a model has been developed based on two theoretical models called the Norm Activation Model and the Theory of Planned Behaviour. The potential consumers in Malaysia were selected to answer questionnaires. Accordingly, 177 valid questionnaires were collected and the influencing factors on the electric vehicles purchase intention were empirically analyzed using a structural equation model. According to the results, perceived value, attitude, the ascription of responsibility, subjective norms, personal norms, perceived consumer effectiveness, and awareness of consequences affected the consumers' electric vehicles' purchase intention significantly and positively. Consumers' behaviour regarding the adoption of electric vehicles can be understood better through the findings of this study, while electric vehicle development can be promoted as well. [ABSTRACT FROM AUTHOR]

Since consumers, governments, and society in general are increasingly concerned about the loss of natural resources, along with pollution of the environment, there is currently a significant tendency to recognize the value of green innovation toward the achievement of sustainable development. Hotels are considered responsible for a considerable proportion of the environmental pollution caused by the tourism industry. Yet, few studies have considered the effects that green innovation may have on sustainable performance in the hotel industry. Consequently, the present study aimed to investigate the factors influencing the adoption of green innovation, and its potential effects on the performance of the hotel industry. Data collection was performed through inspection of 183 hotels in Malaysia. Data analysis was carried out employing the partial least squares method. The two factors of environmental and economic performance were determined to have the strongest influence, affecting the green innovation procedures positively and significantly. The results of the present study have major implications for hospitality research, since they demonstrate the importance and potential of green innovation in promoting sustainable performance in the hotel industry. The proposed model and the identified influencing factors of green innovation can assist policy makers and hotel managers in understanding the drivers leading to the adoption of these practices in the hotel industry. [ABSTRACT FROM AUTHOR]

In light of the rapidly evolving digital landscape, there is an increasing need to explore digital hoarding behavior. This need is driven by concerns regarding its intricate psychological foundations and its impact on individuals within our technology-centric society. This research investigates the influence of various factors, including the fear of missing out, emotional attachment, information overload, and decision fatigue, on digital hoarding behaviors among university students in Iran. Additionally, the study examines the moderating role of maladaptive perfectionism in these relationships. The study involved 275 university students (mean age = 21.62 years; standard deviation = 2.28 years; 65.6% female) selected from four universities in Iran. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that the fear of missing out, emotional attachment, information overload, and decision fatigue significantly predict university students' digital hoarding behavior. Moreover, the findings highlighted the moderating effect of maladaptive perfectionism on the association between emotional attachment and digital hoarding behavior. This suggests that individuals with higher levels of maladaptive perfectionism exhibit amplified digital hoarding tendencies when emotionally attached to their digital data. This study provides a deeper understanding of the relationship between psychological factors and digital hoarding tendencies. These findings have practical implications for educational institutions and mental health professionals, as they can help in developing targeted strategies and interventions to manage digital hoarding behavior in university freshmen and promote healthier digital habits., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)