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Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

Over the last three decades, construction projects have increasingly been delivered through alternative delivery methods. As a result, many owners have a range of delivery methods to choose from and aim to use the right one for each of their projects. Researchers have developed several tools and decision-support processes to facilitate this selection procedure. The purpose of this study is to review and discuss differences and common themes across selection tools developed by academic researchers and project owners.

The study reviews prominent selection processes and tools used for infrastructure projects by conducting an in-depth literature review and using the content analysis method to elicit findings on the methodologies and criteria presented in the literature.

This study presents three principal findings. First, findings show three common themes emerge within the selection criteria—characteristics, goals and risks. Second, while academic studies most commonly suggest employing multi-attribute analysis, this study reveals that, in practice, selection tools most frequently employ a staged or gated evaluation based on the type of criteria and their importance to the decision. Finally, this review further highlights the importance of institutional context in decision-making.

This work contributes to the body of knowledge by providing guidance to practitioners and opening new directions for researchers around the way selection criteria are categorized in the relevant literature and the institutional context considerations when structuring or evaluating a selection process or tool.

In this study, a novel framework was proposed to assess the trust in e-government (e-Gov) services under an uncertain environment. The proposed framework was applied in Iranian municipality websites of e-Gov services to evaluate the readiness score of trust in e-Gov services.

A unique hybrid research methodology was proposed. In the first phase, a comprehensive set of indices were determined from an extensive literature review and finalized by employing the fuzzy Delphi method. In the second phase, interval-valued intuitionistic fuzzy set (IVIFS) -was utilized to model the problem's uncertainty with analytic called IVIFS- hierarchy process (AHP) to determine the importance of indices and indicators by assigning the weights. In the third phase, the fuzzy evaluation method (FEM) is followed for assessing the readiness score of indices in case studies.

The findings indicated that “Trust in government” is the most significant index affecting citizen's trust in e-Gov services while “Maintenance and support” has the least impact on user's intention to use e–Gov services.

The study contributes by introducing a unique research methodology that integrates three phases, including fuzzy Delphi, IVIFS AHP and fuzzy evaluation method. Moreover, the fuzzy sets theory helps to reach a more accurate result by modeling the inherent ambiguity of indicators and indices. Interval-valued intuitionistic fuzzy models the ambiguity of experts' judgments in an interval.

The study helps policy makers to monitor wider aspects of trust in e-Gov services as well as understanding their importance. The study enables policy makers to apply the framework to any potential case studies to evaluate the readiness score of indices and recognizing strengths and weakness of trust dimensions as well as recommending advice for improving the situation.

The study is one of the few to indicate significant indices of trust in e-Gov services in developing countries. The study shows the importance of indicators and indices by assigning a weight. Additionally, the framework can assess the readiness score of various case studies.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors.

Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain.

In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers.

Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias.

The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

Acknowledging previous scholarly focus on functional attributes in understanding technology acceptance behaviour, the current study aims to offer a novel perspective by integrating eight different dimensions of utilitarian and hedonic attributes to examine their influence in delivering a holistic web-based retail shopping experience.

The research model was tested and validated through data collected from 370 online shoppers across both hedonic and utilitarian product ranges. Hypotheses were tested using covariance-based structural equation modelling with multi-group analysis to examine the moderation effect.

The findings strongly support the model confirming eight new utilitarian and hedonic dimensions that influence web-based retail shopping behaviour. The findings also confirm that hedonic attributes remain important even for utilitarian product purchasing.

The key managerial implication is the demonstrated need to balance utilitarian and hedonic attributes in web-based retail platforms, where previously, there has been an overemphasis on functional features. Web-based retailers should consider the optimal blend of utilitarian (e.g. information quality) and hedonic (e.g. aesthetic) attributes in the design of a retail shopping site, irrespective of the product category.

This study integrates multiple dimensions of utilitarian and hedonic attributes into a single model and highlights the interplay of these attributes, thus extending the technology acceptance model. This paper also advances scholarship through its identification of attribute impact across different product categories.

Tracking physical resources at the construction site can generate information to support effective decision-making and building production control. However, the methods for conventional tracking usually offer low reliability. This study aims to propose the integrated Smart Twins 4.0 to track and manage metallic formworks used in cast-in-place concrete wall systems using internet of things (IoT) (operationalized by radio frequency identification [RFID]) and building information modeling (BIM), focusing on increasing quality and productivity.

Design science research is the research approach, including an exploratory study to map the constructive system, the integrated system development, an on-site pilot implementation in a residential project and a performance evaluation based on acquired data and the perception of the project’s production team.

In all rounds of requests, Smart Twins 4.0 registered and presented the status from the formworks and the work progress of buildings in complete correspondence with the physical progress providing information to support decision-making during operation. Moreover, analyses of the system infrastructure and implementation details can drive researchers regarding future IoT and BIM implementation in real construction sites.

The primary contribution is the system proposal, centralized into a mobile app that contains a Web-based virtual model to receive data in real time during construction phases and solve a real problem. The paper describes Smart Twins 4.0 development and its requirements for tracking physical resources considering theoretical and practical previous research regarding RFID, IoT and BIM.

Effective leaders have emerged as the cornerstone of project success. The major purpose of this paper is to propose a framework to categorize and prioritize leadership competencies for project managers in megaprojects.

In the first stage, this study utilizes PMBOK 6th Edition, IPMA ICB 4.0 frameworks to develop a hierarchy-based four clusters of leadership competencies. In the second stage, a Fuzzy-AHP (Analytic Hierarchy Process) approach was employed to prioritize the leadership competencies for an organization dealing in megaprojects. Finally, using ordinal priority approach (OPA), the results of Fuzzy-AHP method are validated.

Based on PMBOK, IPMA and literature, the proposed framework deduced twenty-four leadership competencies and grouped them in four clusters. The Fuzzy-AHP results indicate that among clusters, people competencies cluster is ranked most important, followed by perspective, practice, and innovation competencies. Considering the sub-categories and global weights, culture/values, governance, interpersonal skills, and development and growth emerged as the most important leadership competencies. The results from OPA corroborate the findings of Fuzzy-AHP method.

Megaprojects are characterized by massive investments and extensive economic and social impact. The proposed framework would be an important aid for policymakers to develop suitable strategies and programs to inculcate leadership competencies that would lead to successful project managers and improved project performance.

The need for this research stems from the need to integrate popular project management frameworks in leadership competencies development in project based organizations. The proposed integrated framework, based on PMBOK 6th Edition and IPMA ICB 4.0 competency frameworks, is an original contribution to understand and prioritize leadership competencies for megaproject success.

There is a growing mismatch between the skill demands of the industry and the offerings of academia. One way of reducing this mismatch is by improving collaborations between practitioners and instructors using web-networking platforms. However, it is important to understand practitioners’ considerations while collaborating with instructors. Therefore, this study identified these considerations in order to infer inputs for the design of the graphical user interface (GUI) of a web-based platform for connecting instructors and practitioners.

A mixed method was adopted through a survey and focus group. A survey was used to capture practitioners’ considerations while collaborating with instructors for student development, and a focus group helped uncover an in-depth understanding of the study phenomena. The data were analyzed using descriptive and inferential statistics and thematic analysis.

The results show the willingness of practitioners to collaborate with instructors for student development, the ways by which practitioners are willing to meet instructors' course-support needs and their considerations in deciding to do so. Slight differences were observed between the results of the survey and the focus group regarding the ranking of the practitioners’ considerations. The study highlighted demographic differences in practitioners’ considerations when deciding on meeting instructors' course-support needs. The results provide a basis to deduce the GUI inputs of web-networking platforms for connecting instructors and practitioners.

This study revealed practitioners’ design needs and GUI inputs to facilitate the design of web-networking platforms for connecting instructors and practitioners. This study also contributes to user interface design principles, theories on individual differences and practitioners’ involvement in student professional development.

Following the COVID-19 pandemic, most higher education institutes shifted to online learning as the sole alternative to continuing education while mitigating the risks imposed by the pandemic. This has raised several concerns regarding students’ learning experience, satisfaction and academic achievement, particularly in countries where students have restrained technological resources (i.e. developing nations). The current research aims to investigate the key factors influencing students’ attitudes, satisfaction and academic achievement among university students in an emerging market context (i.e. Morocco). The moderating effect of students’ motivation to study online was also scrutinized.

The authors propose an integrated conceptual framework that combines the technology acceptance model (TAM) with the outcomes of prior literature related to online learning. Based on data collected from 850 Moroccan university students, the authors empirically tested the conceptual model using a partial least squares (PLS) estimation.

First, attitude toward online learning and satisfaction positively impact university students’ academic achievement; at the same time, attitude positively impacts students’ satisfaction with online learning. Second, students’ satisfaction and attitude toward online learning were found to be mainly influenced by instructor performance, ease of use of the online learning platform, information quality, interactivity and perceived usefulness (PU). Finally, student motivation acts as a moderator, e.g. students with higher motivation to learn online are more likely to develop a favorable attitude toward online learning and can, therefore, accomplish better academic performance.

The current study makes a considerable contribution to the literature by contributing to the on-going debate about the potentials and challenges of online learning, particularly in an emerging country where education remains a considerable challenge. The study findings can help higher education institutes gauge the quality of online education programs and design efficient strategies to develop high-quality online learning for students. Our findings have implications not only for educational institutions and instructors in developing markets but also for the vendors of online course delivery software.