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Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The construction industry is complex, human-intensive and driven by monetary values. Hence, disputes are widespread. Initial conflicts among parties may develop into a disastrous dispute that costs the project success and good relationships and affects stakeholders' expectations. There has been a focus on causes of construction-related disputes, and studies over the past three decades have attempted to identify a more comprehensive list of reasons for disputes. Some of these studies' limitations were geographical, project delivery methods and project types. The purpose of this study is to identify the most recent and conclusive list of causes of disputes based on current literature by undertaking a systematic literature review (SLR).

Considering the large number of studies that focused on causes of disputes, this study aims to develop a comprehensive list of causes, using a SLR, as it ensures that all previous articles in multiple databases are reviewed to produce a comprehensive outcome. A six-stage SLR was followed from background study to analysis and reporting.

Not surprisingly, the number of publications has increased over time, most from the Middle East region. The interconnected nature of the causes was widely emphasised. The SLR has produced eight common core causes of disputes. They are: poor contractual arrangements, employer-initiated scope changes, unforeseen site changes, poor contract understanding and administration, contractor’s quality of works, the inability of the contractor to achieve time targets, non- or delayed payments and poor quality of design. The majority of previous authors realised that disputes could be avoided by parties’ involvement during the early stages, avoiding being opportunistic and acting collaboratively.

Even though numerous studies have been carried out to identify the causes of disputes in the construction industry, none did a SLR. This study aggregates all the previous studies that focused on construction-related disputes systematically. Categorising causes based on the party primarily responsible help various stakeholders by providing a distinct list of factors to avoid that contribute to disputes.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

This paper hypothesized that perceived relative advantage and perceived compatibility would have a positive effect on merchants' intention to accept payments via P2P mobile payment services, while perceived financial risks and perceived costs would have a negative effect. The study also explored the differences in gender, age and experience.

The proposed model is based on the valence framework, where positive utility is represented by relative advantage and perceived compatibility, while negative utility is represented by perceived risks and perceived costs. The data for this study were collected from small business owners (merchants) at the largest public market in the Center Department of Mirebalais, Haiti, using a purposive sampling method.

The results of a structural equation modeling on a sample of 339 merchants only confirmed the effect of both perceived comparative advantage and perceived compatibility. Furthermore, the multigroup analysis revealed that the perceived comparative advantage is stronger for female merchants, older age groups and merchants who frequently used P2P m-payment for the transfer of remittances. Perceived compatibility is stronger for male merchants, younger age groups and merchants who occasionally used P2P m-payment for the transfer of remittances.

This study was conducted in the economic context of Haiti, where P2P m-payments are commonly used for transferring remittances. Since there are limited studies that examine P2P m-payment acceptance from the perspective of merchants, this study offers valuable insights.

This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Sustainability is a very important factor to be considered in the supply chain (SC) of any industry. Agricultural industry needs to be addressed even more importantly with the tools of sustainability as it concerns the life of millions. This paper explores the critical barriers (CBs) in the sustainable supply chains (SSCs) of makhana industry located in the northern part of India and seeks to design a model for the researchers and the managers who want to work in this industry.

Initially, the CBs were identified with the help of an extensive literature review of sustainability in SCs for agri-industry and discussion with makhana industry experts (consisting of managers and senior managers) and academicians (consisting of professors and research scholars). The study uses the multi-criteria decision-making (MCDM) technique, namely interpretive structural modeling (ISM) and fuzzy ISM to develop the model. The study finally validates the model using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis.

The obtained results indicate that, in the SSC of makhana industry, the role of “Lack of adoption of organic agricultural management techniques” (CB2), “Lack of modern techniques (CB4)”, “Multiple intermediaries” (CB5), “Weak socio-economic conditions” (CB7) and “Lack of proper knowledge” (CB1) are very significant. These barriers are needed to be addressed first as they have the highest driving power and other barriers are directly driven by these CBs.

The paper has included seven experts, and the interrelationship between CBs has been developed on the basis of their knowledge and discussion, so the results may be a little bias. Moreover, the paper has obtained the results using the ISM and fuzzy ISM by considering ten CBs; the researchers can explore this research by including more CBs and validate the results using other MCDM techniques like fuzzy-decision making trial and evaluation laboratory (DEMATEL), fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best Worst Method (BWM).

This study is unique as per industry point of view and may help the researchers and managers to explore the field of makhana.

The COVID-19 pandemic has disrupted various systems that drove people to adapt to certain technologies, such as electronic government services, for daily survival and to meet social distancing requirements. Therefore, this study aims to determine the antecedents of e-government use based on prospect theory and modified unified theory of acceptance use of technology (UTAUT) during a pandemic.

Convenience sampling of 368 respondents from Vietnam was conducted, and questionnaires were distributed personally or by email. The data were analyzed following a two-stage structural equation modeling (SEM) using SPSS v23 and AMOS v23. The validity and reliability of the instrument were tested and ensured.

Results show that perceived severity drives government support and social influence while perceived security drives government support, social influence and trust. Social influence enhances government support and trust, which both drives e-government use. Mediation analysis shows that government support mediates perceived the influence of perceived severity on e-government use.

The integration of prospect theory and UTAUT brings into light what will drive the adoption of e-government in the context of Vietnam. Supporting mechanisms, such as security measures, trust-building, government support and social influence, will drive citizens to adapt to technologies provided by the government but would also rely on the perceived risks and benefits.

This study integrates prospect theory and a modified version of UTAUT to explain the drivers of e-government use. The results reveal that under uncertainties, government support is critical in driving the use of e-government for people to manage the daily lives for survival.

The purpose of this paper is to develop an interpretive structural modeling (ISM) model to investigate the critical success factors (CSF) and the extent of CSF's influence in the implementation of lean industry 4.0 in manufacturing supply chain.

The study has been carried out with the help of the latest literature followed by brainstorming sessions with experts. The experts were the managers from the industries, assistant professors, and research scholars from academia working in this domain. Finally, a structured model is formed using ISM methodology for the analysis of the CSFs followed by matrice d'impacts croisés multiplication appliquée á un classment (MIAMAC) Analysis for the validation of the model.

The study identifies robotics, virtual and augmented reality and cloud computing as the main CSFs which are responsible to drive all the identified CSFs. However the CSF professional training and development (PTD) has been identified as the weakest driver but having the highest dependent power.

The study has included nine CSFs and the contextual relationships between the CSFs are based on the knowledge and experience of the experts, which may be biased. Moreover, the paper has covered the ISM approach, and the same thing can be validated using the fuzzy-ISM and other multi-criteria decision-making (MCDM) techniques.

This investigation of the CSFs in the lean industry 4.0 is original and the identified CSFs are the result of the literature reviews and an extensive discussion from the experts. The paper uses the complete experience of the respective experts to make this work more effective and original.

This paper aims to anticipate the possible development direction of WAAM. For large-scale and complex components, the material loss and cycle time of wire arc additive manufacturing (WAAM) are lower than those of conventional manufacturing. However, the high-precision WAAM currently requires longer cycle times for correcting dimensional errors. Therefore, new technologies need to be developed to achieve high-precision and high-efficiency WAAM.

This paper analyses the innovations in high-precision WAAM in the past five years from a mechanistic point of view.

Controlling heat to improve precision is an effective method. Methods of heat control include reducing the amount of heat entering the deposited interlayer or transferring the accumulated heat out of the interlayer in time. Based on this, an effective and highly precise WAAM is achievable in combination with multi-scale sensors and a complete expert system.

Therefore, a development direction for intelligent WAAM is proposed. Using the optimised process parameters based on machine learning, adjusting the parameters according to the sensors’ in-process feedback, achieving heat control and high precision manufacturing.