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The intelligent agent object (IAO) system is a multi‐paradigm development environment which can be used to create intelligent agent systems for manufacturing or other domains. The IAO system was developed from the rule‐based object (RBO) system which is a programming environment integrating both the rule‐based and object‐oriented paradigms. Propagation‐oriented programming, access‐oriented programming and group‐oriented programming are among the extensions included in the IAO system. Its most unusual contribution is the propagation‐oriented programming paradigm which is not found in most systems. A key application is the messenger inferencing structure which is a user‐extendable framework supporting multiple knowledge representation, meta‐inference control, and distributed inference. This allows the IAO system to go beyond predicate logic based production rule programming. New developments are also introduced for access‐oriented programming. The IAO system can be used to develop integrated manufacturing systems such as the prototype automated guided vehicle planning and control system, which is briefly described.

The purpose of this study synthesises the body of research revolving around blockchain technology (BCT) whilst drawing on the technology-organization-environment framework, resource-based theory and theory of constraints, to conceptualize capabilities (enablers) and constraints (barriers) of BCT in the hospitality and tourism (H&T) industry.

A systematic literature review of BCT in the hotel and tourism industry has been achieved through two databases, i.e. Scopus and Web of Science. From 544 articles selected between the years 2008 and 2023 (first quarter), a sample of 49 articles was used to structure existing research on this subject.

The findings of this systematic literature review of BCT in the H&T literature establish a solid groundwork for assessing the evolution of this research area over time. Findings are classified into two groups: capabilities (enablers) and constraints (barriers) of BCT based on publication year, different research methods, theoretical underpinnings and applicable contexts.

To the best of the authors’ knowledge, this is one of the first attempts to synthesize studies related to BCT in H&T research by combining three theoretical approaches. It serves as a foundation to evaluate the development of BCT studies in this field.

The aim of this study is to trace the factors that have contributed to the adoption and institutionalization of foresight practices within the Singapore Public Service, Government of Singapore.

This study discusses the history of the adoption and institutionalization of foresight practices in the Singapore Government; this study has carried out content analysis of secondary literature and conducted 11 in-depth semi-structured interviews with elite informants.

This study finds that the adoption and institutionalization of foresight practices in the Singapore Government was brought about by a combination of five factors. The most foundational factor in our model is the role of institutional entrepreneurs, who drew upon the symbolic representation of Singapore’s vulnerability to legitimize the use of foresight, thus resonating well with local technocratic groups to maintain steady economic progress. This study further argues that the underdevelopment of foresight in the local private and academic domains can be at least in part explained by the historical connotations of foresight that were uncovered.

As the findings are fruit of the authors’ interpretation of the secondary literature/interview data, they require further triangulation by future research.

This study presents the interpretation of elite informants’ accounts and historical documents to explain one of the most exemplar yet classified case studies of governmental foresight globally.

With the rise of cryptocurrency and its influence on the financial industry, this paper aims to explore cryptocurrency affordances that lead to approach–avoidance behavioral intentions for non-users (potential) and the intention to continue use for users (actual), drawing upon affordance theory and chasm theory.

The authors collected data from 480 potential and actual users in Germany and used maximum likelihood structural equation modeling (ML-SEM) to analyze it. In particular, the data consisted of 301 cryptocurrency users in Germany\ the authors used ML-SEM to test the post-adoption model. Additionally, logistic regression was utilized to determine the dominant actual usage method (store of value or medium of exchange) for various cryptocurrency coins.

According to the study's results, the perceived value benefits have a positive impact on the behavioral intention of potential users to adopt cryptocurrency, and they influence the intention of actual users to continue using it. However, both perceived volatility and financial risk tolerance are the most crucial factors hindering cryptocurrency adoption, whether in the pre-adoption or the post-adoption stage.

This is the first study to reveal cryptocurrency affordances and examine their effect on behavioral intentions toward cryptocurrency adoption based on the differences between non-users (potential) and users (actual). Furthermore, the authors explore how cryptocurrency holders perceive and invest in different coins (e.g. NFTs), which sheds light on factors such as financial risk tolerance that affect their decision making.

This article aims to present a unique dimension to mentor studies in that unlike most research that focuses on the novice-teachers-mentee, this study explores the influence of mentoring on the mentors themselves. Two main questions were examined: “Which components of mentoring influence the mentors' professional development?” and “What is the differential impact of each of these components as they are internalized by the mentors?”

A total of 765 mentors completed a questionnaire composed of 47 multiple-choice questions and an open-ended question describing the contribution of mentoring. The survey thus generated both quantitative and qualitative data.

The results show three main components of mentoring that influence mentors' professional development in the following order: personal–emotional, didactic knowledge and systemic–organizational. The findings also illustrate how the mentors used a comparison process to develop their professional perception: first, by comparing their role as a teacher and their role as a mentor, and second, by comparing their own professional identity as a teacher with that of the novice-teacher.

The implications of the study point to the importance of mentoring for mentors' professional development. Practical implications of this study encourage mentor training courses that combine a body of knowledge of theory and practice, as well as supervision and mentoring for mentors. It may be important for mentors to develop a professional identity as mentors that is distinct from their professional identity as teachers.

The implications of the study point to the importance of mentoring for mentors' professional development. Practical implications of this study encourage mentor training courses that combine a body of knowledge of theory and practice, as well as supervision and mentoring for mentors. It may be important for mentors to develop a professional identity as mentors that is distinct from their professional identity as teachers.

The manuscript attempts to explore the dynamic relationships within the mentoring process by investigating how mentoring impacts the development of mentors. It presents insights into the benefits of mentoring novice-teachers for the mentors themselves via analysis of a large-scale, nationwide study.

This study focused on unpacking the instructional coaching process, addressing key questions about what happens during a coaching session and what coaching elements predict teacher outcomes.

Using coaching observational data, the research examined critical coaching processes described in the literature: coaching practices (observation, feedback, reflective discussion and planning), the coach–teacher relationship, coaching strategies and coaching duration. The study also developed a path model documenting how coaching behaviors predicted teacher instruction.

Results showed that the coach talked more than the teacher and that most coaching time was spent in reflective discussion. The coach–teacher relationship was promoted by building rapport and reciprocal trust, with use of “we” language demonstrating that coach and teacher were working as a partnership. Most common coaching strategies were clarifying and the coach prompting the teacher to attend to teacher or student behaviors. Path model analysis showed that (a) the coach–teacher relationship quality predicted the level of teacher engagement in coaching and their instructional reflection and (b) the quality of coaching strategies predicted the overall quality of the classroom instruction.

The study provides empirical evidence about the active ingredients of coaching – those underlying processes that impact and improve teacher practice.

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.

In vernacular buildings, many climatic and passive solutions have been used to create indoor thermal comfort. Seasonal occupant movement is an example of a traditional response to increasing thermal comfort. This article investigates the influence of these user behaviours on thermal comfort in courtyard houses.

Parametric models of three different scenarios of courtyard houses are simulated. The courtyard houses are located in Shiraz, Iran, and share the same orientation and construction materials. To enhance the accuracy of the study, the indoor adaptive thermal comfort (ATC) analysis is performed with three different window-to-wall ratios (WWR) of 25, 50 and 75%. The ACT analysis is performed on an hourly basis for summer and winter scenarios.

The results demonstrate that the indoor ATC is 8.3% higher in winter than in the summer in the seasonal zones. During the summer, the amount of ATC is relatively sustained in all zones. Unlike common beliefs, seasonal movement can enhance the ATC, especially during winter, specifically in the northern part of the courtyard. In northern zones, the seasonal movement of occupants improves the indoor ATC from 10.1 to 23.7%, and in southern zones, the improvement is from 2.2 to 4.8%.

This research presents a new numerical investigation into occupants' seasonal movements in courtyard houses during summer and winter. It provides a precise pattern to show how much this seasonal movement can affect the habitant's ATC.

The purpose of this study is twofold. The first objective is to identify the factors that affect Gen Z tourists' M-payment behaviour. Next, this study investigates the inter-relationship between Gen Z tourist’s perception of M-payment benefits, adoption behaviour, usage risk and future usage intention.

The sample comprised Malaysian Gen Z individuals (n = 326) who had experience using M-payment methods while travelling outbound. Given the complex nature of the model and the goal to predict and explain relationships within Gen Z's M-payment usage, partial-least square-structural equation modelling was used to assess the study framework and test the proposed relationships.

This study reveals significant influences on Gen Z tourists' behavioural intentions towards M-payment usage. Perceived benefits, performance expectancy, social influence and perceived trust positively impact behavioural intentions, while effort expectancy exhibits no significant effect. Furthermore, perceived trust is strongly influenced by perceived security, which also positively influences behavioural intentions. A mediated relationship is evident as trust mediating the effect of perceived security on behavioural intentions.

This study’s findings contribute to understanding the intricate relationships influencing Gen Z's M-payment behaviour and underscore trust's pivotal role in mediating the security–behavioural intention relationship.

This study is among the first to consider Mental Accounting Theory and the Unified Theory of Acceptance and Use of Technology as crucial underpinning theories in comprehending the intricate relationships that influence Gen Z travellers' perceptions and behaviours concerning M-payment systems.

The purpose of this study is to investigate consumers’ cryptocurrency adoption through the unified theory of acceptance and use of technology (UTAUT) and complexity theory.

By using a purposive sampling method, a configurational model was developed and a questionnaire-based survey was conducted to gather responses from a Malaysian sample. A total of 223 responses were obtained. Partial least square structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) were adopted to analyze the data.

The PLS-SEM indicated that performance expectancy, effort expectancy, social influence and affinity for technology interaction were positive cryptocurrency adoption predictors, whereas regulation was a negative predictor. Based on the fsQCA, cryptocurrency adoption could be explained by six configurational paths, which comprised combinations of the proposed causal conditions: the UTAUT factors (performance expectancy, effort expectancy, facilitating condition and social influence), environmental factor (regulation) and individual factors (financial knowledge and affinity for technology interaction).

This study offers contributions to the theoretical body of knowledge by articulating the relevance of extended UTAUT and extending the established UTAUT model by integrating external environment and personal factors, also showing the linear and nonlinear interplays of performance expectancy, effort expectancy, facilitating conditions, social influence, regulation, financial knowledge and affinity for technology interaction.

The findings facilitated practitioners’ (cryptocurrency brokers, governments and businesses) fostering of cryptocurrency adoption through the joint consideration of different factors. The factors spanned technological attributes and individual characteristics to regulation. Practitioners should acknowledge that different combinations of the aforementioned antecedents can be equally effective to increase cryptocurrency adoption. The findings suggested that these causal conditions should be considered holistically and that there is no best predictor.

In social terms, the research is expected to contribute to the dissemination of cryptocurrencies and help governments and central banks to develop, regulate and supervise digital currencies, as well as in the implementation of a digital currency ecosystem aligned with sustainable development goals. Economically, the results might foster a high cryptocurrency adoption rate and stimulate crypto-token-based business models and investment opportunities that present new means of revenue generation at individual, organizational and national levels.

This study offers unique perspectives for the body of knowledge and practice in the cryptocurrency domain, using both symmetric and asymmetric methodologies, by delineating the configurational logic involving technological capabilities, social influences, regulation and individual characteristics in facilitating more efficacious dissemination of cryptocurrencies.