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Megaprojects have large impact on the environment and stakeholders should take collective action to ensure that these projects are developed in a socially responsible manner. Hitherto, it is not known whether group and subjective norms and social identity could compel stakeholders to take socially responsible collective actions in megaprojects. The aim of this study is to design and test a model to boost stakeholders' intention to take socially responsible collective action in the context of mega water transfer projects in China.

A quasi-experimental causal research design was adopted to establish cause–effect relationships among the dependent variable (we-intention) and independent variables (subjective norms, group norms, social identity and desire). This study adopts the belief–desire–intention model and social influence theory to empirically investigate how to boost the stakeholders' intention to participate in socially responsible collective action. An online questionnaire survey was conducted and data was collected from 365 respondents who were involved in mega water transfer projects in China. The partial least squares structural equation modeling technique was employed to analyze the data.

The results from partial least squares analyses indicate that the presence of subjective norms, group norms and social identity (collectively known as social influence process) could increase stakeholders' intention to take socially responsible collective action. In addition, the desire to be socially responsible also boosts stakeholders' intention to take collective action. Desire partially mediates the relationship between social influence process and intention to take socially responsible collective action.

This study adds to existing knowledge by discovering social influence process as an antecedent to taking socially responsible collective action in megaprojects. Strong group norms and subjective norms could propel stakeholders to be more socially responsible. The study also adds to knowledge by discovering that stakeholders' desire to fulfill social responsibility also leads them to take concrete actions. Implications and recommendations are provided on how to manipulate different types of social influence processes to facilitate stakeholders to adopt socially responsible collective action in the process of managing megaprojects.

This study aims to investigate the effects of a prospective traveler’s perception of media exposure on their intention to visit a destination (i.e. South Korea). Cultural exposure to a particular country through media affects people’s preference for that foreign country, and may ultimately be a function of the behavior for consuming that country’s cultural products – e.g. traveling to that country. Media exposure has been recognized as a major underlying reason for the desire to visit a destination.

This study examines the impacts of potential travelers’ media exposure in three different language-use groups (i.e. English, Japanese and Chinese) and their perception of the media exposure on their intention to visit the actual site (i.e. South Korea). To enhance the understanding of the intention to visit the destination, this study proposes a research model based on use and gratification theory and the belief–desire–intention model.

Mass and social media exposure had an effect on the intention to visit a destination as a result of the gratification and desire experienced through the content.

This study suggests the synthesis of the use and gratification theory and the belief–desire–intention model and an examination of theoretical and practical implications.

This study involved a sample of users of destination marketing sites. In addition, this study investigated the users’ intentions to visit a real tourism destination taking into consideration mass media (traditional media) and social media (new media) based on the use of gratification theory and the belief–desire–intention model. Practically, the findings highlight the crucial role of social media in the intention to visit the tourism destination.

The purpose of this paper is to understand blog users’ negative emotional norm compliance decision-making in crises (blog users’-NNDC).

A belief–desire–intention (BDI) model to evaluate the blog users’-NNDC (the BDI-NNDC model) was developed. This model was based on three social characteristics: self-interests, expectations and emotions. An experimental study was conducted to evaluate the efficiency of the BDI-NNDC model by using data retrieved from a popular Chinese social network called “Sina Weibo” about three major crises.

The BDI-NNDC model strongly predicted the Blog users’-NNDC. The predictions were as follows: a self-interested blog user posted content that was targeting his own interests; a blogger with high expectations wrote and commented emotionally negative blogs on the condition that the numbers of negative posts increased, while he ignored the norm when there was relatively less negative emotional news; and an emotional blog user obeyed the norm based on the emotional intentions of the blogosphere in most of the cases.

The BDI-NNDC model can explain the diffusion of negative emotions by blog users during crises, and this paper shows a way to bridge the social norm modelling and the research of blog users’ activity and behaviour characteristics in the context of “real life” crises. However, the criterion for differentiating blog users according to social characteristics needs to be further revised, as the generalizability of the results is limited by the number of cases selected in this study.

The current method could be applied to predict emotional trends of blog users who have different social characteristics and it could support government agencies to build strategic responses to crises.

This paper supports the creation of normative models and engineering methods to predict blog users’-NNDC and mitigate their effect in real-world crises.

The drone food delivery service (DFDS) is a revolutionary technology owing to its ability to reduce delivery costs, alleviate traffic congestion, and offer a more environmentally friendly alternative compared to traditional delivery methods. While studies have been conducted to understand consumers’ intentions towards DFDS, very few of them considered internal factors such as personality traits. Considering the aforementioned factors, this research employs the belief-desire-intention (BDI) model and the big five personalities (BFP) traits to explore how personality traits (neuroticism, extraversion, agreeableness, conscientiousness, and openness) moderate the associations between the desire for DFDS and consumers’ intention to use DFDS (ITU).

Survey questionnaires were distributed online via Facebook and WhatsApp. A total of 359 responses were considered valid, and the study employed the partial least squares structural equation modelling approach to test the hypotheses.

The study revealed a positive impact of the desire for DFDS on ITU. Additionally, extraversion and agreeableness were found to moderate the effect of the desire for DFDS on ITU.

This study is the first in DFDS literature to employ the BDI model, showcasing its effective application in understanding the desire-to-intention relationship. Also, this is the first study that explores the moderating role of the BFP traits in the context of DFDS usage intention. The results provide insights for developing marketing strategies to encourage DFDS usage intention based on consumers' personality traits.

Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue.

The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated.

Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling.

The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits.

Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.

One of the greatest challenges facing any intelligent tutoring system is being able to adapt its behaviour based on the student's current knowledge level, ability, needs and wishes within a course. This paper aims to present a framework of BDI agents within an agent‐based intelligent tutoring system (ABITS).

A conceptual discussion approach is taken.

The agents provide the core reasoning ability. In particular, the paper demonstrates how the system sources and refines a particular set of commonly available data. Also shows how these data are incorporated into the agents' belief set so that they may adapt their behaviour to support individual students.

Provides a framework that can improve learning procedures for future users of ABITS.

As Pervasive Computing environments become more richly populated with sensors and computing power it may be possible for the environment to observe and interpret user actions and pre‐emptively provide accurate support for those actions. This requires accurate inference of user intent. Though individual user acts may be inferable from environmental context sensing, the inference of intent for which appropriate support might be offered is more challenging. Numerous researchers have used probabilistic techniques such as Bayesian Analysis techniques to attempt such inference. Unlike a desktop environment, however, pervasive computing environments are extremely heterogeneous, so any heuristics used must be tailored to the physical environment and the users in question. Thus these techniques are only likely to be accurate if configured with accurate knowledge of routine user behavior. Many existing approaches attempt to learn such knowledge from operational data, but this often requires user involvement in training and expert configuration of probabilistic processing structure. In this paper we examine a complimentary approach where users are presented with an intuitive interface to support the direct configuration of probabilistic structure by users with appropriate knowledge. By treating an intent inference system for a particular pervasive computng environment as an autonomic system, we approach the problem as the design of an intuitive governance interface for this system. We then present the design and usability evaluation of this governance interface.