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Past research recognizes the important influence of individual beliefs on technology acceptance and use. This line of research has also identified a variety of factors that drive the formation of these beliefs. One category of variables that has received less attention in this research stream consists of individual preferences, in particular time‐use preferences. In the current study we address the gap in the technology acceptance literature by introducing and empirically testing a new construct labeled computer polychronicity, which captures individuals’ time‐use preferences regarding information technology. A new measure of computer polychronicity is developed and subsequently tested using partial least squares estimation. Computer polychronicity is then theorized as a key driver of perceived usefulness, linking computer anxiety and computer playfulness to perceived usefulness. Overall, the results of model testing support the notion that preferences play an important role in the formation of technology‐related beliefs.

The purpose of this paper is to explore how computer self-efficacy (CSE), perceived information overload (IO) and the digital native perspective predict students’ information literacy (IL) self-efficacy.

The research was conducted in Israel and comprised 117 students from the Information Science Department at Bar-Ilan University. Researchers used five questionnaires to gather personal details: a demographic questionnaire, the IL self-efficacy questionnaire, the CSE questionnaire, technology usage (TU) questionnaire and the perceived IO questionnaire.

The findings confirm that these variables significantly predict students’ IL self-efficacy.

Instructors and librarians should be familiar with the issue of individual differences, as well with the issue of students’ age. These factors may help them choose the most appropriate way when instructing IL skills to their students.

The purpose of this paper is to examine whether different corporate social responsibility (CSR) dimensions, namely, workplace economic responsibility, legal responsibility, ethical responsibility, philanthropic responsibility and environmental responsibility, have different influence on job pursuit intention among undergraduate students in Malaysia.

The target respondents are undergraduate business students from five leading private universities in Malaysia. Primary data were collected through survey questionnaires via face-to-face method. Five-point Likert scale was used to assess the variables and to measure the items. Collected data were tested through SAS software and analysed with descriptive and inferential analyses.

Four out of the five CSR dimensions significantly influence job pursuit intention with ethical responsibility being the exception.

This study proved that the adoption of CSR in an organisation will become a competitive advantage to attract new talents. Also, this is an improved research model by adopting multi-dimensional perspectives of CSR.

This proposed framework with a combination of five CSR dimensions (workplace economic responsibility, legal responsibility, ethical responsibility, philanthropic responsibility and environmental responsibility) is useful to future researchers as it enables them to have a broader view on CSR and its effect on job pursuit intention.

The purpose of this study was to examine the factors that influence the information seeking behaviors of ChatGPT users. Specifically, we investigated how ChatGPT self-efficacy, ChatGPT characteristics and ChatGPT utility affect the frequency and duration of information seeking via ChatGPT. We also tested the mediating roles of ChatGPT characteristics and utility in the relationship between ChatGPT self-efficacy and information-seeking behaviors.

This study adopts a quantitative approach and collects data from 403 ChatGPT users using an online questionnaire. The data are analyzed using linear regression and structural equation modeling (SEM).

The linear regression analyses revealed that ChatGPT self-efficacy is positively and significantly related to the information seeking behaviors in ChatGPT. Second, mediation analyses also showed that ChatGPT characteristics and utility significantly mediate the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT independently and sequentially.

This study is the first to investigate the factors and mechanisms that influence information-seeking behaviors in ChatGPT, a new phenomenon in the media landscape. The findings in this study suggest that ChatGPT self-efficacy acts as an important motivator for information-seeking behaviors in ChatGPT and that ChatGPT characteristics and utility provide information regarding potential mechanisms in the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT. The study contributes to the literature on information seeking, self-efficacy and generative AI.

This paper aims to collect and compile the historical data of Guanfu Salt Farm, officially built by the Song Dynasty (960–1279) within modern Hong Kong territories, to reconstruct its history for the reflection of Hong Kong society of the time.

This paper is largely based on identification and analysis of historical documents, including keyword search on electronic databases and verification with the original sources, with reference to archaeological findings when necessary.

This paper reconstructs the history of Guanfu Salt Farm based on documentary sources with reference to archaeological findings. English translation of Chinese sources is also provided when necessary.

There has been an absence of systematic compilation of historical data of Hong Kong during the Song Dynasty, which are limited in quantity and scattered across different sources. This paper seeks to fill the vacuum of knowledge about pre-colonial Hong Kong, with a more comprehensive reconstruction of the history of Guanfu Salt Farm.

The discontinuity problem of general non‐linear evolutionary equations and the chaos of complex models of poly‐order are studied. The concept of “blown‐up” of Navier‐Stokes equation is discussed, which was introduced by OuYang in 1994 and 1995. The results show that the blown‐up of the model appears during the whole evolution of general non‐linear equations; and that the concept of “chaos” of Lorenz’s model should be restudied questioningly.

Very recently, the South Korean architectural field has seen a rising trend to reinterpret the traditional hanok, meaning Korean-style house. While this presents an interesting diversion from the housing market dominated by high-rise apartments, there is a lack of consensus in determining the scope and definition of hanoks. This is because of many experiments with the features of the hanok, such as inclusion of new material, construction techniques, and even radically new spatial organizations.

This article explores and analyzes the effectiveness of four different approaches of reinterpreting hanoks: apartment hanoks, urban hanoks, rural experimentations, and contemporary versions. At one end of the spectrum, there is the recent integration of a hanok’s features in high-rise apartments otherwise considered modern. Then I move on to discuss examples of urban hanoks built since the start of the industrialization era in the 1920s and afterwards. Urban hanoks are detached houses, most of which show a stylistic preference toward wooden hanoks of the elite literati. The third approach is rural experimentations that involve the development of an unconventional construction method by both architects and non-architects. Finally, this paper turns to reinterpretations of the hanok by architects trained in contemporary architecture. Although each approach differs in the degree of integrating historical hanok features, some commonalities, such as low floor area ratio and the organic integration of an open courtyard, can be detected among the successful cases.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

Despite better accessibility and flexibility, peer-to-peer (P2P) lending has suffered from excessive credit risks, which may cause significant losses to the lenders and even lead to the collapse of P2P platforms. The purpose of this research is to construct a hybrid predictive framework that integrates classification, feature selection, and data balance algorithms to cope with the high-dimensional and imbalanced nature of P2P credit data.

An improved synthetic minority over-sampling technique (IMSMOTE) is developed to incorporate the randomness and probability into the traditional synthetic minority over-sampling technique (SMOTE) to enhance the quality of synthetic samples and the controllability of synthetic processes. IMSMOTE is then implemented along with the grey relational clustering (GRC) and the support vector machine (SVM) to facilitate a comprehensive assessment of the P2P credit risks. To enhance the associativity and functionality of the algorithm, a dynamic selection approach is integrated with GRC and then fed in the SVM's process of parameter adaptive adjustment to select the optimal critical value. A quantitative model is constructed to recognize key criteria via multidimensional representativeness.

A series of experiments based on real-world P2P data from Prosper Funding LLC demonstrates that our proposed model outperforms other existing approaches. It is also confirmed that the grey-based GRC approach with dynamic selection succeeds in reducing data dimensions, selecting a critical value, identifying key criteria, and IMSMOTE can efficiently handle the imbalanced data.

The grey-based machine-learning framework proposed in this work can be practically implemented by P2P platforms in predicting the borrowers' credit risks. The dynamic selection approach makes the first attempt in the literature to select a critical value and indicate key criteria in a dynamic, visual and quantitative manner.

Organic food consumption is a complex process that makes it difficult for organic food businesses to develop appropriate marketing strategies. This study thus adopted the stimuli–organism–response (S–O–R) model to create a comprehensive framework to understand consumers' organic food purchase decisions.

This study collected 592 valid samples in organic food chain stores and markets by random sampling method. Meanwhile, structural equation modelling was adopted to test hypotheses.

The research findings indicate that consumer preference for natural food was the most important factor for enhancing purchase intention, followed by health consciousness, health risk, attitude towards organic food and trust in labelling. Perceptions of nutritional value positively influenced attitudes towards organic food and trust in labelling, followed by perceptions of environmental effects; conversely, attitudes towards organic food labelling had the least effect on increasing trust in labelling. Attitudes towards organic food labelling was the most important factor influencing positive attitudes towards organic food, followed by consumer perception of environmental protection effects.

This study demonstrates the relative influence of different variables on organic food purchase intention. Compared with consumer attitude towards organic food and trust in labelling, consumers' individual health was the most important factor influencing their purchase intention. As health and naturalness are attractive factors for consumers, the organic food industry can emphasize health protection in their marketing strategies.