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Since the launch of the Mainland and Hong Kong Closer Economic Partnership Arrangement (CEPA) in 2003, Hong Kong cinema is believed to have confronted drastic changes. Hong Kong cinema is described to be dying, lacking creative space and losing local distinctiveness. A decade later, the rise of Hong Kong – China coproduction cinema under CEPA has been normalized and changed the once pessimism in the industry. The purpose of this paper is to demonstrate how Hong Kong cinema adjusted its production and creation in the first 10 years of CEPA.

Beginning with a review of the overall development, three paradigmatic cases are examined for reflecting upon what the major industrial and commercial concerns on the Hong Kong – China coproduction model are, and how such a coproduction model is not developed as smooth as what the Hong Kong filmmakers expected.

Collectively, this paper singles out the difficulties in operation and the limit of transnationality that occur in the Chinese context for the development of Hong Kong cinema under the Hong Kong – China coproduction model.

This is the author’s research in his five-year study of Hong Kong cinema and it contributes a lot to the field of cinema studies with relevant industrial and policy concern.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This paper examines the pedagogical features of massive open online courses (MOOCs) for language learning–known as language MOOCs. The mainstream pedagogy of MOOCs typically involves the provision of short videos and reading materials for self-study; discussion forums, mostly for peer-to-peer interaction on course content; and machine-graded quizzes for self-assessment. For language learning, which has been conventionally understood as skill development, the pedagogical features of relevant MOOCs have yet to be comprehensively surveyed.

This study surveyed a total of 123 language MOOCs from the major MOOC platforms. The pedagogical features shown in these courses were identified and categorised according to the types of course materials and learning activities as well as the participation of learners and instructors.

English was the most common language taught in the courses. Over 80% of the courses took not more than six hours to complete. Most of these courses followed the typical approach of xMOOC delivery, with video watching, reading and auto-graded assessment being the most common learning activities. Less than half of the courses included discussion as part of learning, and instructors were involved in less than 30% of the discussion.

The findings show that, despite the technological advances in course delivery, current language MOOCs do not differ substantially from conventional distance language learning. Yet, the utilisation of computer-assisted language learning technology and the massive student base of MOOCs for creating a virtual social community are opportunities for developing learners' language proficiency on this learning environment.

The purpose of this paper is to evaluate the implementation of mobile learning in a nursing course at The Open University of Hong Kong, and identify the potentials of, and constraints on, introducing mobile technologies in the instructional design of nursing education. The paper also considers the pedagogical implications of the expansion of mobile learning in the field of nursing.

The research adopts a qualitative approach to obtain the students’ and teacher’s experiences, opinions, and expectations on mobile learning. Two focus groups with 20 student participants were conducted and an in-depth interview with the course teacher was arranged. The Framework for the Rational Analysis of Mobile Education (FRAME) model was used as the research framework to support data collection and analysis.

The aspects of device usability, interaction learning, and social technology as suggested in the FRAME model were partly fulfilled in the study. Mobile technology enhanced the portability and accessibility of learning information, and networking tools facilitated interaction among students and between students and the teacher. However, the readability of text was limited due to constraints on the user interface and screen size, and concerns over the reliability of learning content were also raised, given the abundance of unfiltered online information. The difficulty in updating the content of multimedia materials and sourcing videos of an appropriate level, together with the problem of device networking, also limited the usefulness of mobile learning. Attention should also be paid to the perceptual differences between students and the teacher on the nature and functions of mobile learning.

This empirical study provides a detailed evaluation of the delivery of mobile learning in a nursing course. The findings reveal the strengths and limitations of using mobile technologies to support the nursing education.

This paper aims to present a review of case studies on the use of learning analytics in Science, Technology, Engineering, (Arts), and Mathematics (or STE[A]M) education. It covers the features and trends of learning analytics practices as revealed in case studies.

A total of 34 case studies published from 2013 to 2018 reporting relevant learning analytics practices were collected from Scopus and Google Scholar for analysis. The features and trends of practices were identified through a comparison of the first (2013–2015) and the second period (2016–2018).

The results showed an increasing adoption of learning analytics in STE(A)M education, particularly in the USA and European countries and at the tertiary level. More specific types of data have been collected for the learning analytics practices, and the data related to students’ learning processes has also been more frequently used. The types of STE(A)M learning practices have become more diversified, with technology enhancement features increasingly introduced. The outcomes of the case studies reflect the overall benefits of learning analytics and address the specific needs of STE(A)M education. There have also been fewer types of limitations encountered in the learning analytics practices over the years, with unknown correlation among variables, small sample size and limited data being the major types.

This study reveals the implementation of learning analytics in relation to the contexts and needs of STE(A)M education. The findings also suggest future work for examining the adoption of learning analytics to cope with the development of STE(A)M and, in particular, how the successful experience of learning analytics in other disciplines could be transferred to STE(A)M.

This study is based on the regulatory focus theory and conservation of resources theory to examine how information confusion leads to users’ discontinuous usage intention of social networking sites (SNSs), with the mediating mechanisms of users’ motivation and affectivity.

Data were obtained from 270 users in different SNSs in Vietnam. Structural equation modeling was used to analyze the sample data and test the hypotheses.

Results show that information confusion has a positive influence on discontinuous usage intention, and prevention focus positively mediates this relationship. Furthermore, the negative affectivity positively mediates the link between information confusion and discontinuous usage intention and that between prevention focus and discontinuous usage intention. In addition, prevention focus and negative affectivity together play a serial mediating effect in the link between information confusion and discontinuous usage intention.

This study extends the regulatory focus theory and conservation of resources theory to validate a model that explains the transitional process of users’ motivation and affectivity in the link between information confusion and discontinuous usage intention of SNSs. The findings of this study provide implications for academic researchers and business managers of SNSs to understand and make better decisions to retain their users.

The purpose of this paper is to combine the elaboration likelihood model and hedonic/utilitarian values into a coherent model of shopping motivational values for online and offline Malaysian shoppers. Once these values were identified, they were compared to one another based on shopping channel, gender, and age group.

The Shopping Motivational Value Questionnaire was developed by the researcher after extensive literature review and measured the respondent’s perception toward shopping motivational values in both shopping channels. Data were collected from 306 Malaysians above the age of 21, and analyzed using exploratory factor analysis and pairwise t-test techniques.

The factor analysis revealed a three-factor model of shopping motivational values. Gratification Values were the most important to shoppers, followed by Essential and Societal Values. Shoppers perceived all these motivational values as important when shopping offline as compared to online, regardless of gender and age group.

The results of the factor analysis revealed a three-factor model of shopping motivational values: Gratification Values which were the most important to shoppers, followed by Essential and Societal Values. These values were then compared to one another based on shopping channel, gender, and age group. Results indicate that shoppers perceived all these motivational values as important when shopping offline rather than online, regardless of gender and age group. Moreover, the perception of shoppers toward specific product categories and other shopping channels were not taken into account.

This study attempted to combine the elaboration likelihood model and hedonic and utilitarian values into a coherent model. A questionnaire was developed by the researcher with these two theoretical models as its foundation. Additionally, the shopping motivational values of this study have been formulated to be compatible to both shopping channels.

This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications.

A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis.

Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification.

This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

Green consumption behaviour (GCB) has been advocated to mitigate the environmental consequences of traditional consumption patterns. Besides the current circumstances, Generation Z college students are a sizable consumer group who are likely to be concerned about the future. Thus, this study aims to examine the factors affecting the college students’ GCB and the moderating effect of government support to provide new evidence from college students in China.

In addition to environmental knowledge and social media influence as the variables, government support was used as a moderator to develop the extended theory of planned behaviour (TPB) model. Purposive sampling was used to obtain 328 valid responses from Chinese college students. The collected data were analysed using partial least squares structural equation modelling.

The findings indicated that subjective norms, perceived behavioural control, environmental knowledge and social media influence substantially affect students’ GCB. Notably, the moderation analysis suggested that government support greatly strengthens the relationship between subjective norms and social media influence on the GCB of Chinese college students.

The study provides several significant practical implications as the findings could be referred by stakeholders, such as government and businesses entities, in formulating policies and strategies to encourage the consumers’ GCB in mitigating ecological consequences.

The extended TPB model that integrated environmental knowledge and social media influence with the government support as the moderator contributes to the extant literature with the evidence derived from Generation Z in China.