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This study examines academic librarians' perceptions and attitudes toward Internet of Things (IoT) applications in Hong Kong academic libraries and the problems and possible improvements in using IoT technologies to strengthen library services.

This qualitative research used video conferencing software for semi-structured, one-on-one interviews. Participants were given introductory material about the IoT and asked to complete an interview. The data were analyzed using inductive theme clustering for this study.

The analysis identified three themes: perception about applying IoT technology to the library, problems and improvements in using IoT. Participants were generally optimistic about the potential benefits of IoT for improving library operations and providing personalized services. However, they also expressed concerns about privacy and security, errors and extra efforts for information literacy training. They suggested improvements such as incorporating facial recognition technology, advanced RFID technology and collections identification technology to enhance user experience.

Most studies examined users' views rather than librarians' on IoT applications, which few studies cover, especially in East Asia.

Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

To a large extent, knowledge in government workplaces resides within the hierarchy as it provides unique services to the public. Governments should preserve and pass on valuable and irreplaceable knowledge in providing their services through learning by sharing. Yet, sharing requires time and effort. This study examines the motivations of civil servants to share knowledge and their knowledge-sharing behaviors.

The authors conducted an online survey to collect quantitative data from civil servants in the Hong Kong Government and obtained 104 useable responses to test the eight hypotheses comprising six constructs by structural equation modeling to explore the participants' knowledge-sharing behaviors.

Results indicated that (1) expected contribution was the primary source of motivation behind knowledge sharing; (2) participants engaged more frequently in informal than formal knowledge-sharing activities; and (3) there was a dire need for knowledge sharing focusing on explicit knowledge of work procedures.

The research focuses on motivational factors for knowledge sharing. Despite abundant literature about knowledge sharing in public or governmental organizations, scant studies focus on the motivations behind why expected rewards and expected associations have less significant impacts on the attitude toward knowledge sharing than expected contribution.

This chapter traces one student teacher's (Joan) experiences of learning to teach English as a second language in a cross-cultural context during a teaching practicum in Hong Kong. The school-based practicum is a core component of many initial teacher education programmes. During this induction period, usually an 8-week block, student teachers are placed in local schools to learn how to integrate theories into practice in real teaching situations. Specifically, I uncover how Joan grappled with the tensions and complexities of teaching young learners from a different cultural and linguistic background, in a small elementary school situated in the borderland between Hong Kong (an autonomous region of China) and Shenzhen (a province of Mainland China).

Critical incidents from Joan's practicum experiences were analysed to uncover how she dealt with the tensions and dilemmas in confronting difference and marginalising practices while learning to teach English as a second language (ESL) in the practicum school. Implications on how to develop initial teacher education programmes so that student teachers learning to teach across cultural contexts can be encouraged to explore, confront and ‘deal with the emotional terrain of understanding difference’ will be discussed (Boler & Zembylas, 2003, p. 123; Zembylas, 2010).

The purpose of this study is twofold: to investigate the role of big data in firms’ co-knowledge and value creation and to understand the underlying drivers behind value creation through big data in the oil and gas industry by underscoring the role of firms’ capabilities, trends and challenges.

Following an inductive approach, semi-structured interviews were conducted with senior managers and analysts working in oil and gas companies across eight countries. The data collected from these key informants were then analysed using the qualitative data analysis software ATLAS.ti.

Value creation through big data is an important factor for enhancing performance. It has a positive impact on both tangible (organisational performance) and intangible (societal) aspects depending on the context. Oil and gas companies understand the importance of big data to creating value in their operations. However, implementing and using big data has been problematic. In this study, a framework was developed to show that factors such as the shortage of data experts, poor data quality, the risk of cyber-attacks and unsupportive organisational cultures impede its implementation and utilisation.

The findings from this study have implications for managers and executives implementing big data and creating value across various data-intensive industries. The research findings, are contextual, however, and should be applied cautiously.

This study contributes to the value creation literature in the big data context. The findings identify the key areas to be considered for the effective implementation and utilisation of big data in the oil and gas sector. This study addresses a broad but under-explored issue (i.e. knowledge creation from big data and its implementation) and strengthens the academic debate within this research stream.

This study aims to study the attitude toward purchasing luxury fashion of young consumers based on an extended model that integrates the constructs of the theory of reasoned action (TRA), identity theory, social identity theory, affect–behavior–cognition (ABC) model of attitude and brand attractiveness.

A purposive sampling approach was used to collect data from 237 young luxury fashion consumers in Hong Kong. Results were analyzed using partial least square.

The findings revealed that self-identity predicts affect-based attitudes (i.e. passive engagement and active engagement), and social identity predicts cognition-based attitude (i.e. attitude toward celebrity endorsement). Moreover, both affect- and cognition-based attitudes were found to be antecedents that enhanced brand attractiveness, which in turn positively affected purchase intention.

This study collected data from Generation Z. Although this generation is the world’s most influential consumer group and is highly engaged in social media, the findings may not be representative of the entire population in Hong Kong. Therefore, the findings should be used cautiously in the whole luxury fashion industry.

This study extends the understanding of luxury fashion purchase intention from TRA to the connection among identity, social identity theories and ABC model of attitude and brand attractiveness. The findings of this study also contribute to practical insights on developing suitable marketing strategies for the Asian luxury fashion market.

Teacher education for social justice aims to enable teachers to work toward equity and justice in society and humanizing the educational experience of their students. Conceptualizing teaching as a political and ethical endeavor, social justice teacher education must engage seriously with the local and lived experiences of both teacher educators and student teachers. How then does teacher education for social justice move across communities and identities, and through cultural, social, geographic and temporal spaces? This chapter presents an autobiographical narrative inquiry into social justice teacher education across sociocultural and sociopolitical contexts, across time, and within different educational communities. Bakhtin's dialogic theory (1981) helps to trace the narrative threads wherein “each word tastes of the context and contexts in which it has lived its socially charged life” (p. 293). The study examines my ideological becoming (Bakhtin, 1981) as a critical teacher educator in the context of a youth mentoring service-learning course for undergraduate teacher candidates. I examine the complexities and tensions in exploring experiences and co-constructing understandings of oppression, privilege and social justice with my student teachers on the youth mentoring course in dialogic struggles with my experiences of justice and education in the USA and Hong Kong as an English-speaking Chinese American. Providing an in-depth examination of the convergence of identity, social relations, place, and time in my knowledge formation, I critically reflect upon the notion of social justice to suggest that social justice teacher education is multi-voiced and lived both locally and globally.

Encouraging college students to volunteer is a supposed but uncharted way to contribute to their career commitment. Clarifying the ways of the contribution is therefore necessary. From the social capital perspective, volunteering and network density among friends represent social capital to reinforce each other. Thus, the purpose of this paper is to study the enhancement of the contribution by the density.

The examination employs a two-wave panel survey of 410 university students to estimate the effects of volunteering and friend network density at Wave 1 on career commitment at Wave 2. Essentially, the examination adjusted for biases due to sample attrition and self-selection into volunteering.

Volunteering at Wave 1 showed a significant contribution to career commitment at Wave 2. Moreover, the contribution significantly increased with friend network density at Wave 1.

Findings from this panel survey of university students in Hong Kong require future research for substantiation. For instance, such research can apply an experimental design to volunteering to guarantee the internal validity of the contribution of volunteering.

Social capital theory is applicable to the promotion of career commitment. Specifically, optimizing the strength of social capital through the combination of volunteering and friendship is promising.

Empirical support for the application of social capital theory to career development is evident. Particularly, the joint contribution of volunteering and friendship is demonstrable.

The librarian and researcher have to be able to uncover specific articles in their areas of interest. This Bibliography is designed to help. Volume IV, like Volume III, contains features to help the reader to retrieve relevant literature from MCB University Press' considerable output. Each entry within has been indexed according to author(s) and the Fifth Edition of the SCIMP/SCAMP Thesaurus. The latter thus provides a full subject index to facilitate rapid retrieval. Each article or book is assigned its own unique number and this is used in both the subject and author index. This Volume indexes 29 journals indicating the depth, coverage and expansion of MCB's portfolio.

Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.