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The importance of multidimensional and engaging instruction for sustainable development goals (SDGs) in higher education cannot be overstated. Such instructions should motivate students not only to memorize and contemplate these goals but also to actively participate in addressing SDG-related challenges. Consequently, this study aims to develop practical and appropriate instructional approaches to education for sustainable development (ESD) in higher education to enhance students’ knowledge, attitudes and behaviors concerning sustainability.

By using a quasi-experimental design, this ESD study was conducted at a university in central Taiwan. A total of 121 students from diverse academic backgrounds participated in the 16-week experiment, which was divided into three groups. Lecturing, thematic teaching and design-thinking strategies were applied to these respective groups.

The thematic-teaching and design-thinking groups displaying improved cognitive performance. However, the quantity results revealed that the design-thinking group surpassed the other two groups in sustainability knowledge, attitudes, behaviors and mind map tasks. The qualitative findings further indicated that design thinking – through multiple practical problem-solving activities – guided college students to think independently and sustainably, as well as enabled them to internalize the value of sustainable development. By implementing these effective approaches, the core goals of ESD-related personal and societal transformations may be realized.

This study proposed a goal-oriented ESD instructional model for educators, demonstrating the efficacy of design thinking in cultivating higher-order thinking and affection for ESD in students. Additionally, this study introduced an innovative evaluation approach – mind mapping – to the ESD domain, which may compensate for the limitations of the survey method.

This study provides empirical evidence for the effectiveness of design thinking and thematic-based pedagogies in sustainable development higher education. Additionally, it also offers a practical ESD instructional model with reference value for scholars and multi-domain instructors. Moreover, the study highlights that by examining organizational governance from the perspectives of design thinking and higher-order affection, sustainable and economic development need not be mutually exclusive concepts. Instead, pursuing SDGs can be viewed as investment opportunities for organizations rather than mere costs.

In drawing on the conservation of resources theory and the broaden-and-build theory, the present research investigates the dynamic of social resources (i.e. servant leadership) and personal resources (i.e. psychological empowerment and positive affect) in the determination of the nurses' optimal performance (i.e. deep acting).

The research involved collecting three waves of data on 481 frontline nurses at a large hospital in Taiwan, each a month apart. The hypotheses were tested using PROCESS mediation and moderated mediation regression models.

The results supported the indirect relationship between servant leadership and deep acting through psychological empowerment as well as the moderating effect of positive affect on the mediation model.

The findings shed new light on the interplay of different resources and also provide practical implications for the development of frontline supervisors and nursing staff to be compatible with a serious orientation toward the quality of their professional functioning.

In recent decades, emerging market multinational enterprises (EMNEs) have predominantly adopted a big step internationalization strategy to expand their business overseas. This study aims to examine the effect of big step internationalization on the speed of subsequent foreign direct investment (FDI) expansion for EMNEs. The authors also investigate the potential boundary conditions.

The authors use the random effects generalized least squares (GLS) regression following a hierarchical approach to analyze the panel data set conducted by a sample of publicly listed Chinese firms from 2001 to 2012.

The findings indicate that implementing big step internationalization in the initial stages accelerates the speed of subsequent FDI expansion. Notably, the authors find that this effect is more pronounced for firms that opt for acquisitions as the entry mode in their first big step internationalization and possess a board of directors with strong political connections to their home country’s government. In contrast, the board of director’s international experience negatively moderates this effect.

This study provides insights into our scholarly and practical understanding of EMNEs’ big step internationalization and subsequent FDI expansion speed, which offers important implications for firms’ decision-makers and policymakers.

This study extends the internationalization theory, broadens the international business literature on the consequences of big step internationalization and deepens the theoretical and practical understanding of foreign expansion strategies in EMNEs.

This paper aims to identify ways to establish an information system to aid users to enhance the effectiveness of self-regulated learning and solve the problem of learning domain unawareness. Many libraries are spacious and with a rich collection of books, the problem a newcomer may encounter in the wide library is spatial unawareness. In addition, people new to a particular field of study often encounter the problem of learning domain unawareness.

This paper presents an overview of self-regulated learning theory first. We realize the essential principles of self-regulated learning model in the library and developed a learning system that utilizes the concept of combining mobile augmented reality (AR), indoor navigation and data mining algorithms.

The proposed NO Donkey E-learning (NODE) system utilizes AR and innovative indoor positioning technology to fulfil the goal of navigation inside a library and solve the problems of spatial and learning domain unawareness. On the one hand, the system allows peers to communicate asynchronously to create a cloud-based information sharing community; the dual-track terminal (the website and the app interfaces) in the system could provide both educational functionalities and mobility for readers. On the other hand, AR navigation function integrates the information of reading paths, the real-space locations, real-time dynamic information, book introductions and readers’ comments to help readers have access to the topic-related books efficiently.

We found that although the library provides the floor plan and signs, such passive and fixed indication may cause spatial unawareness. People need system to show the bookshelf location and dynamic direction indicators when they walk in the wide library. However, most existing library information systems only provide readers with the function of book search, including which floor the book is on, call number and check-out status. In this sense, we propose that self-regulated learning theory integrated the new innovation technology is the solution for the above issues.

The system developed in this study, while viewing the real scenes inside the library through camera lens, provides related virtual educational information services and learning paths on screen and guides the public to do systematic self-regulated learning. With the functions of the “learning topic” and “knowledge sharing”, the learning system promotes the general public to self-monitor their learning progress and to use the sharing mechanism as the system structure to solve the two main problems of spatial unawareness and domain unawareness in learning in libraries, creating a truly innovative people-centred library information system.

The sharing economy is an emerging trend that takes advantage of underused assets and generates enormous amount of economic value. However, little research has been done to understand the factors that drive people to share assets.

This study develops two models, one based on the theory of planned behavior (TPB) and another based on social capital theory (SCT), to explain why individuals want to share their assets with others. An Internet survey is conducted to collect data and test the proposed models. Most of respondents were members of Airbnb or Uber – accommodation sharing and taxi services.

The results show that the TPB model has greater explanatory power than the SCT model. The individual's motivations, attitude, perceived behavioral control, facilitating technological conditions and identification with the community increase their intention to share assets.

This study distinguishes asset suppliers from consumers to clearly understand the factors that influence the intention to provide underused assets. The research findings can help companies participating in the sharing economy to induce people to share.

The purpose of this study is to establish a comprehensive service recovery mechanism by analyzing different behaviors of customers with different personality traits after service failures and by proposing different service recovery measures that service providers could adopt based on diverse customer personality traits.

This paper constructs a service recovery mechanism based on a signaling game theory by considering customers and service providers as two players in the game and analyzing possible outcomes under both separating and pooling equilibria to achieve an optimized strategy and set of actions that allow the highest payoffs for both service providers and customers.

This study successfully simulated the separating equilibrium and pooling equilibrium between service providers and customers in a signaling game with incomplete information. It also provides a reference for service providers to design service recovery strategies after service failures. By using this model, when facing problems related to service failures and service recovery, service providers will have a better chance of increasing the service recovery success rate, improving customer satisfaction and achieving optimal payoffs for both themselves and their customers.

Based on concepts of service science, this study designed a service recovery mechanism by applying the signaling game from game theory and introducing personality traits theory to the service recovery scenario so that service providers are able to execute service recovery after service failures more effectively. This study proposed a service recovery mechanism based on the perspectives of both service providers and customers, considering the mutual influence of key variables related to both of them, as well as the context of service failures, customers’ personality traits and service providers’ available resources. Many studies have applied personality traits to different fields; however, to the best of authors’ knowledge, few studies have applied this concept to service-related subjects, and only the influence of employees’ personality traits on service providers has been discussed.

The purpose of this paper is to examine the great contributions made by the American library expert, Mary Elizabeth Wood, to Chinese library development.

As a pioneer of the modern library movement Mary Elizabeth Wood devoted herself to a Chinese library career. It was structured according to the following theme: setting up the Boone Library and introducing the modern American public library into China; establishing Boone Library School and initiating Chinese library science education; raising money and appealing for China's library development; helping forward the foundation of the Library Association of China; as well as promoting Chinese library intercommunion and cooperation with the West.

With the background of underdeveloped Chinese librarianship, Mary Elizabeth Wood introduced modern American public library spirit into China, opened the gate of Chinese library science, and promoted Chinese library science.

The paper discusses the library history of China and the role of an American librarian in Chinese library history; thus, it should be of wide interest to researchers involved in library history.

Mary Elizabeth Wood devoted herself to a Chinese library career, and promoted Chinese library science greatly, but research on her is limited. This paper considers her contribution to Chinese library science.

This study aims to investigate the antecedents and outcomes of excessive use of personal social media at work. The prevalence of personal social media in the work environment can easily lead to excessive use and negative consequences. Understanding the predictive factors and negative consequences of employees' excessive use of personal social media at work is important to develop their appropriate use of social media and improve their job performance.

Based on dual-system theory and the person-environment fit model, this study develops a research model to examine the effect of habit and self-regulation on excessive use of personal social media at work and that of the outcomes of excessive use on employee job performance through strain. This study conducts a questionnaire survey on 408 employees to test the research model and hypotheses empirically.

Results suggest that the imbalance between habit and self-regulation drives excessive personal social media use of employees at work. Furthermore, excessive use of personal social media has a strong impact on employee strain, which can significantly decrease job performance.

First, this study considers excessive use of personal social media at work as a result of two different cognitive systems, that is, an automatic system and a controlled system, thereby extending the dual-system theory to explain excessive use of personal social media in the work context. Second, unlike previous studies that focused on the outcomes or explored the antecedents of excessive social media use at work respectively, the study employs the person-environment fit model and examines the systematic influence of excessive social media use at work from a broad perspective by linking its antecedents and outcomes.

Special education in China has lagged behind regular education for many years, however, the past few decades, the government has made considerable efforts to develop and improve the special education system. While the citizens of China have had a generic moral interest in disability since ancient times, the development of special education schools did not occur until American and European missionaries started schools for the visually and hearing impaired in the 19th century. The next major influence in the development of the special education system occurred with China’s Cultural Revolution in 1978. Interestingly, there is not any exclusive legislation on special education but in the 1980s, the government started Learning in Regular Classrooms (LRC), which is China’s version of inclusion. LRC has progressed rapidly the past two decades; however, the quality of instruction is low due to a lack of specialists, a shortage of personnel, inadequate funding, and limited technology as well as other barriers that are delineated in the chapter. The chapter emphasizes the government’s recent efforts in in-service teacher training, the preparation of preservice teachers, working with families, developing community rehabilitation training programs, and implementing evidence-based practices. Special education in China today is at a good place but it has quite a way from the ideal situation.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.