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By employing the GMM and SVAR models in this paper, the effects that bond prices, equity prices, gold prices, and domestic credit have on housing prices were analyzed, using data from 2002q4 to 2015q1 for the ASEAN + 2 countries. The GMM results indicated the significant effects of equity prices and gold prices on housing prices and insignificant effects of bond prices and demotic credit on housing prices in selected Asian countries. Furthermore, findings show that worldwide economic crisis has negative impacts on housing prices in Asian countries. Moreover, Impulse response results indicated that housing prices respond simultaneously and positively to equity prices in all countries except Malaysia and Singapore. Likewise, Variance deposition findings demonstrate the importance of gold prices in fluctuation of housing prices in Malaysia and China especially in the long term.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.

We aim to comb the current policies that have been developed to promote the environmental industries in China and analyze them in a comparative manner.

We mainly use the method of text study to study the existing policies that Chinese central government published to promote the development of environmental industry. We built a database of policies and regulations from 1979 to 2015 by searching the official website of the Ministry of Environmental Protection of China.

We find that the existing policies focus on command and control approaches. Policies are more oriented to the stage of production instead of stages of investment and consumption. They rely more on negative incentive when stimulating supply and positive incentive when encouraging demand. Based on existing academic wisdom, we suggest that Chinese government should pay more attention to environmental economic policy and to stimulating demand for environmental products.

Few studies provide a systematic overview of the policy systems that have been developed to promote environmental industry in China in a systematic manner.

Women entrepreneurship is designated as the new engines of economic growth in developing countries. Prior research shows how men and women differ in starting a business, with women normally facing more restrictions on entrepreneurial financing, entrepreneurial growth, and entrepreneurial performance. This has often been explained by gender role, yet we still lack a systematic understanding of how gender roles impact on women's entrepreneurial process in developing countries. In this chapter, we review literatures on female entrepreneurship in developing countries to show how gender role works in developing countries and especially its influence on the intention/entry/business participation and performance of female entrepreneurs.

As the hospitality industry is shifting its focus from service to experience, customers are becoming co-creators of the perceived value of a hospitality service because experiences customers obtained when consuming a hospitality service involve the participation of the customers. Thus, more research is needed to examine the relationships among consumer’s personal factors and their evaluations of hospitality services. This study developed and tested hypotheses that examined the effects of customers’ intrinsic characteristics on their evaluations of a restaurant service. Data were collected from college students in the United States (n = 220) and China (n = 254) using a scenario approach. Findings reveal that customers’ gender, personality, and cultural background had significant effects on their evaluations of a restaurant service. Specifically, female customers rated the same service higher than male customers on the reliability dimension of service quality and overall service quality; customers with personalities of extroversion, conscientiousness, agreeableness, and openness rated the service higher than customers with neuroticism personality on the responsiveness dimension; and customers in individualistic cultures rated the service higher than those in collectivistic cultures on most of the service evaluation measures. Implications of the study’s findings are discussed.

In as much as it is contested, the Belt and Road Initiative (BRI) is also unexplored, underdiscussed, and, as a result, misunderstood. Frequently viewed through the lens of international relations and global economy, the diverse dimensions of collaboration, including business and research-industry clusters, that BRI enhances, tend to be excluded from the analysis. In a similar manner, the role of the Arab Peninsula in the grand strategy underpinning BRI and its implementation is rarely discussed. BRI is a forward-oriented initiative, an attempt to reap benefits of developments and circumstances that are only nascent. This bears two potent implications. First, as China attempts to influence the context in which it operates, it is subject to change itself; the Chinese business sector evolution attests to that. Second, some of China’s not so obvious partners of today, including those in the Arab Peninsula, are about to turn into key interlocutors of tomorrow. BRI taps into opportunities thus created. This chapter elaborates on these issues and, against this backdrop, outlines how the remaining chapters included in this volume add to this discussion.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.