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“The world needs science, science needs women” is the message given by UNESCO in the program for the development of women in science” (UNESCO, 2017). In Vietnam, women’s participation and achievements in scientific research is considered a great and important resource for industrialization and modernization. Even so, are there gender differences in scientific achievement in the social science research institutes in Vietnam? What factors influence the scientific achievement of female social researchers? The answers will be based on data from a 2017 survey with a sample of 756 researchers, of which 77.6% were female. The survey was conducted by the Vietnam Academy of Social Sciences, a leading, ministry-level national center for the social sciences in Vietnam. This chapter analyzed the scientific achievements of researchers through their position as principal investigators of research projects and their publications, and factors that may impact this. Bivariate and multivariate analyses of factors that may affect the scientific achievement of researchers found that gender differences in academic achievement in the social sciences in Vietnam was still prevalent. Female researchers’ scientific achievements were lower than those of their male counterparts. The contribution to science of Vietnamese female researchers was limited by many different factors; the most important were the academic rank of the researchers and gender stereotype that considered housework the responsibility of women.

This paper aims to assess the comparative position of the national innovation system of Chinese state entrepreneurship versus liberal market entrepreneurship. Based on the comparative institutional framework, it asks whether Chinese state entrepreneurship has a comparative disadvantage because of its incoherent institutions in liberal or coordinated economies. Hence, does the Chinese institutional system of innovation lag behind that of US or liberal countries of Organisation for Economic Co-operation and Development (OECD) economies in the transformation of national science into economic products measured as high-technology exports?

This study uses panel data analysis based on 29 OECD economies and the Chinese economy over 23 years. Regarding national science productivity (explorative capabilities), it includes published and patented science streams; regarding technological transformation (exploitative capabilities), it measures the percentage of high-technology exports in gross domestic product (GDP). The interactions between the types of entrepreneurship and national science institutions serve as predictors in the design.

The results show that Chinese state entrepreneurship has a comparative advantage over liberal economies in published science. However, Chinese state entrepreneurship has a comparative disadvantage compared to liberal entrepreneurship in patent science. Regarding the dyadic level of comparability between the national economies, there are mixed results in the transformation of national science.

This study supports the three following theoretical points: national institutions differ regardless of the pressure of convergence through globalization; national science contingencies influence different paths of the transformation of national science to technology; and mixed economies, such as state entrepreneurship, can achieve high performance without fully conforming to liberal markets.

This study emphasizes institutional mechanisms for future research to support the innovation of incoherent institutions and suggests the benefit of cross-pollination of senior managers between state and private organizations for a defined duration.

Theoretically, this research combines an interdisciplinary and interinstitutional level of analysis, and in so doing, it deals with the transformation of national science in scientific publications and patents in the vertical value chain. Empirically, this study links the national published and patented science with the national economic artifacts in high-technology sectors. This novel approach to assess the national and discipline-level interaction sets a context for the future research in other settings. It also informs policy decisions regarding the growth of science, innovation and development.

The socialist political economy with Chinese characteristics reflects the characteristics of ushering into a new era, and the research object thereof shifts to productive forces. Emancipating and developing productive forces and achieving common prosperity become the main theme. Wealth supersedes value as the fundamental category of economic analysis.

The theoretical system of socialist political economy with Chinese characteristics cannot proceed from transcendental theories but is problem-oriented. Leading problems involve development stages and research-level problems.

The economic operation analysis is subject to the goal of optimal allocation of resources with micro-level analysis focused on efficiency and macro-level analysis focused on economic growth and macroeconomic stability also known as economic security. The economic development analysis explores the laws of development and related development concepts in compliance with laws of productive forces. The new development concepts i.e. the innovative coordinated green open and shared development drive the innovation of development theory in political economy.

Accordingly, the political economy cannot study the system only, but also needs to study the problems of economic operation and economic development. Therefore, the theoretical system of the political economy tends to encompass three major parts, namely economic system, economic operation and economic development (including foreign economy). The basic economic system analysis needs to understand the relationship between public ownership and non-public ownership, between distribution according to work and factor payments, and between socialism and market economy from the perspective of coexistence theory, thus transforming institutional advantage into governance advantage.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

While postdoctoral research (postdoc) training is a common step toward academic careers in science, technology, engineering and mathematics (STEM) fields, the role of postdoc training in social sciences is less clear. An increasing number of social science PhDs are pursuing postdocs. This paper aims to identify factors associated with participation in postdoc training and examines the relationship between postdoc training and subsequent career outcomes, including attainment of tenure-track faculty positions and early career salaries.

Using data from the National Science Foundation Survey of Earned Doctorates and Survey of Doctorate Recipients, this study applies propensity score matching, regression and decomposition analyses to identify the role of postdoc training on the employment outcomes of PhDs in the social science and STEM fields.

Results from the regression analyses indicate that participation in postdoc training is associated with greater PhD research experience, higher departmental research ranking and departmental job placement norms. When the postdocs and non-postdocs groups are balanced on observable characteristics, postdoc training is associated with a higher likelihood of attaining tenure-track faculty positions 7 to 9 years after PhD completion. The salaries of social science tenure-track faculty with postdoc experience eventually surpass the salaries of non-postdoc PhDs, primarily via placement at institutions that offer relatively higher salaries. This pattern, however, does not apply to STEM PhDs.

This study leverages comprehensive, nationally representative data to investigate the role of postdoc training in the career outcomes of social sciences PhDs, in comparison to STEM PhDs. Research findings suggest that for social sciences PhDs interested in academic careers, postdoc training can contribute to the attainment of tenure-track faculty positions and toward earning relatively higher salaries over time. Research findings provide prospective and current PhDs with information helpful in career planning and decision-making. Academic institutions, administrators, faculty and stakeholders can apply these research findings toward developing programs and interventions to provide doctoral students with career guidance and greater career transparency.

The purpose of this study was to uncover representative emergent areas and to examine the research area of marketing, tourism and big data (BD) to assess how these thematic areas have developed over a 27-year time period from 1996 to 2022. This study analyzed 1,152 studies to identify the principal thematic areas and emergent topics, principal theories used, predominant forms of analysis and the most productive authors in terms of research.

The articles for this research were all selected from the Web of Science database. A systematic and quantitative literature review was performed. This study used SciMAT software to extract indicators. Specifically, this study analyzed productivity and produced a science map.

The findings suggest that interest in this area has increased gradually. The outputs also reveal the innovative effort of industry in new technologies for developing models for tourism marketing. Ten research areas were identified: “destination marketing,” “mobility patterns,” “co-creation,” “gastronomy,” “sustainability,” “tourist behavior,” “market segmentation,” “artificial neural networks,” “pricing” and “tourist satisfaction.”

This work is unique in proposing an agenda for future research into tourism marketing research with new technologies such as BD and artificial intelligence techniques. In addition, the results presented here fill the current gap in the research since while there have been literature reviews covering tourism with BD or marketing, these areas have not been studied as a whole.

El objetivo de esta investigación fue descubrir nichos representativos de áreas emergentes y examinar el área de Marketing, Turismo y Big Data, evaluando cómo han evolucionado estas áreas temáticas durante un período de 27 años desde 1996–2022. Analizamos 1.152 investigaciones para identificar las principales áreas temáticas y temas emergentes, las principales teorías utilizadas, las formas de análisis predominantes y los autores más productivos en términos de investigación.

Todos los artículos para esta investigación fueron seleccionados de la base de datos Web of Science. Realizamos una revisión sistemática y cuantitativa de la literatura. Utilizamos el software SciMAT para extraer indicadores. Específicamente, analizamos la productividad y elaboramos un mapeo científico.

Los hallazgos sugieren que el interés en esta área ha aumentado gradualmente. Los resultados también revelan el esfuerzo innovador de la industria en nuevas tecnologías para desarrollar modelos de marketing turístico. Se identificaron diez áreas de investigación (“marketing de destinos”, “patrones de movilidad”, “co-creación”, “gastronomía”, “sostenibilidad”, “comportamiento turístico”, “segmentación de mercado”, “redes neuronales artificiales”, “precios”, y “satisfacción del turista”).

Este trabajo es único al proponer una agenda para futuras investigaciones en investigación de Marketing Turístico con nuevas tecnologías como Big Data y técnicas de Inteligencia Artificial. Además, los resultados presentados aquí llenan el vacío actual en la investigación ya que si bien se han realizado revisiones de literatura que cubren Turismo con Big Data o Marketing, estas áreas no se han estudiado como un conjunto.

这一特定研究领域的目标是发现具有代表性的新兴领域, 并考察市场营销、旅游和大数据研究领域, 以评估这些主题领域在1996年至2022年的27年间是如何发展的。我们分析了1152项研究, 以确定主要专题领域和新兴主题、使用的主要理论、主要的分析形式以及在研究方面最有成效的作者。

本研究的文章都是从Web of Science数据库中选出的。我们进行了系统化的定量文献审查, 并使用SciMAT软件来提取指标。具体来说, 我们分析了生产力并制作了一个科学研究地图。

研究结果表明, 人们对这一领域的兴趣已经逐渐增加。本文也揭示了工业界在开发旅游营销模式的新技术方面的创新努力。研究确定了十个研究领域：“目的地营销”、“流动模式”、“共同创造”、“美食”、“可持续性”、“游客行为”、“市场细分”、“人工神经网络”、“定价 “和游客满意度”。

这项研究的独特之处在于提出了未来利用大数据和人工智能技术等新技术进行旅游营销研究的议程。此外, 本文的结果填补了目前的研究空白, 因为虽然有文献综述涉及旅游与大数据或市场营销, 但这些领域还没有被作为一个整体来研究。