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In Germany, various approaches have been taken to tackle the current teacher shortage in technical and vocational education and training (TVET). One attempt to remedy the shortage in Bavaria has been the introduction of an engineering education study programme at universities of applied sciences. Ideal candidates for this programme should have an interest in both engineering and social interaction. For effective recruitment, therefore, it is necessary to know applicants’ characteristics such as their vocational interests. In this study, the vocational interest profiles of students in TVET teacher training programmes were identified and their interest profiles and further characteristics were compared with those of other VET students at universities and universities of applied sciences.

An online questionnaire based on Holland’s interest theory and adapted from the Allgemeiner-Interessen-Struktur-Test-3 (interest structure test) was administered to 85 students in TVET teacher training programmes at universities and universities of applied sciences in Bavaria. Items regarding reasons for choosing a particular study programme, university location and other personal details were added.

The vocational interest profiles of students at universities and universities of applied sciences can be described as similar but weakly differentiated. Insights are provided by the characteristics of students such as the majority being first-time academics in the family. The reasons for choosing the degree programme and university location highlight the fact that a large proportion of students in engineering education would not have chosen a teaching-related degree programme if it had not been offered at the respective university of applied sciences.

Although the sample in this study was small and, therefore, limiting, it represented a high proportion of TVET teacher training students in Bavaria and a substantial proportion of first-year students in TVET teacher training programmes at universities and universities of applied sciences in Bavaria (section 2.2 and 3.1). Thus, the findings provide valuable insights into commonalities in interest profiles between engineering education students at universities of applied sciences and other TVET students at universities. With respect to the domain of the chosen vocational specialisation, differentiated profiles emerged that, for example, showed a stronger artistic orientation among students in construction technology/wood. For further analysis, the previous variable-centred orientation of the analysis can be supplemented by person-centred analyses (e.g. cluster analysis and latent variable mixture modelling, LVMM) (cf. Leon et al., 2021).

The findings in this study reveal the potential for attracting candidates to universities of applied sciences if they prefer to study in rather rural areas close to their hometowns. With the aim to educate prospective teachers for future work not only in metropolitan regions but in rural areas too, offering bachelor degree programmes in rural areas would seem promising. A regional option can boost the recruitment of new students and attract candidates that otherwise would be unable to pursue studies or a career as a teacher in vocational education. The results of this study and those of previous studies suggest that universities of applied sciences can cooperate with universities to help solve the teacher shortage problem.

Overall, it is apparent that the students' interests reached comparatively high values in all interest orientations and thus are only weakly differentiated. If undifferentiated profiles indicate low levels of career readiness, this significantly affects the recruitment of young people for the teaching profession. Assessing career orientation and promoting vocational interests should be prioritised during secondary school education. Vocational orientation measures are essential and should provide insight into typical activities of daily work life in different professions and thus pique and foster interests.

This study provides insight into how to respond to the teacher shortage in VET by identifying important characteristics of engineering education students using vocational interest profiling.

Knowledge-intensive entrepreneurial firms (KIE) strongly rely on scientific and strategic research and development (R&D) capabilities to achieve higher performance levels. Hence, the purpose of this paper is to disentangle the effects of scientific capabilities and strategic R&D on KIE performance; and how the constituent elements of these dimensions can be configured to generate conditions for high performance.

The authors’ empirical setting involves companies that submitted projects to the Innovative Research in Small Businesses (PIPE) program in Brazil. The authors then run partial least square structural equation modeling to verify how scientific and strategic R&D capabilities influence the performance construct. Second, the authors apply fuzzy-set qualitative comparative analysis to identify configurations that are equifinal in terms of generating superior performance.

Findings indicate a strong association between scientific capabilities and KIE performance. The configurational approach outlines the existence of multiple paths to success, but human capital stands as a core condition throughout estimations.

The authors’ assessment has implications for how KIE firms are managed according to their organizational profiles and trajectories. Also, it advances the authors’ comprehension on how entrepreneurship policies can better target these distinct profiles.

The authors’ analysis provides new evidence on the inherent complexity behind the generation of high performance in KIE when addressing their portfolios of knowledge-related capabilities. More than that, the authors were able to identify the existence of heterogeneous profiles that can equally lead to higher levels of performance.

Nowadays, sustainable, clean, inclusive, innovative, and smart mobility in addition to urban transformation is required to achieve sustainable development as a path to preserve the world for future concerns and improve quality of life at the present, even to be kept up with growing citizens' needs. Mobility as an infrastructure component plays fundamental roles in urban transformation, and economic development. In this chapter, and based on the 5^th wave theory, related theories, models, and concepts, modern, clean, and inclusive mobility founded on high future of 4th technologies (which is called 5th technologies), digitalization, smartness, sustainability, and CSR 2.0 strategies is declared as proper clean mobility technologies to create sustainable and smart cities. Such smart cities are able to deal with challenges made by rapid, unplanned urbanization and globalization to achieve sustainable development. In this research, roles of inclusive and smart mobility systems as path to create modern and sustainable urban areas to make the world more sustainable and livable for living are declared. Literature reviews, case studies, interviews, and questionaries are applied as main methods to recognize inclusive and modern mobility and its roles in urban transformation to achieve sustainable development. This chapter is based on know-how and do-how of the author Prof. Hamid Doost on sustainability such as cooperating with Danish Sustainable Platforms Company, working with Erasmus Plus as an academic leader in Germany since 2017, cooperating with Copenhagen's former mayor and researching on sustainability. In this chapter, impact of sustainable mobility, sustainable buildings, and smart cities on CSR 2.0 and social responsibility, how these parameters improve sustainable development and sustainability in social responsibility, corporate social responsibility (CSR), and how social responsibility could influence humanities are explored.

Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing social commerce research frameworks (e.g. the information model).

In the era of Industrial Revolution 4.0 technologies and new social commerce (s-commerce) models, the authors believe that there is an immediate need for a new research framework. The authors analysed the progress of the s-commerce paradigm between 2003 and 2023 by applying longitudinal science mapping. The authors then developed a research framework based on the themes in the strategic diagrams and evolution map.

From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, virtual communities, social shopping and analytic approaches. From 2011 to 2015, it shifted to s-commerce, consumer behaviour, Web 2.0, artificial intelligence, social technologies, online shopping, user studies, data gathering methods, applications, service-based social commerce constructs, e-commerce and cognitive factors. Social commerce remained the primary research paradigm from 2017 to 2023.

The SC framework may be analogous to popular research frameworks such as technology-organisation-environment (T-O-E) and stimulus-organism-response (S-O-R). Based on this SC framework, researchers may gain a better understanding by determining the factors of the social, commercial, technological and behavioural dimensions.

The authors redefined s-commerce and developed an SC framework. Practical guidelines for the SC framework and an exemplary research model are presented. Overall, this study offers a new research agenda for the extant understanding of s-commerce, with the SC framework as the next frontier of the theoretical advancements and applications of s-commerce.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

The major objectives of this study are to explore the perceptions of library practitioners towards IoT and to suggest directions for its proper application in university libraries of Bangladesh.

The study conducted an online questionnaire-based survey of library practitioners working at university libraries in Bangladesh in addition to an email interview with ten librarians. A sample size of 310 library practitioners was obtained using the convenience sampling technique for the survey. Non-parametric tests were performed to examine the differences in demographics and perspectives on IoT, as well as the potential benefits and challenges of IoT deployment in libraries.

A total of 178 valid surveys were completed, and the results indicate that 147 (82.6%) library practitioners have varied degrees of IoT understanding and positive attitudes about IoT applications in libraries. The study identified RFID, wireless technologies, cloud computing, mobile apps, artificial intelligence and semantic search as major IoT enablers. Tracking, book reservation, circulation, user identification, service marketing, selection of resources, information networking and resource sharing, etc. are the main areas of a library where IoT may be applied. The integration of IoT has the potential to transform conventional libraries into smart libraries. However, there are some challenges that Bangladeshi university libraries must overcome to take use of the prospects emerged from IoT.

The study would encourage Bangladeshi librarians adopting IoT-based library and information services and provide guidelines for its successful implementation.

The study is a pioneering effort from Bangladesh’s perspective which portrays the original opinions and perceptions of library practitioners regarding IoT applications in university libraries of Bangladesh.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.