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The purpose of this paper is to understand the impending relationship between the impact of the US–China trade war on Taiwanese firms' spending on R&D and their offshore investment in technologically advanced countries (TAC), the authors examined if changes in these firms' R&D ratios and the growing presence of skilled workers in Taiwan's labour market during the trade war have affected their offshore investments in TAC.

Using a model built on pooled cross-sectional time-series data from 2012–2019, the authors examined whether a change in R&D ratios of domestic firms in Taiwan and the growing presence of skilled workers in Taiwan's labour market have affected the offshore investment by these firms during the trade war. Using data from the Manpower Utilisation Survey, the authors applied differences–in–differences–in–differences and differences–in–differences–in–differences–in–differences estimation methods and found that the trade war indeed gave a boost to Taiwan's job market, particularly for skilled workers.

From the estimation results, the authors noticed a rise in employment opportunities alongside a decline in the earnings of skilled workers in industries where more firms have spent on R&D as well as invested in offshore operations. However, firms in Taiwan that had not heavily spent on R&D from industries where investment in foreign operations was otherwise high have also attracted skilled workers during the trade war.

An in-depth analysis of the impact of the trade war on domestic firms' spending on R&D and their investment in offshore operations in TAC should be helpful to policymakers interested in understanding the effects of the trade war and subsequent changes in firms' spending on R&D on labour market outcomes. If changes in the R&D ratios and a steady supply of skilled workers influenced the outflow of Foreign Direct Investment (FDI) to TAC, this insight could be helpful for those devising policies and measures to curb the impact of the trade war on domestic spending on R&D.

The study findings not only provide broad lessons to policymakers in Taiwan, but the country case study can guide growing economies that are equally careful while perceiving trade war as a significant deterrent to domestic R&D spending and the outflow of FDI.

Literature has recognised entrepreneurship education as the main conduit through which entrepreneurial behaviours, attitudes and actions can be built, enacted and delivered. Since the founding of new ventures is largely a resourceful founder-driven enterprise, entrepreneurship education has largely centred on galvanising and shifting the mindsets and cognition of the entrepreneur. Yet, despite over 60 years of delivering entrepreneurship education programmes, hard evidence of the generation of high-growth-oriented and sustainable ventures has been scarce as student entrepreneurship intentions do not always translate into successful venture creation. This is largely because of the complexities of the practicality of entrepreneurial education particularly, the dissonance between acquired education in business schools and the knowledge and competencies needed in the entrepreneurial field. Such dissonance can be attributed to the lack of clarity on the pedagogical approach that most resonates with entrepreneurial action, the diversity in assessment methods and the scholarly illusion pertaining to how pedagogical approaches can be channelled to the generation of growth-oriented ventures. Drawing on Girox's concepts of transformative critical pedagogy (including pedagogy of repression), Socratic dialogue, Hegelian dialectic and Yrjö Engeström's transformative expansive agency, I demonstrate how a flipped transformative critical pedagogy can be harnessed in digitally enhanced learning environments to create new entrepreneurial possibilities for facilitating critical inquiry, complex problem-solving, innovation for the market and fostering tolerance for failure in ambiguous entrepreneurial contexts.

Prior research on user-generated content (UGC) contributions has primarily focused on self-centered or other-centered motives, paying limited attention to the concept of enlightened self-interest, in which both motives coexist in a single organism. Additionally, the factors influencing enlightened self-interest and their effects in different circumstances are yet to be explored. Drawing on theoretical lenses rooted in the switching barriers perspective and stimulus–organism–response framework, this study posits that dedication-based switching barriers (community–member relationship quality, member–member relationship quality, and content attractiveness) positively relate to enlightened self-interest, whereas constraint-based switching barriers (switching costs) moderate the relationship between dedication-based switching barriers and enlightened self-interest in social media communities (SMCs). Members' enlightened self-interest in turn influences both the creation and co-creation of UGC.

This study comprised two quantitative studies: an online survey-based study (Study 1) and an online scenario-based experiment (Study 2). Study 1 surveyed 613 respondents, while Study 2 included 749 participants. Both studies employed structural equation modeling and bootstrapping techniques for analysis.

The findings indicate that dedication-based switching barriers positively affect users' enlightened self-interest, which in turn is positively associated with UGC creation and co-creation. Switching costs moderate the relationship between relationship quality (community–member and member–member) and enlightened self-interest.

This study complements the current understanding of how the association between dedication- and constraint-based switching barriers and users' enlightened self-interests influence user-generated contributions.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.