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A “state-driven” approach suggests that colonists use census categories to rule. However, a “society-driven” approach suggests that this state-driven perspective confers too much power upon states. A third approach views census-taking and official categorization as a product of state–society interaction that depends upon: (a) the population's lay categories, (b) information intellectuals' ability to take up and transform these lay categories, and (c) the balance of power between social and state actors. We evaluate the above positions by analyzing official records, key texts, travelogues, and statistical memoirs from three key periods in India: Indus Valley civilization through classical Gupta rule (ca. 3300 BCE–700 CE), the “medieval” period (ca. 700–1700 CE), and East India Company (EIC) rule (1757–1857 CE), using historical narrative. We show that information gathering early in the first period was society driven; however, over time, a strong interactive pattern emerged. Scribes (information intellectuals) increased their social status and power (thus, shifting the balance of power) by drawing on caste categories (lay categories) and incorporating them into official information gathering. This intensification of interactive information gathering allowed the Mughals, the EIC, and finally British direct rule officials to collect large quantities of information. Our evidence thus suggests that the intensification of state–society interactions over time laid the groundwork for the success of the direct rule British censuses. It also suggests that any transformative effect of these censuses lay in this interactive pattern, not in the strength of the British colonial state.

This study aims to identify the political alignment and political activity of the 11 Presidents of Britain’s most important scientific organisation, the Royal Society of London, in its early years 1662–1703, to determine whether or not the institution was politically aligned.

There is almost no information addressing the political alignment of the Royal Society or its Presidents available in the institution’s archives, or in the writings of historians specialising in its administration. Even reliable biographical sources, such as the Oxford Dictionary of National Biography provide very limited information. However, as 10 Presidents were elected Member of Parliament (MP), The History of Parliament: British Political, Social and Local History provides a wealth of accurate, in-depth data, revealing the alignment of both.

All Presidents held senior government offices, the first was a Royalist aristocrat; of the remaining 10, 8 were Royalist or Tory MPs, 2 of whom were falsely imprisoned by the House of Commons, 2 were Whig MPs, while 4 were elevated to the Lords. The institution was Royalist aligned 1662–1680, Tory aligned 1680–1695 and Whig aligned 1695–1703, which reflects changes in Parliament and State.

This study establishes that the early Royal Society was not an apolitical institution and that the political alignment of Presidents and institution continued in later eras. Furthermore, it demonstrates how the election or appointment of an organisation’s most senior officer can be used to signal its political alignment with government and other organisations to serve various ends.

Stigma towards people living with complex mental health issues is widespread and harmful, preventing help-seeking behaviour, fostering social exclusion and decreasing self-efficacy. This study aims to investigate the usefulness and drawbacks of a mental health-themed podcast in reducing stigma.

Qualitative interviews were conducted with 12 participants who had each listened to three episodes of the co-designed podcast “On the Same Wavelength”. Interview data were analysed using reflexive thematic analysis.

Four themes were generated: learning from a trustworthy source; connecting with lived experience; reducing stigma, one listener at a time; and a high-quality podcast with limited reach. Listeners appraised the podcast as improving their attitudes and behavioural intentions towards people living with mental illness and their understanding of mental illness and stigma. They perceived the lived experience narratives as its most impactful element, but felt the podcast might not have mass appeal.

Despite the popularity of mental health-themed podcasts, there is limited research examining their potential to reduce stigma. This study provided insight into the usefulness, listener acceptability and impactful elements of such podcasts, by exploring listeners’ perspectives of a new podcast co-designed to reduce stigma.

This study aims to explore the intersection of AI-generated learning materials and active learning strategies in higher education artificial intelligence (AI) is bringing about changes and opening up new possibilities for an improved and more efficient higher education. However, the argument is that its use in education/classroom should be informed by verifiable evidence as well as best practice, which this scholarly work helps build evidence-based research to assess this technology in higher education.

Primary data was collected through structured questionnaire administered online via Google form. Based on the non-probability sampling technique, 300 higher education tutors and students across the UK were purposively targeted, out of which 218 (72.7%) response rate was achieved. Data was analyzed using descriptive statistics with the aid of Statistical Package for Social Sciences, whereby regression, correlation and Chi-square tests were conducted to determine the statistical significance, direction and strength of the relationship between the measured variables.

This study revealed that AI-generated learning materials support active learning strategies that enable students to actively engage in their learning, likewise enabling students to develop deeper understanding of their course content with significantly better knowledge retention, which is critical to the learning process. However, findings further revealed that acceptance/regular use of AI-generated learning materials is still below par across the higher education institutions, and there is major concern that the benefits may not be fully realized due to barriers to adoption.

There are limitations that future studies can improve on, especially in terms of methodology. Pragmatism is a philosophical research stance that integrates quantitative data collection with qualitative data (such as interviews) and will ask in-depth questions to gain holistic quality data for such empirical. Future studies can also improve on the research scope to allow for generalizability of findings and check for potential biases in the data collection, analysis and interpretation processes.

Despite the huge anticipation regarding how AI technology could transform teachers’ roles in higher education, concrete research into AI-generated learning materials and actual impact in facilitating active learning strategies and knowledge retention is currently lacking. This study presents theoretical models on AI acceptance in higher education and explored the Technology, Pedagogical and Content Knowledge framework to inform empirical information on how AI can support active learning strategies and students’ knowledge retention.

This paper comes in the context of the European International Business Academy (EIBA) History project, and the long period analysed was broken down in six time windows (1975–1981, 1982–1988, 1989–1995, 1996–2002, 2003–2012; and 2013–2020), in line with the periodisation followed in writing such history. The main purpose was to identify the key themes of the papers presented at EIBA conferences between 1975 and 2020.

The analysis was carried out drawing on topic modelling, a machine-learning statistical solution that is capable of processing large volumes of text data.

A set of 5,296 Competitive and Workshop papers was processed with the use of topic modelling. The method enabled to identify 24 underlying research topics. These were then grouped into nine higher-level categories. The results show a consistent growth in the number of papers presented, especially in the periods going from 1989–2012. This is a sign of an increasing attractiveness, openness and attendance in EIBA’s annual conferences. Overall, the topics with the highest probabilities were topic 22 (Measuring and Assessing IB performance), followed ex-aequo by topics 13 (Evolutionary Approaches, Matrix Structures and Managerial Challenges) and 20 (Comparative Management Education and Behaviour) and then by topic 4 (Born Globals and International New Ventures). A key finding was the change in methodological approaches over time, with a significant increase in the use of sound quantitative and qualitative methods, instead of broad narratives mostly based on descriptive statistics.

Latent Dirichlet Allocation (LDA), as a quantitative approach to analyse text data, has some limitations. LDA, along with other distributional models, may identify degrees of semantic relations between words, but is not able by itself to specify the kind of relation, entailing a possible loss of contextual information which might have been able to further assist in the study. Another limitation stems from the use of very old paper proceedings, whose quality was sometimes low, making reading difficult.

This research provides a longitudinal perspective of the evolution of the key research topics in international business over about 45 years. Its findings are very important for all those who are interested on the evolution of the IB field.

The research provides an interesting perspective of the development of a scientific field as well as of a scientific community.

The main contributions of this research are fourfold. Firstly, to the best of the authors' knowledge, it provides the most complete longitudinal analysis about the evolution of IB research topics published so far. Secondly, it extracts relevant information about the evolution of the IB research issues addressed at EIBA’s annual conferences, enabling a 46-year longitudinal perspective on research interests as they emerged. Thirdly, it provides a successful application of topic modelling for the analysis of large volumes of textual data. Fourthly, it addresses the entirety of the text documents, as opposed to specific sections or keywords only, ensuring increased analytical depth.