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Doctoral students are promising entrepreneurial actors in university-based ventures, which positively impact the external environment and create value for their universities. In this article, the authors extend current research on academic entrepreneurship by shedding light on the role of university support in the early stage of Ph.D. entrepreneurship. Based on social information processing theory, the authors posit that academic entrepreneurship results from the interplay between doctoral students' human capital and university-level support. A multilevel model is proposed and empirically tested to shed light on the cradle of doctoral students' entrepreneurship by explaining the variance of their entrepreneurial alertness and intentions.

A model is proposed that explains the combined effect of specific human capital and different forms of university support on doctoral students' cognitive transition from entrepreneurial alertness to intentions. The model was then tested through structural equation modeling (SEM) and multigroup analysis (MGA) on a sample of 187 doctoral students enrolled in Italian universities.

The SEM results reveal that doctoral students' entrepreneurial alertness is influenced by perceived educational support and human capital. The MGA demonstrates that those who perceive a higher level of support for concept and business development from universities are more likely to convert their alertness into intentions than those who perceive lower support.

The present paper brings to the stage doctoral students as an extremely promising entrepreneurial target. In doing so, it extends academic entrepreneurship studies by detailing how and when the different forms of university support influence their entrepreneurial decisions, along with individual dimensions.

Despite the increasing attention given to product-service integration (PSI), little is known about this innovation strategy from a key decision-maker’s perspective. To address this gap, our study draws from personality psychology and decision-making (DM) logics theory to better understand why and how companies’ decision-makers strategize for PSI.

Using an abductive, empirics-first approach, we identify the study’s theoretical building blocks, followed by an exploratory quantitative analysis to generate new theory. We propose a fit-as-mediation conceptual framework suggesting that (1) specific personality traits [i.e. honesty-humility (H), emotionality (E), extraversion (X), agreeableness (A), conscientiousness (C) and openness to experience (O) (HEXACO)] make decision-makers more likely to include PSI in their company’s strategy and (2) depending on their personality, they apply different DM logics (i.e. causation or effectuation) to do so. To empirically examine this, we use data from 289 SMEs’ decision-makers.

We report several meaningful relationships among our key theoretical constructs. For instance, we find that conscientious decision-makers are more likely to develop a PSI strategy via causation, whereas extravert decision-makers are more likely to do so via both causation and effectuation.

This service study is the first to apply the well-established HEXACO Personality Inventory to companies’ key decision-makers. Moreover, it contributes to the microfoundations of PSI strategy and DM logic theories.

In Thailand, the number of higher education institutions (HEIs) offering international programmes has surged dramatically. Internationalisation is seen as key to competing in the higher education market, modernising educational programming, and generating new streams of revenue. Yet, such rapid change is disorientating for the internationalisation of higher education (IHE) in the Thai context. That said, there is little disagreement on what it is not; it is not Thai. This chapter investigates apparent efforts to ‘de-place’ Thailand from IHE and considers how these attempts may connect to (post-)colonial tensions between sovereignty and civilisation. Through a synthesis of scholarship in the areas of higher education, cultural geography and Thai studies, the authors construct a framework for exploring how IHE is both imagined and experienced in Thailand. In particular, they re-examine datasets from studies they conducted with stakeholders over recent years, including executive leadership, international faculty members, and university students. Through a series of narrative portraits, a dialogue of voices is constructed that reflect distinct orientations to ‘Thainess’ in the IHE. The authors argue that a wider and more inclusive orientation to internationalisation is not only respectful of local identity but is enhanced by it. Ultimately, the hope of this study is to offer a vision of what an ‘emplaced’ idea of IHE in Thailand might look like, one grounded in an orientation unique to a particular place with its own cultural and social coordinates.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

In the growing information systems (IS) literature on metaverse, augmented reality (AR) technology is regarded as a cornerstone of the metaverse which enables interaction services. Interaction has been identified as a core technology characteristic of metaverse shopping environments. Based on previous human–technology interaction research, the authors further explicate interaction to be multimodal sensory. The purpose of this study is thus to better understand the unique nature of interaction in AR technology and highlight the technology's benefits for shopping in metaverse spaces.

An experiment has been conducted to empirically examine the authors' research model. The authors use the structural equation modeling (SEM) approach to analyze the collected data.

This study conceptualizes image, motion and touchscreen interactions as the three dimensions of multimodal sensory interaction, which can reflect visual-, kinesthetic- and haptic-based sensation stimulation. The authors' findings show that multimodal sensory interaction of AR activates consumers' intention to purchase via a psychological process. To delineate this psychological process, the authors use feelings-as-information theory to posit that experiential factors can influence cognitive factors. More specifically, multimodal sensory interaction is shown to increase multisensory experience and spatial presence, which can effectively reduce product uncertainty and information overload. The two outcomes have been considered to be key issues in online shopping environments.

This study is one of the first ones that shed light on the multimodal sensory peculiarity of AR interactions in the extant IS literature. The authors further highlight the benefits of AR in addressing major online shopping concerns about product uncertainty and information overload, which are largely overlooked by prior research. This study uses feelings-as-information theory to explain the impacts of AR interactions, which reveal the essential role of the experiential process in sensory-enabling technologies. This study enriches the existing theoretical frameworks that mostly focus on the cognitive process. The authors' findings about AR interactions provide noteworthy guidelines for the design of metaverse environments and extend the authors' understanding of how the metaverse may bring benefits beyond traditional online shopping settings.

The use of augmented reality (AR) and experiential learning go hand in hand. Although AR learning opportunities have been well discussed, there is limited empirical research on the use of AR within higher education settings. Drawing from the uses and gratifications theory (U&GT), this study aims to explore the use of AR for learning satisfaction and student engagement, while also examining differences in learning styles.

This study used experiments with higher education students in the UK to explore the use of AR as part of the learning experience. Data from 173 students who experienced AR as part of their learning experience were analysed using partial least square analysis.

The authors found that hedonic, utilitarian, sensual and modality gratifications influence AR learning satisfaction and student engagement. Furthermore, the authors found differences between active and passive learners with regards to utilitarian (information seeking, personalisation) and sensual gratifications (immersion, presence) and effects on learning satisfaction.

This study developed and validated a U&GT framework incorporating different learning styles rooted in Kolb’s learning cycle. Findings provide important implications for the use of commercial AR applications as part of the learning experience within higher education settings.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

As cross-cultural mergers and acquisitions (M&A) have learning effects on organisations, assessing their impacts on corporate performance is crucial. This study aims to explore the impact of inter-firm cultural differences on long-term post-M&A stock market performance.

The authors select domestic M&A transactions of Chinese listed companies during 2010–2021 as the sample. Then, the authors use the partial least squares structural equation model (PLS-SEM) to construct the latent variable of cultural differences in four dimensions to explore long-term stock market performance.

Cultural differences first positively and then negatively impact post-M&A performance. Three transmissions mechanisms are identified: investor sentiment, takeover premiums and information disclosure quality. Further analysis reveals that acquirer stock performance improves with higher analyst coverage and non-local shareholders but worsens if there are business affiliations between the acquirer and target firms.

This study can help optimise information disclosure systems in M&A transactions for regulatory authorities and aid investors’ understanding of post-M&A performance changes. Furthermore, it can improve acquirers’ understanding of the risks and opportunities in cross-cultural M&A, thereby facilitating the adaptation of management practices to the im-pacts of cultural differences.

By integrating the theories of resource dependence and transaction costs, this study examines the reversal effect of cultural differences between merging companies on post-M&A performance. The authors use a PLS-SEM to empirically analyse the main effects and reveal three transmission mechanisms.