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This study examines whether US effective tax rates on foreign income of US multinationals (MNCs) vary according to the favorability of US macroeconomic conditions relative to those of non-US countries. We use the pre-Tax Cuts and Jobs Act of 2017 regime as our setting and present evidence that US effective tax rates on foreign earnings are higher (lower) in periods when macroeconomic conditions in the US are favorable (unfavorable) relative to those elsewhere in the world. These results imply that firms seek to maximize after-tax returns when making asset allocation decisions, even when faced with US repatriation tax costs. We provide further evidence indicating that our primary results vary predictably according to certain firm characteristics, namely the ability to acquire funds for investment through less expensive means than repatriation of foreign profits, high intangible asset intensity, and tax aggressiveness. Finally, we show that economic uncertainty in the US counters the positive effects of favorable US macroeconomic conditions on US effective tax rates on foreign earnings. Our findings have implications for the policy debate around the US taxation of foreign earnings and provide a (partial) explanation for the observed lower-than-expected levels of repatriation activity following the implementation of the Tax Cuts and Jobs Act of 2017.

This study examines potential ways to break the inequality reproduction circle faced by ethnic minority health workers and sustained by key performance indicators (KPIs)-centred management in the National Health Service (NHS) in England. It does so through the lens of signalling theory.

Three years panel data for 2018–2020 covering 207 hospitals was compiled from the annual NHS staff survey and matched with relevant administrative records. Structural equation modelling was used to test the proposed hypotheses at the organisational level.

The moderated mediating model reveals that persistent racial discrimination by managers and coworkers can disadvantage the career progression of ethnic minority health workers, which in turn reinforces and reproduces economic and health inequalities among them. More importantly, we show how the collective agreement that the senior management team acts (SMTA) on staff feedback can break this vicious circle.

While our research focuses on the not-for-profit health care sector, it opens important opportunities to extend the proposed model to understand organisational inequality and how to address it.

Perceived SMTA can send strong signals to reduce deep-rooted discrimination (race, gender, age, etc.) through resource allocations and instrumental functions. This is also a way to address the current staff burnout and shortage issues in the healthcare sector.

This article reveals why the purpose of organisations that provide public service to reduce social inequality was comprised during their business-like operations and more importantly, how to reflect their foundational purpose through management practice.

This study offers a way forward to resolve one of the unintended consequences of KPI-centred management in the not-for-profit sector through unpacking the process of inequality reproduction and, more importantly, how it is possible to break this vicious circle.

Given the importance of interdisciplinary research in addressing wicked problems, this paper aims to explore the development of and prospects for interdisciplinary research through evidence gained from academic accountants in Australia.

Extant literature is complemented with interviews of accounting academics in Australia to reveal the challenges and opportunities facing interdisciplinary researchers and reimagine prospects for the future.

Evidence indicates that accounting academics hold diverse views toward interdisciplinarity. There is also confusion between multidisciplinarity and interdisciplinarity in the journals in which academic accountants publish. Further, there is mixed messaging among Deans, disciplinary leaders and emerging scholars about the importance of interdisciplinary research to, on the one hand, publish track records and, on the other, secure grants from government and industry. Finally, there are differing perceptions about the disciplines to be encouraged or accepted in the cross-fertilisation of ideas.

This paper is novel in gathering first-hand data about the opportunities, challenges and tensions accounting academics face in collaborating with others in interdisciplinary research. It confirms a discouraging pressure for emerging scholars between the academic research outputs required to publish in journals, prepare reports for industry and secure research funding, with little guidance for how these tensions might be managed.

The purpose of the article is to contribute to the body of research on digital transformation among members of the supply chain operating in an emerging economy. This paper researches the digital transformation trends happening across Vietnamese supply chains, by investigating the current adoption rates, predicted impact levels and financial investments being made in key Industry 4.0 technologies.

By using a semi-structured online survey, the experiences of 281 supply chain professionals in Vietnam were captured. Subsequently, statistical techniques examining variances in means, regression analysis and Monte Carlo simulation were applied.

The findings of this study offer a comprehensive understanding of Industry 4.0 technology in Vietnam, highlighting the prevalent technologies being prioritized. Big data analytics and the Internet of things are expected to have the most substantial impact on businesses over the next 5–10 years and have received the most financial investment. Conversely, Blockchain is perceived as having less potential for future investment. The study further identifies several technological synergies, such as combining advanced robotics, artificial intelligence and the Internet of things to build effective and flexible factories, that can lead to more comprehensive solutions. It also extends diffusion of innovation theory, encompassing investment and impact considerations.

This study offers valuable insights into the impact and financial investment in Industry 4.0 technologies by Vietnamese supply chain firms. It provides a theoretical contribution via an extension of the diffusion of innovation theory and contributes toward a better understanding of the current Industry 4.0 landscape in developing economies. The findings have significant implications for future managerial decision-making, on the impact, viability and resourcing needs when undertaking digital transformation.

The aim of this paper, which analyses the use of sports celebrities in advertising discourse, is to understand the strategic use to which brands put them in their commercial and corporate communication on Instagram.

To this end, a content analysis was performed on the Instagram posts of the brands Santander, Movistar, Red Bull and Iberdrola during the period 2021-2022.

The results indicate that, strategically speaking, these brands use the celebrity endorsement strategy to pursue emotional objectives and to adopt a position depending on the type of user. Likewise, these findings show that they single out uniqueness as the principal celebrity characteristic, while also mainly leveraging sports values, especially competence. These values represented by sports celebrities are markedly social in nature, which implies that they enjoy a degree of public recognition that is transferred to the brand to which they lend their image.

The conclusions connect celebrity endorsers with strategic branding issues and aspects of sports.

An empirical approach is followed here to study the representation of sports celebrities in the advertising of well-known brands linked to the sports world.

Artificial Intelligence (AI)-powered technologies are revolutionising the landscape of education, ushering in a myriad of possibilities and challenges. This article delves into the dual nature of AI-driven tools in education, spotlighting pivotal advancements like automated grading, personalised learning algorithms, online monitoring, content filtering, AI-based learning tools, Chat Generative Pre-trained Transformer (ChatGPT) and standardised testing (ST) platforms. Ultimately, the examination reveals a spectrum of advantages, risks and considerations associated with AI-driven educational applications. Employing the PRISMA protocol, this study systematically reviews peer-reviewed literature concerning the implementation and ethical implications of AI in higher education. The analysis incorporates 36 scholarly articles, uncovering the entrepreneurial advantages of AI, such as tailored learning experiences, self-assessment opportunities, heightened efficiency, skill enhancement and reduced educational disparities. Concurrently, the research identifies potential hazards, including user profiling, plagiarism, academic integrity breaches and excessive reliance on technology that may hinder creative learning dynamics. Crucial concerns emerge, encompassing the possible devaluation of educators' roles, privacy issues inherent in personalised learning platforms and the intrusive nature of online surveillance. Additionally, the study highlights biases embedded within AI algorithms and apprehensions regarding job displacement within the academic community. To steer AI integration responsibly within higher education, the investigation explores ethical frameworks and models, offering pragmatic suggestions for institutions. Recommendations advocate for a balanced approach, emphasising judicious AI utilisation and the formulation of institutional policies. This chapter's distinctiveness lies in its innovative stance, striving to reconcile the technical and entrepreneurial benefits of AI applications with the preservation of creativity in higher education contexts.

This study aims to understand the influence of digital transformation, using Industry 4.0 technology, on food and beverage companies in the post-COVID-19 period.

To achieve the aim of this study, the authors have used innovation diffusion theory (IDT), dynamic capability view (DCV) theory and relevant literature, to develop a conceptual model. For the data, we surveyed 14, different sized food and beverage companies in India and the authors took inputs from 312 respondents at those companies to validate the conceptual model using the PLS-SEM approach.

The results from this study suggest that there is a considerable appetite for food and beverage companies to use Industry 4.0 technologies, as a part of their digital transformation journey in the post-COVID-19 scenario.

Food and beverage companies are going through a digital transformation process, which has been accelerated after the COVID-19 pandemic. Managers and leaders of the food and beverage companies need to support activities to adopt Industry 4.0 technologies. Moreover, the leaders should sponsor various readiness and training programs so that their employees successfully adopt these modern technologies.

This is a primary study on food and beverage companies that are using Industry 4.0 technologies or are in the process of digital transformation. The usage of both IDT and DCV to develop the unique conceptual model is an important contribution to the body of knowledge. Moreover, the proposed model has a high explanatory power, which makes the model effective.

The crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play.

We conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling.

Our study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment.

Our research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants.

• (1)

  Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

• (2)

  Verifies the relations among overconfidence, risk propensity, and demographics.

• (3)

  Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

• (4)

  Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

Verifies the relations among overconfidence, risk propensity, and demographics.

Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

This study aims to explore the impact of athlete brand image on fans’ social media engagement, purchase intentions, and also examines the mediating role of emotional attachment on these relationships, as well as the moderating role of perceived price value between emotional attachment and purchase intentions.

The data are based on an online survey conducted in China (N = 572). The PLS-SEM (partial least squares structural equation model) and regression-based estimation method (PROCESS) are employed to test the hypotheses.

The results indicate a positive relationship between athletes’ athletic performance and fans’ social media engagement as well as purchase intentions. The impact of athletes’ attractive appearance and marketable lifestyle on fans’ purchase intentions is sequentially mediated by emotional attachment and social media engagement. Moreover, the mediation effect of athletes’ off-field image and purchase intentions is contingent upon fans’ perceived price value.

Athletes and marketers could integrate and leverage both the on-field and off-field attributes to cultivate emotional connections with fans. Sports organizations and managers need to pay attention to fans’ social media engagement and provide content that increases engagement and converts into transactional behavioural intentions.

The study provides empirical evidence of the mediating role of emotional attachment between athlete brand image and fans’ purchase intentions. The explanatory mechanisms involving emotional attachment and social media engagement (non-transactional behavioural intentions) are anticipated to be a noteworthy addition to the traditional fan transactional behavioural intentions framework. Moreover, the research introduces and confirms perceived price value as a crucial moderating factor influencing the relationship between emotional attachment and purchase intentions.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.