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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.

The aim of this work is to enhance workplace diversity and inclusion by exploring and addressing unique barriers faced by veterans during their transition from military service to civilian occupations.

Building on existing expatriate theory, we introduce the Veteran Employment Transition (VET) model. Drawing parallels between veterans and expatriates, the model illustrates key antecedents crucial for a successful transitional adjustment.

The proposed VET model outlines essential factors contributing to successful veteran transitions. These factors include individual factors such as language skills, job and organization factors such as role clarity and nonwork factors.

The VET model establishes a foundation for future research on veteran transition and answers the call for theory development in the field.

The insights derived from the VET model offer practical recommendations for designing interventions and transition support programs tailored to the unique needs of returning veterans.

The contribution of this paper lies in the development of the VET model, offering a novel perspective for understanding and addressing the distinctive challenges faced by returning United States (US) military veterans.

This paper considers how knowledge has been organized about museum objects and belongings at the Museum of Anthropology, in what is now known as British Columbia, and proposes the concept of historical or provenance warrant to understand how cataloguing decisions were made and are limited by current museum systems.

Through interviews and archival research, we trace how cataloguing was done at the museum through time and some of the challenges imposed by historical documentation systems.

Reading from the first attempts at standardizing object nomenclatures in the journals of private collectors to the contemporary practices associated with object documentation in the digital age, we posit that historic or provenance warrant is crafted through donor attribution or association, object naming, the concept of geo-cultural location and the imposition of unique identifiers, numbers and direct labels that physically mark belongings.

The ultimate goal and contribution of this research is to understand and describe the systems that structure and organize knowledge, in an effort to repair the history and terminologies moving forward.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

This study explores the relationship between firm value and conforming tax avoidance (tax avoidance that does not create a book-tax difference). Tax avoidance provides firms with more cash and creates value. However, conforming tax avoidance has costs, such as lower book income, and these costs potentially lower firm value. As such, it is unclear whether conforming tax avoidance is positively or negatively correlated with firm value. We use a measure of conforming tax avoidance that was recently introduced in the literature, and bifurcate tax avoidance into conforming and nonconforming portions using a large sample. We present evidence that investors place a negative value on conforming tax avoidance for the average firm. We also examine the top quartile based on the measure of conforming tax avoidance and find a positive correlation between firm value and conforming tax avoidance for this subsample.

We conducted this study to examine the relationship between revenue concentration and a firm's financial leverage. We aimed to analyze whether revenue concentration influences a firm's capital structure decisions and whether this relationship is driven by customer-specific investments or the direct effect of revenue concentration itself. Additionally, we investigated the role of asset redeployability in mediating or moderating the relationship between revenue concentration and financial leverage.

The paper investigates the relationship between revenue concentration and a firm's financial leverage. The results indicate a negative association between revenue concentration and financial leverage. This finding holds across various regression models and is statistically significant. Furthermore, the paper explores the potential role of asset redeployability in explaining the relationship between revenue concentration and financial leverage. The results indicate that even after controlling for asset redeployability, the negative relationship between revenue concentration and leverage remains significant, suggesting that revenue concentration affects capital structure decisions independently of the risks associated with relationship-specific investments. Robustness tests are conducted using a three-stage least squares approach to account for the simultaneity between revenue concentration, asset redeployability and capital structure.

Our findings demonstrate that revenue concentration is negatively associated with financial leverage, even after accounting for asset redeployability. This suggests that revenue concentration affects capital structure decisions independently of the risks associated with customer-specific investments. Furthermore, we performed robustness tests to address potential simultaneity issues between revenue concentration, asset redeployability and capital structure.

The study relies on available data sources, which may have inherent limitations in terms of accuracy, completeness or consistency. The quality of the data used in the analysis could impact the robustness of the findings. Time Period: The study focuses on more recent years, which might limit the ability to compare the findings with studies conducted over different time periods. Historical trends or structural changes that could impact the relationship between revenue concentration and financial leverage might not be fully captured.

Firms with higher revenue concentration tend to have lower financial leverage. Recent years show a negative relationship between profitability and market leverage compared to earlier periods. Revenue concentration has a distinct effect on financial leverage, not fully explained by risks from relationship-specific investments or asset redeployability. Insights for firms in managing capital structure decisions, considering revenue concentration and its implications for leverage.

This research is one of the first papers that investigates the impact of revenue concentration on the capital structure choices of firms. By exploring the relationship between revenue concentration and financial leverage, the study contributes to the existing literature by shedding light on an underexplored area. Thus, this study adds originality to the field by addressing a research gap and contributing to the understanding of the relationship between revenue concentration and capital structure choices.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

Supply chains are increasingly incorporating social perspectives into their activities. It is often argued that social initiatives in supply chain management (SCM) are a response to external pressures. However, it is still undetermined whether these initiatives can improve economic performance. Additionally, it is proposed that digital transformation in supply chains, also described as Smart Supply Chain, can support social performance. Therefore, this study aims to analyze the association between digital transformation, social performance in SCM and economic performance.

The study is based on a survey of 473 companies in Brazil, a country where social needs create high pressures on companies to respond to the country’s challenges. The data collected underwent testing using ordinary least squares regression and bootstrapping techniques to examine the mediation effects between Smart Supply Chain, social performance and economic performance.

The findings indicate that digital transformation supports social performance. Additionally, adopting social initiatives helps to increase firm performance in the context of an emerging economy, while social initiatives mediate between digital transformation and firm performance.

This paper provides a new perspective on the social side of supply chains by demonstrating the close relationship between digital transformation and social engagement initiatives. It argues that aligning digital transformation strategies and technologies with social performance is crucial for companies to establish stronger connections with stakeholders.

The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.

The link between theory and practice in innovation studies still has some gaps, despite scholars’ efforts to illustrate and identify them in real-world situations. To fully understand the dynamics of the innovation ecosystem, it is crucial to consider key actors and their roles and recognize their impact on ecosystem outcomes. Therefore, this paper seeks to discuss how analytical structures of innovation ecosystems address the dynamics of actors and their contribution to the ecosystem outcomes.

A research protocol was developed to query the Web of Science database to identify analytical structures of innovation ecosystems based on pre-established criteria.

The dynamics of actors interfere with their contribution to value creation. That is, the actor changes his contribution. Therefore, depending on the ecosystem's value proposition, the activities and dynamics of the actors change over time.

It contributes to advancing the discussion of innovation ecosystems, addressing insights into the dynamics of actors in different analytical structures. The essay proposal considers innovation ecosystems' evolutionary aspects, value propositions and exchange. In addition, the importance of orchestration in the various stages of the ecosystem is highlighted.