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This study aims to examine the determinants of Foreign Corrupt Practices Act (FCPA) violations and consequences of FCPA enforcements.

This paper uses publicly available data from Compustat, I/B/E/S and Thomson Reuters databases, combined with Securities and Exchange Commission (SEC) and Department of Justice (DOJ) cases, to extract insights on FCPA violations and enforcements using econometric approaches.

The main determinants of FCPA violations appear to be firm size, multinational structure, country corruption and Sarbanes-Oxley Act control weaknesses. Traditional misreporting risks (F-score and M-score) do not predict FCPA violations. This study discovers significant differences between FCPA violations by motivation, as in, sale generation, rent extraction or cost evasion. Bribes motivated by sale generation or rent extraction are partially driven by the extent of the firm’s global operations, whereas bribes motivated by cost evasion relate to internal influences. This study also finds that enforcement is more salient for criminal violations (DOJ enforcement), compared to civil violations (SEC enforcement).

This research provides new insights into the determinants of FCPA violations while underscoring the need for effective measures to combat bribery and promote ethical business practices. This research contributes to the ongoing efforts to curtail bribery, offering valuable insights into the characteristics of firms more likely to engage in bribery and contexts in which these activities occur. It provides critical implications for regulatory bodies, highlighting the differential responses of firms to varying types of enforcement, namely, criminal versus civil, as the authors observe greater decreases in internal control weaknesses following DOJ enforcement compared to SEC enforcement.

For enforcement agencies, the findings underscore the importance of rigorous criminal enforcement against FCPA violations, highlighting the improved control environments prompted by DOJ actions. Managers will find this research relevant, as it demonstrates that a firm’s entry into international markets substantially elevates the risk of its representatives engaging in bribery with foreign officials. In addition, the results are of interest to regulators, revealing that the underlying motivations driving a firm’s activities can significantly alter the factors to consider that might lead to an FCPA violation.

This paper is the original work of the authors and explores the determinants and consequences of FCPA violations and enforcement actions since 2002. To the best of the authors’ knowledge, it is the first to explore bribe determinants by their motive and documents industry-wide benefits arising from criminal enforcement.

Online open innovation platforms provide opportunities for product users to participate in the innovation process and contribute their ideas to the platform. Nonetheless, they also present a significant challenge for platform managers, who select high-quality innovations from a massive collection of information with diverse quality.

In this study, the authors employed a machine learning method to automatically collect a real dataset of 2,276 innovations and 30,004 detailed comments from the online platform of IdeaExchange and then conducted empirical experiments to verify the study hypothesis.

Results show that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness and openness to experience and the quality of an innovation.

Results showed that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness, openness to experience and the quality of innovations.

This study combined the Big Five personality traits theory and social network theory to examine the association between user intrinsic personality traits, social network position and the quality of their innovative ideas in the context of online innovation platforms. Additionally, the findings provide new insights for platform managers on how to select high-quality innovation information by considering user personality traits and their social network position.

The authors examine the effect of climate risk on accounting conservatism for a sample of listed companies operating in 26 developing countries.

The authors employ the Climate Risk Index (CRI) developed by Germanwatch to capture the severity of losses due to extreme weather events at the country level. The authors use different approaches to measure firm-level accounting conservatism.

The authors find that greater climate risk leads to a lower level of accounting conservatism. The results hold even after using different estimation methods.

Although the authors' analysis is limited to the period 2007–2016, it could be helpful for standard setters such as International Accounting Standards Board (IASB) and International Sustainable Standards Board (ISSB) as they may consider the potential effect of climate risk in their international standards.

The negative impacts of climate risk on the quality of financial reporting as proxied by accounting conservatism could trigger regulators and standard setters to require disclosure of information relating to climate risks and to incorporate climate-related risks in their risk management systems. In addition, for policymakers, incorporating accounting conservatism as a financial quality reporting standard could help promote greater transparency, accuracy and reliability in financial reporting in the context of climate risk.

The authors add to the literature on international differences in accounting conservatism by showing that climate risk significantly affects unconditional and conditional conservatism. The authors' results provide fresh evidence of the dark side of climate change. That is, climate risk is shown to decrease financial reporting quality.

The purpose of this research is to conduct a theoretical prediction study exploring the effectiveness of different content marketing strategies in expanding the second-hand market for fashion brands, comparing the costs and risks involved in these strategies in practice.

First, the expert interview method is employed to extract the content marketing strategies of the fashion second-hand market. Then, a descriptive space that is able to identify various fashion brand images is established. Then, experts' perceptions of the relationships between content marketing strategies and fashion brand image dimensions are obtained through a subjective evaluation procedure. Data of semantic evaluation were quantified and analyzed using the fuzzy logic method.

When fashion brands expand to the second-hand market, they not only need to focus on improving the individual differentiation of products but also give priority to the quality of products and services and the overall customer experience. Exploring the “social impact strategy” will become an important direction for the development of fashion brands in the future.

The research methodology employed herein exhibits a noteworthy degree of novelty. This study introduces a pioneering theoretical prediction approach utilizing fuzzy logic, marking the inaugural exploration of this emerging and captivating dimension within the context of the study. Simultaneously, the study provides comparative results among content marketing strategies for expanding the fashion second-hand market, offering guidance for market expansion.

This study aimed to design a user-participatory methodology to investigate the post-occupancy sustainability of reused historical buildings and to apply it to a case study.

This study was designed in four stages. In the first stage, the sustainability parameters and sub-parameters were determined in the reused historical buildings based on the literature. The second stage included a field study in which the current situation of the study area was analysed, and the users were reached using the survey technique. In the third stage, the data obtained from the user participation were analysed with importance performance analysis (IPA) and an IPA matrix was created. The fourth stage included an evaluation of the results of the analysis and the development of recommendations.

IPA is a supportive method for ensuring the sustainable use of historic buildings. According to the data obtained from the IPA, it was seen that the functional sustainability of the building was achieved to a great extent. At the same time, there were deficiencies in technical and environmental sustainability. In terms of aesthetic sustainability, it was observed that the importance and performance values given by the users were generally consistent with each other.

The originality of this study is that the performance of the reused historical buildings in the process of use was monitored with appropriate parameters, and a user-participated method was proposed that allows improvement suggestions to be developed in line with the results obtained.