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Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

We investigate how firms’ responses to misconduct change when the institutional environment becomes more stringent. Organizational theory offers conflicting perspectives on whether new legislation will increase or decrease pressure on firms to take remedial action following misconduct. The dominant perspective posits that new legislation increases expectations of firm behavior, amplifying pressure on them to take remedial action after misconduct. A more recent perspective, however, suggests that the mere necessity to meet more stringent regulatory requirements certifies firms as legitimate to relevant audiences. This certification effect buffers firms, reducing the pressure for them to take remedial action after misconduct. Using a temporary, largely arbitrary exemption from a key provision of the Sarbanes-Oxley Act, we show that firms that were not required to meet all the regulatory standards of good governance it required became 45% more likely to replace their CEOs following the announcement of an earnings restatement after Sarbanes-Oxley. On the other hand, those that were required to meet all of Sarbanes-Oxley’s provisions became 26% less likely to replace their CEOs following a restatement announcement. Ironically, CEOs at firms with a legislative mandate intended to increase accountability for corporate misconduct shoulder less blame than do CEOs at firms without such legislative demands.

This chapter provides preliminary evidence to show that financial accounting students would use generative artificial intelligence (AI) tools to improve their learning if given the opportunity to do so by their instructors. Most students who completed the exercises we used in the study did so diligently and modified their answers after using a generative AI tool in a manner that suggests beneficial effects. It appears that the more prior knowledge a student had about the subject matter, the more beneficial was the experience. Pitfalls still exist, however. For example, students without knowledge of the subject matter struggled with crafting queries and judging the efficacy of their answers. Moreover, although a minority, some students tended to duplicate their original answers without utilizing the responses generated by the generative AI tool. Additionally, certain students merely copied the answers generated by the AI tool without providing any additional critique or analysis. Implications for teaching and learning and opportunities for future research are discussed.

Purpose: This study examines the effect of uncertainties on the hospitality industry from different perspectives across the globe. The hospitality industry faces several contemporary issues and challenges that have the potential to impact its growth and development. This study aims to analyse the current problems and uncertainties in the hospitality sector.

Need for the Study: The hospitality industry plays a significant role in the global economy with various services, including accommodation, food and beverage, events, and tourism. However, the sector faces several contemporary issues and challenges that have the potential to impact its growth and development. This study provides an overview of the most significant problems and challenges facing the hospitality industry today.

Methodology: A systematic literature review was conducted to identify and synthesise relevant studies on the effect of uncertainties issues on the hospitality industry. A systematic search of the Web of Science and Scopus databases was conducted to determine relevant studies published between 2010 and 2021. Studies were screened and selected based on pre-defined inclusion and exclusion criteria. A thematic analysis was performed to categorise the uncertainties and issues in the hospitality industry.

Findings: The study identified several uncertainties and issues facing the hospitality industry, including the pandemic uncertainties, financial crisis, whether positive and negative impacts, terrorism attacks on hotels and tourist places, uncertainties in government policies, situational risks like uncertainties, ambiguity, cultural differences, changes in tourist preferences and changing habits of the tourist.

Corporate social responsibility (CSR) promotes society, reduces risk, and encourages ethical business practices. Due to its relevance, we study how CSR influences firms' sustainable development. We analyze data from 427 New York Stock Exchange (NYSE)-listed firms from 2008 to 2022. The Refinitiv environmental and social score is used to measure CSR, whereas for firms' sustainable development we rely on corporate sustainable growth rate (SGR) and market-based metrics. The analysis employs various econometric techniques, including ordinary least square, fixed effect regression, two-stage least square, generalized method of moment, and simultaneous quantile regression. The results indicate that CSR has a positive and significant effect on firms' sustainable development across all models. This relationship supports the notion that socially responsible business can contribute to long-term financial sustainability in line with “stakeholder theory”, indicating that companies should accommodate the concerns of various stakeholders, including society and the environment, to achieve sustainable development. We evaluate how the conditional distributions of SGR and firms’ value are affected by CSR, categorizing them into high, moderate, and low regimes. The quantile regression estimates indicate that the effect of CSR is more pronounced at upper quantiles, followed by moderate and low regimes. These findings underscore the importance of considering CSR in assessing the SGR and enterprises market value. We also confirm that our results are robust under range of different econometrics' methods. Finally, we enlighten current literature, and our research has useful policy implications for management and investors.

When reporting wrongdoing internally, whistleblowers are confronted with the dilemma of weighing up their loyalty toward the organization (e.g., ethical standards) and their co-workers (e.g., the social norm of not snitching on peers). However, the role played by peers in the whistleblowing decision process and in the aftermath has rarely been addressed in existing reviews. We therefore perform a systematic review that identifies seven thematic clusters of peer factors, offering researchers an informative overview of (a) the peer factors that have been examined to influence the whistleblowing decision, and (b) the extent to which the whistleblower experiences adverse consequences from peers in the aftermath of whistleblowing. As peer factors seem to be important to explain and predict internal whistleblowing, researchers are encouraged to address in future works the research gaps our review unraveled.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This chapter aims to provide a comprehensive overview of virtual tourism and its potential contribution to sustainable development in the tourism industry.

In this chapter, a qualitative approach is used to analyse relevant documents and resources to explore the relationship between virtual tourism and sustainability.

The findings of this study indicate that virtual tourism has numerous applications in the tourism industry, with evident potential for the future. Furthermore, this research identifies virtual tourism as a promising alternative for sustainable tourism, offering the potential to address key sustainability issues in the field.

This chapter adds to the existing literature by examining the link between virtual tourism and sustainability, highlighting the potential of virtual tourism as an alternative to traditional sustainable tourism practices. The insights generated from this study can inform the practices of both academics and practitioners in the tourism industry, promoting more sustainable and responsible tourism practices.

Risk preferences play a critical role in almost every facet of economic activity. Experimental economists have sought to infer the risk preferences of subjects from choice behavior over lotteries. To help mitigate the influence of observable, and unobservable, heterogeneity in their samples, risk preferences have been estimated at the level of the individual subject. Recent work has detailed the lack of statistical power in descriptively classifying individual subjects as conforming to Expected Utility Theory (EUT) or Rank Dependent Utility (RDU). I discuss the normative consequences of this lack of power and provide some suggestions to improve the accuracy of normative inferences about individual-level choice behavior.

Purpose: This paper aims to study the impact of adoption eXtensible Business Reporting Language (XBRL) on earnings management.

Design/methodology/approach: This study uses a sample of all firms listed on the Indonesian stock exchange, except for finance and real-estate sectors from 2012 to 2019, with a total of 2,560 firms–years with panel data analysis.

Findings: Four findings in this study are listed as follow. First, the surprising result is that accrual earnings management increase after the adoption of XBRL. Second, after the adoption of XBRL, there was an increase in real earnings management. Third, the results of the study prove that the use of Big 4 auditors will weaken the increase in real earnings management after the adoption of XBRL. Finally, this study shows that after the adoption of XBRL, it turns out that both accrual and real earnings management experienced an increase.

Originality/value: This study contributes to providing an evaluation note to IDX regulators that the goals they want to achieve have not been achieved. This study provides empirical evidence for the debate over whether the adoption of XBRL is beneficial.