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Existing literature in experimental accounting research suggests that accounting professionals and people with accounting backgrounds tend to have a lower level of moral reasoning and ethical development. Motivated by these findings, this paper aims to examine whether chief executive officers (CEOs) with accounting backgrounds have an impact on firms’ earnings management behavior and the level of accounting conservatism.

The authors classify CEOs into those with and without accounting backgrounds using BoardEx data. Using discretionary accruals from several different models, they do not find that CEOs with accounting backgrounds are more likely to engage in income-increasing accruals. However, the authors find that CEOs with accounting backgrounds exhibit lower levels of conservatism, proxied by C-scores and T-scores (Basu, 1997). This finding suggests that CEOs with accounting backgrounds recognize bad news more quickly than good news, consistent with the accounting principle of “anticipating all losses but anticipating no gains”.

The authors show that firms whose CEOs have accounting backgrounds exhibit lower levels of accounting conservatism. However, these firms do not exhibit higher levels of income-increasing discretionary accruals. This study documents the impact of CEOs’ educational backgrounds on firms’ accounting choices and confirms prior findings in experimental accounting research using large sample archival data.

This paper is the first study that investigates the impact of CEOs’ accounting backgrounds on firms’ financial reporting policy. The findings may have some policy implications. If accounting backgrounds of CEOs can make a significant difference on firms’ behavior, it is reasonable to make CEOs accountable for the quality of financial reporting. This paper is one of the first to empirically test inferences drawn by experimental accounting research. There has been a gap between archival and experimental accounting studies. The authors propose that interesting research questions can be addressed by filling in such a gap.

This study is dedicated to systematically collating the distribution and utilization circumstances of geothermal resources in China. Moreover, it endeavors to formulate a comprehensive utilization scheme for geothermal resources during the construction and operation phases of the railway, thereby furnishing robust support and valuable reference for the holistic utilization of geothermal resources along the railway corridor.

Through an in-depth analysis of the extant utilization of geothermal resources in China, it is discerned that the current utilization modalities are relatively rudimentary, bereft of rational planning and characterized by a low utilization rate. Concurrently, by integrating the practical requisites of railway construction and operation and conducting theoretical dissections, a comprehensive utilization plan for the construction and operation periods of railway is proffered.

In light of the railway’s construction and operation characteristics, geothermal utilization models are categorized. During construction, comprehensive modalities include tunnel illumination power generation, construction area heating, tunnel antifreeze using shallow geothermal energy, tunnel pavement antifreeze and construction concrete maintenance. During operation, they comprise operation tunnel antifreeze, railway roadbed antifreeze, railway switch snow melting and deicing, geothermal power station establishment and railway hot spring health tourism planning.

According to the characteristics and actual needs of railway construction and operation, it is of great significance to rationally utilize geothermal resources to promote the construction and operation of green railways.

This research examines stock traders' disposition effects and contrarian/momentum behavior in the Taiwan Stock Exchange (TWSE). Specifically, we first investigate disposition effects across all trader types and then examine the relationships between disposition effects, trader types, and order characteristics. Next, we explore contrarian and/or momentum behavior and analyze the relationships among the contrarian/momentum behavior, investor type, and order characteristics. Finally, the links among trader types, order characteristics, and investment performance are detected. This chapter yields the following findings. (1) Individual investors exhibit the strongest disposition effects compared to other investors. (2) Foreign investors, investment trusts, and individual investors tend to use large orders to sell loser stocks. (3) Investment trusts are inclined to be momentum traders, while individual investors tend to perform contrarian strategies. (4) Institutional aggressive and large orders perform better than individuals' orders. (5) The performance of foreign investors' selling decisions is better than that of retail investors.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.