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Purpose The purpose of this paper is to examine the ethical investment willingness decision-making process to understand how investors evaluate corporate social responsibility (CSR) actions. Design/methodology/approach Data were collected through a survey of 298 individual investors and analyzed using structural equation modeling. Findings Results reveal that perfectionist decision-making style is positively related to perceived moral intensity, substitutability of financial returns, and ethical investment willingness. In addition, perceived moral intensity and substitutability of financial returns are positively related to ethical investment willingness. Finally, perceived moral intensity is positively related to substitutability of financial returns, and a two-factor causal mediation model is supported. Research limitations/implications The limitation of this study was that the pre-tests and sampling methods required all participants to have investing experience; however, procurement of trading information for each investor was impossible; thus, actual investment behaviors were undetermined. This study shed light on the mediating roles of perceived moral intensity and the substitutability of financial returns. Future studies can further investigate the factors influencing perceived moral intensity and the substitutability of financial returns. Practical implications Future ethical investment education can focus on cultivate the ability to distinguish ethical investments and change ethical investment willingness into actual investment behavior. Originality/value Understanding the relationship between these variables can help understand why ethical investment willingness varies among investors and how the traditional financial theory investment decision model should be revised as, internationally, more people have begun to observe CSR and sustainable development.

The environmental pollution caused by Advanced Semiconductor Engineering in October 2013 in Taiwan highlighted the fact that foreign investors tend to support the classical economic ideas of arbitrage and shareholder wealth maximization, which is in conflict with the fact that institutional investors in the current global capital market lean towards the stakeholder theory in ethical investments. Will local investors’ decision-making also be influenced by differences in the perceived ethics of negative environmental corporate social responsibility (ECSR)? Compared to the remedial measures implemented by British Petroleum for the 2010 Deepwater Horizon oil spill, Advanced Semiconductor Engineering, another international corporation, decided to not respond to any news regarding the toxic wastewater incident. In contrast, Advanced Semiconductor Engineering only made clearer promises after extreme public pressure. This study investigated whether remedial measures for negative ECSR are an important factor influencing investors’ decisions. The purpose is to clarify the interactions among perceived moral intensity of negative ECSR, the implementation of remedial measures, and the intention of ethical investment. An experimental design was employed to test the hypotheses. The results indicated that perceived moral intensity has a significant negative impact on the intention of ethical investment. The implementation of remedial measures for negative ECSR affects investors’ intent to invest. Finally, positive ECSR remedial measures also serve as a key moderating variable in the relationship between perceived moral intensity and the intention of ethical investment. This study clarified whether the provision of remedial mechanisms can effectively recover or maintain investor investment intent when companies experience negative ECSR.

Fraud cases have become more common in recent years, highlighting the role of auditors’ legal liability. The competent authorities have called for stricter control and disciplinary measures for auditors, increasing auditors’ legal liability and litigation risk. This study used machine learning (ML) techniques to construct a litigation warning model for auditors to assess audit risk when they evaluate whether accept or terminate an engagement, thus improving audit quality and preventing losses due to litigation. Otherwise, a sample matching method comprised of 64 litigated companies and 128 non-litigated companies was used in this study. First, feature selection technology was used to extract six important influencing factors among the many variables affecting auditors’ litigation risk. Then a decision tree was used to establish a litigation warning model and a decision table for auditors’ reference. The results indicated that the eight outcomes provided by the decision table could effectively distinguish the level of a litigation risk with an accuracy rate of 92.708%. These results can provide useful information to aid auditors in assessing engagement decisions.