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This study attempts to determine whether the level and volatility of interest rates affect the equity returns of commercial banks. Short‐term, intermediate‐term, and long‐term interest rates are used. Volatility is defined as the conditional variance of respective interest rates and is generated by using the ARCH estimation procedure. Two sets of models are estimated. The basic models attempt to determine the effect of contemporaneous and lagged interest rate volatility on bank equity returns, while the extended models incorporate additional contemporaneous macroeconomic variables. Contemporaneous interest rate volatility has little explanatory power, while lagged volatilities do possess some explanatory power, with the lag length varying depending on the interest rate series used and the time period examined. The results from the extended model suggest that the long‐term interest rate affects bank equity returns more adversely than the short‐term or the intermediate‐term interest rates. The findings establish the relevance of incorporating macroeconomic variables and their volatilities in models determining bank equity returns.

The purpose of this paper is to compare and contrast the structure, regulation, and performance of banks in the EU and G‐10 countries. This enables one to identify any significant differences in the structure of banking in the nineteen separate countries comprising these two groups. The regulatory, supervisory, and deposit‐insurance environment in which banks operate in each of these countries is also compared and contrasted. This enables one to identify any significant differences in the regulatory environment that may help explain the structure of banking in the various countries. Beyond this, the effect of the overall structural and regulatory environment on individual bank performance is investigated in order to evaluate the appropriateness of existing regulations in individual countries and any proposals for reforming them. Hence, an exploratory empirical analysis based upon a sample of banks in the different countries is conducted to assess the effect of the different “regulatory regimes” on the performance of individual banks, controlling for various bank‐specific and country‐specific factors that may also affect bank performance. In this way, the paper attempts to contribute to an assessment of the appropriate balance between market and regulatory discipline to ensure that banks have sufficient opportunities to compete prudently and profitability in a competitive and global financial marketplace. In the process of conducting such an assessment, the paper necessarily provides information as to whether the U.S. is “out‐of‐step” with banking developments in other industrial countries.

When librarians think of accounting, they generally think of budgets. However, accounting includes planning and control activities broader than creating budgets. Managerial accounting concentrates on internal decision‐making processes. In many libraries, external budgetary reports have mandated techniques that the library is expected to use. Internal reports and operational budgets are designed by the librarian. The question is: How can internal reporting and operational budgets be used advantageously in the library? Using managerial accounting is one answer.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).