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The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.

We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.

The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.

This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.

This study aims to better understand the effects of speeches as a valuable communication tool for central banks. It extends the analysis of the effects of public speeches on jumps to determine whether individual speakers matter partly because of their name, position or institution.

This study detects intraday jumps using a robust-to-jump volatility estimator that accounts for deterministic seasonality. As a result, this study removes spurious jumps that occur when volatility is high and consider the relatively small jumps that occur when volatility is low. After identifying jumps, this study examines their reactions to senior official speeches and macroeconomic news surrounding the US and European Union (EU) financial crises.

Despite having the most influential individual speakers, this study finds that the impact of the Federal Reserve (Fed) and European Central Bank (ECB) is mitigated because the two institutions have a relatively small impact on currency jumps. This finding shows that the speaker’s name is more important than his or her institution affiliation. While the Federal Reserve Bank President and Chief Executive, as well as ECB board members, significantly reduce jump sizes, particularly during the EU crisis period, both the Fed Chairman and the ECB President increase the magnitude of the jump in both the US crisis and noncrisis periods, contributing to market instability.

The implications of the results include international portfolio management, currency derivatives pricing and hedging, risk management and market efficiency.

The findings contribute to a better understanding of the effects of senior official speech attributes on currency jumps in various economic states. The results raise questions about the speaker’s name, institution and position’s effectiveness in calming markets and reducing uncertainty.