Search results

Based on the stressor-detachment theory, this study aims to investigate the effect of daily customer mistreatment on proactive service performance and ego depletion, mediated by psychological detachment inhibition during the evening. Additionally, this study endeavors to investigate the dual moderating role of prosocial motivation.

A time-lagged, diary daily survey involving 74 participants over 8 consecutive workdays was conducted to test the hypotheses.

The findings indicate that the psychological detachment inhibition during the evening of Day t mediates the impact of Day t’s customer mistreatment on Day t + 1’s proactive service performance and ego depletion. Furthermore, although prosocial motivation was found to intensify the impact of customer mistreatment on psychological detachment inhibition, it alleviated the negative association between psychological detachment inhibition and proactive service performance.

When employees experience customer mistreatment, hospitality managers should not only provide emotional reassurance and resolve any related issues promptly but also encourage employees to engage in activities that distract them and help them to relax and recharge, especially for those who exhibit high prosocial motivation. Moreover, hiring employees with high prosocial motivation is recommended for hospitality organizations to enable them to maintain high service performance.

This study focuses on psychological detachment inhibition during the evening linking within-person design and daily spill-over impact, enriching the mechanisms through which the repercussions of daily customer mistreatment extend beyond the immediate workday and affect individuals’ outcomes. This study also expands upon the existing literature by clarifying the dual aspects – both detrimental and beneficial – of prosocial motivation.

Although the impact of environmental, social and governance (ESG) on firms' innovation has attracted attention, the existing research findings diverge. The authors believe that failure to consider both innovation input and output is an important reason for the divergence of conclusions in the extant literature when discussing the impact of ESG and firm innovation. Thus, based on signaling theory, this study aims to reconcile these divergent findings by examining the impact of ESG performance on firms' innovation efficiency.

To seek empirical evidence to support the authors’ theoretical view, the authors conduct an empirical test based on the Tobit model using 8 years of data from Chinese listed companies.

Although ESG performance effectively improves firms' innovation efficiency, the institutional-level signaling environment (including state-owned firms and regional market development) weakens the positive effect of ESG performance on firms' innovation efficiency. Further tests suggest that financing constraints partially mediate the relationship between ESG performance and firms' innovation efficiency.

By systematically revealing whether, how and under what circumstances ESG performance improves firms' innovation advantages, this study bridges the gap in the existing literature and highlights important implications to suggest how firms can better capture the value associated with ESG.

Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.

The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.

Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.

The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.