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This study examines how asking employees to self-assess their performance during the compensation setting process, when they are unaware of their marginal contribution to firm profit, affects employer welfare. Previous research suggests that giving employees a voice in the compensation setting process can positively affect employee performance and firm profit (Jenkins & Lawler, 1981; Roberts, 2003). However, the study proposes that asking employees to assess their own performance as part of the compensation setting process can have unintended consequences that ultimately lead to higher employee compensation demands. This is because asking employees to assess their performance increases their overconfidence in their own performance and their compensation demands. As a result, employers may face the dilemma of whether to meet these higher compensation demands or risk economic losses due to employee retaliation if their demands are not met. Through experimental evidence comparing a control condition without self-assessments and three self-assessment reporting conditions, the study provides evidence that supports the notion that eliciting employee self-assessments as part of the compensation process reduces employer welfare. Data on employee perceptions of performance further support the notion that asking employees to evaluate their performance leads to an inflated perception of their performance. These findings provide a theory-based explanation of why, in practice, many companies disentangle employee performance assessments from the compensation setting process and that companies are well advised in doing so.

This paper studies how employee effort depends upon the manager’s level of narcissism and the framing of the manager’s incentive scheme. In an online experiment with 356 employee participants, the authors manipulate the description of the manager narcissism (high or low) and the framing of the manager’s compensation scheme (bonus or penalty) and examine the joint effect of these two factors on employee effort to help the manager reach their objectives. Results show that employees exert less (more) effort when manager narcissism is high (low). This effect is mediated by employees’ feelings of envy toward the manager. In line with recent research on the cascading effect of management compensation, the authors also find that a manager’s penalty contract has a negative effect on employee effort when manager narcissism is high. The results have important implications for compensation design in business practice.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

Management discretion in the decision to reduce payroll costs is an important but under-researched issue in management accounting. The authors leverage the experimental environment to test the role of organizational culture (close vs. distant) and managerial communion (concern for others) along with their interaction with sales decline persistence (one vs. two periods) on planned layoff decisions. The authors find that communal managers are hesitant to downsize employees and that a close organizational culture interacts with one period sales declines to reduce layoffs although the influence of culture is reduced with persistent sales declines. The authors also examine the influence of culture and communion on managers’ preference for pay cuts as an alternative to layoffs. The authors find that a close culture and higher communion are associated with decisions to choose pay cuts over layoffs; however, these costs interact such that managers low in communion in a distant culture express a higher preference for layoffs. These findings illustrate the combined influence of economic, organizational, and dispositional factors on manager decisions about the extent and form of labor cost reductions due to sales declines.

This study aims to investigate the integration of blockchain technology into the food supply chain within the restaurant industry. It focuses on how blockchain can be applied to enhance transparency and trust in tracking food sources, ultimately impacting customer satisfaction.

A service design workshop (Study 1) and three between-subjects experiments (Studies 2–4) were conducted.

Results indicate that blockchain adoption significantly improves traceability and trust in the food supply chain. This improvement in turn enhances customer satisfaction through perceived improvements in food safety, quality and naturalness. This study also notes that the effects of blockchain technology vary depending on the type of restaurant (casual or fine dining) and its location (tourist destinations or residential areas).

The findings offer practical insights for restaurant owners, technology developers and policymakers. Emphasizing the benefits of blockchain adoption, this study guides decision-making regarding technology investments for enhancing customer service and satisfaction in the hospitality sector.

This research contributes novel insights to the field of technology innovation in the hospitality industry. It extends the understanding of signaling theory by exploring how blockchain technology can serve as a tool for signal transmission in restaurant food supply chains.

The purpose of this study is to investigate whether the lockdown and the increased spread of food delivery applications (FD Apps) during COVID-19 pandemic have augmented the consumption of fast foods.

A cross-sectional study was conducted on 673 adults from different regions of Saudi Arabia using an online questionnaire.

Data showed that 61% (N = 410) of participants used FD Apps during the pandemic. Among those users, 54.9% (225) were females and 70.5% were in the 18–44 years old group. Most FD Apps’ users were university graduates (74.4%). The increased use of FD Apps during the pandemic significantly affected the eating behavior and the nutritional pattern. It has further significantly augmented the consumption of fast foods (p-value < 0.05).

This study reports on the use of FD Apps during COVID-19 pandemic in Saudi Arabia and its impact on consumer eating pattern. This study shows the need for prudent use of these applications to limit ordering fast foods and consider healthier choices. It further calls for education programs, awareness campaigns, legislative measures and formal policies to rationalize the use of such applications for better nutrition, health and well-being.

This study aims to explore the link between prevalent risk factors for early childhood diarrhea, including hygiene, feeding, weaning practices and maternal education and the occurrence and severity of early childhood diarrhea in Saudi Arabia.

A case-control study was conducted, involving 98 mothers from the Eastern Region of Saudi Arabia (51 cases and 47 controls). Data were collected from both hospital and community sources. The collected data were statistically analyzed and depicted using descriptive statistics and frequency tables.

Demographic data revealed that 60% of mothers were housewives, 75% had normal deliveries and all babies were full term. In the study cohort, 44% of children aged one to two years. Four domains were compared: diarrheal management, hygiene, weaning and feeding practices. Diarrheal management was suboptimal in some cases: 29% increased fluid intake, 10% maintained adequate food intake, 50% sought medical advice, 58% were familiar with oral rehydration solutions and only 37% used them. Hygiene practices were deficient, with 35% using wipes or sanitizers, 64% handwashing before feeding and 52% adhering to the recommended 10-s duration. Controls exhibited better hygiene practices. Weaning practices were generally similar, with no significant differences between the two groups.

To the best of the authors’ knowledge, this is the first study to collectively report on the risk factors linked to early childhood diarrhea in Saudi Arabia. This study yields significant insights, highlighting the crucial role of managing diarrhea, educating mothers and implementing proper household practices in impacting the occurrence and severity of this perilous ailment.

The aim of this study was to investigate consumer preferences and profile their food-related lifestyles, as well as to identify consumer groups with similar attitudes/behaviours in the Euro-Mediterranean fruit and vegetable market.

A structured questionnaire was designed drawing from the food related lifestyles instrument and including other factors relevant to fruit and vegetable consumer preferences. The data were collected in an online survey with 925 participants in France, Greece, and Italy. A principal component analysis was conducted to interpret and examine consumers' fruit and vegetable related lifestyles. In addition, a cluster analysis was performed to identify different consumer segments, based on the core dimensions of the food-related lifestyle approach.

In each country, three primary consumer segments were distinguished. Health-conscious individuals were predominant in France and Greece, while quality-conscious consumers were prevalent in Italy. These classifications were determined considering various factors such as purchase motivation, perception of product quality, health concerns, environmental certifications, and price sensitivity.

The food-related lifestyle approach has been adapted instrument to create a customised survey instrument specifically designed to capture the intricacies of fruit and vegetable consumer preferences and priorities in three Euro-Mediterranean Countries.

This paper undertakes a structured literature review to analyse the literature on performance management and measurement (PMM) in universities over the last four decades. Over that time, PMM has emerged as an influential force in universities that impacts their operations and redefines their identity.

A structured literature review approach was used to analyse a sample of articles on PMM research from a broad range of disciplines over four decades. This was undertaken to understand the impacts of PMM practices on universities, highlight changes over time and point to avenues for future research.

The analysis highlights the fact that research on PMM in universities has grown significantly over the 40 years studied. We provide an overview of published articles over four decades regarding content, themes, theories, methods and impacts. We provide an empirical basis for discussing past, present and future university PMM research. The future research avenues offer multiple provocations for scholars and policymakers, for instance, PMM implementation strategies and relationships with various government programs and external evaluation and the role of different actors, particularly academics, in shaping PMM systems.

Unlike a traditional literature review, the structured literature review method can develop insights into how the field has changed over time and highlight possible future research. The sample for this literature review differs from previous reviews in covering a broad range of disciplines, including accounting.

This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism.

This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion.

The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research.

This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry.

本文旨在系统回顾人工智能（AI）在旅游业中的应用。通过整合人机交互、机器学习、大数据和其他相关技术, 本研究建立了一个全面的研究框架, 探索人工智能与旅游业各方面之间的系统联系。

本文对4048篇与旅游业人工智能相关的文章进行了关键词共现分析。分析确定了主要议题并对其进行了分类, 然后通过主题文献梳理和手动编码对其进行了进一步完善, 以便进行详细讨论。

分析揭示了五个主要主题：人工智能与旅游体验、人工智能与旅游营销和预测、人工智能与目的地管理、人工智能与旅游企业, 以及人工智能在战略和监管框架中的整合。每个主题都进行了回顾, 以构建一个综合讨论, 勾勒出当前的研究格局, 并提出了未来的研究方向。

研究力图突破目前关于旅游与人工智能的碎片化讨论, 建立了一个统一的框架, 旨在加强对旅游业中人工智能现有方法、理论和应用的理解, 还点明了需要突破的关键领域, 以助力旅游业与人工智能共同创造更加人性化和可持续发展的前景。

Este artículo pretende revisar sistemáticamente la aplicación de la inteligencia artificial (IA) en el sector turístico. Mediante la integración de la interacción humano-ordenador, el aprendizaje automático, big data y otras tecnologías relevantes, el estudio establece un marco de investigación exhaustivo que explora las conexiones sistemáticas entre la IA y diversas facetas del turismo.

Este trabajo realiza un análisis de co-ocurrencia de palabras clave de 4.048 artículos relacionados con la IA en el turismo. El análisis identifica y clasifica los temas dominantes, sobre los que se profundiza mediante una revisión temática de la literatura y una codificación manual para su discusión detallada.

El análisis presenta cinco temas principales: El impacto de la IA en la experiencia turística, la IA en el marketing y la predicción turística, la IA en la gestión de destinos, el papel de la IA en las empresas turísticas y la integración de la IA en el marco estratégico y normativo. Cada tema se revisa para construir un debate integrado que trace el panorama actual y sugiera direcciones para futuras investigaciones.

Este artículo expande el análisis fragmentado que suele encontrarse en la bibliografía al establecer un marco unificado que no sólo mejora la comprensión de las metodologías, teorías y aplicaciones existentes de la IA en el turismo, sino que también identifica las áreas críticas para los avances, con el objetivo de inspirar una integración más humana y sostenible de la IA en la industria turística.