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This study aims to assess the effect of job insecurity, customer incivility and work–life imbalance on Lebanese bank employee workplace well-being (EWW), while investigating the moderating role that positive and negative affect might have.

Quantitative data was collected from 202 respondents and analyzed using structural equation modeling system through IBM SPSS and AMOS.

Results revealed that each of the independent variables has a negative, statistically significant effect on Lebanese bank EWW. The positive affect and the negative one are shown to have a moderating effect that lessens and boosts, respectively, these negative effects.

The study adds to the literature on EWW while highlighting the high-power distance and collectivist society that the research took place in.

Limitations include the sample size that was hoped to be larger, in addition to the self-reporting issue and what it entails in the data collection process.

The study has many practical implications, including the validation of a questionnaire in a developing Arab country, hence providing a reliable tool for researchers. HR specialists should lean toward applicants with positive affect, ensuring that their workplace is occupied by members with enhanced resilience. Furthermore, employers should support their employees’ professional growth, thus, boosting their employability during turmoil and consequently making them less vulnerable in times of economic recession.

The study’s unique context, depicted in the harsh economic and financial crisis, makes the findings on EWW of a high value.

This paper aims to develop a comprehensive model on intra- and interpersonal emotion regulation (ER) in hospitality and tourism (H&T) service encounters.

A critical review and reflection of ER research from multiple disciplines was conducted. Methodologies appropriate for investigating ER were also reviewed.

A comprehensive framework was proposed to outline key influential factors, processes and consequences of intra- and interpersonal ER in service encounters in the H&T industry. Methodologies integrating advanced tools were suggested to measure complex and dynamic emotion generation and regulation processes in social interactions from a multimodal perspective.

The researchers developed a comprehensive conceptual model on both intra- and interpersonal ER based on a critical review of the most recent psychological research on ER. Various theoretical and methodological considerations are discussed, offering H&T scholars a solid starting point to explore dynamic emotion generation and regulation processes in complex social settings. Moreover, the model provides future directions for the expansion of ER theories, which have been mostly developed and tested based on laboratory research.

The proposed model addresses two critical issues identified in emotion research in the H&T field: the lack of a dynamic perspective and the neglect of the social nature of emotions. Moreover, the model provides a roadmap for future research.

An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.

This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.

This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.

This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.

This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.

This study investigates the role of emotional energy as a mediator of the relationship between transformational leadership (TFL) and work engagement.

A two-wave survey was conducted through Prolific and 392 valid responses were collected from full-time hospitality and tourism employees. RStudio was used for structural equation modeling and bootstrapping analysis.

Results indicate that the positive relationship between TFL and work engagement is mediated by emotional energy.

The research particularly highlights to practitioners the important role of emotional energy and provides methods to improve emotional energy. Hospitality leaders should monitor the emotional energy levels of their employees and strive to avoid energy depletion. The findings also provide practitioners with some support in explaining the importance of leadership training workshops to leaders and their organizations.

The study provides new insight into the process underlying the TFL–work engagement relationship and highlights the importance of emotional energy as a construct of study in the hospitality industry.

Purpose: The COVID-19 pandemic has been a good example of a Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) world. Higher educational institutions (HEIs) have faced a massive hit because the jobs in this industry have become unexpected. Considering the most valuable assets ‘Teachers’ crunched in the VUCA crisis, the study intends to determine if personal harmony (PH) and organisational citizenship behaviour (OCB) would enhance teachers’ job satisfaction (JS).

Design/methodology/approach: Data are collected from the teachers of Indian HEIs and teachers who have experienced the impact of the COVID-19 catastrophe (VUCA). Considering the pandemic restrictions, data have been collected through an online survey (N = 364).

Practical Implications: PH is an individual’s internal quality and attribute that cannot be developed on force or situational need. Even in an uncertain situation, teachers have tried their best to contribute through professional service. Hence, people who possess PH contribute their best even though unsatisfied with their jobs.

Originality/value: This study has focused on finding the relationship between two different variables, PH and OCB (which has not been explored in Asian countries, majorly in India, where it has a vast cultural diversity and structure influencing the educational policies) that hinders the factors influencing JS, where these two variables are highly influenced by hygiene factors such as values, culture, ethical standards, personal belief, leadership styles, and fair treatment showcased by the organisations/institutions.

The post-purchase behavioral responses of omnichannel shoppers, who mainly rely on physical stores (acknowledged as a crucial channel in providing a seamless shopping experience and fulfilling the dynamic needs of the shoppers), are still understudied. The purpose of this paper is to examine how integrated store service quality (ISSQ) can contribute to a more optimal shopping experience (cognitive, affective and relational) and have a subsequent impact on shoppers’ psychological ownership toward the store, resulting in the generation of (face-to-face, online and social media) word of mouth (WOM).

The research is descriptive, quantitative and cross-sectional investigation. A purposive sampling technique was used for selecting the study respondents. The data were collected from 786 Indian omnichannel shoppers using a validated self-administered questionnaire. The proposed conceptual model was tested using partial least squares structural equation modeling.

The results indicate that all three dimensions of omnichannel customer experience (cognitive, affective and relational) positively mediate the relationship between ISSQ and psychological ownership, subsequently impacting all three WOM behaviors of omnichannel shoppers (face-to-face, online store and social media). The customer’s perceived value with the store and their perceived retailer relationship investment significantly moderated the relationship between ISSQ and different WOM behaviors (face-to-face, online store and social media). This research also demonstrated the direct impact of ISSQ on WOM and the indirect impact through different customer experience dimensions and psychological ownership.

The sample used in the study was not probabilistic and, therefore, presents limitations for the possibility of generalizing the results. The study was performed in a cross-sectional methodology in the Indian context; there is a need for longitudinal investigation.

This study addresses the need to investigate different dimensions of omnichannel customer experience that might influence various post-purchase behavioral responses. This study is the first to show that ISSQ might affect omnichannel shoppers' online, offline and social media word-of-mouth behaviors through different customer experience dimensions and the customer’s sense of belongingness to the store. The moderating effect of customer perceived value with the retailer and their perception of retailers’ investment in a relationship on proposed hypotheses was also tested to give managerial recommendations.

The COVID-19 pandemic has burdened the health-care system and exposed nurses to immense stress. This study therefore aims to investigate nurses’ mental well-being who are working with COVID-19-positive patients. Burnout leads to decreased productivity and manifests as emotional exhaustion, depersonalisation (cynicism) and low personal accomplishment (professional efficacy). Authentic leadership is built on a humanistic value system, which is the core value of nurses and other health-care professionals. This study therefore used authentic leadership as the independent variable.

A cross-sectional quantitative research method was adopted by distributing validated online questionnaires to 1,334 nurses in a private pathology laboratory and 241 questionnaires were analysed with 93.4% female respondents. Multiple linear regression model testing was conducted.

Multiple regression analyses showed statistically significant negative correlations between authentic leadership and emotional exhaustion, cynicism, job stress and job-stress-related presenteeism, and a positive correlation between authentic leadership and professional efficacy.

This study provides empirical data to encourage organisations to focus on developing authentic leaders to decrease nurses’ burnout, job stress and presenteeism. The health-care sector should strive to create an environment where nurses are valued and their talent is recognised to increase employee engagement and commitment.

There were two contributions in this study: first, to determine whether there is a relationship between authentic leadership job stress and job-stress-related presenteeism. Second, to determine whether there is a relationship between authentic leadership and the three sub-constructs of burnout.

Despite extensive research on the detrimental work-related impact of customer mistreatment, there has been limited investigation into the outcomes that encompass both positive and negative connotations (i.e. unethical pro-organizational behavior [UPB]). This study aims to test whether, how and when daily customer mistreatment leads to hospitality employees’ daily UPB.

This study conducted a two-phase daily diary study. In the first phase, participants completed measures of their sense of power and provided demographic information. During the subsequent two-week period, participants completed questionnaires twice daily. The analysis included data from 87 hospitality employees, with 781 surveys remaining. This study performed multilevel analyses using Monte–Carlo simulations.

This study revealed that hospitality employees experiencing daily customer mistreatment exhibited heightened perceptions of status threats, resulting in increased daily UPB. The moderating effects of employees’ sense of power were found to be significant in both direct and indirect relationships.

Hospitality managers should recognize that customer mistreatment can threaten employees’ social status and result in daily UPB. To protect employees, implementing daily training programs is essential. Moreover, hotels and managers should provide HR management/recognition programs and empowerment initiatives to boost employees’ sense of power and counteract the harmful effects of customer mistreatment on their status.

This study makes contributions to the existing literature on customer mistreatment by establishing a positive relationship between daily customer mistreatment and daily UPB through the mechanism of status threat. Furthermore, thise study highlights the importance of enhancing hospitality employees’ sense of power as a protective factor against the negative consequences of customer mistreatment.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

In the competitive realm of destination branding, understanding the factors that lead tourists to develop a deep emotional connection with a destination’s brand is essential. At the heart of this emotional bond lies destination brand love – an exceptionally profound sentiment transcending mere satisfaction or preference. This concept has recently garnered attention from both tourism academia and industry practice. However, there remains a void in exploring the factors and mechanisms that pave the way for tourists to develop deep affection for a destination’s brand. This study examines the influences of self-congruence and destination immersion on destination brand love. In addition, the linkages from the four facets of self-congruence to destination brand love via the mediating role of destination brand immersion are also scrutinized.

With data collected from 421 tourists in Vietnam, the partial least squares structural equation modeling (PLS-SEM) approach was applied to test the research model and hypotheses.

The results reveal that (1) ideal and ideal social self-congruence impact destination brand love, (2) all four facets of self-congruence impact destination brand immersion and (3) there is an indirect effect of the four facets of self-congruence on destination brand love, mediated by destination brand immersion.

The study’s findings provide evidence that destination characteristics must be symbolic of the destination brand and consistent with its image among tourists, which is essential in understanding the complex behavior of tourists. Indeed, the results indicate that facets of self-congruence significantly influence both destination brand immersion and destination brand love. Therefore, destination marketers should develop marketing strategies that emphasize personal relevance to the tourist destination to increase their sympathy and love for the destination.

Adapting self-congruence theory and the psychology of flow theory, this study presents a model that elucidates both the direct and indirect relationships among the four facets of self-congruence: destination brand immersion and destination brand love based on the aspects of congruence between tourists and destinations. From this, it helps to explore the factors and mechanisms that pave the way for tourists to develop deep affection for a destination’s brand.