Pharos University in Alexandria

PurposeThe purpose of this study is to delve into the intricate ways through which exhibitionism (EX) and voyeurism (VO) affect employees' fear of missing out (FOMO). Transactional theory of stress is applied to reveal such mechanisms and to explore the FOMO psychological processes that result from the interplay of workplace phubbing (WP) and compulsive social media use, thereby affecting employees' behavioral responses.Design/methodology/approachThis study used data extracted from 834 front-line employees in five-star chain hotels.FindingsThe findings verified that phubbing at the workplace and excessive use of social media have a great positive influence on the feeling of FOMO. In addition, these two psychological factors, WP and compulsive social media use, are major positive psychological outcomes of FOMO. Surprisingly, these psychological outcomes have a significant effect on the behavioral reactions of employees.Practical implicationsUnderstanding how FOMO influences performance and workplace incivility can assist Egyptian hotel managers in creating a more supportive work environment. The negative impacts of FOMO can be mitigated by implementing measures such as developing a work-life balance, providing stress management resources and encouraging positive interpersonal connections.Originality/valueThis study presents a novel dual-pathway model that demonstrates how FOMO leads to two distinct but linked behaviors, phubbing and compulsive social media use and how these behaviors influence crucial job outcomes. The creativity of this model consists in the recognition of EX/VO as the major digital stressors and FOMO as the different strain that eventually leads to two opposite and functionally different coping behaviors avoidant phubbing versus active compulsive use. Thus providing a novel, mechanism-rich extension of stress theory. Thus providing a novel, mechanism-rich extension of stress theory.

PurposeThis study adopts a mixed methods approach in exploring corporate digital responsibility (CDR) in relation to organizational innovation, corporate reputation and digital sustainability in Egypt's five-star hotels. Three major theoretical views were integrated as the research foundations: stakeholder theory, resource-based view (RBV) and institutional theory.Design/methodology/approachThe research adopted the mixed-methods approach. During the quantitative phase, 366 managers were questioned, each providing and contributing to reliable statistical information analyzed through the partial least squares structural equation modeling (PLSSEM). Conversely, interview data were collected with 26 participants on the Qualtrics platform; these data deepened insights into the workings observed in CDR within the hospitality industry.FindingsThe findings conclude that CDR influences organizational innovation, corporate reputation and digital sustainability, thereby creating organizational growth. Organizational innovation and corporate reputation act as mediators between these relationships and provide the greatest facilitation in digital sustainability. In addition, top management commitment moderates the positive effects of CDR on innovation and reputation and makes a case for the need for strong leadership to develop a culture of responsibility.Practical implicationsThe research emphasizes corporate digital responsibility for hospitality professionals, highlighting its role in compliance, growth, market differentiation and stakeholder engagement while fostering ethics and sustainability in digital strategies.Originality/valueThis study contributes to the literature by providing empirical evidence concerning the interrelationships between these constructs in the Egyptian hospitality context, thus presenting practical implications for managers who want to improve their organizational strategies in concert with global sustainability goals.

PurposeThis study aims to research delves into the relationships among sustainable leadership, frugal innovation (FRN) and sustainable performance (STP) in small and medium enterprises (SMEs) in Oman. It seeks to unveil the mechanics of how leadership enables innovative practices that eventually lead to better sustainability outcomes, using the resource-based view, knowledge-based view and transformational leadership theory as a theoretical foundation.Design/methodology/approachBy means of a quantitative research design, data were gathered from a total of 414 employees who worked in SMEs in Oman. To validate the proposed relations among the variables, including the mediating function of FRN and the moderating influence of information credibility (IC) and knowledge sharing (KS), partial least squares structural equation modelling was used.FindingsThe findings corroborate the theory that sustainable leadership has a good impact on FRN and the latter, in turn, glorifies the STP of the company. Additionally, the influence of FRN on the relationship between leadership and performance outcomes was also detected. Moreover, the very IC and KS were the significant factors that moderated these relationships, thus increasing the advantages of sustainable leadership in encouraging both innovation and performance. These discoveries underline the important position of transformational leaders who have the ability to turn the organisation into an innovation-driven one relying on the unique resources and knowledge assets for value creation.Originality/valueThe research adds new insights to the discussion on leadership and innovation in resource-constrained environments by proposing a comprehensive model that connects sustainable leadership, FRN and STP. It also gives practical advice to policymakers and SME leaders in Oman and comparable places, stressing the importance of leadership and innovation strategies for their competitiveness and sustainability in the long run.

Hepatocellular carcinoma (HCC), the third leading cause of cancer-related mortality, is often diagnosed late due to lack of early symptoms. Electronic nose (eNose) technology has been used to detect volatile organic compounds (VOCs) in exhaled breath with moderate accuracy. This study investigated eNose versus gas chromatography–mass spectrometry (GC-MS) for volatolomic analysis of blood and urine headspace to improve HCC diagnosis. A prospective study enrolled 110 volunteers at Alexandria University Hospital, with 98 included (48 HCC patients, 50 matched controls) under strict criteria and ethical approval. Blood and urine samples were collected, clinically characterised, and analysed for alpha-fetoprotein (AFP) and haematological parameters, then blindly tested using a portable eNose with ten metal oxide sensors. Complementary GC-MS analysis with rigorous calibration enabled robust identification and quantification of halogenated and aromatic VOCs. HCC patients and controls were similar in age and sex, but HCC patients had lower body weight, haemoglobin, and platelet counts, with markedly elevated AFP and most classified at advanced Barcelona Clinic Liver Cancer (BCLC) stage IIIB. eNose analysis revealed distinct VOC signatures, with PCA outperforming LDA (> 85

Purpose This study aims to present a framework for applying natural language processing techniques to analyze and classify hotel customer reviews. Design/methodology/approach Using a data set of over 500,000 hotel reviews, a supervised machine learning model is developed to predict whether a review is good or bad based on its textual content. The approach involved a comprehensive data preprocessing pipeline, including tokenization, stop-word removal and lemmatization. For feature engineering, a combination of sentiment analysis scores (using valence-aware dictionary and sentiment reasoner), basic text metrics and advanced text vectorization techniques is integrated such as Doc2Vec and TF-IDF. Findings Given the significant class imbalance in the data set, with a very low percentage of negative reviews, the model performance is rigorously evaluated using the precision–recall curve and the average precision (AP) metric, which are better suited for such scenarios than the traditional receiver operating characteristic curve. The final model, a random forest classifier, achieved an AP of 0.37, demonstrating its effectiveness in identifying the minority class of negative reviews. The results indicate that sentiment analysis features are the most influential in predicting reviewer satisfaction. Practical implications The framework created allows recognizing negative reviews in time to take action to facilitate immediate service recovery and proactive reputation management. The model can be applicable on a strategic level to uncover recurring operational issues, track customer satisfaction trends and derive marketing insights used in the reviews. Originality/value This paper provides a foundation for developing automated systems that enable hotels to better understand and respond to customer feedback in real time.