Search results

This paper aims to utilize an extended involvement-commitment and trust commitment model to examine post-consumption decisions related to food delivery app use.

A self-administered online survey was used to collect data from food delivery app users in the USA.

Findings validate a favorable role of perceived app security and menu description on trust in app recommendations. Trust was found to be positively related to involvement, commitment and willingness to provide feedback. The positive moderating role of perceived convenience and rewards and incentives was also confirmed in relation to consumers’ trust in app recommendations, and involvement and commitment

A key contribution of this study includes the development of a comprehensive model to understand postconsumption decisions related to the usage of food delivery apps. To the best of the authors’ knowledge, this study is also the first to unveil the antecedent and moderating factors related to food delivery app users’ willingness to provide feedback, share personal data and to pay more.

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.

In this study, we aim to investigate entrepreneurial intention (EI) among potential entrepreneurs who were students at Pakistan’s higher education institutes (HEIs) of technical and vocational education and training (TVET).

We used a quantitative and correlational method in this study, and we based its theoretical framework on the theory of planned behavior (TPB) and the entrepreneurial event model (EEM). We based this study’s findings on 367 samples collected from Pakistan’s HEI TVET students who were potential entrepreneurs.

By employing path analysis, the findings reveal that TPB constructs, such as personal attitudes (PA), subjective norms (SN) and perceived behavioral control (PBC), have a positive and significant effect on EI. The findings show, also, that EEM constructs, such as perceived desirability (PD), perceived feasibility (PF) and propensity to act (PT) are positive and significant predictors of EI. Moreover, self-efficacy (SE) and the quality of TVET (QTT) positively and significantly affect EI.

This study’s findings support the improvement of Pakistan’s HEIs in developing TVET to enhance individuals’ skills and, ultimately, to create employment and socioeconomic circumstances. They also assist Pakistan’s HEIs in developing EI among their TVET potential entrepreneurs to ensure that they are sufficiently equipped for the job markets.

This study’s findings empirically confirm that TPB, EEM, SE and the QTT provide an integrated path for Pakistan’s entrepreneurs.

The role played by corporate governance mechanisms on corporate deleveraging policies has not been clarified. Empirical evidence is confined to developed economies, even with conflicting and inconclusive results. This paper aims to examine the role of corporate governance mechanisms, such as ownership structure, board composition and CEO dominance, in explaining corporate deleveraging policies.

Using a sample of listed Pakistani firms between 2010 and 2022, this study resorts to binary response models to examine the effects of governance mechanisms on firms’ decision to go debt-free.

A greater ownership concentration, institutional ownership and family ownership increase the propensity for zero leverage. Board gender diversity decreases the propensity for deleveraging policies, which seems to indicate that the presence of females reinforces the monitoring function of the board. Finally, lower managerial ownership or CEO dominance decreases the propensity toward zero leverage (interest convergence hypothesis), but higher managerial ownership or CEO dominance increases the propensity toward zero leverage (managerial entrenchment hypothesis).

Risk-averse managers who prefer to control a firm using little or no debt will find it easier to implement these financing policies in firms with greater ownership concentration and where institutional holders have a substantial stake. For shareholders, this study suggests that investing in firms with females on board reduces the risk of corporate deleveraging policies being adopted for entrenched reasons.

The presence of females on board seems to decrease the propensity of managers to adopt opportunistic actions and may also contribute to enhancing human welfare and society in developing countries.

To the best of the authors’ knowledge, this is the first study considering the effect of board diversity on zero leverage. Another singularity is that this study exhibits a nonlinear relationship between managerial ownership and corporate deleveraging policy.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

The Technical Education and Vocational Training Authority (TEVTA) plays a crucial role in the socioeconomic development of a country. Still, it is often stigmatized as a secondary choice in the Global South. This study explored the interrelationships and impacts of factors such as family, school, and society on the perception and reputation of TEVTA.

By employing quantitative methods, the analysis focused on how family, society, and school support influence these perceptions and reputations within TEVTA programs. Social Cognitive Theory is the theoretical underpinning of this study, in which 350 students from 13 TEVTA institutes participated by filling out questionnaires. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and IBM SPSS 28.

This study indicates that family and societal influences significantly shape students' perceptions, confirming their pivotal role in enhancing the reputation of these programs. School support also emerged as a critical factor, significantly impacting students' perceptions but not directly influencing the programs' reputation. The analysis underscores the importance of understanding the sociocultural context to develop effective strategies for the TEVTA sector in Pakistan. This clear understanding is essential for developing effective strategies to improve the reputation of TEVTA programs in this setting. Moreover, this research offers policy suggestions to make vocational education more attractive and accessible to diverse students, ultimately contributing to the country's socioeconomic development.

This study applied Social Cognitive Theory (SCT) to explore how individual thoughts, environmental influences (such as family, school, and society), and behaviors interact within the context of TEVTA programs. This approach fills gaps in current research and offers a clearer understanding of what affects TEVTA's perception and reputation.

Social exchange and social learning theories are widely used in many disciplines, but there is little research on the relationships between supervisor support and job performance in a developing context. Therefore this study aims to examine the links between supervisor support and job performance with the mediating role of psychological factors; empowerment and self-confidence.

Data for this study were collected from 364 employees working at Pakistan Telecommunication Company Limited. Partial least square structural equation modeling was used for the analysis.

The results revealed that supervisor support stimulates job performance. Empowerment and self-confidence both partially mediate the relationships between supervisor support and job performance.

This study adds to the current body of literature by providing insight into the influence of perceived supervisor support on job performance through the mediating role of psychological factors.

To the best of the authors’ knowledge, this is one of the very few studies exploring the relationships between supervisor support and job performance in developing contexts, particularly focusing on the mediating mechanisms of empowerment and self-confidence.

Thermophoresis deposition of particles is a crucial stage in the spread of microparticles over temperature gradients and is significant for aerosol and electrical technologies. To track changes in mass deposition, the effect of particle thermophoresis is therefore seen in a mixed convective flow of Williamson hybrid nanofluids upon a stretching/shrinking sheet.

The PDEs are transformed into ordinary differential equations (ODEs) using the similarity technique and then the bvp4c solver is employed for the altered transformed equations. The main factors influencing the heat, mass and flow profiles are displayed graphically.

The findings imply that the larger effects of the thermophoretic parameter cause the mass transfer rate to drop for both solutions. In addition, the suggested hybrid nanoparticles significantly increase the heat transfer rate in both outcomes. Hybrid nanoparticles work well for producing the most energy possible. They are essential in causing the flow to accelerate at a high pace.

The consistent results of this analysis have the potential to boost the competence of thermal energy systems.

It has not yet been attempted to incorporate hybrid nanofluids and thermophoretic particle deposition impact across a vertical stretching/shrinking sheet subject to double-diffusive mixed convection flow in a Williamson model. The numerical method has been validated by comparing the generated numerical results with the published work.

Grounded in the cognitive appraisal theory, this paper aims to investigate how employees cognitively evaluate and respond to the introduction of service robots, with a particular focus on generational differences.

Data was collected from hospitality employees across different generations in the USA, and 279 responses were analyzed using partial least squares structural equation modeling.

The results indicate negative service robot awareness and perceived risk significantly contribute to increased turnover intentions, with job insecurity mediating these associations. Notably, Generation Z employees exhibit distinct attitudes toward service robots compared to older generations, indicating a varying response pattern across different generational cohorts.

Organizations operating in the hospitality industry can use these findings to tailor interventions aimed at addressing concerns related to job insecurity and turnover intentions arising from the integration of service robots. Recognizing the diverse perspectives among different generational groups, organizations can implement targeted approaches to ensure a smoother transition and enhance employee acceptance of service robot technologies.

This study contributes to the literature by shedding light on the nuanced interplay between employees’ cognitive evaluations, generational differences and the introduction of service robots in the hospitality sector. The insights generated offer valuable guidance for both academics and industry practitioners, facilitating the development of strategies to foster a mutually beneficial integration of service robots into the workforce.

本研究基于认知评估理论, 探讨员工如何对引入服务机器人进行认知评估和响应, 特别关注世代差异。

采集了来自美国不同世代的酒店员工的数据, 分析了279份回应, 采用了部分最小二乘结构方程建模（PLS-SEM）分析方法。

研究结果显示, 对服务机器人的负面认知和感知风险显著增加了员工的离职意向, 工作不安全感在这些关系中起到了中介作用。值得注意的是, 与老一辈相比, Z世代员工对服务机器人表现出明显不同的态度, 显示出不同世代群体对服务机器人的响应模式各异。

本研究通过揭示员工认知评估、世代差异和服务机器人引入在酒店业中的微妙互动, 对文献做出了贡献。所得的洞见为学术界和行业从业者提供了宝贵指导, 有助于制定策略, 促进服务机器人与工作人员的互利融合。

在酒店业运营的组织可以利用这些发现来定制干预措施, 解决由引入服务机器人引发的工作不安全感和离职意向问题。认识到不同世代群体的多样化观点, 组织可以实施有针对性的方法, 确保服务机器人技术的平稳过渡, 增强员工对其的接受度。

Scientists have been conducting trials to find ways to reduce fuel consumption and enhance heat transfer rates to make heating systems more efficient and cheaper. Adding solid nanoparticles to conventional liquids may greatly improve their thermal conductivity, according to the available evidence. This study aims to examine the influence of external magnetic flux on the flow of a mixed convective Maxwell hybrid non-Newtonian nanofluid over a linearly extending porous flat plate. The investigation considers the effects of thermal radiation, Dufour and Soret.

The mathematical model is formulated based on the fundamental assumptions of mass, energy and momentum conservation. The implicit models are epitomized by a set of interconnected nonlinear partial differential equations, which include a suitable and comparable adjustment. The numerical solution to these equations is assessed for approximate convergence by the Runge−Kutta−Fehlberg method based on the shooting technique embedded with the MATLAB software.

The findings are presented through graphical representations, offering a visual exploration of the effects of various dynamic parameters on the flow field. These parameters encompass a wide range of factors, including radiation, thermal and Brownian diffusion parameters, Eckert, Lewis and Soret numbers, magnetic parameters, Maxwell fluid parameters, Darcy numbers, thermal and solutal buoyancy factors, Dufour and Prandtl numbers. Notably, the authors observed that nanoparticles with a spherical shape exerted a significant influence on the stream function, highlighting the importance of nanoparticle geometry in fluid dynamics. Furthermore, the analysis revealed that temperature profiles of nanomaterials were notably affected by their shape factor, while concentration profiles exhibited an opposite trend, providing valuable insights into the behavior of nanofluids in porous media.

A distinctive aspect of the research lies in its novel exploration of the impact of external magnetic flux on the flow of a mixed convective Maxwell hybrid non-Newtonian nanofluid over a linearly extending porous flat plate. By considering variables such as solar radiation, external magnetic flux, thermal and Brownian diffusion parameters and nanoparticle shape factor, the authors ventured into uncharted territory within the realm of fluid dynamics. These variables, despite their significant relevance, have not been extensively studied in previous research, thus underscoring the originality and value of the authors’ contribution to the field.