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This study examines the role of psychological safety as an antecedent to meaningful work and as a mediator between transformational leadership (TFL) and meaningful work. Additionally, it explores customer incivility as a precursor to psychological safety and as a moderator in the relationship between psychological safety and meaningful work.

Data from 368 hotel employees in Bangladesh were purposively sampled and analyzed using SmartPLS.

Psychological safety positively predicted meaningful work and served as a mediator in the nexus between TFL and meaningful work. Additionally, customer incivility was identified as a negative predictor of safety and acted as a moderator, reversing the association between psychological safety and meaningful work.

TFL exhibits mixed correlations, being negatively associated with meaningful work but positively linked to psychological safety. Therefore, workplaces should prioritize cultivating a psychologically safe environment and minimizing customer incivility to increase meaningful work.

The results add value to the conservation of resources and self-concept theories by examining the mediating role of psychological safety and the moderating influence of customer incivility from the perspective of hotel employees.

The efficiency of each of an organization’s individual workers determines its effectiveness. The study aims to explore the relationship between human resource management (HRM) practices and organizational effectiveness with employee performance as a mediating variable.

Data were collected from 800 police officers in the Greater Accra and Tema regions. The data were supported by the hypothesized relationship. Construct reliability and validity was established through confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling.

The results show that career planning and employee performance were significantly related. Self-managed teams and employee performance were shown to be nonsignificantly related. Similarly, performance management and employee performance were shown to be nonsignificantly related. Employee performance significantly influenced organizational effectiveness. The results further indicate that employee performance mediates the relationship between HRM practices and organizational effectiveness.

The generalizability of the findings will be constrained due to the research’s police service focus and cross-sectional data.

The study’s findings will serve as valuable pointers for the police administration in the adoption, design and implementation of well-articulated and proactive HRM practices to improve the abilities, skills, knowledge and motivation of officer’s to inordinately enhance the effectiveness of the service.

By evidencing empirically that employee performance mediates the relationship between HRM practice and organizational effectiveness, the study extends the literature.

This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

This study aims to clarify why and when digital business strategy (DBS) helps manufacturing firms generate value co-creation (VC) with different stakeholders in the digital context of China. This study considers external network capability (ENC) and internal network capability (INC) as mediation mechanism, and strategic flexibility (SF) as theoretical boundary.

Questionnaires were used and filled out by executives from manufacturing firms. The manufacturing samples from 289 different fields in China were used for hypothesis testing, and the structural equation model was the main analytical method.

This study found that DBS of manufacturing enterprises has a positive impact on VC. Specifically, DBS affects firm-partner VC and firm-consumer VC through the indirect positive effect of ENC, and affects firm-employee VC through INV. The positive effects of ENC on firm-partner VC and firm-consumer VC, as well as INC on firm-employee VC, are weak at high (or low) SF, and are strongest at moderate SF.

This study provides manufacturing firms with practical insights into why and when they can implement DBS to generate VC, with a particular emphasis on the weighted role of SF.

This study spotlights gaps in the literature on why and when manufacturing firms can reap the benefits of DBS, focusing on one important business outcome – VC. The authors clarify the mediating role of differences in ENC and INC, as well as the inverted U-shaped moderating role of SF.

The major purpose of this paper is to examine the mediating role of job competency in the effect of transformational leadership to performance of university lecturers. This article also attempts to examine the direct effect of transformational leadership on job competency and lecturer performance.

For the purpose of the study, quantitative research was applied by conducting an empirical survey with the active participation of 223 lecturers. The survey was conducted in ten high-ranked private universities in Jakarta, Indonesia. Structural equation modeling (SEM) was employed for the measurement and structural model analyses.

The results reveal that the effect of transformational leadership on lecturer performance is expressed only by indirect effect (through lecturer competency). Even though transformational leadership has a positive direct effect on lecturer performance, it is not statistically significant. This paper highlights the crucial role of lecturer competency in the performance of academic scholars. The findings suggest transformational leadership is fundamental in fostering competencies, which, in turn, improve the work performance of university lecturers.

This study makes significant contributions to the understanding of the interaction between transformational leadership and performance in higher education, and the statistical significance of lecturer work competency in mediating this relationship. The results of this study provide a snapshot of the contextual mechanism linking transformational leadership and lecturer performance.

The present study aims to investigate the relationship between institutional influence and performance, mediated through transformational leadership (TL) and moderated by barriers, situational factors, communication and implementation.

Using a structured survey instrument, data were collected from 370 faculty members from 31 higher educational institutions in southern India. After checking the psychometric properties of the instrument, the authors used Hayes’s PROCESS to test the direct hypotheses and three-way interactions.

The results revealed that TL mediated the relationship between institutional influence and performance. Further, the findings supported the three-way interactions between (1) institutional influence, barriers and communication positively affecting TL; and (2) TL, situational factors and implementation affecting the performance of faculty members.

This study underscores the importance of TL for the smooth functioning of higher educational institutions and achieving superior performance, especially in the new normal context after the global pandemic.

This study makes several significant recommendations to administrators in higher educational institutions, in addition to contributing to the vast literature on TL. The study suggests that administrators must invest resources in developing TL skills so that employees reach their fullest potential and contribute to achieving organizational goals. In addition, leaders in organizations need to exercise a transformational style to combat the new normal post-pandemic academic environment.

This study provides new insights into the importance of TL style and institutional influence to enhance performance. To the best of our knowledge, the conceptual model developed and tested the first of its kind in India, significantly contributing to theory and practice.

While travelling, tourists like to use mobile technology to share their travel experiences. This study aims to understand how the social value gained by tourists from sharing a travel experience with mobile technology affects their satisfaction with the travel experience through onsite mobile sharing behaviour.

A second-order hierarchical model is constructed to examine the moderated mediating role of onsite mobile sharing behaviour in improving tourists’ travel satisfaction. Through systematic sampling, 304 responses were collected at ten attraction points in Guangzhou and Shenzhen, China.

The results show that, compared with self-centred values (self-presentation and self-identification), other-centred values (building social connection and reciprocity) contribute more to forming social values of sharing. In addition, onsite mobile sharing behaviour partially mediates and moderates the effect of social values on travel satisfaction.

This study applies the social capital theory to identify the value gained by sharing travel experiences and empirically evaluates the impact of these values on the overall value of sharing travel experiences. This study also contributes to tourism research by examining the moderated mediating role of onsite mobile sharing behaviour in improving travel satisfaction. This study helps destination marketing to make strategies to motivate tourists to use mobile technology to share their travel experiences while travelling.

Studies have shown that customer orientation has a substantial impact on a business's success. This study examines the effects of positive personality on salespeople's proactive customer orientation (PCO) and responsive customer orientation (RCO) by incorporating the effects of job enthusiasm and transformational leadership.

A questionnaire survey is conducted. A total of 511 questionnaires are received from Taiwan's life insurance salespeople. Partial least squares (PLS) regression is used to test the hypotheses.

The results show that positive personality influences PCO and RCO both directly and indirectly through job enthusiasm. The effect of transformational leadership is also found to be significant. Financial service companies should be concerned about the important role of positive personality and transformational leadership in promoting job enthusiasm, PCO and RCO among salespeople.

Previous studies mostly focused on the direct relationship between customer orientation and organizational outcomes, neglecting the role of individual personality. This gap leaves us wondering how a positive personality influences a salesperson's proactive and responsive customer orientation. To the authors' knowledge, this is the first study to examine the mechanisms of a positive personality, job enthusiasm, and transformational leadership on salespeople's PCO and RCO.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.