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Highly committed, motivated and engaged employees assure organizational success and competitiveness. The study aims to examine the association between human resource development (HRD) practices and employee engagement with organizational commitment as a mediating variable.

Data were collected from 760 employees of 13 star-rated hotels comprising 5 (five-star) and 8 (four-star). The data supported the hypothesized relationships. Structural equation modeling was used to evaluate the proposed model and hypotheses. Construct validity and reliability were established through confirmatory factor analysis.

The results indicate that HRD practices and affective commitment are significantly associated. HRD practices and continuance commitment were shown to be non-significantly associated. HRD practices and normative commitment were shown to be non-significantly associated. Employee engagement and organizational commitment are significantly associated. The results further show that organizational commitment mediates the association between HRD practices and employee engagement.

The generalizability of the findings will be constrained due to the research's hotel industry focus and cross sectional data.

The study's findings will serve as valuable pointers for stakeholders and policymakers of the hotel industry in the adoption, design and implementation of proactive HRD interventions to keep highly engaged and committed employees for organizational competitiveness and sustainability.

By evidencing empirically that organizational commitment mediates the nexus between HRD practices and employee engagement, the study extends the literature.

This research aimed to investigate the mediating role of social media engagement in the relationship between differentiation-oriented content and purchase intentions. Additionally, this research studies the moderating impact of entrepreneurial social media skills in the relationship between social media engagement and purchase intentions.

The research proposes a positivist research philosophy, deductive research approach and survey research strategy. Data were collected from followers of social media pages of small and medium businesses operating in the fields of groceries, food items, apparel and supplies in Pakistan. Respondents were selected randomly. The descriptive statistics were calculated first, followed by reliability and validity analysis as part of the measurement model. Finally, mediation and moderation analyses were run by using structural equation modeling.

Results of the study confirm that differentiation-oriented content has a positive relationship with purchase intentions and social media engagement mediates this relationship. Results further confirm that the social media skills of entrepreneurs moderate the relationship between social media engagement and purchase intentions.

From a practical point of view, this study will potentially help entrepreneurs in Pakistan unveil the undiscovered potential of social media and understand the importance of social media marketing campaigns in crisis situations. It will unlock the importance of entrepreneurial training and development to better adapt to the dynamic and vibrant world of social media.

This is the first study that investigates the relationship between differentiation-oriented content and purchase intentions. Additionally, the current study adds to existing knowledge by proposing entrepreneurial social media skills as moderators in the relationship of social media engagement with purchase intentions.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.