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Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

Generation Z (Gen Z) is entering the hotel workforce and will soon become the dominant group. This has called for a better understanding of this workforce’s attitudes and perceptions towards working in the hotel industry. This study aims to examine the effect of organizational socialization on the retention of Chinese Gen Z employees, the mediating role of person–environment fit (P-E fit) and the moderating effect of career commitment.

Time-lagged data were collected from 426 Gen Z new employees from 20 upscale hotels at two different times (2 weeks and 12 weeks after the employees entered the hotel). Confirmatory factor analysis, structural equation modeling, bootstrapping analysis and moderated hierarchical regression analyses were used for data analysis.

Organizational socialization positively affects employee retention via person–environment fit. Moreover, career commitment positively moderates the relationship between person–environment fit and employee retention.

Hotels must view organizational socialization as a long-term investment in Gen Z talent management by offering effective training through diverse methods, creating a collaborative environment and helping them develop career plans to enhance their career commitment.

This study unpacks the four dimensions of organizational socialization and investigates their differential effects on Gen Z employees’ retention through P-E fit. The moderating role of career commitment is also examined. This study contributes to the growing body of hospitality human resources management research on this new generation of workforce in China.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

A prime objective of the Swachh Bharat Abhiyan (SBA; Clean India Mission) is to motivate people to segregate their household waste. The purpose of this study is to assess the ground reality of waste management behaviour of Delhi residents with the help of a modified Value–Belief–Norm (VBN) model. Past researches point the need to include cost as a variable in the VBN model. This study fulfils this need and tests cost as one of the variables on the gathered data.

The research data were gathered by interacting with the people and the civic staff in the jurisdiction of the three Delhi municipalities through a stratified sampling technique (N = 250). The structural equation modelling was used to analyse the collected data.

The modified VBN model explains the waste management behaviour, but the variables do not follow the exact causal chain. Values, awareness of consequences, ascription of responsibility and personal norms all explain the resident's waste management behaviour. However, cost limits the resident's waste management behaviour.

The study could only achieve a moderate model fit; its sample size was small; and data were collected through self-reported questionnaire.

Three main practical implications of the study are: (1) While designing waste management solutions, due importance must be given to the cost to be borne by people for adopting these solutions. (2) Design such interventions that target residents' values to convince them to make the desired behavioural change. (3) People need be educated about the ways to sort waste and made aware of the importance of waste segregation in eradicating the urban waste mess.

The paper is an original contribution to testing a modified VBN model in predicting waste management behaviour. The modified model includes cost as a variable missing in the previous research. This research is useful in the backdrop of the SBA and provides suggestions for policymakers and pro-environment researchers.

Industrialisation has contributed to global environmental problems, especially in developed countries, but increasingly so in developing ones as well. The rising public concern for the natural environment is compelling business entities to revise their business models towards green lean (GL) management. Most manufacturing firms have realised that GL implementation is a critical factor that drives their success. Therefore, keeping in view the above said aspects, the purpose of this paper is to empirically assess the complementary impact of GL practices on environmental performance.

Data from a sample of 124 Indian manufacturing industries are analysed using a structural equation modelling technique.

Evidence suggests that GL practices such as top management commitment, government support, human resource management, health and safety of employees and public pressure and legislature have significantly positive effect on environmental performance of manufacturing industries.

The sample is limited to Indian manufacturing industries situated in northern region, with a low response rate.

Successful implementations of GL practices can lead to improved environmental performance. Manufacturing industries within emerging economies like India can improve on their GL practices by incorporating these findings into their business models, while research could be guided to focus their inquiries on this and related genres of scholarly work.

To the best of the authors’ knowledge, this study is one of the first to empirically assess the complementary impact of GL practices on environmental performance within the Indian context.

In this study, the service quality of an automobile authorized service center was investigated based on the European Customer Satisfaction Index (ECSI) model. The ECSI model includes image, customer expectations, perceived quality, perceived value, customer satisfaction, customer complaints and customer loyalty.

In the study, an attempt was made to improve the ESCI model by adding the trust factor as a moderating variable. After an extensive literature review, measurement questions were developed to best represent the factors in the research model. Partial least squares structural equation modeling (PLS-SEM) was used to test the fit of the research model and test the hypotheses.

As a result of the analysis, only one of the 13 hypotheses tested was not supported. According to the results of hypothesis testing, the highest effect was found in the relationship between customer satisfaction customer complaints, customer expectations and perceived quality. In addition, customer expectations affect customer satisfaction indirectly rather than directly. In this case, customer expectations, perceived value and perceived quality influence customer satisfaction.

The customer satisfaction quality index score of the authorized automobile service whose service quality was measured was calculated as 72.75. Although customers were generally satisfied with the authorized service, their expectations were not fully met.

In the study, an attempt was made to improve the ECSI model by adding a trust factor. Trust, which was added to the model as a moderator variable, fit the model. As a result, it was revealed that trust has an increasing regulatory effect on the relationship between perceived quality and customer satisfaction.

The purpose of this paper is to examine the factors that influence older adults' intention to use virtual fitting room technology during the COVID-19 pandemic based on the extended technology acceptance model (TAM).

An online survey was conducted with a sample of older adults from 60 to 90 years old (n = 819). A structural equation modeling was conducted to test a proposed research model.

The results revealed that older adults' behavioral intentions were positively influenced by perceived usefulness and ease of use, and fear of infection during the pandemic was significantly related to the perceived usefulness. Fit concern was not significantly related to perceived usefulness of virtual fitting room technology.

This research extends the TAM by adding antecedents to perceived usefulness in explaining older adults' adoption of virtual fitting technology.

This study develops and tests a comprehensive model that examines whether dimensions of supervisors’ job demands and resources influence their work motivation through their job strain levels while managing disability accommodation (DA).

The proposed model leverages the assumptions of established job demand and resources theories, including demand-ability fit, job demand-control, job demand-control-support, and effort-reward balance models. Then, we tested with the quantitative data from 335 British, Canadian, American, Australian, Dutch, and German supervisors with recent DA experience.

This study found support for the proposed model. Job control and social support directly affected work motivation, while job strain did not mediate the relationship between job control and social support and work motivation. The results suggest that employers looking to improve the likelihood of DA success should focus on providing adequate job control, social support, and rewards to supervisors responsible for accommodating employees with disabilities.

This research enhances our understanding of how additional DA responsibilities impact supervisors and aids in the development of effective DA management policies and interventions, providing robust support for practitioners.

This study contributes to extending the DA literature by testing the applicability of different theoretical models to explain the effect of the additional DA responsibility on supervisors’ job demand, strain, and motivation levels and identify the resources to mitigate them.

This study examined the influence of social media use on graduateness and the employability of exit students in South Africa.

The study used quantitative and descriptive research designs to test the proposed hypotheses. An online survey was used to collect the data from a study sample. A sample of 411 respondents was received, with structural equation modelling (SEM) being used to assess the model fit.

The study found that the direct effect of social media use on graduateness skills is significant. Secondly, the direct effect of graduateness skills on perceived employability is also significant. The results also showed existence of support for the mediation of graduateness skills on the relationship between social media use and perceived employability.

The study provides empirical evidence to the proposed model and infers the potential role of social media in addressing issues related to graduateness and the employability of exit students.

In addressing the challenge of unemployment, the use of social media can potentially aid in matters of skills acquisition.

The results demonstrate how technology through the use of social media potentially fits within enhancing graduateness and employability skills.