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This study aims to examine the relationship between Achievement Motivation (AM), Smart Work (SW), and human resources (HR) performance. It questions how moral global leadership (MGL) could moderate the relationship between AM, SW, and HR performance. A theoretical model was developed and tested on sample data representing 219 employees, educators, and educational staff of Islamic Higher education (IHE). The data were collected through surveys and applied to structural equation modeling using SEM-PLS. This study found that AM and SW significantly affect HR performance. While MGL substantially moderates the relationship between AM, SW, and HR performance. This study contributes to the literature on MGL, AM, and SW in creating HR performance that has yet to be studied so far. This study offers the concept of MGL, which plays a central role in moderating the relationship between AM, SW, and HR performance.

This study aims to explore the mediating role of self-efficacy and the relationship between job satisfaction and employee commitment.

The study is designed based on social cognitive theory. To collect the data, survey method is used in the present study. Questionnaires were distributed to the 75 randomly selected textile industry registered and located in Bengaluru city, India. Employees of 71 organizations participated in the survey. Out of 700 employees who received the email survey, 452 valid responses were considered for the data analysis. To examine the defined research hypothesis, a structural equation model is used.

The mediating analysis explored that the direct effect is 0.700, the indirect effect is 0.1730 and the total effect is 0.8731; it indicated that self-efficacy mediates the relationship between job satisfaction and employee commitment. Research also reveals that there is a positive relationship between self-efficacy, employee commitment and job satisfaction of employees working in the textile industry. The proposed measurement model statistics are as follows: CMIN = 2.322; df = 49; GFI = 0.958; AGFI = 0.934; NFI = 0.943; RFI = 0.923; IFI = 0.967; TLI = 0.955; CFI = 0.966. All these indices were nearer to unity.

The research findings provide insights to the management, practitioners and employers about the status of job satisfaction, self-efficacy and commitment of employees in textile organizations which will help make the strategies to increase the overall performance of the organization by enhancing the levels of job satisfaction, self-efficacy and commitment of textile industry employees.

To the best of the authors’ knowledge, this is the first study that tests the relationship among self-efficacy, job satisfaction and the mediating effect of self-efficacy of employees in Indian textile industries.

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.

Examining emotional solidarity that drives tourists’ nostalgia has received significant attention, offering valuable insights that can aid in the selection of a travel destination. However, tourists’ nostalgia, along with its antecedents—perceived safety risk and emotional solidarity, has gained less attention within the setting of Pakistan. Therefore, this research has been carried out and validated a research framework using the stimulus-organism-response model to investigate the links between perceived safety risk, emotional solidarity—welcoming nature, emotional closeness, sympathetic understanding, perceived nostalgia, and destination advocacy intentions. In addition, this model employed destination attachment—place identity and place dependence—as a boundary condition on the relationships between emotional solidarity and advocacy intentions.

This study collected 545 responses through two methods: offline distribution of print copies of the questionnaire and online surveys from domestic tourists who visited scenic destinations last year. Structural equation modeling (SEM) was performed using AMOS 26, and the PROCESS macro was conducted using IBM SPSS 28.

The findings highlighted that perceived safety risk negatively influences emotional solidarity—welcoming nature, emotional closeness, sympathetic understanding—, resulting in a positive effect on perceived nostalgia. Furthermore, tourists’ perceived nostalgia positively triggers advocacy intentions. The findings also confirmed the boundary conditions of destination attachment—place identity and place dependence—on the association between emotional solidarity and perceived nostalgia.

Three major practical implications of these findings: First, ensuring visitor safety with smart security measures, digital tools for quick response, and local community involvement. Second, highlighting local markets, art, and architecture to enrich cultural experiences and promote accessibility and diversity. Finally, using marketing to generate nostalgic experiences through local collaborations, professional storytelling, and engaging social media content to build emotional ties and curiosity.

In terms of originality, this is pioneering research intended at developing and validating the model in the context of Pakistani destinations. Furthermore, this marks the initial step in examining the proposed relationships between perceived safety risk and emotional solidarity in fostering tourists' perceived nostalgia, ultimately leading to a strong desire to advocate for the destination.

This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor.

The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances.

The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%.

This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.