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This study investigates the crucial link between sustainable practices and organizational development, focusing on sustainable knowledge management (SKM), green innovation (GI) and corporate sustainable development (CSD) in diverse Pakistani organizations.

This study employs a comprehensive research methodology involving advanced statistical techniques, such as confirmatory factor analysis, structural equation modeling and hierarchical linear modeling. These methods are instrumental in exploring the complex interrelationships between SKM, GI, moderating factors and CSD.

This research generates significant findings and actively contributes to sustainable development. The following sections (Sections 4 and 5) delve into the specific findings and in-depth discussions, shedding light on how industry regulation, organizational sustainability priorities, workplace culture collaboration and alignment between green culture and knowledge management practices influence the relationships between SKM, GI and CSD. These findings provide valuable insights for the research community and organizations striving for sustainability.

The study’s findings have practical implications for organizations seeking to enhance their sustainability efforts and embrace a socially and environmentally conscious approach to organizational growth.

This study contributes to the literature on sustainable practices and organizational development. Researchers and business people can learn a lot from it because it uses advanced econometric models in new ways and focuses on the link between knowledge management, GI and sustainable corporate development.

With rapidly changing marketing landscape, nowadays, the formulation of various marketing strategies is increasingly focused on how consumers tend to make decisions. To meet the highly demanding consumer expectations, market segmentation can be used as an important marketing strategy. Due to gender marketing concept familiarity in the contemporary world, gender difference is one of the reference features in the process of market segmentation for marketers. This research is aimed to examine various determining factors that foster consumer purchase decision-making and the differences between consumers of different genders while making shopping and purchase decisions with special reference to an emerging economy, i.e. Pakistan.

Based on a cross-sectional sample of 367 consumers, the study adapted Sproles and Kendall's (1986) Consumer Style Inventory (CSI) to scrutinize the decision-making of both genders in Pakistan. For data analysis, the exploratory and confirmatory factor analysis in addition to the structural equation modeling has been used.

The study emphasized that, with the exception of quality awareness, brand consciousness, fashion consciousness, option overload and price consciousness greatly affect buying decisions. In addition, when it comes to consumer purchase decision-making, significant gender variations were discovered for both fashion consciousness and price consciousness.

Drawing upon the distinctive cultural characteristics of Pakistan and its people, in-depth research was conducted on purchasing behaviors of Pakistani consumers and the decision-making characteristics of customers of different genders were summarized. The outcomes are expected to make a significant contribution to the field of gender marketing by organizations.

This study empirically tests a conceptual framework that shows how integration practices are significantly associated with supply chain (SC) performance. This study also intends to achieve the following purposes: first, how the performance is influenced by the integration practices, i.e. internal and external; second, to measure the mediating effect of organizational antecedents (market orientation, learning orientation) between integration practices and firm’s SC performance.

In a noncontrived study environment, a cross-sectional study design was used with a questionnaire. The study used a stratified proportionate random sample of 205 managers from manufacturing firms in China. Six hypothesized relationships were examined using the structural equation modeling (SEM) technique in AMOS software, and five were shown to be valid. The proposed model was validated through various techniques.

Results of this study indicate that both external and internal integration influence SC performance and confirms the mediating role of organizational antecedents between integration practices and SC performance. According to the findings, five out of the six hypotheses are accepted. Findings of this research also offer very expedient insights for the companies’ management which can help them to ensure optimal output by giving due importance to external as well as internal integration.

The data for the study were only obtained from one province, which was Henan Province, and one industry, which was manufacturing; this constrained the generalizability of the study. The findings may be further validated in the future by expanding the scope of the studies to include various cultural contexts and types of businesses. Second, this study used data from a cross-sectional analysis; however, future research may potentially make use of a longitudinal design in order to more thoroughly confirm the findings.

Findings of this study offer substantial managerial insights suggesting various ways to develop better internal as well as external integration to get better results. Management of the company should focus and give more importance to job rotation, trainings and management commitment as part of internal integration. Moreover, management should strive for improving the capabilities of integration in internal functions prior to external integration as internal collaboration, teamwork and interaction within the company are considered as a precondition to maintain integration with external stakeholders. It is also a social process which needs to be built up over a longer period of time.

The authors contribute to the literature by experimentally evaluating the effects of integration practices on SC performance using a conceptual model drawn from current theories. The study also offer additional empirical evidence for Han et al. (2007), who found that SCI enhances firm performance through quality management in their analyses of the relationships between SCI, quality management practices and firm performance.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. 10; 10; 10; 10;

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Increased workplace diversity gives rise to both opportunities and threats for businesses. Effective training which considers gender differences can help exploit the positives of diversity and positively impact on job satisfaction and affective commitment of employees.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This study aims to investigate the impact of job rotation on employee performance and explores the mediating role of human resources (HR) strategy and training effectiveness on this relationship, within the petrochemical industry, which represents a highly specialist and hazardous industrial context.

Data was collected through a questionnaire which was distributed among the experts working in an Iranian petrochemical organization. Previously validated scales were used to measure job rotation, employee performance, HR strategy and training effectiveness, and partial least squares structural equation modeling was used for hypothesis testing.

The research findings indicated that job rotation had a negative effect on employee performance, while training effectiveness and HR strategy positively mediated the relationship between job rotation and employee performance. This highlights the importance of ensuring effective training and a HR strategy to support job rotation of skilled and specialist employees.

Managers of employees in specialist and hazardous industries, such as petrochemical workers, interested in job rotation to support employee career development, should be mindful of potential negative implications on employee performance. To support and improve employee performance, job rotation should be considered alongside HR strategy and training.

Previous research has largely focused on the value of job rotation to develop managers’ organizational understanding and to reduce injury within blue-collar work, which has led to a paucity of research into job rotation within highly skilled and specialist industrial roles. It is highlighted within the literature that it remains unclear what supports effective job rotation. This study addresses this lacuna by investigating how job rotation affects employee performance in a highly skilled and specialized industry and how strategy and training effectiveness mediate this effect.

The purpose of this study is to analyse the perception of employees towards training and also examine the mediation effect of employee engagement between training and task performance.

An integrated model has been developed highlighting the relationship of the motivation for training and support for training and their implications on task performance through the mediating role of employee engagement. Using the sample of 397, structural equation modelling has been used with the help of SPSS and AMOS to validate the hypothesized relationship and evaluate the responses of employees working in travel agencies operating in Delhi (capital), India.

The findings of this study demonstrated a positively significant relationship between training and task performance of employees in the tourism and hospitality industry. Simultaneously, employee engagement positively mediates the relationship between training and task performance directly and indirectly. This study goes over the ramifications of the findings and offers some suggestions for practical implementations.

The findings of this study can be used by managers and HR professionals to organize exclusive training programs for improving employees’ performance based on the dimensions used in this study. This study also suggests that training program enhances employee engagement in organizational activities which leads to build up team work and overall organizational as well as individual performance.

This study also introduces a conceptual model and theoretical framework that provide a significant contribution to the training and task performance of employees. This study provides a strong theoretical foundation by incorporating the social exchange theory to confirm the role of employee engagement in performance. Further, this novel piece of research explores the relationship between training and task performance with employee engagement as a mediator, especially in the Indian tourism and hospitality industry.

This study aims to investigate whether the impact of effectiveness of diversity training on affective commitment is different for men versus women and whether affective commitment mediates the relationship between effectiveness of diversity training and employee outcomes of turnover intention and job satisfaction.

The data were collected from 134 employees working in an Australian manufacturing organization by using an employee survey. Structural equation modeling was used to analyze the data using AMOS.

The results of this study support the proposed hypotheses, demonstrating a significant, indirect effect of effective diversity training on job satisfaction and turnover intention via affective commitment. Moreover, gender moderates the relationship between effective diversity training and affective commitment.

The findings suggest that organizations can enhance affective commitment and job satisfaction and reduce turnover intention by providing effective diversity training to employees. Gender of employees should also be considered when evaluating the effectiveness of training.

This study provides pioneering evidence of the following relationships: diversity training effectiveness and turnover intention via affective commitment; diversity training effectiveness and job satisfaction via affective commitment; and diversity training effectiveness and affective commitment for men versus women.

The purpose of this paper is to find out the corporate tax gaps in India and the possible reasons behind it.

The objectives were achieved by looking at the existing tax system in the country, problems in the area of corporate taxation and the reasons for these problems. The authors also look at how we can address a few of these problems in comparison with other emerging countries.

Emerging countries play an important role in the world economy. India is undeniably rising as an important and vital country, not only among emerging countries but also in the global economy. Tax environment in India is often seen as a complex system with the multiplicity of indirect taxes, overburdening litigations and lack of certainty. Tax policies can be an effective mechanism to promote investments, both international and domestic, which India needs in abundance. The existing high fiscal deficit in the country necessitates the need to make revenue increase a primary concern of tax policy in India. Although indirect taxes have become an important source of development funds in developing countries due to their wide coverage, we feel that corporate tax can become a potential source of revenue in the future.

This paper should help in changing the way policy decisions relating to tax should be looked at.

Studies in the Indian context are limited. This paper should be of some use to those intending to understand the tax system, especially with reference to corporate taxes in India.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.