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To comprehend the influence of green HR practices (GHRM) on employee outcomes, strategic HRM researchers are gradually adopting an employee-centric approach, a subject that has sparked recent discussions among scholars in the field of green HR. These scholars have emphasized the need for studies that shed light on the reasons behind the differences in employees' perceptions of GHRM. To address this concern, we investigated (1) supervisors perceived GHRM (SUP-GHRM) and subordinates perceived GHRM (SUB-GHRM) as the fundamental source of variation in employee eco-friendly behavior and green performance, (2) the association between SUP-GHRM and SUB-GHRM, (3) the mediation role of SUB-GHRM toward green performance and eco-friendly behavior, and (4) the moderation of perceived HRM system strength (HRMSS) on supervisor-subordinate perceived GHRM.

Applying a survey approach, we collected data from 217 supervisors and 624 subordinates from Large-Scale Manufacturing Organizations in the Textile sector of Pakistan. Since the data is hierarchical, we applied the Hierarchical Linear Model (HLM) and bootstrapping techniques to examine the hypothesized relationship.

The results of HLM revealed that (1) the SUP-GHRM and SUB-GHRM were key in determining green performance and eco-friendly behavior, (2) the SUP-GHRM significantly influenced SUB-GHRM, (3) the SUP-GHRM indirectly affected the eco-friendly behavior and green performance through SUB-GHRM, (4) the HRM system’s strength positively moderated the association between the SUP-GHRM and SUB-GHRM.

The corporations need to ensure that both supervisors and subordinates have a consistent understanding of GHRM practices and foster positive relationships between them. It is also important for companies to actively enhance supervisors' knowledge of GHRM and encourage them to effectively communicate the company’s GHRM practices to their subordinates. This is vital for improving employee job-related outcomes. Furthermore, corporations should emphasize developing a strong HRM system designed to create a climate where employees understand the behaviors and responses that are valued and recognized, leading them to perceive situations in line with their managers.

This study suggests SUP-GHRM and SUB-GHRM as critical factors that influence eco-friendly behavior and green performance, and HRMSS is key to aligning the perception gaps between subordinates and supervisors about what GHRM is in place in their organization, which is empirically analyzed in a developing country context.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

This study aims to investigate the relationship between management support, quality infrastructure, staff training and the effectiveness of quality enhancement cells (QECs) established in higher education institutions.

Data were acquired via a structured questionnaire dispatched to faculty members across 12 public and private universities, primarily situated in Punjab, Pakistan. Among the 200 questionnaires distributed, 180 were retrieved and 140 were deemed valid. The proposed relationships were examined using SPSS–25 and PLS–SEM.

The results show a positive and significant relationship between management support, quality infrastructure and staff training with QECs' effectiveness. The study also highlights that the effectiveness of QECs is “Good” in only two of the 12 universities, while in most universities it is “Barely Acceptable”. Furthermore, QECs' effectiveness is slightly better in public universities compared to private institutions.

The study employs convenience sampling and a cross-sectional approach, focusing on faculty members from 12 universities, primarily in Punjab, Pakistan. To enhance future research, larger samples and probability-based sampling should be considered, while involving quality managers and students for a broader perspective.

The research suggests policymakers and university leaders should strengthen their support by providing resources, quality infrastructure and training for academic and administrative staff. This would enhance the effectiveness of QECs and improve the overall quality of education in both public and private universities.

This study contributes to the literature on quality assurance in higher education by emphasizing the significance of QECs concerning management support, quality infrastructure and staff training – areas that are often overlooked in Pakistani universities.

The present study aims to investigate the impact of corporate governance proxies by ownership structure and firm-specific characteristics, i.e. firm size, leverage, growth opportunities, previous year dividend, firm risk, profitability, and liquidity on dividend behavior of the Pakistan Stock Exchange (PSX) listed firms.

Final sample of the study consists of 140 PSX-listed firms. The study covers a period of six years, starting from 2015 to 2020. Dividend payout dummy, dividend payout ratio, and dividend yield were used to assess the dividend behavior of the sample firms. The appropriate regression procedures (logistic, probit, ordinary least square (OLS), and fixed effect regression) are used to test the study hypothesis. To check the robustness of the result, a system GMM estimation technique is also used in the present study.

The study reveals that institutional ownership, foreign ownership, and individual ownership have a significant positive whereas managerial ownership has a significant negative impact on the dividend decision of sample firms. Among firm-specific characteristics, it was found that liquidity, profitability, and the previous year's dividend were significantly positive, while growth opportunities were significantly inversely associated with dividend payout decisions of PSX-listed firms.

This study sheds light on the relationship between dividend policy, ownership structure, and firm-specific factors in the context of an emerging market like Pakistan. The study's findings have important implications for managers, minority shareholders, lawmakers, and investors looking for guidance on the dividend policy of publicly-traded non-financial firms.

The literature lacks studies that together analyze the ownership characteristics and firm-specific variables on dividend decisions, particularly in the context of developing economies. The current study aims to fill this gap.

This study investigates economic sustainability through orientation and absorptive capacity.

The researchers developed a conceptual framework based on vigorous literature for this investigation. This study targeted managers from Pakistan's SME sector as respondents and employed cross-sectional data. In total, the authors based this study's findings on 192 valid cases.

The structural equation modeling (SEM) results highlight that innovation orientation (IO), customer orientation (CO), supplier orientation (SO), network orientation (NO) and absorptive capacity (AC) have significant effects on economic sustainability (ES). Moreover, this study's findings show that ES significantly predicts environmental sustainability (ENS). Finally, the results also demonstrate that ES and ENS positively and substantially affect financial performance (FP).

This study's findings help SMEs continue sustainable business practices by avoiding adverse environmental effects and ongoing climate changes. This study's findings contribute also to the manufacture of eco-friendly environmental products to reduce the contamination of the environment. Financial institutions and policymakers would boost SME owners' capacity and the obtainability of financial resources to improve Pakistani SMEs’ sustainable economic and environmental performance.

This study's findings help to enrich environmental and economic sustainability and, more significantly, for developing countries.

Activity sampling is a technique to monitor onsite labourers' time utilisation, which can provide helpful information for the management level to implement suitable labour productivity improvement strategies continuously. However, there needs to be a review paper that compiles research on activity sampling studies to give readers a thorough grasp of the research trend. Hence, this paper aims to investigate the activity sampling techniques applied in earlier research from the angles of activity categories formation, data collection methods and data analysis.

The method used in this paper is a systematic review guided by the PRISMA framework. The search was conducted in Scopus and Web of Science. The inclusion and exclusion criteria were applied, selecting 70 articles published between 2011 and 2022 for data extraction and analysis. The analysis method involved a qualitative synthesis of the findings from the selected articles.

Activity sampling is broadly divided into four stages: targeting trade, determining activity categories, data collection and data analysis. This paper divides the activity categories into three levels and classifies the data collection methods into manual observation, sensor-based activity sampling and computer vision-based activity sampling. The previous studies applied activity sampling for two construction management purposes: labour productivity monitoring and ergonomic safety monitoring. This paper also further discusses the scientific research gaps and future research directions.

This review paper contributes to the body of knowledge in construction management by thoroughly understanding current state-of-the-art activity sampling techniques and research gaps.

This study aims to examine the relationship between tool adoption and information literacy within smallholder farmers (SHFs).

A structured questionnaire was used to gather data for this quantitative study from 225 SHFs. Structural equation modelling was done to test the hypotheses.

The findings established that tool adoption dimensions (Information and communication technologies (ICT) acceptance, language use and information culture) positively and significantly influenced information literacy. Information culture had the strongest impact.

The study enriches the situated learning theory (SLT) literature by introducing tool adoption as a predictor of information literacy in a new context of SHFs. Use of tools as independent variables is a positive deviation from previous studies that have used them as mediating variables. Despite the contributions, the cross-sectional design study undermines the ability to solicit more detailed perspectives from the lived in experience of the respondents.

Managers should promote usage of context-specific tools like local radio stations and mobile phones, but also use language tailored to farmer contexts when disseminating information. Policymakers should leverage on social and cultural settings when designing information interventions.

The study highlights critical factors that significantly promote information use for improved productivity for SHFs, cumulatively increasing the country’s gross domestic product (GDP). Socially, findings may reduce on their poverty levels of farmers.

This study offers a novel perspective in information literacy domain by using the SLT to delineate contextual tools that are paramount in predicting of information literacy in an under research informal context of SHFs.