Many farmers worldwide resort to choosing various income-earning options for diversifying their income sources as a means of risk-avoidance, social protection, and, above all, to finance agricultural operations. Non-farm income generation among farm families has become an imperative part of livelihood earning strategies in recent years amid fast-evolving climatic and sociodemographic changes. In this regard, this study seeks to identify the patterns and socioeconomic factors responsible for the uptake of various non-farm income diversification sources among agricultural households in southern Punjab, Pakistan. For this purpose, a total of 290 farm households were sampled using a random sampling technique to collect relevant data through structured questionnaires. Results show that approximately 79% of the surveyed farmers were involved in non-farm income generation activities, whereas, the income from these sources accounts for about 15% of total household income. The majority of the respondents offered labour for off-farm work followed by self-employment ventures. The major reason to pursue non-farm work includes low income from agriculture, mitigating risks associated with farming, and acquiring funds to finance farming operations, along with the desire to increase family income. A range of socioeconomic and infrastructure-related variables are associated with the decision to participate in specific off-farm activity, such as age, education, family size, farm income, dependency burden, farming experience, and distance to the main city. Results imply the provision of technical support to increase livelihood from farming operations to ensure food security and curb rural-urban migration. However, vocational training can enhance the rural inhabitants’ skillset to diversify on the farm through agribusiness development within rural areas, enabling them to employ local people instead of populating urban centres.

This study aimed to investigate the impact of ethical leadership on knowledge-hiding behavior of the employees working in the financial services sector under the mediating role of meaningful at work and moderating role of ethical climate. For this purpose, data were collected from two hundred and fifteen employees of financial services providing organizations. The already-established scales were followed to develop an instrument that was used to obtain responses from the respondents. Collected data were analyzed by applying the structural equation modeling through Smart PLS and Process Macro. The results indicate that ethical leadership and meaningful work (MW) reduce knowledge-hiding behavior of employees at work, while ethical leadership positively impacts the influential work of employees at the workplace. Further, the relationship between ethical leadership and knowledge-hiding behavior is partially mediated by MW. Similarly, ethical climate moderated the relationship between ethical leadership and knowledge-hiding behavior. This research makes valuable contributions to the existing literature on leadership and knowledge management. From a practical point of view, this study stresses that managers at work should promote ethical leadership styles to promote MW, which will reduce knowledge hiding. Thus, in this way, it will enhance the innovation and creativity within organizational circuits. The limitations and future directions of this study are also listed.

Purpose: The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability.Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality.Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

The adoption of blockchain technology (BCT) in a supply chain holds great potential for textile industries by executing transactions among stakeholders in a most reliable and verifiable way. Textile industries in emerging economies, like Pakistan, confront severe economic pressures and uncertain environment and strive to achieve sustainable supply chain excellence through blockchain implementation. This study is an initiative to analyze the key barriers in adopting BCT-related practices within the textile industry. This study conducts an extensive review of the literature using fuzzy Delphi approach for finalizing the barriers and applied fuzzy analytical hierarchy process (AHP) for prioritizing the barriers under uncertain environment. Based on the extensive review of the literature and panel discussions with experts, a total of five main barriers and 21 sub-barriers were categorized and ranked. The results and findings prioritize technological and system-related barriers (TSB) first, and human resources and R&D (HRB) barriers second among the other barrier dimensions. This paper highlights the need for an inclusive understanding of the various technological, environmental, and socio-economic perspectives to create blockchain applications that work for the textile sector. This study’s key findings and policy guidelines can assist concerned stakeholders in making strategic decisions for adopting BCT within the textile supply chain. The managerial implications are provided for the industrial decision-makers and policymakers aiming to integrate BCT into the supply chain processes. Presently, there exists no research in the context of Pakistan that highlights the challenges faced during the adoption of BCT in the supply chain. For this purpose, an approach in the form of an integrated model based on fuzzy set theory is developed. Finally, the robustness of the proposed model is checked through sensitivity analysis.

Sustainable production is becoming an important slant through which business enterprises prepare for achieving sustainable development goals. The concept of sustainable manufacturing deals with the development of various items that may utilize minimum resources and are safer for the society at a reasonable price. The present research problem explores and evaluates the production indicators pertaining to the achievement of sustainability in a textile supply chain. In this research, five main indicators and twenty five sub-indicators were determined through extensive review of literature and experts confirmation. This study proposes a novel TESCO-based fuzzy AHP approach for the evaluation of sustainability dimensions of textile sector under uncertain environment. This paper investigates the key sustainable dimensions based on technical (T), ecological (E), socio-economic (S), core competencies (C) and operational (O) considerations to prioritize the sustainable production indicators (SPIs). The fuzzy analytical hierarchy process (FAHP) approach has been applied to compute the weight vectors of dimension impacting the (SPIs). The findings depict that technical indicators have significant impact whereas operational indicators have the least impact in the decision-making process of a textile sector. Furthermore, a sensitivity analysis is applied to check the robustness among the criterion ranking and suggest possible measures to improve operational business excellence. Finally, the paper brings out a more systematic decision support toolkit and implications for the concerned stakeholders to formulate the policies regarding physical production and distribution of products.