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PurposeThe food industry is crucial in delivering healthy products for life saving of the society. The identification of key barriers of knowledge management (KM) is desired to enhance the sustainability of the industry. KM has been seen as a part of sustainable development by reducing the bullwhip effect in the entire supply chain. The core objective of the existing research is to prioritize the essential factors of KM adoption in sustainable supply chain (SSC) based on fuzzy analytical hierarchy process (FAHP) method.Design/methodology/approachIn order to fulfill objectives of this study, an extensive review of literature and a questionnaire-based field visits were conducted. A total of five major barriers categories and 22 sub-barriers categories were identified in food sector of Pakistan using experts' inputs. This study employed fuzzy analytical hierarchy process (FAHP).FindingsManagerial barriers, innovation and technological barriers categories are found to be highly prioritized among others. Further, the sensitivity analysis is applied to check the incremental changes of ranked barriers. This prioritization of barriers and incremental changes in them is expected to serve food sector for long-term sustainability and competitive advantage for importers and exporters. Finally, the findings of this research are very helpful for industrial experts, practitioners, consultants and government officials in effectively developing policies regarding KM adoption in line with sustainable goals.Research limitations/implicationsThe present work is conducted in the Pakistani context; however, the benchmark model may be tested and applied to other developing countries to compare the outcomes. For further research, the identified barriers may also be evaluated to establish their inter-relationships, using ISM, DEMATEL, ANP, etc. Similarly, the results of this study can also be compared with that of other fuzzy multi-criteria techniques like fuzzy TOPSIS, fuzzy VIKOR, fuzzy ELECTRE, fuzzy PROMETHEE, or fuzzy VIKOR.Practical implicationsThis research study can facilitate policymakers, government bodies, stakeholders and supply chain professionals to recognize the key barriers they may encounter in adopting KM practices in their SSC. Additionally, this work helps managers to evaluate the identified barriers by computing their relative importance in adopting KM practices at managerial levels like strategically, tactically and operationally activities in business. This study also facilitates industrial management in formulating policies and action plans in case of implementation, eliminating the barriers in adoption of KM, and SSC successfully.Originality/valueFew research studies were conducted on KM adoption in industries of China, India, Turkey, Saudi Arabia and Malaysia, but due to workforce diversity these industries have dissimilar views of experts about KM adoption. This study significantly contributed to fill the existing literature gap for prioritization of key barriers against KM implementation in Pakistani context.

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.

Abstract: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 measure (EVM) and genetic algorithm with -188-Journal of Industrial Engineering and Management -https://doi.org/10.3926/jiem.2078 weighted sum approach for solving the multi-objective problem. This research focus on a multiobjective 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 an...

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 research aims to identify, rank, and create an interplay among the psychological barriers to adopting Industry 4.0 technologies in the manufacturing sector. A comprehensive literature review tracked by a discussion with industry and academic experts recognized 20 barriers. Based on three widely acclaimed statistical techniques, hybrid AHP-TOPSIS (Analytical Hierarchy Process-Technique for Order Performance by Similarity to Ideal Solution) and ISM (Interpretative Structural Modeling), critical psychological barriers have been investigated. A group of 8 experts from industry and academia with at least 10 years of experience was consulted for AHP and ISM techniques. Whereas TOPSIS was conducted by 443 operational-level users, including managers and supervisors of different functional areas of the manufacturing industry located in Pakistan. The findings reveal that ‘Fear of job losses’, ‘Fear of data loss/Risk of security breaches, ‘Lack of advanced & continued education of employees’ and ‘Lack of standards and reference architecture’, with highest importance weights, emerged as the most prominent psychological barriers in developing economies. Then the interrelations among these barriers resulted in a four-layered structural model. The driver barriers identified in the final model advocate that development in ‘advanced & continued education of employees’, ‘standards & reference architecture’ and ‘minimization of fear of job & data loss’ can expedite the adoption of industry 4.0 (i4.0) technologies. The study uniquely develops hierarchical relationships among the psychological barriers for adopting i4.0 in the manufacturing context using AHP-TOPSIS and ISM techniques. The study would be valuable for practitioners, decision-makers and companies that wish to focus their efforts and resources on removing the most critical barriers and challenges for the seamless implementation of Industry 4.0.

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