Suchergebnisse

Ready-made garments (RMG) are one of the most critical sectors in the economy of the South Asian region in terms of the labor force employed and export earnings. This research study aims to determine the Corporate Social Responsibility Stakeholders dimension and its influence on textile firms Performance. The study used organizational legitimacy as mediating variable between the CSR stakeholders and firms' performances. The research study was used in the quantitative analysis approach to determine the cause and effect of the relationship between CSR and Textile firm's financial and non-financial performance. Though the study collected primary data & secondary data from 250 respondents using survey questionnaires, the researcher obtained secondary data by analyzing the audited annual and sustainability reports of various RMG companies. We have collected data by conducting a focus group interview forming a team of employers, top-level managers, and CSR officers. We asked them all the questions, filled it, tapped it, reserved it for the interpretations. We have surveyed 67 industries, but it enabled us to collect the data from the 50 sectors—the data collected from 2016 April to 2018 December. Our study has some limitations in that the sample size is small compared to the other research. SPSS-23 & MS-Excel were used to analyze the collected data. CSR practices benefitted RMG companies in terms of long-term sustainable development by increasing the firm's financial and non-financial performance of the RMG sector.

Background: There is increasing concern around the use of antibiotics in animal food production and the risk of transmission of antimicrobial resistance within the food chain. In many low and middle-income countries, including Bangladesh, the commercial poultry sector comprises small-scale producers who are dependent on credit from poultry dealers to buy day-old chicks and poultry feed. The same dealers also supply and promote antibiotics. The credit system is reliant upon informal relationships among multiple actors as part of social capital. This paper aims to describe dependencies and relationships between different actors within unregulated broiler poultry production systems to understand the social and contextual determinants of antibiotic use in low-resource settings. Methods: We used a cross-sectional qualitative design including in-depth interviews among purposefully selected commercial poultry farmers (n = 10), poultry dealers (n = 5), sales representatives of livestock pharmaceutical companies (n = 3) and the local government livestock officer as a key-informant (n = 1). We describe the food production cycle and practices relating to credit purchases and sales using social capital theory. Findings: Poultry dealers provide credit and information for small-scale poultry farmers to initiate and operate their business. In return for credit, farmers are obliged to buy poultry feed and medicine from their dealer and sell their market-ready poultry to that same dealer. All farms applied multiple antibiotics to poultry throughout the production cycle, including banned antibiotics such as colistin sulfate. The relationship between dealers and poultry farmers is reciprocal but mostly regulated by the dealers. Dealers were the main influencers of decision-making by farmers, particularly around antibiotic use as an integral part of the production cycle risk management. Our findings suggest that strategies to improve antibiotic stewardship and responsible use should exploit the patron-client relationship which provides the social and information network for small-scale farmers. Conclusion: Social capital theory can be applied to the patron-client relationship observed among poultry farmers and dealers in Bangladesh to identify influences on decision making and antibiotic use. Within unregulated food production systems, strategies to promote the prudent use of antibiotics should target commercial feed producers and livestock pharmaceutical manufacturers as a first step in developing a sustainable poultry value chain.