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Proceedings of the 2018 International Conference on Multidisciplinary Research
Abstract
This book is the proceedings of the 2018 International Conference on Multidisciplinary Research.
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The educational loan landscape in Kerala reflects a complex interplay between rising disbursement statistics, increasing Non-Performing Assets (NPAs), and the imperative of leveraging human resources for economic growth. Despite the potential for remittances and demographic advantages, challenges persist in realizing the full utility of educational loans. This study delves into the problems and prospects faced by loan recipients in commercial banks of Kerala, examining factors such as loan processing time, coverage, lending bank sector, and collateral requirements. Policy discussions center on balancing commercial interests with social responsibility, with calls for revising loan terms and enhancing borrower utility. While policymakers advocate for leniency, banks emphasize stringent monitoring amid rising bad loans and recovery costs. The study aims to reconcile these divergent interests and address socioeconomic challenges in higher education financing. By analyzing the association between loan variables, default rates, household dynamics, and alternative finance sources, the study affirms that loan-related factors significantly impact borrower utility. Utilizing basic statistical tools, the paper provides insights into the complexities of educational loan administration and proposes avenues for policy reform to optimize the efficacy of student loan schemes.
This paper, based upon a field research project commissioned by the Panos Institute SouthernAfrica, investigates the communicative efficacy of the radio listening clubs project implementedby the Institute in Malawi and Zambia. The investigation takes the form of a ‘second-orderinterpretation’ of the key findings of the field research. The findings are analysed in terms of theparticipatory communication model of development communication. The paper argues that theclubs live up to some of the ideal-typical attributes of participatory communication. This is evidentin the following areas: (i) a propensity for social mobilisation; (ii) acquisition of skills and knowledge;(iii) communally induced motivation to listen to the radio; (iv) the possibility of interpersonal influencewithin groups; (v) the benefit of being ‘organised’ structures; (vi) the ‘massive’ reach of the clubs;and (vii) the dialogic interchanges between the rural-based groups and the urban-based policymaking elites.
Trip attraction rates are major inputs needed in traffic impact assessment of commercial developments. The objectives of this study are to develop models to estimate trip attraction rates for person trips and vehicle trips and to develop parking standards for three-wheelers, motorcycles, cars/vans for supermarkets in the Kandy area. The number of vehicles entering and leaving the supermarkets and the number of people visiting the supermarkets at every fifteen minute interval during peak hours will be surveyed using a smart-phone based Android application. Multiple linear regression analysis will be used to analyse data. Central limit theorem will be used to develop parking standards. For each category a sophisticated model for supermarket designers and simplified model for local authorities will be developed. The results show that the wine shop area is the governing factor of the trip attraction rate and parking standards in the Kandy area.
Organizations collect a vast amount of data of different types, from various sources, and through different channels. Primarily, these data are used by these organizations to facilitate their core business processes. However, today we witness a growing tendency to use these data for other purposes than that they are collected for. To this end, the data from one information system are combined with those of other information systems. Subsequently, the combined data are analyzed with advanced data analytics tools. Although there is a strong and practical need to apply such findings of data analytics to improve, among others, organizations’ (social) services, it is often not straightforward how to apply these findings in practice. This is due to many challenges arising from legal, ethical, and data quality concerns. In this chapter, we discuss the main reasons that hamper the application of data analytics findings, particularly pertaining to data collection processes and data analysis processes (like data mining and statistics). These reasons include inadequate transformations of statistical truths to individual cases, chances to fall into the trap of system realities, and required efforts to deal with the evolving semantics of data over time. The latter is due to the fact that our (social) environment is subjected to constant changes. We discuss two strategies to harvest data analytics findings in a responsible way. By means of some real-life examples in the field of social services we illustrate the applications of the strategies in practice. Furthermore, we argue that the findings from data-driven analytics may augment real-world ecosystems if they are applied with caution and responsibly.
