Department of Management

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1930

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Author: search.filters.author.Chanda, UdayanSubject: search.filters.subject.Bayesian networks

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Now showing 1 - 4 of 4
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    Bayesian network on labour dissonance: a social sector development challenge to India
    (Taylor & Francis, 2016-01) Chanda, Udayan
    India's unorganized labour force contributes about one third of the total labour sector. The scenario is even worse in the Indian automotive industry which employs a little over 7% on a permanent basis. Problems get exaggerated due to the outdated labour laws, ironically established to support and protect workers. The disappointing areas in the labour contract act and labour laws have led to unfair wage practices and a hostile work environment, giving way to labour discord. This research paper discusses the key issues of labour dissonance in the Indian automobile industry using a Bayesian network analysis. Real-life case-study examples from the Indian automobile industry were considered to identify the rationale behind labour unrest. Bayesian analysis of a set of 250 responses helped us to understand the associations among key attributes of labour dissatisfaction.
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    A Bayesian Network Model on the association between CSR, perceived service quality and customer loyalty in Indian Banking Industry
    (Elsevier, 2017-04) Chanda, Udayan; Goyal, Praveen
    Corporate Social Responsibility has become a buzzword in the contemporary era. Decision makers are including CSR as important part of company’s corporate strategy. Indian banking industry is also facing huge challenges and looking for the avenues to create competitive advantage to retain and attract the customer. This study aims at identifying the association of various CSR initiatives on the perceived service quality and customer loyalty in the Indian perspective by using Bayesian Network analysis. Results of the study show the different dimensions of CSR that establishes relationship with the perceived service quality and customer loyalty.
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    A Bayesian network model on the interlinkage between Socially Responsible HRM, employee satisfaction, employee commitment and organizational performance
    (Taylor & Francis, 2019-08) Chanda, Udayan; Goyal, Pravin
    In recent years several studies have been made to understand the impact of Socially Responsible HRM practices on Organizational Performance. Employee progress, community and environment play an important role in the sustainable growth of an organization. Thus, organizations are always looking for the ways to improve the employee satisfaction vis-a-vis commitment to improve the performance. Recent studies have shown that as employees are important stakeholder, hence formulating proper Socially Responsible HRM practices may help organization to better the returns on assets. The main objective of the study is to identify the relationship among various dimensions of Socially Responsible HRM practices with dimensions of employee satisfaction, employee commitment and organizational performance for Indian manufacturing sector by using Bayesian Network approach. Results of the study establish the relationship between dimensions of Socially Responsible HRM and Organizational Performance.
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    Supply-side risk modelling using Bayesian network approach
    (Taylor & Francis, 2022-02) Chanda, Udayan; Sharma, Satyendra Kumar; Routroy, Srikanta
    Organisations’ vulnerability to risks exponentially increased in the past decade, thereby highlighting the need to develop additional effective risk management strategies. This research uses a systematic literature review as a foundation for designing a supply risk model that uses a Bayesian belief network. The proposed model aims to identify the most critical objective and subjective risk factors influencing supply chain networks. Moreover, the proposed methodology has been demonstrated through a case study conducted in an Indian manufacturing, in which inputs were taken from supply chain and risk management experts. Hugin Expert software was used to design and run simultaneous simulations on the Bayesian network. The top three factors found to influence business profitability were delays, product technology, and fuel price. The proposed model can be reengineered as conditions change and new information becomes available, thereby ensuring that risk analysis remains current and relevant along the timeline of the any disruption.