ISM approach to model financial risks in Indian KPO organization

Naveen J; Himanshu Payal; Prashant Chauhan

doi:10.52756/ijerr.2023.v32.003

Naveen J Department of Mechanical Engineering, Sharda University, Greater Noida, Uttar Pradesh, India https://orcid.org/0000-0002-0046-1080
Himanshu Payal Department of Mechanical Engineering, Sharda University, Greater Noida, Uttar Pradesh, India
Prashant Chauhan Department of Mechanical Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India https://orcid.org/0000-0003-0769-8504

DOI: https://doi.org/10.52756/ijerr.2023.v32.003

Keywords: Interpretive Structural Modeling, financial risk factors, knowledge process outsourcing

Abstract

Globalization has entered a new phase due to significant advancements in technology. This progress has allowed organizations to reduce costs while enhancing their market responsiveness, thus gaining a competitive edge. Nowadays, organizations prioritize knowledge process outsourcing (KPO) as a crucial element in their strategies, particularly in the service sector. However, research indicates that several challenges are negatively impacting the performance of the supply chain network when it comes to KPO. One major challenge is the financial risk associated with this environment. Various factors, such as switching costs, adaptation costs, and layoff costs, influence this financial risk. The aim of this research is to comprehend and identify the interconnectedness of these financial risk factors, which can affect the operational performance of KPO. The authors have meticulously reviewed relevant literature to identify these financial risk factors. Based on this subject matter, an integrated model has been constructed using Interpretive Structural Modeling (ISM) to address the finance-related challenges impacting KPO. Additionally, the Cross-Impact Matrix analysis evaluates the dependency and driving power of financial challenges. This analysis assists decision-makers in classifying and identifying important factors, as well as understanding the direct and indirect effects of each factor on operations related to KPO. The results indicate that switching costs, disputes & litigation, and measurement problems have strong driving power and weak dependency, making them independent factors. By mitigating these independent factors, the overall performance of the model can be improved.

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