Document Type : Original Research Paper
Authors
1 Department of Management, University of Qom, Qom, Iran
2 Departmentn of Management, University of Qom, Qom, Iran
Abstract
BACKGROUND AND OBJECTIVES: Big data is increasingly becoming a major organizational enterprise force to reckon with in this global era for all industries. It seemingly offers more features for acquiring, storing and analyzing voluminous generated data from various sources to obtain value-additions. Despite the advantages of big data analytics in enhancing performance and achieving the competitive advantage, there is substantial evidence that many organizations have faced some barriers to adoption and implementation of big data technologies. The insurance industry is no exception. However, the adoption and implementation of big data analytics in insurance organizations is relatively lagged and there is no study addressing this phenomenon so far in Iran insurance industry. Therefore, the main purpose of this study is to identify and analyze various barriers that affect the adoption and implementation of big data analytics in the insurance industry in the Iranian context and to investigate the inter-dependences between these barriers.
METHODS: The current research is an applied study in terms of objectives, a descriptive study in terms of research design, as well as a survey study in terms of data collection method. First, using a comprehensive review of existing literature and obtaining confirmatory opinions of industry managers, a list of barriers to adoption and implementation of big data analytics in the Iranian insurance industry have been identified. Then, Total Interpretive Structural Modeling (TISM) with matrice d' impacts croises multiplication appliqué an classement (cross-impact matrix multiplication applied to classification, abbreviated as MICMAC) analysis was used to map the interrelationships and develop a hierarchical structure among the identified barriers.
FINDINGS: The major barriers to adoption and implementation of big data analytics were identified and classified into 10 categories including cost of investment, lack of compatibility with technical infrastructure, weak organizational culture, lack of top management support, time constraints, staff resistance, lack of collaboration among departments, lack of access to experienced and skilled expertise, customer data privacy and security, and lack of regulations. In addition, lack of access to experienced and skilled expertise, lack of top management support as well as weakness or lack of regulations are the root barriers to the adoption and implementation of big data analytics in the Iranian insurance industry
CONCLUSION: Combining the literature review findings with the opinions of managers and industry practitioners, and analyzing them by total interpretive structural modeling with MICMAC led to the development of a framework for better understanding of barriers to the adoption and implementation of big data analytics in the Iranian insurance industry. This framework helps policymakers and managers to prioritize issues and develop effective strategies for the development of big data analytics. This study is the first of its kind to theorizing big data analytics adoption and implementation barriers and develops hierarchical relationships between them using ISM and MICMAC methodology in the Iranian insurance context. Finally, the paper provides several effective solutions to coping with barriers to adoption and implementation of big data technologies and recommended some future directions of research in this field.
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