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المؤلفReddy, Rajesh Chidananda
المؤلفMishra, Debasisha
المؤلفGoyal, D. P.
المؤلفRana, Nripendra P.
تاريخ الإتاحة2024-03-03T10:20:36Z
تاريخ النشر2023-09-28
اسم المنشورBenchmarking
المعرّفhttp://dx.doi.org/10.1108/BIJ-03-2023-0160
الاقتباسReddy, R. C., Mishra, D., Goyal, D. P., & Rana, N. P. (2023). A conceptual framework of barriers to data science implementation: a practitioners' guideline. Benchmarking: An International Journal.
الرقم المعياري الدولي للكتاب1463-5771
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85172163872&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/52554
الملخصPurpose: The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints. Design/methodology/approach: The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework. Findings: The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable. Originality/value: The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.
اللغةen
الناشرEmerald Publishing
الموضوعBarriers
Categorization
Data science
Fuzzy MICMAC
Hierarchical model
ISM
العنوانA conceptual framework of barriers to data science implementation: a practitioners' guideline
النوعArticle
dc.accessType Full Text


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