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Enhancing Quality and Productivity: A Deep Dive into Technology Adoption Models for Operational Excellence

In: Decision Sciences for Quality and Productivity Improvement

Author

Listed:
  • Ved Prabha Toshniwal

    (Malaviya National Institute of Technology, Department of Mechanical Engineering)

  • Rakesh Jain

    (Malaviya National Institute of Technology, Department of Mechanical Engineering)

  • Gunjan Soni

    (Malaviya National Institute of Technology, Department of Mechanical Engineering)

  • Bharti Ramtiyal

    (Faculty of Management and Commerce Poornima University)

  • Shrishti Gupta

    (Malaviya National Institute of Technology, Department of Mechanical Engineering)

  • Vijay Raj

    (Malaviya National Institute of Technology, Department of Mechanical Engineering)

Abstract

The importance of technology adoption theories is unmatched when it comes to discovering the barriers and enablers in consumer behavior in adoption of a new technology. Various theories are available in literature within the realm of consumer behavior study, each emphasizing different facets of adoption, be it behavioral or technological. While analyzing the adoption of emerging technology, a theory that considers both technological and human behavioral factors is essential. Most studies do not have a clear approach to selecting a technology adoption model for their research. In this study, our main focus is to compare the models that predominantly focus on the technological aspects and identify a best fit model to study the adoption of emerging technologies in the pharmaceutical sector. To achieve this, we employed a combination of Multi-Criteria Decision Making (MCDM) methods. Seven criteria were formulated in collaboration with experts from the pharmaceutical industry. Five technology adoption models were chosen that focused on technology-related aspects. The weights of the criteria were determined using the PIvot Pairwise RElative Criteria Importance Assessment method, while the technology adoption models were ranked based on ratings provided by experts, utilizing multiple methods such as MARCOS, TOPSIS, EDAS, ELECTRE and CODAS. The obtained rankings were subsequently compared to validate the methodologies employed. Notably, the Task Technology Fit (TTF) model emerged as the top choice across all three MCDM methods, showcasing its efficacy in examining technology-related factors influencing the adoption of Pharma 4.0.

Suggested Citation

  • Ved Prabha Toshniwal & Rakesh Jain & Gunjan Soni & Bharti Ramtiyal & Shrishti Gupta & Vijay Raj, 2026. "Enhancing Quality and Productivity: A Deep Dive into Technology Adoption Models for Operational Excellence," Springer Books, in: Indrajit Mukherjee & Raghu Nandan Sengupta & Bhaskar Basu & Jitendra Kumar Jha (ed.), Decision Sciences for Quality and Productivity Improvement, chapter 0, pages 215-245, Springer.
    • Handle: RePEc:spr:sprchp:978-981-95-7545-9_9
    DOI: 10.1007/978-981-95-7545-9_9
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