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

Ved Prabha Toshniwal (), Rakesh Jain (), Gunjan Soni (), Bharti Ramtiyal (), Shrishti Gupta () and Vijay Raj ()
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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

Chapter Chapter 9 in Decision Sciences for Quality and Productivity Improvement, 2026, pp 215-245 from Springer

Abstract: 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.

Keywords: Technology adoption model; TOPSIS; MCDM; Task technology fit; Pharma 4.0; CODAS (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-981-95-7545-9_9

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