Exploratory Analysis of Factors Influencing Agile New Product Development Adoption in Manufacturing Industries

Abstract

Purpose: Global competition, individualized customer requirements, and volatile market conditions create an environment conducive to agile new product development (ANPD). This research seeks to identify the key factors that influence ANPD adoption along with the development of a conceptual framework for the identified factors. Design/methodology: Through a literature review, eight factors having 47 sub-factors pertinent to ANPD adoption and its performance improvement were identified. Considering all of these factors, the development of conceptual framework and research hypotheses was carried out. A structured questionnaire was used to collect 118 online responses from both domestic and foreign subject matter experts. The structural equation modeling (SEM) approach was used for validation of the conceptual framework along with the research hypotheses testing. Findings: This study supported six hypotheses: “Technology management competencies”, “Product development competencies”, “Organizational management competencies”, “Human resource competencies”, “Software management competencies”, and “Policy management competencies”. These supported hypotheses influence ANPD adoption significantly. However, the analysis did not support the two more positive factors, namely “Integrated system competencies” and “Supply chain competencies”, showcasing the necessity for a better understanding of them among the product development experts. Research limitations: As the proposed methodology relies on qualitative data, it is somewhat complex and time-consuming. While SEM can verify the linear relationship, a hybrid approach involving the SEM-MCDM technique can be employed to comprehend the impact of ANPD adoption and performance improvement. Practical implications: The findings of this study will assist product development experts, manufacturing executives, and managers in developing effective ANPD adoption policies. It will help in improving the new product development success rate and highlighting the causes of poor performance. Originality/value: This is a one-of-a-kind and highly beneficial structural modeling-based decision-making tool. This framework can be effective across multiple domains, and incidents of ANPD adoption failure can be mitigated.