Exploring the impact of dynamic capabilities on sustainable supply chain firm's performance using Grey-Analytical Hierarchy Process

摘要

Dynamic capabilities (DCs) are the inherent capabilities developed by firms through the implementation of Sustainable Supply Chain practices. These DCs serve as a backbone for the firms to achieve competitive advantages in contemporary ever-changing markets. The present study focuses on exploring the impact of the DCs on the firm's performance, by offering a review of literature to identify the various associated DCs and performance Measures of a firm cited in peer reviewed journals. The impact of the Collected DCs on the enhancement of firms' performance is then analyzed with the help of six performance measures identified from the literature. First, through a questionnaire survey with industrial experts from south India as respondents, the initial list of 77 DCs is confined to a list of 40 essential DCs. Second, the shortlisted 40 DCs are grouped under six performance measures; a logical interpretation of which DC will best enhance which performance measure was selected with the help of the industrial experts. Third, Analytical Hierarchy Process pair-wise comparisons of the seven groups of DCs are done to identify the influential DCs in each of the seven performance measure categories. Priority weights and rankings are obtained both within the category and globally. Finally, a Grey-Analytical Hierarchy Process is used to get a better understanding of the overall linpact of the DCs in enhancing the performance measures of the firm for a sustained competitive advantage. The obtained results help industrial managers to identify specific influential DCs for each performance measure category by overcoming the limitations of few experts through the application of the grey multi criteria decision making system. Identifying and prioritizing performance-specific DCs allow sustainable supply chain oriented firms to concentrate on the DCs most relevant to the specific performances needing improvement.(C) 2017 Elsevier Ltd. All rights reserved.