An integrative approach for structuring and prioritising eco-innovation determinants with a survey in knowledge-based companies

Abstract

Purpose

This study aims to develop a comprehensive set of determinants and sub-determinant of eco-innovation (EI) and to propose an approach for their structuring and prioritisation.

Design/methodology/approach

A framework was developed based on a literature review, interpretive structural modelling (ISM) was used to structure the main determinants and the results were transferred into a six-phase quality function deployment (QFD) for prioritising the sub-determinants. The statistical population included 15 experts and the middle/top managers of 130 knowledge-based companies.

Findings

Based on the results of the driving power and dependence diagram from the ISM, a seven-level classification of the main determinants has been used. The QFD results indicated ‘meeting environmental law standards’; ‘scale to support innovative strategies’; ‘commitment to continually improve and make eco-innovations’; ‘technological advisory oriented to environment’; ‘product and process EI oriented methods’; ‘improvements in energy efficiency across the company and the market’ and ‘reputation, brand image, and profit margin’ as the priorities of the sub-determinants of eco-innovation in the phases of the developed QFD.

Practical implications

The findings of this study provide evidence of the usefulness of decision-making approaches such as ISM and QFD in resolving environmental problems, which is helpful to analysts and practitioners in the field of EI.

Originality/value

The novel ISM+EID₂ approach proposed distinguishes this study from previous studies. The approach allows elaboration on the model presented by de Pacheco et al. (2017) by adding nine empirically derived sub-determinants.

Citation

Shahin, A., Imanipour, N., Shahin, A. and Wood, L.C. (2020), "An integrative approach for structuring and prioritising eco-innovation determinants with a survey in knowledge-based companies", Journal of Manufacturing Technology Management, Vol. 31 No. 4, pp. 799-824. https://doi.org/10.1108/JMTM-03-2019-0110