Assessing sustainability performance of high-tech firms through a hybrid approach

Cui, L., Zhai, M., Dai, J., Liu, Y. and Zhang, P. (2019) Assessing sustainability performance of high-tech firms through a hybrid approach. Industrial Management and Data Systems, 119(8), pp. 1581-1607. (doi: 10.1108/IMDS-02-2019-0066)

Abstract

Purpose: In light of the lack of subjective criteria and scientific rationality in current sustainability performance assessment, the purpose of this paper is conducted to improve the sustainability performance assessment of high-tech firms by developing a hybrid approach that integrates quantitative and qualitative research methods. Design/methodology/approach: This study proposed a hybrid approach that integrates word frequency analysis, cluster analysis, grey theory and the decision-making and trial evaluation laboratory (DEMATEL) method. Specifically, this study identifies useful criteria using quantitative word frequency analysis as well as qualitative literature research. Then, cluster analysis is used to divide these criteria into different categories. Subsequently, this study applies the grey theory associated with the DEMATEL method to assess the sustainability performance of high-tech firms. Findings: The results reveal that the socio-environment is an important aspect underlying the corporate sustainability performance of high-tech firms. Therefore, high-tech firms should enhance their pollution emission control capabilities and increase investment in energy-conservation and emission-reduction technologies to drive sustainable development. In addition, increasing green product sales revenue and improving the guiding capability of green consumption are core issues that firms must address. Originality/value: This study assesses the sustainability performance of high-tech firms by applying a hybrid method. This method can be used to construct a framework for scientific sustainability performance assessment and to provide a clear direction for the sustainable development of firms.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Kurt
• Authors: Cui, L., Zhai, M., Dai, J., Liu, Y., and Zhang, P.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Industrial Management and Data Systems
• Publisher: Emerald Publishing Limited
• ISSN: 0263-5577
• ISSN (Online): 1758-5783
• Copyright Holders: Copyright © 2019, Emerald Publishing Limited
• First Published: First published in Industrial Management and Data Systems 119(8):1581-1607
• Publisher Policy: Reproduced in accordance with the publisher copyright policy