PRASIAS: A New Preference Ranking Model for Comparing Organizational Performance Under Disruption

Sanjib Biswas (), Prasenjit Chatterjee and Edmundas Kazimieras Zavadskas ()
Additional contact information
Sanjib Biswas: Amity University, Major Arterial Road, AA II, Newtown Kadampukur, West Bengal 700135, India
Prasenjit Chatterjee: ��Chief Research Fellow, Faculty of Civil Engineering, Institute of Sustainable Construction, Laboratory of Smart Building Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania‡College of Engineering, Yuan Ze University, Taoyuan City, Taiwan§Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah 711204, West Bengal, India
Edmundas Kazimieras Zavadskas: �Institute of Sustainable Construction, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 06, 1771-1800

Abstract: This paper aims to investigate the impact of COVID-19 on the organizational performance of automotive, fast-moving consumer goods, consumer durables, and healthcare during both pre-and post-pandemic periods, forming a dual objective. To achieve this, the paper uses a set of market price-based ratios including Price-to-Earnings (P/E), Price-to-Book value (P/B), Price-to-Sales (P/S), and Price-to-Cash Flow (P/CF), along with market risk, as indicators for performance comparison. The scope of the assessment is limited to the financial performance analysis. The paper also attempts to relate performance to long-term growth prospects measured by Tobin’s Q model. Second, the paper proposes a Multi-criteria Decision-Making (MCDM) model called Preference Ranking based on Similarity to Ideal Average Solutions (PRASIAS) to compare the organizations. PRASIAS is a novel extension of the recently introduced Preference Ranking on the Basis of Ideal-Average Distance (PROBID) method. PRASIAS uses similarity measures in terms of inclination angle and considers positive and negative ideal solutions, as well as the average solution, to provide a comprehensive decision-making framework. To determine criteria weights, the paper applies a recently developed model called Logarithmic Percentage Change-driven Objective Weighting (LOPCOW). The paper validates the results by comparing them with the outcomes of other MCDM methods, and it also performs sensitivity analysis to examine the stability of the ranking. From the results, it is evident that there are some variations in the comparative positions over the years. Cipla Ltd., Dr. Reddy’s Laboratories Ltd., Bajaj Auto Ltd., and Britannia Industries Ltd. secured the top positions. P/E ratio remains a dominant influencing factor, as indicated by the calculated weights. A positive association is observed between financial stability and the comparative ranking of the organizations, while Tobin’s Q has not proven to be a significant influencer. It is also noteworthy that COVID-19 has impacted the performance of these entities.

Keywords: Market-based performance; Tobin’s Q; COVID-19; Preference Ranking-based on Similarity to Ideal Average Solutions (PRASIAS); Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622025500208
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:24:y:2025:i:06:n:s0219622025500208

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622025500208

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().