Business resilience and growth strategy transformation post crisis

Abstract

AbstractThe recent crisis world experienced (Covid-19) undoubtedly changed many aspects of business, such as operations, growth, sales, etc. A business needs capability and strategy to survive, and this paper considers resilience and growth suitable for encountering a crisis. Following that, this paper argues that resilience not only results from a crisis, as mentioned in previous studies but is a process. We also argue that growth strategy and its stages differ between regular times and times of crisis. This paper aims to elaborate on changes in business resilience and growth strategies as a reaction to a crisis by building line-by-line coding, patterns, and themes. This study develops a conceptual model for forming business sustainability after a crisis with resilience and growth as pillars. This qualitative study used snowball sampling with 13 case studies and conducted semi-structured interviews, Focus Group Discussion (FGD), and observation with dependability, credibility, confirmability, and transferability as the triangulation processes. This inductive study demonstrates resilience and growth steps that can assist entrepreneurs in avoiding misleading strategies. The findings also indicated that digital imperatives could hasten both processes to attain sustainability. This paper draws on organizational resilience theory to discuss the theoretical implications of this research. This study discovers three main stages in the resilience process: resilience awareness, adaptation, and action, and growth stages in crisis time, namely no growth, growth support, growth force, and sustainable growth. On a practical level, the proposed model can also assist entrepreneurs in dealing with a crisis regarding what to do and understanding their conditions. This study contributes to the ongoing debate on how to respond to crisis and provides complete stages in resilience response and growth strategies for a business. For future research, this paper recommends a longitudinal study to understand and analyse the implementation and effectiveness of the models.