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ABSTRACT
In this paper, we examine in a holistic manner how entrepreneurs’ beliefs in climate change combine with psychological attributes and firm contextual conditions to produce high levels of personal resilience. To do so, we draw from complexity theory and employ a configurational approach with fuzzy set qualitative comparative analysis (fsQCA) using survey data from153 small business owners from Aberdeenshire, Scotland and Wyoming, USA, two regions where the local economies have traditionally been reliant on the fossil fuel industry. The analysis indicates that high entrepreneurial resilience arises from four unique configurations that capture the complex interactions of entrepreneurs’ beliefs about climate change with their psychological attributes of self-efficacy, optimism, and locus of control, along with the size and profitability of their firms. Analyzing patterns across the configurations reveals that efficacy-driven, optimistic internals (EOIs) develop high resilience through psychological synergy. Entrepreneurs who are climate conscious optimistic internals (CCOIs), climate conscious efficacy-driven internals (CCEIs) and climate conscious efficacy-driven optimists (CCEOs) use a mechanism of climate conscious empowerment shifting to achieve high levels of resilience. Demonstrating how beliefs about climate change complement other psychological attributes that impact entrepreneurs’ abilities to deal with adverse situations has important implications for entrepreneurship research and economic development policy.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. Although recently the price of Brent crude oil has been close to $100/barrel, at the time of the survey, Brent crude was approximately $65/barrel (https://oilprice.com/oil-price-charts/#Brent-Crude)
2. The individual KMO values are as follows: entrepreneurial resilience (0.771), belief in climate change (0.768), entrepreneurial self-efficacy (0.824), optimism in the external environment (0.844), and locus of control (0.754).
3. The truth table is shown in Appendix A.
4. The Quine-McCluskey algorithm is used to simplify the truth table by identifying the prime implicants, which are the essential combinations of conditions that cover the cases where the outcome of interest occurs followed by identifying redundant combinations and consolidating them to create a more concise and manageable set of causal configurations (Thiem and Duşa Citation2013).