Identifying Non‐Adopter Consumer Segments: An Empirical Study on Earthquake Insurance Adoption in Turkey

Abstract

In recent years, steadily climbing natural disaster losses have increased the need to promote new financial risk transfer mechanisms, including insurance, as a mitigation tool to build resilient communities to recover faster after disaster occurrence. However, while the societal need for such policies is high, demand for natural disaster insurance typically is still low. While there is ample research on positive adoption decisions, reasons for non‐adoption has not yet received the attention it deserves. Using the case of earthquake insurance in Turkey, this study investigates how public policy makers and insurance companies can differentiate non‐adopter segments and consequently develop targeted strategies to stimulate the uptake of disaster insurance. Our study develops a non‐adopter typology consisting of four segments—state reliant positivist, dependers, adversaries, and uninformed loners. Differences among segments provide policy makers and insurance companies with meaningful insights to design and consequently introduce affordable natural disaster insurance to the market.

Identifying consumer segments based on COVID‐19 pandemic perceptions and responses

“…We used the poLCA package (PolytomousVariable Latent Class Analysis) within Rversion 3.6.3 (Holding the Windsock; https://www.r-project.org/). In conducting the latent class analysis (LCA), we followed Vermunt and Magidson's (2005) and Adigüzel et al's (2019) procedure.…”

“…Although BIC and AIC are both well‐accepted information criteria for model selection based on the parsimony criteria in LCA, the BIC statistic is most often used to determine the appropriate number of latent classes in a data set because of its relative simplicity (Forster, 2000; Lin & Dayton, 1997; Linzer & Lewis, 2011). Compared to AIC, BIC also helps avoid selecting over‐fitted models and is effective in detecting correct models (Adigüzel et al, 2019; Vermunt & Magidson, 2005).…”

“…Compared to AIC, BIC also helps avoid selecting over-fitted models and is effective in detecting correct models (Adigüzel et al, 2019;Vermunt & Magidson, 2005).…”

“…These advantages include the ability to provide statistical tests to assess model fit, the ability to provide formal criteria to make decisions about the appropriate number of clusters, the choice of a cluster criterion being less arbitrary due to the underlying statistical model, the ability to simultaneously estimate clusters and covariate effects on the clusters, the ability to include observed variables of different scaling and measurement levels, and less biased estimations of class‐specific means due to the probabilistic nature of cluster membership in LCA (Wedel & Kamakura, 2002; Karnowski, 2017). LCA has been used to identify and differentiate segments of consumers in various research contexts such as logistics (e.g., Garver et al, 2008), business traveling (e.g., Millán et al, 2016), and non‐adopters of earthquake insurance among Turkish consumers (Adigüzel et al, 2019).…”

Uncovering the factors that affect earthquake insurance uptake using supervised machine learning

“…The current surge in the occurrence, intensity, and severity of natural disasters (e.g., hurricanes/cyclones, earthquakes, floods, and others) around the world has called for the need for various insurance packages as a risk management tool to guarantee the safety of nations and their citizenry 1 – 3 . However, the extent to which people see and understand the consequences of natural disasters as well as the degree to which the disaster threatens their safety are rarely considered in earthquake insurance design.…”