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Corporate Reputation and Financial Performance of Life Insurers

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Abstract

This paper analyses empirically the relationship between corporate reputation and the financial performance of life insurers. First, it investigates the factors contributing to the corporate reputation of life insurers and finds that financial strength as well as underwriting service quality are crucial determinants. Second, this study shows that corporate reputation has a significantly positive impact on profitability because it helps to bring in new business and premiums for investment. Third, underwriting and investment constructs of insurance operations are linked to analyse the sources for profitability. Additionally, this paper finds that a sustained reputation may increase profitability over time, because reputation built previously can keep an insurer in a favourable position in future market competition. The empirical findings of this paper suggest that life insurers can improve their profitability through promoting corporate reputation, which highly relies on the service quality of underwriting.

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Notes

  1. Schanz (2006).

  2. Gatzert and Schmit (2016).

  3. Gatzert et al. (2016).

  4. Fombrun and Van Riel (1997).

  5. Gatzert (2015).

  6. Dowling (1986).

  7. Boonpattarakan (2012).

  8. Sobol and Farrelly (1988).

  9. Robinson (1997).

  10. Zboron (2006).

  11. Csiszar and Heidrich (2006).

  12. See Gatzert (2015) for a comprehensive review.

  13. Cummins et al. (2006).

  14. Ishihara (2006).

  15. Rindova et al. (2010).

  16. Vegholm (2011).

  17. Walsh et al. (2009).

  18. Walters (1978).

  19. Guenzi and Georges (2010).

  20. Venetis and Ghauri (2004).

  21. Page and Fearn (2005).

  22. Roberts and Dowling (2002).

  23. Cummins et al. (1999).

  24. According to the income statements of life insurance companies in Taiwan during 2004–2013.

  25. McGahan and Porter (1999).

  26. Roberts and Dowling (2002) use “America’s Most Admired Corporations” surveyed by Fortune magazine as the measurement for reputation.

  27. This ranking has been conducted since 1993.

  28. McGuire et al. (1990).

  29. According to Greene (2000, pp. 332–334), stepwise regression, Akaike’s information criterion (AIC) or Amemiya’s prediction criterion (PC) can help to select variables for a model when there is no solid theory to support which variables are to be included; however, stepwise regression can be a more palatable alternative. This study conducts all three methods and finds the outcomes of AIC and PC consistent with those based on stepwise regression. Owing to the length of paper, only the result of stepwise regression is explained in the section “Empirical Resuults”, with AIC and PC reported in Table 3.

  30. This paper uses complaint ratio (i.e. the unsatisfaction rate per policy), instead of the number of complaints, to measure an insurer’s service quality. Since the number of policies sold greatly differs between large and small insurers, the number of complaints itself is not a good measurement for service quality. The insurance commissioner also publishes the complaint ratio.

  31. ROA and Debtr are lagged variables due to the disclosure time of financial statements.

  32. Wooldridge (2002).

  33. The selection criterion of stepwise regression is based on the significance level of F statistics, but the coefficients of multiple regression are judged based on t statistics. Therefore, the significance level may be somewhat different. Although the coefficient of ROA is insignificant, the multicollinearity is not a problem according to the variance inflation factor (VIF) shown in Table 4.

  34. ρ(CorpRP t , lnPremNC t ) =0.534. The variance inflation factors (VIF) for the explanatory variables are low, as shown in Table 6, which suggests that the problem seems not due to multicollinearity.

  35. ROA is not significantly related to FHC or FI under OLS (Table 6). Under 2SLS (Table 7), ROA has a significantly positive relation with FI but its negative relation with FHC is not significant enough.

  36. ROA=earning/total assets, so small assets may push up ROA.

  37. The result of 2SLS is similar to that of 3SLS and thus omitted.

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Authors and Affiliations

  1. Department of Risk Management and Insurance, National Chengchi University, 64, Section 2, ZhiNan Road, Taipei, 11605, Taiwan

    Tsai-Jyh Chen

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  1. Tsai-Jyh Chen
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Appendices

Appendix A

Table A1

Table A1 Candidate variables for corporate reputation and sources of references
Full size table

Appendix B

More underwriting expenses imply higher underwriting service quality, and complaints usually result from poor service. To understand the net effect of service quality, this paper applies factor analysis method to find the latent variable—service quality as shown in Eq. (A1).

The regression result for corporate reputation conducted based on the net effect of underwriting service quality is shown in Table A2. The underwriting service quality remains the most important determinant for corporate reputation. ROA of previous period is also a significant factor for reputation as expected. However, the adj-R2 of this revised model is somewhat lower than that based on the original variables (see Table A2).

Table A2 Multiple regression for corporate reputation based on service quality
Full size table

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Chen, TJ. Corporate Reputation and Financial Performance of Life Insurers. Geneva Pap Risk Insur Issues Pract 41, 378–397 (2016). https://doi.org/10.1057/gpp.2016.8

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