Intuitively, we would expect that trusting investors engage in less monitoring. In contrast, we documented positive associations between trust in stakeholders and our two monitoring proxies: financial accounting information acquisition and the exercise of shareholder voting rights. In the following, we use structural equation modeling to examine potential mecha-nisms driving these associations. Especially, the positive relation between trust and monitor-ing might partly be due to backdoor effects via investor characteristics that are associated with both concepts. Our proposed structural equation model to be tested is summarized in Figure 5.
First, we model a direct effect of trust on monitoring, and we expect it to be negative. Trust is a latent construct measured inversely by the respective four questionnaire items for agency risk perception as reflective indicator variables (omitted in the figure). Monitoring is either a latent construct for financial accounting information acquisition or the variable VOTING.
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Second, we use ECON_EDU as an inverse proxy for the costs of monitoring and thus, we expect it to have a positive effect on monitoring. The extent of trust in stakeholders might depend on the acquired knowledge about economic activities and interrelations. Therefore, we allow ECON_EDU to have an effect on trust, as well. However, we do not have an expecta-tion for the respective sign. Third, we introduce stock market exposure, a latent construct to represent the benefits of monitoring. The variables %STOCKS, #FIRMS and HORIZON are used as reflective indicator variables for stock market exposure (omitted in the figure). Higher levels of %STOCKS should increase the stake at risk, and higher levels of #FIRMS or HORI-ZON should increase the accumulated probability that stakeholders negatively affect an indi-vidual investor’s wealth position. We expect investors with a greater stock market exposure to engage more in monitoring. Furthermore, investors with low levels of trust are expected to rather shun the stock market and thus to have lower stock market exposure. This would imply a positive effect of trust on stock market exposure. Finally, we expect investors with high lev-els of ECON_EDU to have a greater stock market exposure.
[Figure 5 about here]
We use maximum-likelihood estimation for the structural equation modeling. For each la-tent construct in the model, the path to one of its indicator variables is fixed to one in order to scale the latent construct. The degrees of freedom are greater than zero for each model esti-mated, thus satisfying the t rule as necessary condition for the identification of a model (Bol-len 1989). All estimated covariance matrices are positively definite and no negative variances are encountered during the estimations. We present standardized solutions in which the vari-ances of independent variables are fixed to one and parameter values are bound by zero and one. To assess the fit of our model estimations, i.e., how well a model estimation is able to replicate the covariance matrix of the data, we use (1) the Adjusted Goodness of Fit Index (AGFI), (2) the Comparative Fit Index (CFI), and (3) the Root Mean Square Error of
Approx-imation (RMSEA). Typically, AGFI and CFI greater than or equal to 0.90 (0.95) are consid-ered as a reasonable (good) fit (Lei and Wu 2007), and a RMSEA less than or equal to 0.08 (0.05) is considered as a reasonable (good) fit (MacCallum, Browne, and Sugawara 1996).
Additionally, we provide the Chi² values for the null hypothesis that the covariance matrix of the data equals the covariance matrix implied by the model. However, it is not uncommon that for large samples this hypothesis is rejected although the model might fit the data well.
Figure 6 presents the modeling solution with financial accounting information acquisition as monitoring proxy. This latent construct is measured by the eight questionnaire items for the intensity of use of financial statements components as reflective indicator variables (omitted in the figure). The direct effect of trust on financial accounting information acquisition is in-significant. All remaining parameters are significant and show the expected sign. The effect of ECON_EDU on trust is positive. Of the indicator variables, HORIZON is the only one with an insignificant parameter. The fit measures indicate a reasonable fit. Taken together, the solu-tion suggests that the positive associasolu-tion between trust and financial accounting informasolu-tion acquisition we have documented is a joint effect of three mechanisms. First, individual inves-tors with an educational background in economics or business tend to be more trusting and to engage more in information acquisition. Second, these investors tend to have greater stock market exposure which tends to result in more information acquisition, as well. Third, more trusting investors tend to have greater stock market exposure in general and thus tend to en-gage more in information acquisition.
[Figure 6 about here]
Figure 7 provides the modeling solution with VOTING as monitoring proxy. In terms of parameter signs and significance levels, it is in line with the previous solution for financial accounting information acquisition. AGFI and CFI suggest a good fit while RMSEA is close to the good-fit threshold. The same mechanisms suggested above seem to jointly explain the
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overall positive association between trust and the exercise of shareholder voting rights we have documented. Again, the solution suggests no direct effect of trust on monitoring.
[Figure 7 about here]
Table 13 presents a summary of the fit measures for the two modeling solutions presented above. Our final sample consists of questionnaires only which provide a full set of answers on all monitoring items used throughout this paper. As a robustness check, we re-estimate our proposed model using relaxed samples allowing monitoring items to be missing if not re-quired for the respective modeling solution. Thus, our sample size increases by 68.6% for the solution regarding financial accounting information acquisition and by 129.6% for the solu-tion regarding VOTING. The respective fit measures are presented in Table 13, as well, and are in line with the fit measures of the solutions based on the final sample. However, when using relaxed samples, the parameter for the direct effect of trust on monitoring is significant in both cases. Since the signs are—against expectation—positive, this might hint at the exist-ence of additional mechanisms connecting trust and monitoring.
[Table 13 about here]