Descriptive statistics
Table 2 reports the descriptive statistics for the variables. From the descriptive statistics in Table 2, we can see that in the sample of this article, the average value of digital transformation of enterprises (DT) is 0.461, the maximum value is 2.811, the minimum value is 0, and the standard deviation is 0.551, which show that there are certain differences in the level of digital transformation between different enterprises. The maximum value of the proportion of academic senior executives (r_xsgg) is 1.000, indicating that some enterprises have reached 100% of their senior executive team having an academic background in individual years, and the average value is 0.087, indicating that 8.7% of the observation samples have hired senior executives with academic backgrounds. The descriptive statistical results of the remaining control variables are shown in Table 2 and are not individually described.
Benchmark regression analysis
To ensure the validity of the model estimation, this paper also conducted a variance inflation factor (VIF) test for all variables. The VIF values for all variables are below the threshold of 10, with an average VIF value of 1.27, indicating that there is no multicollinearity among the variables. Next, the Hausman test was used to determine whether the fixed effects model or the random effects model should be used. The results of the Hausman test strongly reject the hypothesis of using the random effects model; therefore, this article uses the panel fixed effects model. Based on the setting of research Model (1), the OLS regression method was used to empirically test H1; that is, it was used to examine the specific impact of the academic background of senior executive teams on the digital transformation of enterprises (DTs).
Table 3 reports the regression results for the impact of the academic background of senior executives on the digital transformation of enterprises. Columns (1) report the regression results without the use of control variables, and Columns (2) report the regression results incorporating the use of control variables. The estimated coefficient for r_xsgg1 in column (1) is 0.2439, and it is significant at the 1% level; after including control variables, the estimated coefficient for r_xsgg1 in column (2) is 0.1967, significant at the 1% level, indicating that the academic background of executives effectively promotes corporate digital transformation. In summary, H1 is supported.
(1) The t values displayed in brackets are corrected by robust heteroskedasticity; (2) *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively (the same applies below).
Test of the mediating effect
The report results are shown in Table 4, where columns (1) and (2) respectively report the regression results with and without control variables. In column (2), the estimated coefficient for r_xsgg1 is 2.9170 (P < .01), indicating that the academic background of executives will intensify the company’s innovation investment, thereby enhancing innovation resources. Continuous and stable innovation investment is an important driving factor for the development of corporate digital transformation55. The more innovation investment increases, the more it is conducive to the development of digital transformation in enterprises. Thus, Hypothesis 2 is verified.
Moderating effect test
The report results are shown in Table 5, where columns (1) and (2) respectively present the regression results with and without control variables. In column (2), the coefficient of the interaction term between executive academic background and industry competition intensity is 0.6326 (P < .01), indicating that the intensity of industry competition has a positive moderating effect on the relationship between executive academic background and corporate digital transformation, thus verifying Hypothesis H3 of this paper.
Robustness test
To solve the possible endogeneity problem, a variety of methods are used for testing.
Endogeneity treatment
Instrumental variables
In this article, the instrumental variable method is also used to solve the endogeneity problem. We select the proportion of senior executives with an academic background in other enterprises in the same industry in the same year to serve as the instrumental variable and use the two-stage least squares method for robustness testing, the test results are shown in Table 6. The reason behind the choice of this instrumental variable is that the proportion of senior executives with academic backgrounds in other enterprises in the same industry in the same year can be reflected in the industry competition during that year. However, senior executives with academic backgrounds more frequently serve as independent directors. Due to the employment system of independent directors, an independent director can serve as an independent director of multiple enterprises. The proportion of senior executives with academic backgrounds in other enterprises in the same industry in the same year is closely related to the proportion of senior executives with academic backgrounds in the enterprise, but these executives do not participate in the decision-making of the enterprise itself and thus do not directly affect the digital transformation of the enterprise. Therefore, the proportion of senior executives with academic backgrounds from other enterprises in the same industry in the same year is not correlated with the digital transformation of enterprises. The regression results obtained using 2SLS are shown in Table 6. The proportion of senior executives with an academic background still has a significant positive impact on the digital transformation of enterprises.
Propensity score matching method
To solve the problem of sample bias, the propensity score matching method is used to conduct a robustness test based on whether the enterprise has senior executives with academic experience. The test results are shown in Table 7. With DT (the digital transformation of enterprises) used as the explained variable, logit regression was performed for enterprise size (lnsize), enterprise capital structure (Lev), board size (Board), return on assets (Roa), operating net cash flow (CFO), main business income growth rate (Growth), ownership concentration (Shrcr5), senior executives’ shareholding ratio (Sr), two-job combination (Both) and enterprise age (Age), as well as industry dummy variables and year dummy variables to calculate the propensity score, based on which multiple regression analysis was performed, and the results show that the matching effect is good. In the sample regression after matching, the estimated coefficient of r_xsgg is significantly positive at the 1% level. In summary, the results of this article remain robust to various testing methods.
Alternative variable
To ensure the effectiveness and scientific nature of the results in this paper, we changed the measurement method of the dependent variable, using the proportion of the year-end intangible asset details related to digital transformation disclosed in the financial report notes of listed companies to the total amount of intangible assets to measure the degree of corporate digitalization (DT2). Table 8 reports the statistical results, with the estimated coefficients of DT2 being 0.0277 and 0.0178, significantly positive at the 1% and 5% levels, respectively. This indicates that companies with a higher proportion of academic executives can better promote corporate digital transformation.
Since the CEO is the primary decision-maker in corporate operations and the most influential decision-maker in the company, they play an irreplaceable role in promoting, planning, and implementing corporate digital transformation, directly affecting its success or failure. To further test the robustness of the conclusions of this paper, we use the CEO’s academic background (CEO_xsgg) as an alternative variable for the proportion of academic executives (r_xsgg) to examine whether executives with an academic background can effectively promote corporate digital transformation. In addition, we also use (isxsgg) as an alternative variable for the proportion of academic executives (r_xsgg). The reported results are shown in Tables 9 and 10, with coefficients of 0.0504 and 0.0569, respectively, both significant at the 1% level. This indicates that CEOs with an academic background can effectively promote corporate digital transformation.
Changing the measurement method
Although the overall distribution of the digital transformation of enterprises is scattered within a positive range, some enterprises’ digital transformation averages around 0. Therefore, the samples herein can be applied to the Tobit model. In this article, the original OLS regression is changed to Tobit regression, and the results show that the estimated coefficients of DT are all significantly positive at the 1% level, which demonstrates the robustness of H1.
Heterogeneity analysis
In the empirical test of the benchmark regression analysis part, the positive impact of executives’ academic background on corporate digital transformation was verified, with innovation input being a potential pathway, and the degree of industry competition moderating the relationship between the two. So, under different circumstances, what are the differences in the positive relationship between executives’ academic background and corporate digital transformation? This paper further examines the specific contexts in which the executives’ academic background and corporate digital transformation play a role, such as the ownership nature of the enterprise, whether it is a high-tech industry, and the region where the enterprise is located. This paper uses group testing to distinguish whether the positive correlation between executives’ academic background and corporate digital transformation, as found in the previous text, is affected by factors such as the ownership nature of the enterprise, whether it is a high-tech enterprise, and the region where it is located.
Enterprise heterogeneity
Since senior executives with academic backgrounds have accrued rigorous, forward-looking and innovative characteristics through their past work, and these imprints lead to a high degree of acceptance of enterprise digital transformation, regardless of enterprise ownership, they all value the benefits brought by digital transformation. At the same time, however, the rigorous academic thinking and prudent logical thinking cultivated by senior executives who engaged in academic research in their early careers prevent their old imprints that were formed in their past work from being easily replaced by “new imprints” from their new environment34. However, compared with non-state-owned enterprises, the natural connection between state-owned enterprises and the government enables state-owned enterprises to obtain a large amount of scarce resources, which includes government subsidies, and senior executives with academic backgrounds in state-owned enterprises are more motivated to promote, plan and implement digital transformation of enterprises with greater advantages. In summary, we speculate that senior executives with academic backgrounds can significantly promote the digital transformation of enterprises, regardless of whether they are in state-owned enterprises or not. The positive correlation between the academic background of senior executives in state-owned enterprises and the digital transformation of enterprises is thus strengthened.
Table 11 lists the test results as grouped by business ownership. The results show that the correlation coefficients between the proportion of senior executives with an academic background (r_xsgg) and the digital transformation of enterprises (DT) in the two groups, state-owned enterprises and nonstate-owned enterprises, are 0.2186 and 0.1169, respectively, which are significant at the 1% level. A coefficient test between groups was also conducted for this study, and the difference was significantly positive at the 5% level. The above test results are consistent with our expectations. Senior executives with academic backgrounds can better promote the digital transformation of enterprises regardless of ownership type. Compared with that of nonstate-owned enterprises, the positive correlation between the academic background of senior executives in state-owned enterprises and the digital transformation of enterprises is shown to be enhanced.
Industrial heterogeneity
In a relatively fairly competitive environment, compared with high-tech enterprises, non-high-tech enterprises have less demand for innovation and exhibit lower levels of corporate governance; additionally, such industries are generally more mature, and the level of information asymmetry between enterprises and external investors is lower. Therefore, in the context of non-high-tech enterprises, senior executives with academic backgrounds are more motivated to promote the digital transformation of enterprises in an attempt to bridge the “digital gap.” In summary, we speculate that senior executives with academic backgrounds can significantly promote the digital transformation of enterprises, regardless of tech level; however, the positive correlation between senior executives with academic backgrounds in non-high-tech enterprises and the digital transformation of enterprises is greater than that in high tech enterprises.
Table 12 lists the test results as grouped by tech level. The results show that the correlation coefficients of the ratio of senior executives with academic backgrounds (r_xsgg) and the digital transformation of enterprises (DT) in the high-tech and non-high-tech groups are 0.1999 and 0.0990, respectively, which are significant at the 1% level. A coefficient test was also conducted between groups for this study, and the difference was significantly positive at the 1% level. The above test results are consistent with our expectations. Senior executives with academic backgrounds can better promote the digital transformation of both high-tech enterprises and non-high-tech enterprises. Compared with those in non-high-tech industries, senior executives with academic backgrounds in non-high-tech enterprises are more strongly correlated with the digital transformation of enterprises.
Regional heterogeneity
Compared with that in the eastern region, the noneastern region is relatively economically underdeveloped, exhibits a lower marketization process, and faces relatively less intense market competition. Since the digital transformation of enterprises in less active areas lags behind that in active areas, the demand for digital transformation is greater. Therefore, in the context of the other regions aside from the eastern region, senior executives with academic backgrounds can more effectively improve the digital transformation of enterprises. In summary, we speculate that senior executives with academic backgrounds, regardless of region, can significantly promote the digital transformation of enterprises, but the positive correlation between senior executives with academic backgrounds s and the digital transformation of enterprises is greater in noneastern region.
Table 13 lists the test results grouped according to the regional location of enterprises. The results show that the correlation coefficients of the ratio of senior executives with academic backgrounds (r_xsgg) and digital transformation of enterprises (DT) in the eastern region and that in the central and western regions are 0.1843 and 0.0216, respectively, which are significant at the 1% level. A coefficient test between groups was also conducted, and the difference was significantly positive at the 1% level. The above test results are consistent with our expectations. Senior executives with academic backgrounds can better promote the digital transformation of enterprises regardless of region. Compared with those of enterprises in the western region, the positive correlations between senior executives with academic backgrounds and the digital transformation of enterprises are greater for enterprises located in the eastern and central regions.
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