Empowerment

Partial least squares (PLS) are used to analyze the model. This technique is a new type of multivariate data analysis method, with more reliable and stable calculation results compared with other methods. In addition, this method is suitable for analyzing small data samples and can simultaneously realize modelling prediction, the comprehensive simplification of multivariable systems and correlation analysis between two sets of variables, which can effectively solve the problem of collinearity. The main purpose of this method is to build a regression model between multiple dependent and independent variables. Moreover, when constructing the model, PLS can set the external relationship type in the structural equation flexibly according to the actual situation, that is, it supports the constitutive model and reflective model. SmartPLS 3.0 is used in this study to analyze the model.

Path coefficient and hypothesis test

The path coefficient indicates the strength of the relationship between the independent and dependent variables. The results of the path coefficient analysis of the study model are presented in Figure 1 and Table 4. All seven hypotheses are supported.

Figure 1. Model path and significance level.

Table 4. Hypothesis testing results.

R² is the variance variability explained by the dependent variable. In this study, the bootstrapping repeated sampling method is used to select 3,000 samples to calculate the t-value of the significance test. The interpretation degree of Personal Development Support, Delegation of Authority and Innovation Behavior is 0.393, 0.351 and 0.395, respectively, thereby indicating that the model has a satisfactory interpretation effect.

In this study, the bootstrapping method is used to test the significance of the path coefficients of the structural model, and the results are shown in Table 4. The effect of Personal Development Support on Innovation Behavior is unverified (β = 0.073, t = 1.069), thus, H1 is unconfirmed. Participative Decision Making has a significant positive influence on Innovation Behavior (β = 0.396, t = 4.925), thereby supporting H2. The effect of Delegation of Authority on Innovation Behavior is unverified (β = −0.051, t = 0.852); thus, H3 is unconfirmed. Participative Decision Making has a significant positive influence on Personal Development Support (β = 0.630, t = 16.204), thereby supporting H4. Participative Decision Making has a significant positive influence on Delegation of Authority (β = 0.595, t = 14.421), thereby supporting H5, and Delegation of Authority has a significant positive effect on Vigor (β = 0.326, t = 5.595), thereby supporting H6. Vigor has a significant positive effect on Innovation Behavior (β = 0.326, t = 4.714), thereby supporting H7. Learning has a significant positive effect on Innovation Behavior (β = 0.258, t = 4.606), thereby supporting H8.

Moderating effect test

To test the moderating effect of Personal Development Support on the relationship between Participative Decision Making and Innovation Behavior, hierarchical regression analysis is employed. This study investigated the role of variables at the level of Personal development support and Participative decision-making on the dependent variable. On this basis, it continues to investigate whether the variable Participative decision-making will affect the slope between the independent variable and the dependent variable at the Personal development support level, so as to obtain the slope prediction model, namely the full model.

Before verifying the moderating effect, centralising the variables of the cross terms to avoid collinearity is necessary. Next, the variables processed through centralization are multiplied to construct the interactive items. In this study, the independent and adjusted variables are processed centrally to construct the product terms of Personal Development Support and Participative Decision Making with Innovation Behavior for the multilevel regression analysis. Personal Development Support has a significant regulatory effect on the relationship between Participative Decision Making and Innovation Behavior. In the study, Personal Development Support is divided into high, medium and low conditions, which can facilitate the clear display of the role of the regulatory variables. Excel is used to plot the degree of influence of Participative Decision Making on Innovation Behavior in the high, medium and low conditions of Personal Development Support. The main effect (Participative Decision Making) is −0.15, the moderating variable effect (Personal Development Support) is −0.331 and the moderating effect (Participative Decision Making × Personal Development Support) is 0.143, p = 0.023. The moderating effect is shown in Figure 2, and the results reveal that when Personal Development Support is high, the influence of Participative Decision Making on Innovation Behavior increases, thereby supporting H8. Personal development support has a significant moderating effect on the relationship between participative decision making and innovative behavior.

Figure 2. Moderating effect of Personal Development Support on the relationship between Participative Decision Making and Innovation Behavior.