From the total of 2560 employees of the seven organizations, representative sample size was included in the study. Based on Morgan table, the sample size was estimated to be 334. Selecting a larger sample size to compensate for the likelihood of response rate lower than 100% is recommended by authors [11]. The researcher believes that the issues to be raised in this research are very sensitive and respondents may hesitate to give response to them. Taking into account the research culture of the country, the researcher adjusted the sample size assuming the response rate of 65%. The adjusted sample size is 513.

### Krejcie and Morgan [46] formula

$$S = chi^{2} , N , P , left( {1 – P} right)/d^{2} left( {N – 1} right) + chi^{2} , P , left( {1 – , P} right)$$

where *s* = the required sample size, *χ*^{2} = the table value of Chi-square for one degree of freedom at the desired level of confidence level = 95% = 3.841 = (1.96*1.96), *N* = population size, *P* = the population proportion (assumed to be 0.5). Krejcie and Morgan recommended 0.5 as an estimate of population proportion as this proportion will result in the maximization of variance and produce maximum sample size [46], *d* = the degree of accuracy expressed as a proportion (0.05)-error the researcher wants to accept.

Using the above formula

$$begin{aligned} & S = 3.842*2560*0.5*0.5/ , 0.5^{2} left( {2560 – 1} right) , + , 3.841*0.5left( {0.5} right) \ & S = 334 \ end{aligned}$$

So the adjusted sample size is 334/0.65 = 514.

Seven items from the Herold et al.’s [19] instrument were used to measure the independent variable change leadership. The organizational culture (mediating variable) instrument was adapted from Glaser et al. [24], which has 13 items for the six sub-constructs of teamwork and conflict (2 items), climate and morale (3 items), information sharing (2 items), involvement (2 items), meetings (2 items), and supervision (2 items) (2 items). Employees’ readiness for change was measured using nine items from an instrument developed by Dave et al. [18] (three items for each of the three sub-constructs: intentional, emotional, and cognitive readiness). The three instruments’ items are all rated on a Likert scale of 1–5, with 1 indicating strong disagreement and 5 indicating strong agreement.

A total of 340 instruments were collected out of 514, with a good response rate of around 65%. Thirteen cases were removed from the data set because they had more than 10% missing values. In addition, the data were screened for respondents who were uninterested in participating. In this case, 11 people were excluded because they answered yes or no to the majority of the questions. There were no outliers, and missing values were identified and imputed using the marching response method [45]. In terms of skewness, the indicators and all other variables have a fairly normal distribution. Statistical Package for Social Sciences (SPSS) version 22 and structural equation modeling method of analysis using Analysis of Moment Structure (AMOS) version 23 were used to analyze the data Using Structural Equation Modeling has some advantages. One advantage is that latent variables are more reliable measures than observed variables because measurement errors are estimated and removed. Another advantage is that it easily allows the researcher to examine models with multiple dependent variables [15]. It permits the estimation of the goodness of fit of an entire model. AMOS is the appropriate software for the analysis of structural equation modeling. It also has a user-friendly graphical interface and the potential to enhance conceptual understanding and communication of results [15, 45].

### Study result

The demographic profiles of the respondents were described before the hypotheses were tested. Table 1 shows the demographic characteristics of respondents, including their age, gender, educational status, and work experience. The majority of the workers (66.8%) are between the ages of 25 and 44. In addition, 16.8% of respondents are between the ages of 45 and 55, 16.1% are under the age of 25, and only one (0.3%) is over the age of 55. Male respondents make up 70.9% of the total, while female respondents make up 29.1%. When it comes to educational attainment, 61.1% have a bachelor’s degree and 27.5% have a master’s degree. Diploma, third-degree, and certificate holders make up small percentages of the respondents, accounting for 9.5%, 1.3%, and 0.6%, respectively.

The majority of respondents (36.1%) have between 2 and 5 years of work experience, while 30.4% have between 6 and 10 years of work experience. Only 5.7% of respondents have less than one year of experience, with 27.8% having more than ten years of experience. Employees’ perceptions of change leadership that are rated as most important to them and their readiness for the change were determined using descriptive statistics. The descriptive table represents scores from subscales of the 316 sample when reporting the results. It is calculated descriptive statistics on employee responses to change leadership, organizational culture, and employee readiness for change. The mean, standard deviations, skewness, kurtosis, and zero-order Pearson correlations are shown in Table 2. A check for multi-collinearity between variables was also performed. If Pearson R-values exceed 0.90, a multi-collinearity problem will be assumed [26].

### Factor analysis exploratory

The researchers used principal component factoring to condense a total of 29 Likert scale items into the three required variables. Due to their low-reliability scores, eleven items (four from organizational culture, two from change leadership, and five from readiness to change) were reduced. In the confirmatory analysis, the remaining 18 items were used. For change leadership, organizational culture, and employees’ readiness for change, the Kaiser–Meyer–Olkin sampling adequacy value was 0.0.876, 0.790, and 0.876, respectively, which is higher than 0.70 (Table 3). This indicates that each variable can be predicted with a sufficient number of items. When we look at the KMO and Bartlett’s test results, we can see that the data from the questionnaire are suitable for confirmatory factor analysis.

### Confirmatory factor analysis (measurement model)

A confirmatory factor analysis was conducted, which included 18 items and explained three major latent variables. Table 4 shows the results of construct and convergent validity for each of the three latent constructs, including Cronbach alpha (EFA), composite reliability (hereafter CR) of the scales, and average variance explained (hereafter AVE) (Table 4). To test the measurement model, major goodness-of-fit (GoF) measures were used [10, 12, 15, 45]. Chi-square statistics to the degree of freedom (CMIN/DF), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), normed fit index (NFI), Tucker–Lewis index (TLI), also known as the non-normed fit index (NNFI), comparative fit index (CFI), and root-mean-square error of approximation are some of the most commonly used measures (RMSEA).

The model fit the data well, with GFI = 0.905, AGFI = 0.876, NFT = 0.900, and CFI = 0.942, and the hypothesized model adequately described the sample data. The TLI value in this study is 0.930, indicating that there is a good fit (Table 5). The hypothesized model’s RMSEA is 0.063, with a 90% confidence interval of 0.047 to 0.059 and a p-value of 0.176 for the test of the closeness of fit. This means that we can be 90% confident that the true RMSEA value in the population will be between 0.053 and 0.072 (Table 5). This represents a high level of precision, and it can be concluded that the model that was initially proposed fits the data well.

The overall results of the structural model analysis using SEM are shown in Table 6. The structural model is well-fitting. The Chi-square index (CMIN/DF) 2.589 with a p-value of 0.000, as well as other fit indices (GFI = 0.892; AGFI = 0.856; NFI = 0.894; TLI = 910; CFI = 0.924; RMSEA (CLOSE) = 0.071(000), can be used to determine this (Table 6). All of these model fit indices are above the recommended level, indicating that the structural model has an acceptable goodness-of-fit (GoF) to the sample [10, 12, 15, 26, 45].

The researcher compared the hypothesized model to two alternative models to see if it was robust. First, alternative model 1 specified a mediation-only model that differed from the original model only in that the direct link between change leadership and employees’ readiness for change was set to zero.

The model has a lower good fit (2 = 0.145) than the others. The descriptive fit indices were nearly identical (AGFI = 0.001TLI = 0.001, CFI = 0.001), with the CFI being marginally better. The second model was the direct effect only model (alternative model 2). Only the direct effect of change leadership on employees’ change readiness was allowed in this study, while the other two relationships were set to zero. The model was found to be less accurate than both the original and alternative models (1(2 = 63.617 *df* = 1). The descriptive fit indices GFI = 0.010, AGFI = 0.012, NFI = 0.001, TLI = 0.019, CFI = 0.017, and RMSEA (RMSEA = 0.007) all decreased. As a result, when compared to the alternative model, the original model produced a better fit. Furthermore, the original model is less resource-intensive than the two alternatives.

### The variables’ structural relationships

The structural part of the specified model was examined in addition to testing the appropriateness of the measurement model. Figure 1 shows the outcome of the analysis. Standardized coefficients and significant numbers were used to confirm or reject the research hypotheses (Table 7). The full hypothesized model shows sufficient model fit (GFI = 892; AGFI = 856; TLI = 910; CFI = 924, and RMSEA (PCLOSE) = 0.071) (2 = 333.835 df = 129 GFI = 892; AGFI = 856; TLI = 910; CFI = 924, and RMSEA(PCLOSE) = 0.071) (000).b The direct and indirect effects were accounted for in the structural model. The model shows a negative and insignificant direct path from change leadership to employee readiness for change, which contradicts H1. Organizational culture is significantly and positively linked to change leadership (= 0.42; *p* 0.01). As a result, H2 is accepted (see also Fig. 2).

Organizational culture, according to H3, should be positively related to employees’ willingness to change. This hypothesis is supported by the data (= 0.15; *p* 0.04). A direct link between a predictor and an outcome variable is not required to postulate a mediation effect, according to one argument. As a result, we put the proposed organizational culture mediation of change leadership and employee readiness to change to the test (H4). The data show that change leadership has no indirect effect on employees’ readiness for change through organizational culture (= 0.063). To claim that there is mediation, the coefficient for the indirect effect must be significantly lower than the direct effect. However, the indirect effect (= 0.063) has a higher coefficient than the direct effect (= − 0.03). As a result, H4 is not supported (Table 8).

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