4.1. The Analyses of the Results by Multivariate Factor Analysis
In order to reach data about the key factors that influence employees’ attitudes and perceptions of CSR and possible differences of CSR implementation in the analyzed countries, as well as in the public and private sector, eight independent variables were defined. The control variables were two groups of respondents (public and private sector employees), as well as respondents from five countries (Montenegro, Serbia, Bosnia and Herzegovina, Albania, and Northern Macedonia).
Responding to the main research questions was preceded by determination of the reliability of the research results. The reliability coefficient for the independent variables is 0.613, and represents the acceptable value of this coefficient in social science research [
116].
As previously mentioned in the methods part, validation of factor analysis was tested by The Kaiser-Meyer-Olkin (KMO), as well as the Bartlett test of sphericity. In order to achieve a sufficiently high level of Bartlett’s test of sphericity and statistically significant KMO indicator, variables were selected with respect to their contribution to the model. Therefore, out of the total number of questions in the survey which were potential variables, by the analysis of the correlation matrix it was decided that the factor analysis will be realized with eight variables. The Kaiser-Meyer-Olkin (KMO) measure ranges from 0 to 1. If the KMO value is less than 0.5, it indicates that the correlation matrix is inappropriate for factor analysis, i.e., a value of 0.6 is recommended as the smallest value acceptable for appropriate factor analysis [
116,
117].
Bartlett’s test of sphericity is used to test the hypothesis that the correlation matrix is identical to the identity matrix (whose nondiagonal values are zero and on diagonals there are number ones). Bartlett’s test of sphericity should be statistically significant, i.e.,
p < 0.05. If the
p-value obtained is large, the hypothesis that the matrix does not differ significantly from the identity matrix is accepted, and in that case the justification of applying principal component analysis should be considered. The results of Kaiser-Meyer-Olkin and Bartlett’s test are given in the
Table 1.
The results of the tests show that the use of factor analysis for a given sample and a set of variables is justified, because the KMO measure is 0.627, which is higher than the recommended lower limit, while the probability of making an error of hypothesis about the existence of an identity matrix for the correlation matrix is 0%.
Factor analysis requires a pattern of relationships among a large number of variables. Therefore, the analysis begins by determining the correlation ratios of the original variables. The most commonly used measure for correlation analysis is the Pearson correlation coefficient, which shows the strength and direction of the relationship between the two variables. The resulting correlation coefficient table may contribute to better identification, naming and understanding of factors. There must be sufficiently high correlation coefficients in the correlation matrix in order to make sense of applying factor analysis. The obtained results of Pearson correlation coefficient analysis is given in the
Table 2.
On the basis of the correlation matrix, it is concluded that it is justified to continue conducting factor analysis, since among the analyzed variables there is a sufficient number of correlation coefficients whose values are greater than 0.3, as well as a sufficient number of statistically significant correlation coefficients.
Further analysis identifies common factors found in the correlation coefficient table. This step is usually performed using the method of main components. The method of main components analysis identifies groups of variables that have high coefficients within the group and small coefficients relative to other groups. These few major components represent factors. Factor rotation was done using Varimax rotation with Keiser normalization and the obtained results are given in the
Table 3.
The first goal of the research was achieved by applying factorial analysis, which identified three main significant factors that influence the attitudes and perceptions of employees, as well as their understanding of CSR practices and initiatives. Specifically, using the method of main components, three factors with an eigenvalue greater than 1 were extracted. These three factors explain 54.629% of the total variation.
The scheme (
Scheme 1) shows the eigenvalues of the components starting from the largest. It is easy to notice a spot where the line changes direction suddenly and becomes horizontal, and that point is called a breaking point. Only those factors that are above the breaking point are considered relevant to keep. Based on the scheme, it is concluded that the breaking point is in the fourth component, so that for the purposes of analysis it is necessary to keep the first three components, which explains 54.629% of the variance.
In the following segment of the analysis, factor loadings after rotation are observed and the results are presented in the table above (
Table 4). In order to assign adequate names to the factors, factor loadings for each variable are observed in order to determine its role and contribution in defining the structure of the factors. The predictors of factor loadings are interpreted as for any other correlation coefficient, which means that for positive factor loadings, the factor and the variable are positively related and otherwise negative. Factor loads greater than 0.50, regardless of the sign, represent large and moderate loads that show how the variable is related to the factor. Based on the results presented in
Table 4, it is concluded that: The first factor has the highest values of factor loadings for the last four variables whose analysis can define the name of the CSR in the company; the second factor stood out for the first two variables, which are also control variables, so the name of this factor is the Environment for implementation of CSR, while the third and the last factor was extracted thanks to the third and fourth variables, so the name of this factor is Understanding of the importance of CSR.
Since factor loadings of variables have different values per factor, on the basis of their values, the most significant variables for each factor will be extracted. The highest factor loading of CSR factor in a company has a variable that examines the key benefits of CSR implementation, and the factor loading of this variable is 0.749. When asked what are the main benefits of CSR implementation, in most of the cases (45.85%), the respondents stated improvement of the company’s reputation through sustainable practices, as well as recognition of the brand as responsible/sustainable (44.71%). On the other hand, a large number of employees (61.65%) believe that key CSR activities are focused on improvement of workplace conditions (better pay and employee treatment) and greater employees’ engagement.
The second factor is mostly correlated with the variable related to the country in which the respondent lives. The factor loading value of this variable is 0.642.
Understanding the importance of CSR is the factor most closely associated with the variable that examines the meaning of CSR for employees. The factor loading value in the case of this variable is –0.609.
Further, it is necessary to answer the research question—whether all three factors, identified in theory and in this analysis, are equally and statistically significant. The rotational sum of variance of factor loadings showed that these three factors explain 54.629% of variations in CSR implementation in public and private companies of the analyzed countries. The first factor, identified as CSR in the company, explains 22.861% of the variations, the second, the Environment for implementation of CSR, explains 16.057% of the variations, while the third factor, Understanding the significance of CSR, is slightly less significant and explains 15.711% of the variations. Based on the results, it is possible to conclude that the answer to the research question RQ1 was given, i.e., that key factors that influence employees’ attitudes and perceptions can be identified on the basis of extracted variables.
The last part of the analysis examines whether there is a difference in the degree of significance of these factors, depending on whether they are public or private sector companies, or depending on the country from which the respondent comes from. The result of descriptive statistics for private and public companies are given in the
Table 5.
An impact of three identified factors on the implementation of CSR in companies depending on whether they are public or private will be performed by applying a T-test. Having on mind that the difference of influence of individual factors for the two groups of respondents is analyzed, the ANOVA test is not necessary.
Since the size of the sample of public companies is significantly smaller than that of private companies, it is logical that larger deviations of individual averages will occur compared to the group average of the mentioned variable.
Testing of the differences in the average values of the analyzed variables related to CSR in private and public companies is carried out by applying the T-test and the results are given in the
Table 6.
One of the most important conditions for applying the T-test is the homogeneity of variance. It is, in this case, examined by applying the Levene variance equality test. By implementation of this test, it is concluded that the variance is homogeneous in the case of a variable which refers to the Company’s chances for success by implementation of CSR, and in the case of a variable that relates to the assessment of the Position of CSR in future. Other variables do not satisfy the condition of variance homogeneity. Since only two variables satisfy the condition of variance homogeneity, for the other variables the value of the T-statistic is corrected in order to consider one that does not assume the equality of the varyingly analyzed groups of respondents. With a level of significance of 5%, it is concluded that the test results in private and public companies were the same for each variable, except in the case of a survey on the Involvement of company in CSR and sustainability, as well as in examination of the respondents’ opinion on whether their employer should be more turned to CSR business. Thus, more than half of the analyzed variables (control variables were not included) have the same survey results regardless of the type of the sector from which the respondents come from. Based on the above, we can conclude that the answer to the research question RQ2 was given.
At the end, the results of the survey were checked depending on the country from which the employees came from. Since the survey was conducted in five countries, there are five groups of respondents available for testing, so the hypothesis of equality of results across the variables, i.e., the identified CSR factors is carried out using the ANOVA test. Analysis of variance (ANOVA) is an analytical model used for testing the significance of differences. When the country is taken as the control variable, then it is necessary to use the ANOVA test to compare the expected values of the research variables, since the number of groups included in the analysis is greater than two.
The ANOVA test of equality of expected values of variables begins with the analysis of the results of descriptive statistics, given in the
Table 7.
Although the sizes of individual groups of respondents from five countries are approximately the same, the analysis of descriptive statistics showed that there is a significant deviation of the average values of the variables by country compared to the common average value of the given variable for all respondents, regardless of their country of their origin, which is evidenced by the high standard deviation of the mean.
For the first variable which represents Understanding of CSR, the highest expected value was recorded for Montenegro, 0.61, and this value is significantly higher than the average of all five countries, whose value is 0.46, but also than the lowest value of the variable which was measured for Serbia and is 0.31. It is interesting that 58.9% of the total number of employees perceive CSR as a concept that enables the generating of new values and success of the company, as well as welfare for the whole society.
For the second variable which examines the Company’s chances for success by implementation of CSR, differences in values were measured—between the states as well as by comparing these values with the common expected value of the variable for all states. The highest value of the variable was achieved for Serbia (1.9), and the lowest for Montenegro (1.53), while the expected value of all countries was 1.69. Additionally, the survey showed that for 51% of total number of employees, CSR activities and sustainable practices are directly related to business success, while 11% have the opposite view, and others (38%) cannot evaluate. More than half of the respondents (59.3%) believe that companies that develop and implement CSR and sustainable practices are more likely to succeed, while 12.78% have the opposite view, and 27.88% of respondents do not recognize a correlation between CSR, sustainable practices, and chances of success.
For the third variable, which refers to Involvement of company in CSR and sustainability, this variable for all countries is 0, while for Serbia this value is 0.38 and for Albania, −0.27. It is interesting that 22.69% of the total number of respondents consider that CSR is a widespread practice today, while 41.86% of respondents say that it is widespread much less than it is desirable. On the other hand, 15.31% consider CSR not to be a widespread practice today, while 20.12% cannot estimate.
The fourth variable explores Benefits of implementation of CSR, and the average values of this variable across countries vary from a minimum of 2.21 measured in Albania to a maximum of 2.42 measured in Montenegro and Bosnia and Herzegovina. The average value of this variable for all countries is 2.33. As previously mentioned, in most of the cases, the respondents stated improvement of the company’s reputation through sustainable practices as well as recognition of the brand as responsible/sustainable (44.71%) as the key benefits of CSR implementation.
The fifth variable examines Long-term orientation on CSR and sustainable business, and its expected value for all countries is 1.35. An analysis of the expected values of this variable by country shows a large range in varying its value from minimum of 1.18 in Albania to a maximum value of 1.67 in Serbia. When it comes to employees’ attitudes towards long-term orientation of companies on CSR and sustainable business, it is interesting that 80.66% of the total number of respondents think that their employer should be more socially responsible and always focused on sustainable business, while only 4.06% of them think that their employer is already quite devoted to the concept of CSR.
The last variable deals with the Position of CRS in future. Negative expected values of this variable across countries ranged from −0.51 for Montenegro to −0.30 for Serbia, while the average for all countries was −0.40. Namely, when it comes to the position of corporate social responsibility in the coming years, 22.86% of the total number of respondents believe that CSR will be in the same position as today, and only 6.05% think that it will be in decline, i.e., that a significant number of companies will neglect CSR in the future. 24.93% of respondents believe that the CSR position will be improved and will include more social and environmental issues, as well as more sustainable practices, while the largest number (46.14%) think that CSR will grow significantly. and more businesses will incorporate CSR into their operations.
On the basis of the previous results of descriptive statistics, it may be concluded that it would be logical to expect the rejection of the hypothesis of equality of expected values for all variables for individual countries, and that it is possible to give a positive answer to the third research question RQ3.
The results of the ANOVA test are given below, in the
Table 8.
The initial hypothesis of the ANOVA test states that the expected values of the variables for the states included in the research are the same. Based on the results of the descriptive statistics, it is noticed that there is a high value of the deviation of the average value of the variables per country compared to the common average value of the variable for all countries. The calculated value of F statistics, obtained by applying the factor-residual variance, but also the associated probability, indicates that for each individual variable, it is necessary to reject the assumption of other goals of the research on the equality of the expected values of the variable measured for individual countries. The level of significance which is made by rejection of null hypothesis is less than 5% for each individual variable. Based on the results obtained previously, we may conclude that the answer to the research question RQ3 has been positive.
The survey showed that the government (41.9%) has the greatest influence in shaping the corporate social responsibility strategy, and that, apart from being a factor in its own right, it is most often connected with the local community, employees, and consumers. Additionally, when it comes to the way in which the government should encourage companies to operate in accordance with CSR principles, respondents in 62.86% of cases think that, above all, there should be tax deductions for companies that allocate funds for CSR, while in 37.84% of cases respondents indicated the highlight of socially responsible companies (certification, CSR index and national sign).
Therefore, according to 25 questions given in the questionnaire and the results on which multivariate factor analysis was applied, eight variables were identified as statistically significant. In line with all mentioned above, it is possible to describe the connection between research questions, variables, and factors. Namely, RQ1 refers to the identification of key factors on employees’ attitudes and perceptions of CSR (the variables explained by this question are Understanding of CSR, Company’s chance for success by implementation of CSR, Involvement of company in CSR and sustainability, Benefits of implementation of CSR, Long-term orientation on CSR and sustainable business, Position of CSR in the future, Sector, Country). Based on these eight variables using the multivariate factor analysis method, three key factors were identified: F1 (CSR in company), F2 (Environment for implementation of CSR) and F3 (Understanding of the importance of CSR). The correlation of variables and factors is given in detail in Table 11. Furthermore, RQ2 refers to identifying differences in attitudes and perceptions about CSR in private comparing to public sector (all variables are included in the analysis as well as for RQ1, where the variable Sector is control variables). The t-test method was used for the RQ2 analysis because the control variable Sector has only two modalities (private and public). RQ3 answers the question if there is a difference between the attitudes and perceptions of employees in the analyzed Western Balkan countries (all variables are included in the analysis as for RQ1, where the Country is control variables). The ANOVA method was used for the analysis of RQ3 because variable Country has more than 2 modalities (more precisely 5 modalities).
4.2. The Analysis of the Results by Implementation of Structural Equation Model
The structural equation model (SEM) in this study was used to test additionally the results provided by applying factor analysis. Structural equation model includes a set of statistical methods that aim to explain the complex relationship between one or more independent variables and one or more dependent variables. The validity of the structural equation model specification is examined using a large number of tests [
118]. The most commonly mentioned tests refer to model validity indices, GFI, and AGFI. In order to consider this model valid, these two indexes should have values greater than 0.9. In our model, the GFI index has a value of 0.951, while the AGFI index has an acceptable value of 0.9. Here is the result of another widely accepted test, the RMSEA test whose value is 0.1. This value is satisfactory, so the model can be characterized as appropriate.
The path scheme (
Scheme 2) presents the relationship between extracted factors and independent variables or to be more precise questions from the survey that refer to the level of development and sustainability of corporate social responsibility in companies.
Factor F1, as noted earlier, represents CSR in a company, Factor F2 is called the Environment for implementation of CSR, while Factor F3 is Understanding the importance of CSR. All three factors are represented by an oval shape. The rectangular shape shows the measurable elements of the model or the independent variables based on which defined factors are given and extracted, using factor analysis. The structural equation model also includes a random error of the model, denoted by “e”. Thus, for example, error e1 represents a random error related to the ability of a variable, which represents the Involvement of company in CSR and sustainability, to completely explain the variance of the factor F1, etc.
On the path scheme above the straight arrows, which connects the independent variables and factors, the values of the standardized regression coefficients are presented. The higher their value is, the specific independent variable may be more considered as a good indicator of a given factor. The values of the coefficients above the arrows, which go from a random error to an independent variable, show the amount of variance in the independent variable that may be explained by the unobservable variable or factor. The larger the number, the larger the unobserved variable that can explain the variance in the independent variable.
In order to justify the results of factor analysis using SEM analysis, it will be examined whether the regression coefficients in SEM are statistically significant, as well as the validity of the specification of the defined model. The results of regression coefficients are given in the
Table 9.
All regression parameters of the estimated structural equation model are statistically significant. The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). In the case of estimated SEM, with a 5% level of significance, all regression coefficients are statistically significant. For the estimation of SEM by each factor, a limit is defined, according to which the value of the regression coefficient is between one independent variable and the observed factor F1. Since the value of a given regression coefficient is predefined, no statistical significance is tested for it. In our model, a value of 1 is defined for the coefficients that correlate the impact of the Benefits of implementation of CSR and the first factor variables, then the Country and the second factor variables, and the Company’s chance for success by implementation of CSR variable and third factor.
The correlation coefficients between the factors are also statistically significant, and the results are presented in following table (
Table 10).
The corresponding probabilities for each correlation coefficient testify that all correlation coefficients are statistically significant. Another advantage of the SEM model is that it can be used to determine which independent variable most influences the extraction or definition of a particular factor. The higher the value of the regression coefficient, the more significant the influence of a given independent variable on the observed factor. The table below (
Table 11) lists the rankings of independent variables by the importance of influencing the defining factor.
Based on the previously conducted testing, it may be concluded that the results of the SEM can be considered as valid as well as confirm the previously obtained conclusions provided by factor analysis.