Method used in this study

For the purposes of this study, first, descriptive statistics will present the measures of central tendency and measures of dispersion of the data. During the bivariate analysis, Pearson’s r will be used to check for multicollinearity between the predictor variables via a correlation matrix. Usually, correlation values higher than 0.90 indicate a substantial collinearity (Hair et al., 2010). Then, the sample will be divided into two subsamples depending on the level of CSR disclosure of each unit and a t-test is performed to examine and compare the means of the two groups for each dependent variable. The coefficient of determination, R-squared, is taken into account as it assesses the prediction accuracy of the regression model. It measures the amount of total variance in the predicted variable explained by the predictor variable (Neelankavil, 2015). The values of R-squared range from 0, standing for no prediction, to 1, which indicates perfect prediction of the model (Hair et al., 2010). In line with previous studies (Patten, 1991; Cormier et al., 2005; Hackston & Milne, 1996; Gamerschlag et al., 2011; Naser et al., 2006; Roberts, 1992), and since the assumptions of OLS regression are met, this method will be applied to test the formulated hypotheses of this study. In addition and in contrast to other regression methods, OLS regressions seems to be the most appropriate approach given the choice of variables, sample and data, which will be discussed next. Thus, the following equation is designed:

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CSR disclosureit = β0 +β1Profitabilityit + β2Leverageit +β3Sizeit +β4Company visibilityit

+ β5Foreign ownershipit +β6Industry sensitivityit +β7Ageit + εit 4.4. Dependent variable

Since the aim of this study is to explore the determinants of CSR disclosure of companies

operating in Bulgaria, level of CSR disclosure is the dependent variablе. Due to its equivocal

nature, the concept of CSR is measured by different means such as CSR ratings (Paredes- Gazquez et al., 2016), indices (Hopkins, 2005), content analyses or surveys (Galant & Cadez, 2017). However, written form of CSR disclosure in companies’ annual reports or web pages presupposes the use of content analysis, which is indeed the most prevalent method for measuring CSR disclosure in the literature dealing with this matter (e.g. Gamerschlag et al., 2011; Naser et al., 2006; Chiu & Wang, 2015; Hussainey et al., 2011; Andrew et al., 1989; Hackston & Milne (1996) etc.).

Content analysis is a research method for making valid and replicable inferences by coding and interpreting texts with regards to their meanings, intentions and contexts of use (Krippendorff, 2018; Bhattacharyya, 2018). This method aims to quantify content based on predetermined categories and criteria (Bryman & Bell, 2015). Thus, it allows the conversion of qualitative data into quantitative data. In addition, this technique is highly flexible and can be applied for analysis of large amounts of data (Bryman & Bell, 2015). It also enables the longitudinal analysis of a topic. Brown & Deegan (1998) conduct a content analysis in five different periods between 1981 and 1994 to test the relationship between media coverage to industries’ environmental implications and the level of corporate environmental disclosure in annual reports of Australian companies. Similarly, Welbeck et al. (2017) perform a content analysis of annual reports over a 10-year period to examine the determinants of environmental disclosure by companies in Ghana. Some of the limitations of this method are that generalizability may be threatened if the examined documents no longer exist or are unavailable. Another potential problem may arise in the coding process, which almost always involves some interpretation by the coders. In addition, content analysis alone cannot explain causal relationships (Bryman & Bell, 2015).

In line with previous literature, content analysis is applied in this study to measure the level of CSR disclosure of companies operating in Bulgaria since we are interested in disclosed CSR information in corporate reports and web sites. Content analysis requires the identification of units of analysis and units of coding. In this research, the units of analysis are corporate webpages and reports, where CSR information can be detected. The units of coding refer to those parts of the unit of analysis that can be meaningfully interpreted with respect to CSR disclosure and are divided into two categories, environmental disclosure and social disclosure, depending on the CSR topics to which they relate. Pre-defined keywords serve as units of coding. Consistent with other studies (Tagesson et al., 2009; Welbeck et al., 2017; Reverte, 2009; Gamerschlag et al., 2011), the coding units, or keywords, in this study are selected

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based on some of the CSR indicators issued by the Global Reporting Initiative (GRI), which

provides the first and most widely adopted standards for sustainability reporting since 199719.

Furthermore, some of the keywords reflect the legal (e.g. “compliance”, “employee safety“, “forced labor”, “child labor”), ethical (“environmental protection”, “recycling”, “employee training”, “sustainability”, employment of minorities”) and philanthropic (“charitable activities/donations”, “investment in community”) responsibilities as identified by Carroll (1979; 1991). A total of 29 items have been selected, 10 of which cover subjects of environmental disclosure and 19 refer to issues of social disclosure. A table with the categories and the list of coding units can be seen in Appendix II.

Major weakness of using keywords for content analysis is that the context, in which they are used, is often ignored by the researcher. Moreover, it is possible that some words (e.g. “labor”) are detected in a context, which does not relate to CSR. Thus, in order to improve the validity and reliability of the analysis, both words and phrases are used as coding units and the whole sentences or paragraphs are considered. Although this is a time-consuming process, it aims to reduce the probability that irrelevant keywords are being added into the analysis. On the other hand, the use of keywords and phrases in a computer-aided content analysis simplifies the data collection process from sources with large content.

Following Gamerschlag et al. (2011), the total level of CSR disclosure consists of the amounts of environmental and social disclosure:

CSRTOT = CSRENV + CSRSOC.

Thus, three dependent variables are produced, which allows for three separate tests and for comparison of their results. In this regard, it can be explored whether the same determinants of disclosure apply when providing only environmental or only social information compared to total disclosure.

In contrast to other studies that measure CSR disclosure level based on word count (Gamerschlag et al., 2011; Juhmani, 2014; Campbell et al., 2001; Welbeck et al., 2017; Aribi & Gao, 2010), for the purposes of this study a CSR coding scale is developed. A coding scale would make certain that irrelevant or unnecessary information does not fall within the two categories of CSR disclosure (Cormier et al., 2005), while using word count can be deceptive and may lead to unreliable results because the meaning of the words and the context are mostly not considered (Welbeck et al., 2017). In this regard, the relevance of each detected keyword is assessed by reading the entire sentence or paragraph of the text, where it is mentioned. Accordingly, 1 point is assigned for every mention of a single item only in a context, which relates to the CSR context. This means that the same keyword detected 5 times for example will correspondingly receive 5 points. Since this study deals with level of CSR disclosure rather than quality, covering different CSR topics and comprehensiveness of the

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disclosed CSR information are not decisive for the calculation of the CSR rating. The CSRTOT rating of each company consists of the points collected for both CSRENV and CSRSOC.