El interés para obrar como requisito de procedencia en un proceso judicial

The data analysis in this thesis consists of three stages:

a. Data verification and validation (numeric);

b. Data assessment;

c. Data verification and validation (panel discussions).

Data verification and validation (numeric)

The data verification and validation aims to examine the validity and reliability of the research design. The results of data verification and validation shall also deliver empirical evidence to answer the following research questions.

RQ 1 What are the cultural factors that influence perceptions of target-based pay?

RQ 2 Why are these cultural factors an important influence on the perceptions of target-based pay?

RQ 3 How do these cultural factors influence perceptions of target-based pay?

The first step of the numeric data verification and validation is the validation of the data dimensionality by means of a factor analysis in IBM SPSS.

For this research, the analysed variables are divided into independent and dependent variables.

According to Field’s definition, an independent variable (also known as predictor variable) is a variable considered to cause an effect. A dependent variable (also known as outcome variable), on the other hand, is one that reacts to the changes of an independent variable (Field, 2013: 8).

In the factor analysis, the independent variables are to be subjected to orthogonal rotation analyses (principal axis factoring using the Varimax procedure in SPSS), in order to increase the reliability of the scale. By eliminating variables with low loadings and factors with too few items, the factor structure is to be improved iteratively. Parasuranman et al. applied a similar method of factor analysis (Parasuranman, Berry & Zeithaml, 1988) for the measurement of service quality. The principle of this statistical method appears however appropriate for this study because of similarities in statistical requirements, therefore is to be employed in this thesis with slight modifications.

Based on the content of the underlying attributes in the outcome of the factor analysis, each factor is then to be mapped with a cultural dimension in the extended SVM framework as defined in chapter 2. This construct is henceforth used as an assessment instrument for the cultural influence on perceptions of target-based pay.

Next, in order to assess the reliability of the cultural factors determined by the factor analysis, the Cronbach’s alpha for each dimension is to be computed. The reliability coefficient Cronbach’s alpha indicates if the applied variables as determinants for the further tests share a

Subsequently, the construct validity of the research procedures is to be examined by using the correlation matrix of all cultural factor attributes. The construct validity is aimed at establishing a set of operational measures for the research procedures. This is meant to keep the researcher from using subjective judgments, in order to reduce the researcher’s biases (Yin, 2009: 10-45).

Following the example of van Ree, for an overall assessment of construct validity, the convergent validity (CV) for each attribute is to be computed as the average of within-dimension correlations and the discriminant validity (DV), as the average of cross-within-dimensional correlations (van Ree, 2009).

The final part of the numeric data verification and validation in this study is the validation of cultural influence on perceptions of target-based pay. This is to be organised in two steps.

First, the relationships between the dependent and the independent variables are to be tested by a simple regression analysis. The results are to be reported based on the following key figures (Field, 2013: 293-355):

R Square Amount of variance in the outcome that can be explained by the model

b-value Magnitude of influence of each independent variable on the dependent variable, if the effects of the other independent variables are kept constant

ρ Significance of b-value

The results of the regression analysis shall indicate how much of the outcomes may be explained by the dependent variables in the database.

Finally, the association between the cultural factors and the pay perceptions of target-based pay is to be examined by means of a correlation analysis. The correlation analysis shall indicate the degree of linear dependence between the independent and the dependent variables in the database. The correlation between the independent and the dependent variables is to be measured by the correlation coefficient r and the significance ρ. The correlation coefficient

referred to in this thesis is defined as Person’s correlation coefficient and is defined as follows (Field, 2013: 262-291):

Correlation coefficient r =^{∑ (𝑋𝑖− X)}

n𝑖=1 (Yi−Y)

(𝑁−1)𝑆𝑥𝑆𝑦 (3.1)

(X = mean of Xi; Y = mean of Yi; N = number of observations; Sx = Standard deviation of X;

Sy = Standard deviation of Y)

For more details of the statistical concepts and methods see Appendix II.

Data assessment

The data assessment is to be organised in two comparison studies.

a. Cross-country comparison; and b. Organisational position comparison.

The comparison studies are aimed to assess how cultural factors influence the perceptions of target-based pay. The comparison studies shall provide further indications for the effect of cultural factors on perceptions of target-based pay in respect of the national cultures, as well as of the organisational positions.

The analysis method for both comparison studies is mean comparison. The following key figures are to be analysed for each cultural factor and the effect on perceptions of target-based pay, each with all the underlying attributes.

 Mean (N)

 95% confidence interval for mean (including lower bound and upper bound)

 Median

 Minimum

 Maximum

 Standard deviation

The key figures are defined as follows (Field, 2013: 262-291):

The mean is the average score calculated by the sum of all scores divided by the total number of scores.

The lower bound and upper bound of 95% confidence interval for mean indicate the likely values in the popullation with a likelihood of 95%.

The median is the middle score when scores are ranked in order of magnitude.

The minimum is the smallest value in the popullation.

The maximum is the largest value in the popullation.

The standard deviation is computed as follows:

Standard deviation (s) = √^{∑ (𝑥𝑖− x)}

n𝑖=1 2

𝑁−1 (3.2)

(N = Number of entities in the data set; (x) = the mean)

For more details of the statistical concepts and methods see Appendix II.

In the comparison study based on organisational positions, due to the limited size of samples in the group of general managers, no statistic analysis is to be conducted for this specific group of employees.

Data verification and validation (panel discussions)

As described in Section 2.5, an expert panel for this thesis was established in early summer 2013, involving four general managers, eight intermediate managers and two high-level administration staff members in the observed business organisation with locations in Germany, the Czech Republic and China.

The results of data assessment are to be presented to the expert panel for subsequent discussions. The panel discussions are to provide valuable insights into target-based pay practices, so as to verify and validate the results of data assessment.

QUALITY OF RESEARCH DESIGN