## 2 Materials and Methods
### Data Processing  
Data processing was performed based on verified statistical methods. This processing took place in the software environment of MS Excel and Statistica. The obtained data combined quantitative and qualitative data collection methods, using values of 1 to 5 based on the distribution of the Likert scale, were the basis for subsequent statistical processing. Based on a random selection of a research sample representing the real situation of the construction market, the statistical extremes were adjusted to ensure they did not distort the results. The relative importance index (RII) was used to determine the quantification and significance of individual implementation factors resulting from the impact of the implementation process of information and communication technology on buildings' life cycle costs. This is a scientific method that is commonly used to determine rankings, often in construction surveys. RII is calculated using the following expression:  
\[\text{RII}=\frac{\sum_{i=1}^{5}\text{w Xi}}{\text{A x N}} \tag{1}\]  
RII = relative importance index  
w = weighting given to each benefit by respondents and it ranges from 1 to 5  
x = frequency of the i-th response given for each cause  
A = highest weight (i.e., 5 in this case)  
N = total number of participants  
The ranking for each area is considered important to discuss construction life cycle cost management. Comparing and focusing on cost parameters is highly important in \begin{table}
\begin{tabular}{l c c} \hline \hline
**Type of Enterprises (_n = 125_)** & **Frequency** & **\% Share** \\ \hline
**Participant of construction** & & \\
**project** & & \\ Contractor & 65 & 52.00 \\ Sub-contractor & 21 & 16.80 \\ Investor & 14 & 11.20 \\ Designer & 25 & 20.00 \\
**Enterprise size** & & \\ Micro enterprises & 40 & 32.00 \\ Small enterprises & 42 & 52.5 \\ Medium-sized enterprises & 38 & 47.5 \\ Large enterprises & 5 & 4.00 \\
**Business funding origin** & & \\ International private & 13 & 10.40 \\ National private & 112 & 89.60 \\
**Working experience in work** & & \\
**position** & & \\
0-5 years & 35 & 28.00 \\
6-10 years & 57 & 45.60 \\
11-20 years & 23 & 18.40 \\
21 years and more & 10 & 10.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structure of research participants. research. The implementation factors for each area did not reach the same ranking. It is essential to look at the key values and compare them in life cycle cost management. This means their significance in terms of individual stages of the construction project. The areas related to the research area of construction life cycle management were specified for total costs and costs associated with the construction project's management. The significance of individual implementation factors can be assessed based on interval values, including specific measured values. According to [PERSON] [44], five critical levels are transformed from RII values:  
* high (H) (0.8 \(\leq\) RII \(\leq\) 1),
* high-medium (HM) (0.6 \(\leq\) RII \(\leq\) 0.8),
* medium (M) (0.4 \(\leq\) RII \(\leq\) 0.6),
* medium-low (ML) (0.2 \(\leq\) RII \(\leq\) 0.4) and
* low (L) (0 \(\leq\) RII \(\leq\) 0.2). This is crucial because these values can be clearly specified and directly classified based on a proven scientific method. The results in the form of rankings were compared with the intervals, and preliminary conclusions were drawn. In this case, it is necessary to also verify these results by statistical significance. Based on the research sample distribution, it was evaluated that it is best to perform this distribution using Kruskal-Wallis tests for a given type of data. The Kruskal-Wallis test was chosen for statistical testing. This test was chosen because the researchers worked with an ordinal variable. As the dependent variable was ordinally scaled, the Kruskal-Wallis test was required. Applying the given tests allowed determination of whether statements and assumptions examined by the current research were statistically significant.