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ŠUMARSKI LIST 1-2/2023 str. 43     <-- 43 -->        PDF

1-5 – the median was calculated as a measure of central tendency and mod (the most frequent mark). For each scale Cronbach’s coefficient alpha was calculated as the measure of internal consistency and unique mark of scale reliability (Eisinga et al., 2013). If the coefficient’s value is greater than 0.7, it is defined as “accepted”, and above 0.8 as “good” (Malhotra et al., 2006; Ivy, 2008; Grubor, 2012). Also, for each scale the median mark by the respondents was calculated.
In the next step a SWOT matrix including the following elements was created: strengths and weaknesses (internal factors), and opportunities and threats (external factors) (Kotler et al., 2007). SWOT analysis is a tool used in strategic planning and management in organizations (Helms, Nixon, 2010; Kajanus et al., 2012). The concept of the SWOT analysis is based on recognition and usage of one’s own strengths and opportunities from the surroundings, as well as one’s own weaknesses, external threats and the reactions to them (Lovreta, 2004; Milisavljević et al., 2004; Schmithüsen et al., 2006). In each part of the SWOT matrix, optional answers were offered, to which the respondents gave a mark of 1-5. In case that the respondent did not recognize some optional strengths, weaknesses, opportunities and threats in the company’s business, no mark was given. Also, for each of the four elements blanks were left so that the respondent could individually write the internal and external elements of the SWOT matrix. Since the SWOT analysis does not enable measurements and estimations, the research was integrated with the Analytical Hierarchy Process (AHP) (Saaty 1988; Stojčetović et al., 2016; Abdel-Basset et al., 2018; Ahmed et al., 2019), which represents one of the most common tools in multi-criteria decision-making (Vaidya et al., 2006; Bayram, Üçüncü, 2016).
AHP is a technique for dissolving a complex problem into a hierarchy (Ramlan et al., 2016). On the first level in the hierarchy is the goal, while the criteria, sub-criteria and alternatives are on the next level. AHP is in principle flexible because it offers a solution for relatively complex problems with many criteria and alternatives to relatively easily find relationships between influencing factors recognize their explicit or relative importance and determine the dominance of one factor over another (Atthirawong, McCarthy, 2002). Based on the formulated hierarchy model of the problem, the decision-maker compares the elements in pairs on every level of the hierarchy in relation to the elements on the higher level. This way, the weighting coefficients of all elements are obtained, which is the basis for the evaluation of alternatives. For the decision-maker, the weighting coefficient represent the measure of relative importance of elements (Forman, Gass, 2001; Russo, Camanho, 2015; Karthikeyan et al., 2016; Taherdoost, 2017). By integrating the AHP model in the already formulated matrix with five dominant characteristics regarding strengths, weaknesses, opportunities and threats, a detailed evaluation of every element within the matrix could be achieved (Elwakil, 2016; Taherdoost, 2017; Goepel, 2018). In this way, it was possible to identify the factors according to which it is necessary to direct actions, with the aim of overcoming the internal weaknesses of the company or acting preventively towards the identified dangers.      
All the tables below represent the results of the research, based on the collected surveys.
RESULTS AND DISCUSSION
REZULTATI I DISKUSIJA
Descriptive statistics (number and percentage) of ownership, type of respondents, location of the company, number of employees and season workers are shown in Table 1.
Out of the analysed companies, 96.9% are privately owned. Companies are mostly situated outside of the cities, which is justified by the intention that the location of the company and processing industry is near the raw material base. The majority of the companies (40.6%) employ 51-250 employees, and only a small percent of them employ season workers (3.1%). Companies from the field of wood processing mostly belong to the category of small and medium enterprises, which are mostly oriented towards the production of sawn timber (Marčeta et al., 2018). Small and medium enterprises in forestry and wood processing are an important factor in rural areas (Šporčić, 2018; FAO/UNECE, 2020; Mijoč et al., 2021), especially due to the fact that they create employment opportunities for the local population (Jelić, Jovanović, 2018), which is why they represent the link between forestry and the wood industry (Ranković et al., 2012).
The average number of employees is 112 (Table 2). The average percentage of women is 23.3% and men 76.7%, which indicates the predominance of the male population in the