Rural Community Prosperity Versus Tourism Progress …
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progress are statistically significantly different, depending on the vital issues
they concerned—monthly income, the sustainable community growth and general
economic connections with tourism activities. It states that attitudes of the residents
toward community-based tourism development in the selected locations show statistically significant differences depending on better living conditions achieved. In the
paper’s results section, the H1 will be accepted or disproved by using a one-factor
variance analysis. It involves three sub-hypotheses (Fig. 2):
H1a: Attitudes of the local residents toward community-based tourism development are statistically significantly different depending on the extent to which the
activity they do is related to tourism.
H1b: Attitudes of local residents toward community-based tourism development are statistically significantly different depending on whether they have an
impression that the community in their place has grown in number.
H1c: Attitudes of local residents toward community-based tourism development
are statistically significantly different depending on the level of their personal
monthly incomes.
4 Results
4.1 Factor Analysis Findings
According to several former studies (Lankford and Howard 1994; Lankford et al.
1994; Schneider et al. 1997; Harrill and Potts 2003; Wang et al. 2006; Wang and
Pfister 2008; Woosnam 2012; Petrovi´ c et al. 2017a, b) and for the analysis of the
components in this paper, all the 27 original variables were taken. Bartlett’s test
of sphericity has achieved the needed statistical significance (p = 0.000), which
confirms the justification of the application of the exploratory factor analysis. Additionally, according to Kaiser (1974), the Kaiser–Meyer–Olkin measure value was
0.79, which exceeds the recommended value of 0.60. The principal component analysis revealed the presence of the four elements with characteristic values above one
(1.00). It is explained by 16.98% (F1), 11.11% (F2), 9.74% (F3) and 8.99% (F4) of
the variance (Table 2). After creating the factors, the Varimax rotation was done. The
reliability of the measurement instrument was checked by using Cronbach’s Alpha
Reliability Coefficient (α). Although Lehman et al. (2013) underlined the fact that
the ideal validity of internal consistency value is in the interval between 0.80 and
0.90, sometimes even the coefficients above 0.70 are accepted (Nunnally 1978).
The coefficient value for the F1 = 0.87, F3 = 0.71, F4 = 0.71 exceeds the
recommended value of 0.70, while the value of the second factor is close to the
recommended value (F2 = 0.68). According to several studies (in the column “Previous research”), with a similar grouping of the items, the factors are entitled in the
way presented in Table 2. Cronbach’s coefficient for the whole scale of 23 items is
F1–F4 = 0.77, which is above the value of 0.70. After the conveyed factor analysis,
117
progress are statistically significantly different, depending on the vital issues
they concerned—monthly income, the sustainable community growth and general
economic connections with tourism activities. It states that attitudes of the residents
toward community-based tourism development in the selected locations show statistically significant differences depending on better living conditions achieved. In the
paper’s results section, the H1 will be accepted or disproved by using a one-factor
variance analysis. It involves three sub-hypotheses (Fig. 2):
H1a: Attitudes of the local residents toward community-based tourism development are statistically significantly different depending on the extent to which the
activity they do is related to tourism.
H1b: Attitudes of local residents toward community-based tourism development are statistically significantly different depending on whether they have an
impression that the community in their place has grown in number.
H1c: Attitudes of local residents toward community-based tourism development
are statistically significantly different depending on the level of their personal
monthly incomes.
4 Results
4.1 Factor Analysis Findings
According to several former studies (Lankford and Howard 1994; Lankford et al.
1994; Schneider et al. 1997; Harrill and Potts 2003; Wang et al. 2006; Wang and
Pfister 2008; Woosnam 2012; Petrovi´ c et al. 2017a, b) and for the analysis of the
components in this paper, all the 27 original variables were taken. Bartlett’s test
of sphericity has achieved the needed statistical significance (p = 0.000), which
confirms the justification of the application of the exploratory factor analysis. Additionally, according to Kaiser (1974), the Kaiser–Meyer–Olkin measure value was
0.79, which exceeds the recommended value of 0.60. The principal component analysis revealed the presence of the four elements with characteristic values above one
(1.00). It is explained by 16.98% (F1), 11.11% (F2), 9.74% (F3) and 8.99% (F4) of
the variance (Table 2). After creating the factors, the Varimax rotation was done. The
reliability of the measurement instrument was checked by using Cronbach’s Alpha
Reliability Coefficient (α). Although Lehman et al. (2013) underlined the fact that
the ideal validity of internal consistency value is in the interval between 0.80 and
0.90, sometimes even the coefficients above 0.70 are accepted (Nunnally 1978).
The coefficient value for the F1 = 0.87, F3 = 0.71, F4 = 0.71 exceeds the
recommended value of 0.70, while the value of the second factor is close to the
recommended value (F2 = 0.68). According to several studies (in the column “Previous research”), with a similar grouping of the items, the factors are entitled in the
way presented in Table 2. Cronbach’s coefficient for the whole scale of 23 items is
F1–F4 = 0.77, which is above the value of 0.70. After the conveyed factor analysis,