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In general, most research articles stipulate that the number of visits is affected directly by tourist satisfaction, since a pleased tourist is more likely to return to a specific destination or to advocate it to others Kozak and Rimmington, ; Kozak, This study examines the impact of tourist satisfaction, socio-demographic and economic determinants on visits to Alba County areas.

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The regression model consists of 33 initial predictors, included the intercept, and the authors apply different estimation techniques to data on Romanian and foreign tourists between and Consequently, the main objective of this study is to analyse the average number of visits of tourists to Alba County. Our review is partially i. Monizwho studied the underlying reasons behind repeat visits to the Azores Islands i.

Alegre and Claderawho analysed the effect that repeat visitation rates have on the purpose to revisit mature sun and sand holiday destinations and on tourists level of satisfaction i.

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Monizwho investigated the fundamental motives behind repeat visits to the Azores Islands. We suggest a model that integrates number of days, number of days i.

The last category of independent variables used in the model refers to demographic variables, described in detail in the Research Design and Methodology section.

Repeat tourists are normally those who are pleased with the journey s end Kozak, ; Moniz,are indifferent to price Alegre and Juaneda,already know and like the destination and who have a positive experience of the destination Hong et al.

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Over the last quarter of a century, substantial research has dealt with the theme of repeat visits Ross and Iso-Ahola, ; Oppermann,; Kozak and Rimmington, ; Kozak, ; Caneen, ; Greiner and Rolfe, ; Ledesma et al. Also, in this manner, there is a strong relationship between destination image, service quality, tourist motivation, tourist satisfaction, and destination loyalty Crompton and Ankomah, ; Weaver et al.

Consequently, it can be concluded that independent variables like expenditure, tourist satisfaction, service quality, number of days and socio-demographic variables, respectively, can influence the number of visits, and also, these covariates in all the regressions considerably better fit the data.

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Research Design and Methodology All the data specific to predictand and predictors i. The total number of tourists who were subject to our research and who responded to questionnaires came to It should be noted that respondents are tourists from Romania i.

Alba County and other counties and from other countries.

przewodnik po rumunii online dating

The period submitted for analysis is As far as the independent variable: Also, there were tourists reporting expenses between 10 and 9, lei i. Finally, following the data processing, it has been discovered that there are some high-value observations, which could influence both the variables statistical significance in the regression model we wanted to elaborate, and the coefficient of multiple determinations for multiple regressions.

To conclude, we used a logarithmic transformation of the covariate i. We have selected three quantitative variables: Also, we have selected 14 qualitative interaction variables as follows: Similarly, we selected 12 dummy variables as follows: Ambiance1 ; special room facilities criteria, Disagree assessment i.

Tariff ; unfavourable restaurant quality service assessment i. In terms of the qualitative variables, to conclude we mentioned that data were classified into two categories, as follows: In our scientific approach, we want to establish the average number of visits to Alba County for the period of timeand how this responds to the independent variables highlighted above.

The options we have chosen in the equation estimation i. Coefficient covariance matrix and Weights directed us, in the end, to introduce interactions or separate variables in the regression model. Thus, the specific function is: We have also tried to use the ANCOVA model which provides a method of statistically controlling the effect of the covariate.

Tom complete the analysis, the following modelwas considered: Log Z average number of visits; u error term. Data were introduced in an unbalanced, undated worksheet and afterward processed by means of the Eviews 7. Therefore, according to the application software, into Equation Estimation, Least Squares Options, we had the possibility to specify two additional settings for the estimation: Type, Weight series and Scaling.

Long and Ervin emphasised that tests based on a Heteroscedasticity Consistent Covariance Matrix i. It is well acknowledged that the EViews offers built-in tools for estimating the coefficient covariance under the assumption that the residuals are conditionally heteroskedastic.

In this case, the coefficient covariance estimator is named a Heteroskedasticity Consistent Covariance White.

Finally, we outlined the following estimator: D a diagonal matrix containing the scaled w along the diagonal z and X matrices associated with z t and x t Performing tabulation of expenses series, we noted: Consequently, in the estimation equation process, the logarithm of the controlled variable and independent variables respectively was carried out i.