Data analysis‘ purpose is to prepare, understand and draw conclusions from the data collected from the respondents (Tustin et al., 2005).
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Table 2 summarizes the demographic profile of the survey respondents in this study.
Of the 161 respondents, 55.9% were male (n=90). Age groups 18-25 years, 26-35 years, 36-45 years, and 46-55 years accounted for approximately 91.4% of the respondents, followed by age group 55 years or older. For level of education, 53.4%
of the respondents had completed college degree (n=86), followed by graduate degree (30.4%, n=49) and high school graduates (14.3%, n=23). Concerning their occupation, 34.1% of respondents are full time employees (n=55) followed by self- employed (26.1%, n=42) and part time employees in 14.9 % (n=24).
Table 3 summarizes the travel habits and past online purchase of accommodation services. More than half of the respondents traveled 2 to 4 times a year (59%, n=95) and 14.3% traveled 5 to 11 times a year (n=23) as well as respondents use to travel once a year in the percentage of 13.7% (n=22). All the respondents of the questionnaire had past online booking experience; this is the reason why their answers are accepted for the survey completion. The majority (75.8%) made an online booking through travel agent (n=122) in contrast with the online bookers using the hotel owned websites (24.2%, n=39). The primary purpose of visiting travel websites for most respondents was to check prices and availability (44.7%), right after with 29.7% to gather information, 13% of them for having fun by browsing hotel facility photos and 12.6% for direct intention to book a room.
Prior to analyzing the conceptual model, variables (useful information, usability, accessibility, perception of privacy/security, aesthetics/design, personalization/customization, perceived website service quality, overall satisfaction, return intention, and customer loyalty) were examined for accuracy of data entry, missing values, and outliers for reliability and validity of data distribution. Table 4 illustrates the alpha values for reliability confirmation; all the measurement scales measuring the constructs of this research study can be considered reliable, since they exhibit very good internal consistency reliability. The tool used for conducting the research is the Statistical Package for the Social Sciences (SPSS 23.0 version).
Concerning validity of data entry, the following table summarizes the correlations among variables:
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Table 6: Correlation coefficient matrix of variables
Correlation is significant at the 0.01 level; correlation is practically significant when r > 0.10 (small effect), r > 0.30 (medium effect) and r > 0.50 (large effect) (Cohen, 1988).
It is evident from table 5 that information sub-dimension of e-servicescape correlates practically and statistically significant with usability sub-dimension (large effect; r=.526), accessibility sub-dimension (medium effect; r=.473), privacy/security sub-dimension (medium effect; r=.455), aesthetics design sub-dimension (large effect;
r=.641), personalization/customization sub-dimension (large effect; r=.574), perception of website service quality construct (large effect; r=.654), overall customer satisfaction construct (large effect; r=.659), customer return intention construct (large effect; r=.610), customer loyalty construct (large effect; r=.625).
Concerning usability sub-dimensions of e-servicescape correlates practically and statistically significant with accessibility sub-dimension (medium effect; r=.433), privacy/security sub-dimension (medium effect; r=.457), aesthetics/design sub-
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dimension (large effect; r=.609), personalization/customization sub-dimension (medium effect; r=.427), perception of website service quality construct (large effect;
r=.609), overall customer satisfaction construct (large effect; r=.563), customer return intention construct (large effect; r=.611), customer loyalty construct (large effect;
r=.653).
Pertaining to accessibility sub-dimension of e-servicescape correlates practically and statistically significant with privacy/security sub-dimension (small effect; r=.202), aesthetics/design sub-dimension (medium effect; r=.485), personalization/customization sub-dimension (medium effect; r=.430), perception of website service quality construct (medium effect; r=.436), overall customer satisfaction construct (medium effect; r=.469), customer return intention construct (medium effect; r=.388), customer loyalty construct (medium effect; r=.457).
Privacy/security sub-dimension of e-serviscape correlates practically and statistically significant with aesthetics/design sub-dimension (medium effect; r=.442), personalization/customization sub-dimension (large effect; r=.506), perception of website service quality construct (large effect; r=.536), overall customer satisfaction construct (large effect; r=.520), customer return intention construct (large effect;
r=.514), customer loyalty construct (large effect; r=.543).
Aesthetics/ design sub-dimension of e-serviscape correlates practically and statistically significant with personalization/customization sub-dimension (large effect; r=.549), perception of website service quality construct (large effect; r=.769), overall customer satisfaction construct (large effect; r=.737), customer return intention construct (large effect; r=.740), customer loyalty construct (large effect;
r=.815).
Personalization/customization sub-dimension of e-servicescape correlates practically and statistically significant with perception of website service quality construct (large effect; r=.650), overall customer satisfaction construct (large effect; r- .650), customer return intention construct (large effect; r=.549), customer loyalty construct (large effect; r=.548).
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Perception of website quality construct correlates practically and statistically significant with overall customer satisfaction construct (large effect; r=.810), customer return intention construct (large effect; r=.772), customer loyalty construct (large effect; r=.805).
Overall customer satisfaction construct correlates practically and statistically significant with customer return intention construct (large effect; r=.794) and customer loyalty construct (large effect; r=.812).
Lastly, customer return intention construct correlates practically and statistically significant with customer loyalty construct (large effect; r=.849).
Hence, all the measurement scales measuring the constructs of this research study can be considered valid since they all exhibit content, construct and criterion validity.
The type of analysis used in this research is the Factor analysis. According to literature, Factor analysis is a multivariate statistical approach commonly used in psychology, education, and more recently in the health-related professions; this multivariate statistical procedure that has many uses as reduces a large number of variables into a smaller set of variables, establishes underlying dimensions between measured variables and latent constructs, and provides construct validity evidence of self-reporting scales. Factor analysis has two types; Exploratory Factor analysis (EFA) and Confirmatory Factor analysis (CFA). In EFA, the researcher does not expect the number or nature of the variables and as the title suggests as it is exploratory in nature allowing the researcher to explore the main dimensions to generate a theory, or model from a relatively large set of latent constructs often represented by a set of items. On the other hand, in CFA the researcher desires to test a proposed theory or model (CFA is a form of Structural Equation Modeling-SEM), and in contrast to EFA, the assumptions and expectations are set beforehand regarding the number of factors, and which factor theories or models best fit (Williams et al., 2010). In this research, for the evaluation of how well the observed data and measurement model fit the structural model SPSS software used provided us with the Factor analysis tool.
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Resulting from Factor analysis in SPSS, the following table shows that the significance level of Bartlett‘s Test of the model is greatly accepted as approaching zero value according to Hoe (2008) as his recommended cut-off points are:
<0.05=good fit; ≤0.08=acceptable fit; ≤0.10=average fit. For KMO value reaching .939 the fit is perfect as according to Blunch (2011) the recommended cut-off points are ≥ 0.95 or ≥ 0.90 = acceptable fit. These fit indices represent the overall model fit for this research study‘s data.
Table 7: Results of the Structural Equation Modeling (SEM) analysis
At this point, it is important to illustrate the strength of the hypothesized linkages among variables and relationships between the constructs by referring to the Analysis of Variance (ANOVA). An n-way ANOVA (with n being the number of independent variables) attempts to determine if there is a statistically significant difference among variables. A factorial ANOVA compares means across two or more independent variables; for some statisticians, the factorial ANOVA doesn‘t only compare differences but also assumes a cause-effect relationship, inferring that one or more independent, controlled variables (factors) cause the significant difference of one or more characteristics. The way this works is that the factors sort the data points into one of the groups, causing the difference in the mean value of the groups (Turner
& Thayer, 2001).
The null hypothesis for an ANOVA is that there is no significant difference among the groups. The alternative hypothesis assumes that there is at least one significant difference among the groups. After cleaning the data, F-ratio was calculated and the associated probability value (p-value). In general, if the p-value associated with the F is smaller than .05, then the null hypothesis is rejected and the alternative hypothesis is supported. If the null hypothesis is rejected, one concludes
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that the means of all the groups are not equal. Post-hoc tests tell the researcher which groups are different from each other.To determine whether each main effect and the interaction effect are statistically significant, compare the p-value for each term to your significance level to assess the null hypothesis. Usually, a significance level (denoted as α or alpha) of 0.05 works well.If the p-value is less than or equal to the significance level selected, then the effect for the term is statistically significant.If the p-value is greater than the significance level selected, the effect is not statistically significant.
For this research‘s purpose, a Factorial ANOVA analysis was adopted to examine the entire causal linkages among variables using the Statistical Package for the Social Sciences (SPSS 23.0 version).
Concerning the relationship of H1 hypothesis about e-servicescape dimensions affecting positively the customers‘ perception of website service quality, From correlation coefficient matrix, perceived website service quality was highly correlated with e-servicescape dimensions noting high scores of r values (.436; .536; .609; .650;
.654; .769).Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of e-servicescape dimensions on perception of website service quality (p<.05). Particularly, it was demonstrated that e-servicescape dimensions positively affect the perception of website service quality.
Table 8: E-servicescape dimensions are positively associated with perceived website service quality of a hotel owned website.
Concerning the relationship of H2 hypothesis about e-servicescape dimensions affecting positively the customers‘ overall satisfaction, from correlation coefficient
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matrix, customers‘ overall satisfaction was highly correlated with e-servicescape dimensions noting high scores of r values (.469; .520; .563; .650; .659; .737).
Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of e-servicescape dimensions on overall satisfaction (p<000). Particularly, it was demonstrated that e-servicescape dimensions positively affect the overall customer satisfaction.
Table 9: E-servicescape dimensions of a hotel owned website are positively associated with customer’s overall satisfaction.
Concerning the relationship of H3 hypothesis about e-servicescape dimensions affecting positively the customers‘ return intention, from correlation coefficient matrix, customers‘ return intention was highly correlated with e-servicescape dimensions noting high scores of r values (.388; .524; .549; .610; .611; .740).
Additionally, according to ANOVA analysis it was demonstrated that there is a statistically significant effect of e-servicescape dimensions on return intention (p<.05). Particularly, it was indicated that e-servicescape dimensions positively affect the customer return intention.
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Table 10: E-servicescape dimensions of a hotel owned website are positively associated with customer return intention to the particular website.
Concerning the relationship of H4 hypothesis about overall customer satisfaction construct affecting positively the customers‘ return intention, from correlation coefficient matrix, customers‘ return intention was highly correlated with overall customer satisfaction noting high score of r value (.794). Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of overall customer satisfaction on customer return intention (p<.05).
Particularly, it was demonstrated that customer overall satisfaction positively affect the customer return intention.
Table 11: Overall customer satisfaction of a hotel owned website is positively associated with customer return intention to the particular website.
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Concerning the relationship of H5 hypothesis about e-servicescape dimensions affecting positively the customer loyalty, from correlation coefficient matrix, customer loyalty was highly correlated with e-serviscape dimensions noting high scores of r values (.457; .543; .548; .625; .653; .815). Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of e- servicescape dimensions on customer loyalty (p<.05). Particularly, it was demonstrated that e-servicescape dimensions positively affect customer loyalty.
Table 12: E-servicescape dimensions of a hotel owned website are positively associated with customer loyalty toward the particular website.
Concerning the relationship of H6 hypothesis about customer return intention affecting positively the customer loyalty, from correlation coefficient matrix, customer loyalty was highly correlated with customer return intention noting high score of r values (.849). Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of return intention on customer loyalty (p<.05). Particularly, it was demonstrated that customer return intention positively affects customer loyalty.
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Table 13: Customer return intention to a hotel owned website is positively associated with customer loyalty toward the particular website.
Concerning the relationship of H7 hypothesis about perceived website service quality affecting positively the customer overall satisfaction, from correlation coefficient matrix, customer overall satisfaction was highly correlated with perceived website service quality noting high score of r value (.810). Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of perception of website service quality on customer overall satisfaction. Particularly, it was demonstrated that perceived website service quality positively affect customer overall satisfaction.
Table 14: Hotel website service quality is positively influencing the customer’s overall satisfaction.
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Concerning the relationship of H8 hypothesis about customers‘ overall satisfaction affecting positively the customer loyalty, from correlation coefficient matrix, customer loyalty was highly correlated with customers‘ overall satisfaction noting high score of r value (.812). Additionally, according to ANOVA analysis it was indicated that there is a statistically significant effect of customer overall satisfaction on customer loyalty (p<.05). Particularly, it was demonstrated that customer overall satisfaction positively affect customer loyalty.
Table 15: Customer overall satisfaction of the use of a hotel owned website is positively associated with customer loyalty.
Thus, all the hypotheses set in the conceptual framework according to the review of literature are strongly supported as it is illustrated in the following table:
Table 16: Hypotheses test results.
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