Monthly family income is distributed variously: 3% (12) of respondents have an income of less than $1000 per month, 12% (48) have an income of $1000-$2000, 36% (144) have an income of $2000-$3000, 35% (140) have an income of $3000-$6000, and 15% (56) have an income above $6000 per month.
The education degree of respondents is as follows: approximately 8% (32) of respondents had less than a high school education; 17.0% (68) of respondents received a grammar school or high school education, 25% (100) of respondents had some college education, 37% (148) of respondents held a bachelor’s degree, and 13% (52) of respondents held a graduate degree .
The results showed that 9% (30) of respondents are retired, 19% (76) worked in management/executive positions, 23% (92) were professionals, 16% (64) worked in government, 13% (52) were freelancers, 18% (72) worked in clerical or sales positions, and 2% (8) were unemployed.
Internet Use in Life
In the collected study, 85% (340) of respondents used the internet in their daily life, and 15% (60) had no online experience or used it very seldom.
Regarding respondents who used the internet, 15% (51) used it 1-2 times per week, 25% (85) used it 3-4 times per week, and 45% (153) used it more than 5 times per week.
Among respondents with online experience, 3% (10) used the internet for less than 6 months, 24% (82) used it for around 6 months to 1 year, 21% (71) used it for around 1 to 2 years, and 52% (106) had more than 2 years of experience using the internet.
The results of the study identified the purpose of using the internet. It shows that 20% (68) of all respondents used the internet for work, 8% (27) used it for online education, 10% (34) used it for online shopping, 26% (89) used it for email communication, 20% (68) used it for entertainment, and 16% (54) used it for personal information.
BEHAVIORAL CHARACTERISTICS REGARDING ONLINE RESERVATION EXPERIENCE
Two hundred and sixty of all respondents (75%) had never had an experience with online reservations. 35% (140) of respondents purchased hotel services and merchandise via online reservations. 3% (4) of respondents with online reservations did not make online reservations in the last 12 months. Forty-two (30%) of respondents made online reservations only 1 time. Seventy (50%) of respondents made online reservations 2 to 3 times in the last 12 months.
Seventeen (12%) of respondents made online reservations 4 to 5 times. Seven of all respondents (5%) made online reservations more than 5 times. Regarding respondents that did not have any online reservation experience, 25% (65) made telephone reservations directly with hotels, 40% (104) arranged their hotel services and merchandise via travel agents, and 35% (91) by corporate aid.
FACTOR ANALYSIS
The identification of important underlying dimensions that have an influence on Kuala Lumpur hotel customers’ online reservation intentions was conducted through explorative factor analysis using main components with varimax rotation.
Five factors in the varimax-rotated factor matrix with eigenvalues greater than 1 were eventually removed and illustrated 63.5% of the overall discrepancy. The five factors that were taken were called: 1. information demands, 2. service performance and reputation of hotels, 3. convenience and security, 4. technological disposition, and 5. price benefits. Reasonably higher loads in appropriate factors with a clean structure were created.
Most of the variables were heavily loaded into other factors and did not load heavily into other factors, meaning that there was minimal coincidence between these factors and that all factors were independent. In order to test the appropriateness of the factors analysis, the Bartlett Test of Sphericity and Kaiser-Meyer-Olkin (KMO) were conducted.
The results showed that the significance of the correlativity matrix was 0.0, with the Bartlett Test of Sphericity value of 2179.612, indicating that it was nearly multivariate normal and acceptable for factor analysis because the data did not form an identity matrix. Kaiser-Meyer-Olkin (KMO) was meritable because the quantity of the sampling adequateness was 0.81. The variables shared common factors and were reasonably interrelated.
Additionally, Cronbach’s alpha was calculated to test the internal consistency and reliability of all factors. The results demonstrated that alpha coefficients varied between 0.71 and 0.86 for all factors. The results of the factor analysis in this research are shown to be very reliable or reasonably reliable, because the minimum value for acceptance is 0.50.
Following are the five factors that underlie Kuala Lumpur hotel consumers’ online reservation purpose features:
- Information demands
Information demands include six items and account for 28.36% of the variance with an Eigenvalue of 5.38. They include items related to the information needs of hotel consumers during their online reservation processes, such as: practical experience of available amenities, appropriate product and service information, ease of comparing hotels, a variety of product/brand choices, clarity of product/service information, and the latest product and service information.
- Service performance and reputation of hotels
This factor accounts for 10.18% of the variance and has a characteristic root of a square matrix of 1.93. It includes three items: brand name, company reputation and credibility, and product quality.
- Convenience and security
This factor includes three items and the factor analysis shows that it accounts for 9.57% of the variance with an Eigenvalue of 1.82. The three items are: safe payment process, ease of placing and canceling orders, and 24-hour availability. Three other items were removed: security of sensitive information, ease of obtaining information, and freedom from hassle.
- Technological disposition
This factor includes three items and the factor analysis shows that it accounts for 7.81% of the variance with an Eigenvalue of 1.48. It includes items such as: receptiveness to new technology innovation, past satisfaction with e-commerce, and familiarity with e-commerce.
- Price benefits
This factor includes two items and the factor analysis shows that it accounts for 7.31% of the variance with an Eigenvalue of 1.38. It includes items such as: reduced purchase-related costs and discounted price.
DEMOGRAPHIC PROFILE AND ONLINE RESERVATION INTENTION
In order to demonstrate whether there is a significant difference between demographic groups and the means of the five factors, an ANOVA was performed. When analyzing age, gender, educational degree, monthly income, and business with the five factors, there was no significant difference. As a result, the first hypothesis cannot be confirmed.
MULTIPLE REGRESSION ANALYSIS
DETERMINANTS OF THE GUESTS’ OVERALL SATISFACTION LEVELS
Two multiple regression analyses were conducted in order to analyze whether the dependent variables (Kuala Lumpur hotel guests’ satisfaction with online reservation practice and the likelihood of making an online reservation by guests without online reservation practice) have a significant influence on the independent variables (5 factors). The input variables for this analysis were the factors from the factor analysis.
The multiple correlation coefficient R, coefficient of determination (R²), and F ratio were considered to predict the integrity of the regression model. The five purchase purpose factors were justified by an adjusted R² of 0.439, indicating about 43.9% of the variance in the dependent variable. Guests that had positive and relatively high satisfaction levels with the five factors showed an R of independent variables in the dependent variable (guests’ satisfaction with online reservation) of 0.692.
The results of the regression model could be observed by chance explained by F-ratio and showed a significance level of 0.0000 and a value of 12.103. In this study, the implemented regression model might not have occurred, and it is considered significant. A t-statistic test was used to determine whether the dependent variable (satisfaction level with online reservation) contributed to the five independent variables.
The t-statistic test demonstrated that four factors, such as brand reputation of hotels, convenience and security, technological disposition, and information demands, were significant variables in this manner l (p ≤ 0.05). On the other hand, Factor 5 was not significant (p = 0.08). One could determine the influence of each variable on the dependent variable based on the coefficient of each independent variable. “Information needs” were the most important determining factor in understanding guests’ satisfaction level.
It showed the highest T value and the highest coefficient value of 0.42 as well. Other variables showed the following importance: “technological inclination” (b = 0.22), “service performance and reputations of hotels” (b = 0.37), and “convenience and security” (b = 0.15). The “Price benefits” variable showed that it is not significant, and the coefficient value is of less importance. The satisfaction level of guests is illustrated as depending on the following four variables.
As a result, it can be concluded that the important factors of guests’ satisfaction are these four variables. In other words, guests’ satisfaction level increases when there is a higher level of satisfaction in these dimensions.
DETERMINANTS OF THE GUESTS’ PROBABILITY OF MAKING ONLINE RESERVATION
This section demonstrates the same analysis as shown in the previous section, identifying whether the five factors had a significant influence on the likelihood of guests without online reservation pattern making an online reservation the following time, by using the same regression model. The sum of the dimensions of the five factors resulting from the factor analysis was regressed to guests’ likelihood of making an online reservation.
The results of the analysis of guests’ likelihood of making an online reservation in relation to the regression analysis. The adjusted R2 of 0.277 shows that the regression equation of “likelihood of making online reservation” explains about 27.7% of the variation in “likelihood of making online reservation”. The F-ratio of 19.687 was significant, indicating that the results of the equation were hardly random.
The t-statistic test was used to prove that all five independent variables contributed to the dependent variable “likelihood of making online reservation”. The analysis demonstrated that all five factors, namely technological disposition, information demands, perception of hotels, convenience and security, and price benefits, were significant variables. One could find the influence of each variable on the dependent variable based on the coefficient of each independent variable.
Information demands were the most important deciding factor in understanding guests’ satisfaction degree with the highest coefficient value of 0.31 and the highest t-value as well. In terms of importance, other variables showed the following: service performance and reputations of hotels (b=0.15), technological disposition (b=0.17), convenience and security (B=0.12), and price benefits (b=0.21). Guests’ satisfaction degree with online reservation information on hotel websites illustrated that it depends on all five variables.
As a consequence, it can be concluded that the determining factors of guests’ satisfaction with online reservation information on hotel websites were these five variables. In other words, when guests’ satisfaction degree increases, there is a higher degree of satisfaction with these dimensions.
