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Travel Agent Analysis and Development Forecast Dissertation

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Updated: May 15th, 2021

Introduction

The study generated findings from the data collected from participants (N = 325) who provided complete information in their questionnaires. Data analysis derived findings from raw information in the questionnaires by highlighting patterns and trends of information. Lacort (2014) asserts that descriptive statistics and inferential statistics are critical in data analysis for they provide valid and reliable findings. Descriptive statistics were used to evaluate the current state of travel agents in the online and high street business platforms. Essentially, descriptive statistics provide patterns and trends of demographic information and travel behaviour of participants.

Moreover, the study employed inferential statistics, namely, analysis of variance, Pearson’s correlation test, Chi-square test, and simple linear regression analysis, to establish the nature and strength of relationships within, between, and among demographic data and travel behaviours of participants. In line with the research objectives, these tests were used to evaluate the current state of high street travel agents, identify their strengths and weaknesses, and assess primary threats to their existence in the tourism industry in the United Kingdom.

As the study hypothesized that the presence of mobile applications and gender determine the number of times services were booked in the high street in the past year (2017), the study utilized analysis of variance. Regarding the hypothesis that preferred services associates with determinants such as age and the mode purchase, the study used the Chi-square test. Pearson’s correlation was used to test the hypothesis that salary has a positive relationship with age and the number of times services were booked in 2017.

Simple linear regression analysis was used test hypotheses that booking experience and technology are predictors of service quality, whereas annual net salary is a predictor of price consideration in both online and physical travel agents. Therefore, the study presented findings using tables and charts to demonstrate demographic characteristics and travel behaviours of participants in the tourism industry.

Demographic Information

The analysis of demographic information of participants reveals some patterns and drifts that are integral in interpreting and inferring findings. Gender, age group, and annual net salary of participants are the demographic variables that the study analysed.

Gender

The distribution of gender (Figure 1) indicates that males form 55.08% (179), whereas females constitute 44.92% (146) of the total participants of the study.

Pie chart showing the proportion of each gender
Figure 1. Pie chart showing the proportion of each gender (Author 2018).

Age Group

The analysis of the distribution (Figure 2) shows that most participants (31.69%) fall in the age group of 29-40 years. Approximately the same proportions of participants are in the age groups of 16-28 years (20.31%) and 41-50 years (23.69%). The proportion of participants under the age of 18 years formed 14.46%, while the proportion of those above 66 years is 3.69%. The participants in the age group of 51-65 years constituted 6.15%.

The distribution of participants according to their respective age groups
Figure 2. The distribution of participants according to their respective age groups (Author 2018)

The analysis of normality using the Q-Q plot demonstrates that the distribution of participants according to their ages follows the normal distribution (Figure 3). The plot shows that the distribution of participants fits the population of study.

The distribution of participants according to age groups
Figure 3. The distribution of participants according to age groups (Author 2018).

Annual Net Salary

Figure 4 demonstrates that the participants earn different levels of salary annually. The majority of participants (32.31%) earns between £30001 and £45000 annual net salary. Additionally, the distribution indicates that 24.62% of participants earn between £15001 and £3000. Comparatively, while 14.77% of participants earn below £15001, 28.31% of them earn more than £45000.

The distribution of participants as per their annual net salary
Figure 4. The distribution of participants as per their annual net salary (Author 2018).

The test of normality using Q-Q plot illustrates that the distribution of participants according to their net annual salaries follows the normal distribution (Figure 5). In this view, the normality test shows that the distribution of participants fits the population.

The distribution of participants according to the annual net salary
Figure 5. The distribution of participants according to the annual net salary (Author 2018).

Current State: Travel Behaviour and Booking Experience

The analysis of data shows that the participants have different travel behaviours and booking experiences. Since the objective of the study is to examine the current state of high street travel agents, the analysis of travel behaviours and booking experiences is necessary. In this view, the analysis focused on the booking experience, quality of services, technology, and price considerations among participants.

Booking Applications

Table 1 indicates that the majority of participants (74.5%) have booking applications in their mobile phones, while the minority of them (25.5%) does not have these applications. This distribution shows tourists and travellers have shifted from physical travel agents to online platforms in accessing booking services in the tourism industry.

Table 1. The Proportion of Participants with Booking Applications
Frequency Percent Valid Percent Cumulative Percent
Valid Yes 242 74.5 74.5 74.5
No 83 25.5 25.5 100.0
Total 325 100.0 100.0

Frequency of Bookings

The number of times the participants have booked services in the high street travel agents varied from one to more than seven times in 2017 (Table 2). About half of the participants (52.9%) made one-two times bookings in the high street travel agents. Frequency distribution shows that 17.8 %, 3.4%, and 2.2% of participants booked traveling services 3-4 times, 5-6 times, and 7 or more times. However, 23.7% of the participants stated that they did not book any services from the physical travel agents. What is apparent is that there are declining numbers of times the participants made bookings in the high street travel agents in the past year (2017).

Table 2. The Proportion of the Number of Physical Bookings in 2017
Frequency Percent Valid Percent Cumulative Percent
Valid None 77 23.7 23.7 23.7
1-2 Times 172 52.9 52.9 76.6
3-4 Times 58 17.8 17.8 94.5
5-6 Times 11 3.4 3.4 97.8
7 or More Times 7 2.2 2.2 100.0
Total 325 100.0 100.0

Table 3 contrasts the frequency of physical bookings since the proportion of the number of times booked online in 2017 exhibit increasing trends. The majority of participants (45.8%) booked more than seven times through online platforms followed by 33.5% of those who booked for five-six times. Moreover, 16.3% and 4.3% of participants booked for services through online three-four times and one-two times respectively.

Table 3. The Proportion of the Number of Online Bookings in 2017
Frequency Percent Valid Percent Cumulative Percent
Valid 1-2 Times 14 4.3 4.3 4.3
3-4 Times 53 16.3 16.3 20.6
5-6 Times 109 33.5 33.5 54.2
7 or More Times 149 45.8 45.8 100.0
Total 325 100.0 100.0

Preferred Services

The analysis of the preferred services shows variation in bookings done in the high street travel agents and online. Figure 6 depicts that most participants (44.92%) who book in the high street travel agents prefer hotels. Additionally, 22.15% and 13.54% of the participants prefer booking whole trip services and travel tickets in the high street travel agents respectively. A considerable proportion of participants (19.38) do not have preferential services in the high street travel agents.

Showing distribution of participants according to their preferred services booked in the high street travel agent
Figure 6. Showing distribution of participants according to their preferred services booked in the high street travel agent (Author 2018).

Figure 7 demonstrates that there is variation in the preferred services that participants make on the online platform. Most participants (48.62%) prefer whole trip services followed by 26.77% who prefer hotel services on the online platform. While 14.46% prefer tickets, 4.31% of them do not have preferences on the online services. Additionally, 5.85% of participants prefer booking other services using online travel agents.

Showing the distribution of participants as per their preferred services booked online
Figure 7. Showing the distribution of participants as per their preferred services booked online (Author 2018).

Booking Experience

Table 4 below shows that the participants prefer online as the source of information on trips, consultations, and the mode of booking. Descriptive statistics show that participants agree (M = 3.90, SD = 0.944) that they obtain information about their travel destinations through online. However, the same participants disagree that they get information about their travel destination from high street travel agents (M = 1.88, SD = 0.728).

In the aspect of engaging in detailed consultations with travel agents, the participants showed different levels of engagements. The participants disagree (M = 1.90, SD = 0.702) that they engage their travel consultants in their physical offices, but they agree (M = 3.60, SD = 1.427) that they engross them in online platforms. Concerning booking mode, participants agree (M = 3.71, SD = 1.343) that they book their trips always through online, but they remain undecided (M = 2.10, SD = 0.943) that they book them via high street travel agents.

Table 4. Descriptive Statistics of the Sources of Information
N Minimum Maximum Mean Std. Deviation
I always get information about my destination online before my trip 325 2 5 3.90 .944
I always get information from my travel agents before my trip 325 1 4 1.88 .728
I can engage in detailed consultations with my travel agent 325 1 3 1.90 .702
I can engage in detailed consultations with my online travel agents 325 1 5 3.60 1.427
I always book my trip online 325 1 5 3.71 1.343
I always book my trip through a travel agent 325 1 4 2.10 .943
Valid N (listwise) 325

Quality of Service

Descriptive statistics (Table 5) depicts that the service quality of travel agents differ according to their mode of provision, that is, online and physical agents. The assessment of the quality of service shows participants strongly agree (M = 4.20, SD = 0.749) that online travel agents provide timely services, whereas they are undecided (M 2.19, SD = 1.076) that high street travel agents offer satisfactory services. Regarding the importance of information, participants agree (M = 3.69, SD = 1.276) that online travel agents are always helpful.

However, participants remain undecided (M = 2.19, SD = 1.076) concerning the importance of information they obtain from high street travel agents. During emergencies, the participants agree (M = 3.81, SD = 1.324) that the online travel agents offer timely and helpful rescue, while they remain neutral (M = 2.39, SD = 1.278) on the help they obtain from high street travel agents. The analysis of convenience of booking indicates that the participants agree (M = 3.56, SD = 1.333) that they can book a holiday from the online travel agents at any time, whereas they disagree (M = 1.94, SD = 1.138) that they can book from high street travel agents at any time.

Table 5. Descriptive Statistics of the Quality of Service
N Minimum Maximum Mean Std. Deviation
I am satisfied with the quality of service I get from high street travel agents 325 1 4 2.19 1.076
I am satisfied with the quality of service I get from online travel agents 325 3 5 4.20 .749
The information I get from high street travel agents is always helpful 325 1 5 2.50 1.203
The information I get from online travel agents is always helpful 325 1 5 3.69 1.276
If I got in trouble on my trip, the after sales service from the high street travel agent is timely and helpful 325 1 5 2.39 1.278
If I get in trouble on my trip, the after sales service from the online travel agents is timely and helpful 325 1 5 3.81 1.324
I can book a holiday from high street travel agents at any time 325 1 5 1.94 1.138
I can book a holiday online at any time 325 1 5 3.56 1.333
Valid N (listwise) 325

Technology

The analysis of the role of technology in the tourism industry reveals that it has a significant influence on the booking experience and travel behaviour of tourists (Huang, Goo & Yoo 2017). The participants agree (M =3.5, SD = 1.360) that the booking applications on their mobile phones are very convenient to use (Table 6). Moreover, the participants strongly agree (M = 4.30, SD = 0.639) that they trust online payment system in the websites of travel agents (Table 6). When choosing a holiday destination, the participants agree (M = 3.41, SD = 1.499) that they consider the online feedback of customers. Therefore, the participants agree that technology plays a central role in making reservations, paying for services, and choosing travel destinations

Table 6. Descriptive Statistics of Technology
N Minimum Maximum Mean Std. Deviation
The booking application on my phone is convenient 325 1 5 3.50 1.360
When I use a website to book my trip, I always trust their online payment system 325 3 5 4.30 .639
Previous customers’ online feedback is important for me in choosing my holiday 325 1 5 3.41 1.499
Valid N (listwise) 325

Price Considerations

Since the price has a marked impact on the purchasing behaviours of customers (Amaro & Duarte 2015), comparison of considerations of customers in high street travel agents and online travel agents is essential. The participants agree (M = 3.71, SD = 1.265) that the price of undertaking online bookings is lower than that of high street travel agents (Table 7). Additionally, the participants agree (M = 3.40, SD = 1.120) that price is the only factor that would make them procure services on the online platform. The participants disagree (M = 1.80, SD = 1.120) that a slightly more expensive price on the high street would not discourage them (Table 7). Regarding the preference of high street travel agents, the participants remain undecided (M = 2.40, SD = 1.281) that price would influence them.

Table 7. Descriptive Statistics of Price Considerations
N Minimum Maximum Mean Std. Deviation
The price of online booking is always lower than high street travel agents 325 1 5 3.71 1.265
I will choose to buy on the high street or online solely based on price 325 2 5 3.40 1.120
I will choose to buy on the high street even if they are slightly more expensive 325 1 3 1.80 .749
I will choose to buy on the high street regardless of price 325 1 5 2.40 1.281
Valid N (listwise) 325

Important Relationships between Demographic Variables

The Number of Times Booked in 2017 and Mobile Applications

The analysis of variance was used to test the hypothesis that the presence of applications in mobile phones encourages customers to purchase services online frequently. To determine if presence of mobile applications determines the number of times services were booked in high street in the past year (2017), the study utilized analysis of variance. Since presence of mobile applications in phones is a categorical variable and the number of times services were booked is a continuous variable, the appropriate test is analysis of variance. Comparison of the number of times booked in 2017 and the use of mobile applications exhibited variation in online and physical travel agents.

Table 8 indicates that the number of times booked in the high street travel agents is almost the same for participants with (M = 2.03, SD = 0.868) and without (M = 2.19, SD = 0.833) mobile applications. Concerning services booked from online travel agents, participants with mobile applications (M = 4.11, SD = 0.971) depicted a higher number of times than those without mobile applications (M = 3.59, SD = 1.22).

Table 8. Descriptive Statistics of the Number of Times Booked versus Mobile Applications.

N Mean Std. Deviation Minimum Maximum
How many times have you booked a service in the high street travel agent in the last one-year (2017)? Yes 242 2.03 .868 1 5
No 83 2.19 .833 1 5
Total 325 2.07 .861 1 5
How many times have you booked a service online in the last one-year (2017)? Yes 242 4.29 .798 2 5
No 83 3.59 1.220 1 5
Total 325 4.11 .971 1 5

Table 9 shows that there is no significant difference between the number of times the participants with and without mobile application booked services from high street travel agents in 2017(F(1, 323) = 2.134, p = 0.145). However, there is a significant difference in the number of times participants with and without mobile applications booked services from online travel agents. These findings demonstrate that the presence of applications in mobile phones encourages online booking of services (Baldwin 2016).

Table 9. ANOVA of the Number of Times Booked in 2017 and Mobile Applications
Sum of Squares Df Mean Square F Sig.
How many times have you booked a service in the high street travel agent in the last one-year (2017)? Between Groups 1.576 1 1.576 2.134 .145
Within Groups 238.651 323 .739
Total 240.228 324
How many times have you booked a service online in the last one-year (2017)? Between Groups 29.832 1 29.832 34.988 .000
Within Groups 275.399 323 .853
Total 305.231 324

Gender and the Number of Times Booked in 2017

The analysis of variance was used to test the hypothesis that gender determines the number of times services that were booked in high street in the past year (2017). This analysis is relevant because gender (the independent variable) comprises of two categories and the number of times were booked (the dependent variable) is on a continuous scale. Table 10 shows that the number times the participants booked services from the high street travel agents is higher for females (M = 2.28, SD = 0.803) than males (M = 1.91, SD = 0.872). In contrast, the number of times the participants booked services from the online travel agents is higher for males (M = 4.29, SD = 1.05) than females (M = 3.88, SD = 0.810).

Table 10. Descriptive Statistics of Gendered Number of Times Booked in 2017.

N Mean Std. Deviation Minimum Maximum
How many times have you booked a service in the high street travel agent in the last one-year (2017)? Males 179 1.91 .872 1 5
Females 146 2.28 .803 1 5
Total 325 2.07 .861 1 5
How many times have you booked a service online in the last one-year (2017)? Males 179 4.29 1.052 1 5
Females 146 3.88 .810 1 5
Total 325 4.11 .971 1 5

The analysis of variance (Table 11) shows that the number of times male participants booked services through high street travel agents is statistically significantly lower than that of female participants, F(1,323) = 16.026, p = 0.000. In contrast, the number of times female participants booked services via online agents is statistically significantly lower than that of male participants, F(1,323) = 14.734, p = 0.000.

Table 11. The Significance of Gendered Number of Times Booked in 2017
Sum of Squares df Mean Square F Sig.
How many times have you booked a service in the high street travel agent in the last one-year (2017)? Between Groups 11.356 1 11.356 16.026 .000
Within Groups 228.872 323 .709
Total 240.228 324
How many times have you booked a service online in the last one-year (2017)? Between Groups 13.316 1 13.316 14.734 .000
Within Groups 291.914 323 .904
Total 305.231 324

Preferred Services and Platform

To test the hypothesis that a business platform associates with the nature of preferred services, the study utilized Chi-square test. Since preferred services (travel tickets, hotel, whole trip, and others) and the platform of business (online and street) exist on categorical scale, Chi-square test is appropriate in determining their associations. Cross-tabulation (Table 12) indicates that there is an association between preferred services booked in the online and high street travel agents. The participants indicated that they (146) prefer to book hotel services (44.9%) via the high street travel agents and 158 of them (48.61%) prefer to book whole trip services through the online travel agents. Comparatively, the preference for travel tickets and other services do not exhibit significant variations.

Table 12. Preferred Services Booked in the Online and High Street Travel Agents
What are services you prefer to book in the high street travel agent? Total
Travel Tickets Hotel Whole Trip None
What are services you prefer to book online? Travel Tickets Count 0 22 13 12 47
% within What are services you prefer to book online? 0.0% 46.8% 27.7% 25.5% 100.0%
Hotel Count 16 45 7 19 87
% within What are services you prefer to book online? 18.4% 51.7% 8.0% 21.8% 100.0%
Whole Trip Count 26 60 46 26 158
% within What are services you prefer to book online? 16.5% 38.0% 29.1% 16.5% 100.0%
None Count 1 9 2 2 14
% within What are services you prefer to book online? 7.1% 64.3% 14.3% 14.3% 100.0%
Others Count 1 10 4 4 19
% within What are services you prefer to book online? 5.3% 52.6% 21.1% 21.1% 100.0%
Total Count 44 146 72 63 325
% within What are services you prefer to book online? 13.5% 44.9% 22.2% 19.4% 100.0%

Chi-square test (Table 13) demonstrates that there is a significant association between preferred services and the mode of booking them through online and high street travel agents, χ(12) = 28.721, p = 0.004. In this case, it is valid to claim that participants prefer booking whole trips through online travel agents and hotels via high street travel agents.

Table 13. Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 28.721 12 .004
Likelihood Ratio 37.358 12 .000
Linear-by-Linear Association .774 1 .379
N of Valid Cases 325

Number of Times Booked and Annual Net Salary

Pearson’s correlation was used to test the hypothesis that age and the number of times services were booked have positive relationship. Essentially, this test is suitable because age and the number of times services are on continuous scale and exhibit linear relationships. Table 14 depicts that there is a moderate positive relationship, which is statistically significant (r = 0.451, p = 0.041), between the number of times the participants booked services from online travel agents and the annual net salary.

Furthermore, correlation shows that annual net salary has a weak positive relationship, which is statistically significant (r = 0.206, p = 0.000), with the number of times the participants booked services from high street travel agents. These relationships imply that customers with higher levels of salary tend to prefer online bookings than those with lower levels of salary.

Table 14. Correlation of the Number of Times Booked and Annual Salary
Booking from High Street Travel Agents Online Booking Annual Net Salary
How many times have you booked a service in the high street travel agent in the last one-year (2017)? Pearson Correlation 1 0.211 .206
Sig. (2-tailed) .067 .000
N 325 325 325
How many times have you booked a service online in the last one-year (2017)? Pearson Correlation .211 1 .451
Sig. (2-tailed) .067 .041
N 325 325 325
What is your net salary annually? Pearson Correlation .206* .451 1
Sig. (2-tailed) .000 .041
N 325 325 325

Age Group and Annual Net Salary

Furthermore, correlation analysis was used to test the hypothesis that age group and salary have positive relationship. Given that that age group and salary have continuous scales and depict linear relationship, the study employed Pearson’s correlation to assess their relationships. Correlation analysis (Table 15) indicate that there is a statistically significant positive relationship between age and annual net salary that the participants earn (r = 0.717, p = 0.000). Since the number of services booked in the past year increased with the annual net salary, it implies that the target customers in the tourism industry are adults and old individuals.

Table 15. Correlation of Age Group and Salary
What is your age group? What is your net salary annually?
What is your age group? Pearson Correlation 1 .717**
Sig. (2-tailed) .000
N 325 325
What is your net salary annually? Pearson Correlation .717** 1
Sig. (2-tailed) .000
N 325 325

Patterns and Trends of Travel Behaviours and Experience

Since the study sought to test the hypothesis that booking experience has statistically significant influence on the service quality, it used simple linear regression analysis. Additionally, the study used simple linear regression analysis to test the hypothesis that annual net salary has significant influence on price consideration among participants. Simple linear regression analysis is appropriate because it measures the strength and the degree of the influence of an independent variable on a dependent variable. In this case, simple linear regression analysis was used to test the hypotheses that booking experience

Booking Experience and Service Quality of High Street Travel Agents

Simple linear regression model (Table 16) shows that the booking experience in high street travel agents has a weak relationship with service quality (R = 0.267). In this case, booking experience explains 7.1% of the variation in the service quality (R = 0.071).

Table 16. Model Summary of High Street Travel Agents
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .267a .071 .069 .46293
a. Predictors: (Constant), Booking Experience in High Street Travel Agents

The regression model (Table 17) is statistically significant in explaining the influence of booking experience on the quality of services that customers get in the high street travel agents, F(1,323) = 24.868, p = 0.000. This finding means that simple linear regression model effectively predicts the effect of booking experience on the quality of services that customers get in the tourism industry.

Table 17. ANOVAa of High Street Travel Agents
Model Sum of Squares df Mean Square F Sig.
1 Regression 5.329 1 5.329 24.868 .000b
Residual 69.221 323 .214
Total 74.551 324
a. Dependent Variable: Service Quality Offered by High Street Travel Agents
b. Predictors: (Constant), Booking Experience in High Street Travel Agent

Coefficients of regression (Table 18) shows that the booking experience of high street travel agents is a statistically significant predictor of the quality of service (β = 0.209, p = 0.000). Essentially, the regression coefficients means that a unit increase in booking experience causes the service quality to increase by 0.21 units of the Likert scale used. Regression equation made from these coefficients in that:

Service quality = 0.209 (Booking experience by high street travel agents) + 3.03

Table 18. Coefficientsaof Service Quality in the High Street Travel Agents
Model Unstandardised Coefficients Standardised Coefficients t Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 3.030 .159 19.056 .000 2.717 3.343
Booking Experience .209 .042 .267 4.987 .000 .127 .292
a. Dependent Variable: Service Quality Offered by High Street Travel Agents

Booking Experience and Service Quality of the Online Travel Agents

The regression analysis (Table 19) indicates that the booking experience has a strong positive relationship with the service quality obtained from the online travel agents (R = 0.649). Booking experience accounts for 42.1% of the variation in the service quality provided by online travel agents.

Table 19. Model Summary of Online Travel Agents
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .649a .421 .419 .36402
a. Predictors: (Constant), Online Booking Experience

The regression model (Table 20) is statistically significant in predicting the influence of booking experience on the quality of services that customers get from online travel agents, F(1,323) = 234.452, p = 0.000. This finding implies that the simple linear regression model is valid in assessing the effect of booking experience on the quality of services.

Table 20. ANOVAaof Online Travel Agents
Model Sum of Squares Df Mean Square F Sig.
1 Regression 31.067 1 31.067 234.452 .000b
Residual 42.801 323 .133
Total 73.868 324
a. Dependent Variable: Service Quality Offered by Online Travel Agents
b. Predictors: (Constant), Online Booking Experience

Table 21 illustrates that booking experience is a statistically significant predictor of service quality offered by online travel agents (β = 0.508, p = 0.000). The coefficients mean that a unit increase in booking experience causes the quality of service to increase by 0.51 units of the Likert scale. Thus, the findings imply that booking experience determines the quality of services that customers get in the tourism industry. In this case, the regression equation is that:

Service quality = 0.508 (Booking experience by online travel agents) + 1.259

Table 21. Coefficientsaof Online Travel Agents
Model Unstandardised Coefficients Standardised Coefficients t Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 1.259 .068 18.500 .000 1.125 1.393
Online Booking Experience .508 .033 .649 15.312 .000 .443 .573
a. Dependent Variable: Service Quality Offered by Online Travel Agents

Technology and Service Quality by the Online Travel Agents

As technology has revolutionised the mode of service delivery in the tourism industry (Kim & Kim 2017), an examination of its relationship with the quality of service is critical. Regression analysis (Table 22) indicates that there is a moderate positive relationship between technology and the quality of service travel agents deliver online (R = 0.535). Moreover, technology explains 28.7% of the variation in the level of service quality offered by online travel agents (R2 = 0.287).

Table 22. Model Summary of Technology and Service Quality
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .535a .287 .284 .69215
a. Predictors: (Constant), Technology

The regression model (Table 23) applied to the prediction of the service quality by the technology is statistically significant (F(1,232) = 129.753, p = 0.000). The significance of predictor means that travel agencies can use technology in improving the quality of their services in the tourism industry.

Table 23. ANOVAaof Technology and Service Quality
Model Sum of Squares Df Mean Square F Sig.
1 Regression 62.161 1 62.161 129.753 .000b
Residual 154.741 323 .479
Total 216.903 324
a. Dependent Variable: Service Quality Offered by Online Travel Agents
b. Predictors: (Constant), Technology

Table 24 illustrates that technology is a statistically significant positive predictor of the service quality (β = 0.420, p = 0.000). In this view, the regression coefficients mean that when technology increases by a unit, the level of service quality increases by 0.42 units of the Likert scale. The regression equation of this model is that:

Service quality = 0.42 (Technology) + 1.662

Table 24. Coefficientsaof Technology and Service Quality
Model Unstandardised Coefficients Standardised Coefficients t Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 1.662 .126 13.222 .000 1.415 1.910
Technology .420 .037 .535 11.391 .000 .347 .492
a. Dependent Variable: Service Quality Offered by Online Travel Agents

Annual Net Salary and Price Consideration

Based on the annual net salary, it is apparent that participants have different economic conditions. Regression analysis (Table 25) indicates that there is a weak relationship between the annual net salary and the consideration of price in booking services from travel agents in both online and high street agents (R = 0.219). R-square (0.048) shows that the annual net salary explains 4.8% of the variation in price consideration among the participants.

Table 25. Model Summary of Annual Net Salary and Price Consideration
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .219a .048 .045 1.237
a. Predictors: (Constant), Annual Net Salary

Table 26 demonstrates that the regression model is statistically significant in predicting the effect of the annual net salary on the perception of price as a factor of considering booking preference among participants, F(1,323) = 16.275, p = 0.000. This model means that linear regression analysis accurately predicts price consideration based on the annual net salary of customers.

Table 26. ANOVAa of Annual Net Salary and Price Consideration
Model Sum of Squares df Mean Square F Sig.
1 Regression 24.887 1 24.887 16.275 .000b
Residual 493.926 323 1.529
Total 518.812 324
a. Dependent Variable: The price of online booking is always lower than high street travel agents
b. Predictors: (Constant), Annual Net Salary

Coefficients of regression analysis (Table 27) shows that the annual net salary is a statistically significant negative predictor of price consideration among participants (β = -0.27, p = 0.000). The coefficients imply that a unit increase in the annual net salary causes the price consideration among participants to decline by 0.27 units of the Likert scale employed in the study. These coefficients also show that the regression equation is that:-

Price consideration = -0.27 (Annual net salary) + 4.45

Table 27. Coefficientsaof Annual Net Salary and Price Consideration
Model Unstandardised Coefficients Standardised Coefficients t Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 4.450 .196 22.743 .000 4.065 4.835
Annual Net Salary -.270 .067 -.219 -4.034 .000 -.401 -.138
a. Dependent Variable: Price Consideration

Summary

The analysis of the data revealed main findings that travel agents need to consider in their operations to remain relevant and competitive in the United Kingdom (Table 28). Demographic trends show that most of the people have mobile applications, use online platforms in booking services from the travel agents, and register a high frequency of bookings. While hotels form a preferred service in the high street travel agents, whole trips are common among online travel agents. Booking experiences and travel behaviours show that most customers prefer online travel agents because they are cheap, trustworthy, convenient, and offer satisfactory services (Agag & El-Masry 2016). Additionally, the regression analysis shows that booking experience, salary, technology, and price are statistically significant predictors of the quality of services among online travel agents.

Table 28. Summary of Findings
Variables Tests Outcomes
Mobile applications and booking rates Analysis of variance Participants with mobile applications had higher booking rates than those without
Gender and booking rates Analysis of variance Males higher booking rates in online travel agents, while females have high booking rates in high street travel agents
Preferred services and business platform Chi-square test Preferences for hotels in the high street travel agents and whole trip services in online travel agents
Annual net salary and booking rate Pearson’s Correlation A moderate positive relationship (r = 0.451, p = 0.041) in online and a weak positive relationship (r = 0.206, p = 0.000) in high street
Age and annual net salary Correlation A strong positive relationship (r = 0.717, p = 0.000).
Booking experience and service quality in high street Simple linear regression Explains 7.1% of the variation in the service quality
Booking experience and service quality in the online platform Simple linear regression Accounts for 42.1% of the variation in the service quality
Technology and service quality Simple linear regression Explains 28.7% of the variation in the level of service quality
Annual net salary and price consideration Simple linear regression Explains 4.8% of the variation in price consideration

Reference List

Agag, GM & El-Masry, A 2016, ‘Why do consumers trust online travel websites? Drivers and outcomes of consumer trust toward online travel websites’, Journal of Travel Research, vol. 56, no. 3, pp. 1-23.

Amaro, S & Duarte, P 2015, ‘An integrative model of consumers’ intentions to purchase travel online’, Tourism Management, vol. 46, no.1, pp. 64-79.

Baldwin, R 2016, The great convergence: information technology and the new globalisation, Harvard University Press, Cambridge.

Huang, CD, Goo, J & Yoo, CW 2017, ‘Smart tourism technologies in travel planning: the role of exploration and exploitation’, Information & Management, vol. 54, no. 6, pp. 757-770.

Kim, D & Kim, S 2017, ‘The role of mobile technology in tourism: patents, articles, news, and mobile tour app reviews’, Sustainability, vol. 9, no. 1, pp. 1-45.

Lacort, MO 2014, Descriptive and inferential statistics: summaries of theory and exercises solved, Lulu Press, Morrisville, NC.

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