applied business methods report

Report

APPLIED

BUSINESS

METHODS:

RESEARCH

PROJECT

What Determines

Hotel Customer-

Review Scores?

Group 6A

Dennis Johannisse (370759)

Zhengchen Wei (36907)

Mike Lien (356159)

2

What Determines Hotel Customer-Review Scores?

Dennis Johannisse, Zhengchen Wei, Mike Lien

Table of Contents

Applied Business Methods: Research Project

Erasmus University Rotterdam

TABLE OF CONTENTS

Table of Contents ............................................................................................. 2

Table of Figures ............................................................................................... 3

Chapter 1 Introduction ................................................................................. 4

1.1 Background information and research question .............. 4

1.2 Outline of the report ........................................................... 4

1.3 Research hypothesis and causal relations scheme ......... 5

Chapter 2 Univariate Analysis....................................................................... 7

2.1 Dependent Variable ............................................................ 7

2.2 Independent Variables ....................................................... 8

2.3 Bonus Variables .................................................................. 9

Chapter 3 Bivariate Analysis ......................................................................... 9

3.1 T-Test ................................................................................... 9

3.2 One-way ANOVA F-Test ..................................................... 10

3.3 Coefficient of Correlation ................................................. 13

3.4 Chi-Squared Contingency Table Test ............................... 14

3.5 Bonus Variable Tests ........................................................ 16

Chapter 4 Multivariate Analysis ................................................................. 17

4.1 Two-Factor ANOVA Analysis .............................................. 17

4.2 Regression Model I (RM1)................................................ 18

4.3 Regression Model II (RM2)............................................... 21

4.4 Comparison of Model I and Model II ............................... 22

Chapter 5 Conclusion and Evaluation ....................................................... 23

5.1 Interpretation and Significance of Results ..................... 23

5.2 Recommendations for Hotel Managers and Owners ..... 24

5.3 Evaluation of Data and Research .................................... 25

Appendix A: Bivariate Analysis Tests ............................................................ 27

Appendix B: Two-way ANOVA Tests ............................................................... 28

Appendix C: Multivariate Analysis Tests ....................................................... 29

Appendix D: Bonus Variables ........................................................................ 30

3



Table of Figures



TABLE OF FIGURES

Figure 1 causal relations scheme .................................................................................. 7

Figure 2 cr_score histogram (Left: Original, Right: Removal of outlier) ....................... 8

Figure 3 Scatter plot between price and cr_score ...................................................... 14

Figure 4 Comparison of number of hotels by stars between hotels that advertise

(left) and hotels that do not (right) ............................................................................... 15

Figure 5 Scatter plot of dAirport and cr_score ............................................................ 16

Figure 6 Plot of mean of treatments within luxury versus cr_score ........................... 17

Figure 7 Graph presenting advertising*star interaction effect .................................. 18

Figure 8 Table listing chosen international airports and their coordinates............... 31

Cover page picture shows the seven star Burj Al Arab seven-star hotel, located in Dubai,

UAE. Courtesy of Ito Joi (Source: http://www.flickr.com/photos/joi/2086020608/)

4



Introduction



Chapter 1 INTRODUCTION

1.1 Background information and research question

The main goal of this report is to find out which factors influence customer reviews, as well

as understanding how they influence them, which in turn can influence a hotels revenue or

profitability. The significance of customer reviews has become increasingly more important

after recent developments in the travel industry across the United States (US), customer

satisfaction is therefore an important determinant which will be researched for this case.

For researching this case, data about 1562 hotels spread across 6 geographical markets in

the US has been gathered. This report aims at finding out what factors might affect

customer reviews for different hotels, as it could help managers determine how hotels can

increase customer satisfaction which in turn can improve the revenue and profitability of

the hotels.

Thus, the main research question for this report is as follows:

What are the determinants of hotel customer-review scores on Orbitz for hotels with

different characteristics?

1.2 Outline of the report

This report is made up of five sections and four appendices (A, B, C and D). In section one,

the introduction and summary of research data and hypotheses, the background

information and research question for the report will be presented. In addition, our research

hypothesis and causal relation scheme along with a short introduction of all relevant

variables will be presented here. The second section, the univariate analyses will reveal the

characteristics and properties of the aforementioned chosen variables. In our third section

we will present the bivariate analysis, which analyses the proposed relationships from the

causal relation scheme. We will use the t-test, one-way ANOVA, correlation coefficient and

contingency table to analyse this section. Then, we will use the outcomes of the

aforementioned tests to evaluate the presence, nature and strength for each of the

proposed relationships. Relevant 7-steps schemes will be presented in appendix A. Our

fourth section on multivariate analysis consists of a two-factor ANOVA analysis which

involves the response variable, cr_score, and two other factors from our causal relationship

scheme. The relevant 7-steps scheme for this test will be provided in appendix B. We will

also formulate a regression model on the basis of our original causal relationship scheme.

Relevant computations can be found in appendix C. A second regression model will then be

5



Introduction



presented, and the results will be discussed. Finally, in section five will conclude with the

results and evaluate.

1.3 Research hypothesis and causal relations scheme

Before we show our established causal relations scheme, all included independent

variables and their proposed relations to the dependent variable, cr_score, will be

presented below. In addition, we also present a few additional cross variable relationships.

Independent variables

Destination ID

Each destination has different features and popular places, customers might have different

preferences or ideas what they want to do during their holiday or travel. In addition, some

destinations might be perceived better and have an overall higher, or at least different,

score as compared to other locations. Therefore, we believe that destination ID has an

influence on the central cr_score variable.

Price

Price is an important aspect of a customer review. Though not a determinant of its own, it

does play an important role when compared to the quality: is a stay at this hotel worth its

money or is it way too expensive? Thus, we conclude that price has an influence on

customer reviews however we are not sure whether this will be positive or negative.

Stars

We suspect that hotels with a higher star ranking will have a higher customer review score

on average. A star is awarded for good quality and service which, logically speaking, is a

determinant of positive customer reviews.

Employee Satisfaction

If employees like their job, generally the hotel has a more favourable and enjoyable

atmosphere. We believe that this too can positively influence the customer reviews.

Therefore, we believe that employee satisfaction is positively related to the dependent

variable customer review.

Chain

Being associated with a certain hotel chain can be viewed as a symbol of prestige. Being

part of a chain requires hotels to fulfil certain quality conditions in order to be eligible to be

part of it. Such requirements, for example hygienic standards or certain luxurious furniture

such as a TV can also mean that customer reviews tend to be more favourable. We propose

that there exists a positive relationship between being in a chain and a higher customer

review score.

6



Introduction



Property type

Different sorts of hotels serve different needs. It is important to remember that, for example,

a full service hotel is rated differently than a simple youth hostel. We believe that a

difference in ratings exists between these groups of property.

Advertisement

A hotel with more online ad impressions is on average more well-known. A hotel advertises

its main selling points, which in turn attracts customers. If the perceived quality of the hotel

matches the quality that it has advertise, customers may provide favourable rating for the

hotel. The degree of advertisement is ranked into five categories based on degrees of ad

impression. We believe that hotels which advertises have more favourable rating on

average.

Cross-Variable relationships

dest_idprice

Some locations are generally more expensive because of their touristic attractions and

popularity. We therefore assume that the outcome of the destination_id variable has an

influence of the price variable. We believe that differences in the means of prices exists

between locations.

stars price

A high star ranking shows that a hotel possesses certain degrees of quality or prestige.

Accordingly, the hotel has a legitimate reason to ask a higher price for a stay. We therefore

believe a positive relationship exists between star ranking and price.

property_type price

Different hotel types charge different prices. For example, a full-service hotel would charge

a higher price than a simple bed & breakfast. We suspect that some difference exists

between the average prices of property types. Thus, we identified property type as an

influencing factor of the variable price.

advertisement price

We expect advertisement to have at least some sort of influence on the price of hotels. We

suspect it is a positive influence.

advertisement stars

An interesting relationship is to examine is whether (heavily) advertising hotels have a

higher star ranking. In case a hotel claims to have certain features and they are deemed

great, perhaps it receives a higher star ranking if the best is deemed accordingly great as

well. We therefore suspect a positive relationship between advertisement and amount of

stars in the star ranking variable.

7



Univariate Analysis



Bonus variable relationships

dAirport cr_score

We expect this variable to be inversely correlated with customer review score, assuming

that easier access to an airport yields a higher customer rating. We are interested in this

relationship as we put a lot of effort in creating this bonus variable.

luxury cr_score

We expect a positive relationship between the two variables, as one would expect a more

luxurious hotel to receive a better score.

Figure 1 causal relations scheme

Chapter 2 UNIVARIATE ANALYSIS

This chapter analyses the data within each individual variable.

2.1 Dependent Variable

cr_score

The histogram plot of cr_score demonstrates a slightly positively skewed distribution, with a

mean of 3.58 and standard distribution of 0.702.

There is also an unusually high frequency of recorded 1.00 and 5.00 scores, whilst

statistically speaking have to be near zero. A z-score test has been utilized to determine

whether this has an outlier effect on the overall distribution.

8



Univariate Analysis



The z-score test produces a value of -0.01817, therefore implicating that the 1.00 and 5.00

cr_score values therefore have little impact on the overall distribution. The removal of these

data points should thus not be necessary.

Figure 2 cr_score histogram (Left: Original, Right: Removal of outlier)

2.2 Independent Variables

price

The price distribution is strongly negatively skewed with a mean of 132.89 and standard

deviation of 90.696. The most frequently occurring prices are centralized around 90.

Again, an outlier analysis in an attempt to minimize statistical distortion. The chosen cut-off

point are data points which lie beyond 400. This resulted in a z-score value of -0.08877,

which therefore indicates that the removal of such datapoints have an insignificant impact

on the price distribution as a whole.

emp_sat

On average, emp_sat has a value of 3.85, and is standardly deviated at 0.625. The

distribution is slightly positively skewed, with most data points being centralized around the

4.2 region. It can be visually determined through a histogram plot of emp_sat that there are

no significant data points which would be indicative of being outlier.

Destination_id

The sample distribution in this variable are more or less even, however if it were truly evenly

distributed each destination would have a frequency of 16.67%. Most variables are near

this percentage, with the exception of Las Vegas (8,3%) and Los Angeles (23,5%). Thus,

more observations of hotels in Los Angeles than Las Vegas are included in the dataset.

Stars

The distribution of samples within stars not optimal either; the data clearly has more

observations of the 2 stars and 3 stars: 33,4% and 40,7%. The mode of this variable is a

9



Bivariate Analysis



star ranking of 3. The star ranking 0 is most likely an error in processing the data or

because there is no information available, as stars are always awarded on a 1 to 5 scale.

Chain

The dataset reveals that a large amount of hotels, 1220 out of 1562 (78,1%) are part of a

chain.

Property_type

The frequency table of property_type indicates that 1386 of 1562 hotels are full service

hotels, which occupy an extremely high proportion (88.7%) in the database. This is followed

by motel and resort with 5.6 and 4.4 percent respectively. The rest of the specialized hotels

only occupies less than 1.5% in our analysis.

Advertisement

A frequency table for this level suggests that 1302 out of 1562 hotels do not advertise at

all or very low (part of the bottom 5% advertisers). Distribution of this variable is thus far

from desired.

2.3 Bonus Variables

dAirport

A univariate analysis reveals that significant numbers of hotels are located within 20km

range from and international airport (60.2%), therefore indicative that a significant number

of the sampled hotels are built to facilitate customers who make use of aerial

transportation. The remaining 621 hotels are located outside a 20km radius.

Luxury

The frequency table indicates that most hotels have at least one or two luxury facility

(67.5%), while only a few hotels have a full luxury rating (3%). 219 hotels (14%) have none

of the aforementioned facilities

Chapter 3 BIVARIATE ANALYSIS

In this section, the relations specified in the causal relation scheme will be analysed using

various statistical tests. We explain the tests and comment on the outcomes for each test.

3.1 T-Test

For examining relationships between one qualitative variable (which has two treatments

thus K=2) and one quantitative variable, we use the t-test. This particular technique is used

to evaluate the differences between the means of two variables. Before that, we need to

use the F-test to examine whether the variances are equal or not. This is important, as

10



Bivariate Analysis



equal variances result in different results as compared to unequal variances. Results of

such a test can be found in appendix A.

chaincr_score

Due to the rise of travel industry, many companies implemented an expansionary strategy

through founding chain hotels, building a profound brand image to customers. An

investigation into whether there is a relation between a chain or non-chain hotel with their

respective cs_score.

Before testing the variables, an F-test was conducted to analyse the equality of variances

for chain and non-chain hotels. The test result shows an equal variance in our variable

(F=0.860, sig.=0.9554). Based on this result, a T-test was conducted under the equal

variance situation, and we found that the chain variable is not related to the cr_score due

to the value of p-value of 0.974, which is significantly bigger that 5%. In fact, hotels that

were part of a chain in fact had a slightly lower mean than independent hotels (3.5827 and

3.5841 respectively). Nevertheless this difference is negligible, and therefore, we conclude

that chain is not an influential factor for the customers comment to the hotel.

3.2 One-way ANOVA F-Test

Analysis of variance, ANOVA in short, is used to test the relationship between one

qualitative variable (with more than 2 treatments, thus K>2) and one quantitative variable.

The advantage of such a test is that it estimates both the variances in and between

treatments. Again, results of such a test can be found in appendix A.

stars cr_score

Star rating, constructed on the industrys standard, is an essential criterion for many

customers choosing hotels. Logically, those high star-ranking hotels could offer customers a

better all-round service during their stay, therefore leading to a higher cr_score. To test the

assumption of the positive relationship between stars and cr_score, a one-way ANOVA test

is conducted. We would like to highlight that there are hotels with no star ranking within the

database (indicated as zero by SPSS). These hotels with missing (zero) star rankings are

omitted within all further analyses involving the stars variable so as not to distort the test

analyses.

The SPSS test result confirmation our prediction. At 5% significance level, the mean of

cr_score significantly differs between different star-ranking hotels (F=154.185 sig=0.000),

and the descriptive analysis shows the positive relationship between these two variables.

Specifically, the cr_score will be increased as the increasing start- rating, with lowest score

(2.77) for 1 star hotel and the highest score (4.43) for 5 star hotels. It can be concluded

that customers recognition is positively influence by the star of the hotel.

11



Bivariate Analysis



advertisecr_score

The fierce competition in travel industry forces hotels to promote themselves by

advertisement to attract more customers. However, it is an illusion to assume that

advertisement could influence customers evaluation without having physically experienced

the hotels services. Therefore, we would like to inquire into whether advertisement has an

effect on cr_score.

The one-way ANOVA test partially confirmed our hypothesis. At 5% significance level, the

relationship between these two variables exists (Sig=0.00). The ANOVA table shows the

value of MST (2.96) is relatively high than MSE (0.49), indicating there is a significant

difference in cr_score between different adverting level groups of hotel. As the descriptive

table shows, non-advertising hotels get the lowest cr_score with 3.54. However, instead of

hotels which advertise the most (category 1) having the highest cr_score, the peak (3.86)

appears in the middle-level advertising hotels (category 3). Nevertheless, hotels which do

not advertise still have the lowest cr_score on average.

It also should be mentioned due to relatively small number of hotels which advertise, the

validity of this result could be disputed.

advertiseprice

Irrespective of the industry, when firms and companies advertise, they often promote

special offers in the form of discounts to customers in order to make a positive

advertisement impression. Therefore, we are interested in whether the level of advertise

has an effect on price. We expected this relationship to be inversely correlated.

The SPSS test would indicate otherwise, however. At 5% significance level, it could be

inferred that the means within the advertise treatment are equal to each other, therefore

indicating that the differences in advertising do not result in a difference in price (F=2.278

sig.=0.059).

To analyse this result in more depth, the Fischers Least Significant Difference (LSD) post

hoc analysis was utilised. With the exception being between category 1 and 3 (sig.=0.027)

and category 3 and 5 (sig.=0.008), the result indicates that most pair-wise differences were

insignificant at 5% significance level. This pair-wise insignificance also resulted in the

insignificance of the advertiseprice relationship overall.

It can also be determined that hotels which are between the top 25% and bottom 25%

percentile in terms of ad impressions has on average the highest mean price (158.56),

However, hotels that are in the top 5% percentile in ad impressions do indeed have the

lowest average price (113.50), thus partially confirming our inverse correlation expectation.

12



Bivariate Analysis



While a significance of 5.9% does mean there are insignificant differences between prices

and advertising categories, this is a relatively weak inference considering that the p-value is

only 0.9% off 5% significance level. An increase in the total number of samples should

provide a more concrete conclusion in future research.

Nevertheless, statistically speaking, any price variation is not determined by advertise.

property_typeprice

While we have established under the univariate analysis of property_type that almost 90%

of the entries are full service hotels, it is still interesting to determine whether a difference

in the property that has an influence on its respective price. Our prediction is that there is a

difference in price between each property type.

Statistical analysis supports that our hypothesis (F=10.349 sig.=.000), therefore implying

that there is a strong indication that there is a pricing difference between property types.

This is as expected, as each property types functions already takes into account its target

group, which in turn also takes their respective incomes and levels of spending (e.g. motels

in the US mostly serve the trucking and freight handling customers, while youth hostels

target the adolescents and backpackers).

Further analysis of this relationship indicate that it is difficult to determine whether the

variances of each treatment is equal to each other, as there are too little samples for some

of the treatments to draw such conclusions (e.g. there is only one sample within the

apartment hotel treatment, and thus has been omitted from the ANOVA analysis all

together). A descriptive analysis reveals that the apartments have the highest average

price (197.20) and motels the lowest (67.26).

destination_id price

With this ANOVA-test, the relationship between the destinations and the price could be

determined. We have expected there to be a difference.

From the results, an F-test value of 62.701 and a corresponding p value of .000 was

derived, therefore indicting that there is overwhelming evidence to reject the null

hypothesis; the means among the categories do differ. Such a large F-test value implies

MSE is a lot smaller than MST; the difference in prices between destinations is much larger

than the difference within location.

Las Vegas had the lowest mean price: 73.69. In addition, New York City had the highest

mean price: 194.60. This result was quite unexpected. We have no clue what the reason for

this could be, as most observations in Las Vegas were really low in comparison to other

locations. Perhaps Las Vegas really is cheaper than we perceived it to be.

property_typecr_score

13



Bivariate Analysis



An ANOVA test was also conducted for the relationship property_type and cr_score. The

computed ANOVA shows an F-test value of 26.731 and a p-value of 0.00; meaning that

there is significant evidence to conclude that at there is a difference in means between at

least 2 groups. With MSE being much smaller than MST, we can therefore conclude that

the differences of review scores between property types are much greater as compared to

the differences in scores within one group of hotels.

There was only one observation of the apartment hotel property type (property type 8) and

had a relatively high cr_score of 4 out of 5. We therefore decided to remove this variable,

as this distorted the results of the analysis. Upon the removal of this treatment, the highest

average score is to be found for the resort types of hotels (3.9088) and lowest for

apartments (2.640). This is quite logical, as a resort is normally possesses many luxurious

features and services. Naturally, one would expect such a hotel to have a high score.

starsprice

With this ANOVA test, the relationship between the number of stars and the price of hotel

has been examined. The corresponding F-test shows a value of 304.735 and the p-value is

0.00. We can thus conclude that there is sufficient evidence to infer that a difference

between at least two means exists. We would like to point out that the difference between

star rankings is much higher than the differences within one star ranking, attributed to a

large MST when compared to MSE. As mentioned previously, hotels with supposed zero

stars have been removed from the analysis.

To conclude, the number of stars positively correlates to the average price as expected. A

strong linear relationship therefore exists between the star ranking and price variables.

3.3 Coefficient of Correlation

The coefficient of correlation can be used to measure the strength of a linear relationship

between two quantitative variables. Results are between -1 and 1, with each extreme

resulting in a perfect negative of positive linear relationship and a result of 0 implying both

variables are independent. Results can be found in appendix A.

pricecr_score

In order to analyse the relationship between price and cr_score, a correlation coefficient

test was conducted. There was some variation in price at both ends of the cr_score variable

which could have looked like an outlier to us, but filtering those results out gave a

correlation coefficient which gave roughly the same value. We therefore decided not to

exclude those values, as the amount of observations was not large enough to make a

significant difference.

14



Bivariate Analysis



Both the one-tail and two-tail test show a p value of .000 and a Pearson correlation

coefficient of 0.487. Thus, a positive and moderate relationship between price and

cr_score exists.

The scatter plot between the two variables (figure 3) would this relationship is non-linear,

however. The plots would seem to indicate more a negative exponential distribution, thus

indicating that this variable may be unsuitable to linear regression analysis. Furthermore,

the variance within this graph also seems to be high.

Figure 3 Scatter plot between price and cr_score

emp-satcr_score.

Employee satisfaction could be seen an essential criterion to judge a hotels overall

performance. We assume that when employees are working in a better work environment,

employees are more motivated to offer customers the best service, which, in return, will

positively increase cr_score. To test the relation between these two variables, the

correlation coefficient test was utilised.

This assumption is proven to be correct by the SPSS test result. At 5% significance level,

there is a strongly positive correlation between these two variables (sig=0.000). Further

analysis reveals that adding 1 unit in the employee satisfaction will increase the cr_score

by 0.821.

This relationship was also proven to be linear in a scatter plot, indicating that this variable

would be suitable for further analyses involving linear regression models.

3.4 Chi-Squared Contingency Table Test

The chi-squared test is used to infer relationships between qualitative variables. Results

can again be found in appendix A.

15



Bivariate Analysis



advertisestars

It is hypothesised that the level of advertisement, expressed through the variable advertise,

has a positive correlation with the perceived level of quality of hotel, stars. Basing off the

assumption that the degree of advertisement is proportional to a hotels physical size and

its willingness and need to advertise, it can therefore be concluded that the better the hotel

fits the two aforementioned criterions, the more they will, on average, also inherently have

a better quality in terms of stars.

The chosen test for testing this assumption is chi-squared contingency table test. However,

because several elements under the advertise variable do not fulfil the n=5 chi-squared

test requirement, it was decided that a new advertisement variable, advertise_NEW, will

used. This variable does not distinguish between the degrees of advertisement, but rather

classify hotel into either very little to no advertisement (previously category 5 hotels) or

there is some advertisement (all category 1 to 4 hotels). This new variable is then tested

with the stars variable.

The contingency table indicates that most hotels that do not advertise have a star rating of

2 or 3. In addition, the actual numbers of hotels within 4 or 5 stars are notably lower than

the expected values. The converse is true for hotels who do advertise, however; the actual

number of hotels within the 4 and 5 star ratings is almost double to that of the expected.

This result reflect upon in figure 4, demonstrating that there are a greater number 4 to 5

star hotels when hotels do advertise (in total 40%) than when hotels do not (in total 17%).

The SPSS output confirms our previously made assumption that, indeed, advertisement

does correlate with stars (2=112.237 sig.=.000). The fact that the p-value is near zero, it

can thus also be inferred that this correlation is strong.

Figure 4 Comparison of number of hotels by stars between hotels that advertise (left) and hotels that do not (right)

1% 12%

47%

32%

8%

1

2

3

4

5

5%

38%

40%

14% 3%

1

2

3

4

5

16



Bivariate Analysis



3.5 Bonus Variable Tests

For our bonus variables, we conducted one-way ANOVA tests for both.

dAirportcr_score

Airports, particularly those who serve international passengers, are often seen as symbols

of commerce and international trade. Businesses flourish when they are easily accessible

to a large airport, and thus it could also be assumed that this level of business prestige also

translates on to hotels, which in turn positively reflect on cr_score. Thus, the distance to the

nearest international airport (dAirport) is the variable we are interested in. If our hypothesis

is true, then this means that cr_score is inversely correlated with dAirport.

While the SPSS ANOVA alaysis strongly confirms our hypothesis (F=8.134, sig.=.000), the

results, however are slightly different from our expectations. It seems that hotels located

within the 10-15km radius have the highest cr_score (3.7116) rather than within the 20km radius, have on average the lowest overall cr_score

(3.4584).

Figure 5 shows that the relationship between these two variables is suitable to be tested

with the One-way ANOVA test due to the fact that the variance of each treatment seem

similar.

Figure 5 Scatter plot of dAirport and cr_score

luxurycr_score

It is predicted that there is a positive correlation between luxury score and cr_score, as it is

logical to assume that the more recreational facilities there are available to the customer

leads to greater customer satisfaction.

17



Multivariate Analysis



This hypothesis is proven to be correct, as the SPSS output demonstrates that there is

enough evidence to indicate that different luxury scores have different cr_scores

(F=63.636, sig=.000).

Further analysis reveals that there is almost perfectly linear relationship between the two

variables. Hotel with zero luxury have a mean review score of 3.099, while fully luxury

hotels (with a score of four) have a mean of 4.050.

In addition, the observations of the highest luxury score lie much higher as compared to the

lower rankings as can be seen below in figure 6. This enforces our established view on this

matter that a higher luxury rate results in better review ratings.

Figure 6 Plot of mean of treatments within luxury versus cr_score

Chapter 4 MULTIVARIATE ANALYSIS

The following section analyses the multifactor influences on the central dependent variable.

4.1 Two-Factor ANOVA Analysis

advertise*starscr_score

It was determined under the bivariate analysis the stars correlated with the central

dependent variable, cr_score, while advertise did not. It was also established that in a chi-

squared contingency table test that advertise and stars variable are dependent on each

other. This relationship led us to consider whether these two variables combined

interaction would have an effect on cr_score.

A two-factor ANOVA test was used for this purpose. The SPSS output reproduced the

bivariate tests for both treatments, and again it can be seen that there is cr_score

correlation for one of the factors (stars), while there is none for the other (advertise) (the p-

value of these two correlations are sig.=.000 and sig.=0.778 respectively).

18






The SPSS output further indicates that a the stars*advertise do not interact to have an

effect on cr_score (F=.543 sig.=.888). Because the p-value of stars*advertise is much

higher than the standard 5% significance level, the test strongly does not reject the null

hypothesis.

This outcome can also be seen graphically in figure 7. Note the relatively parallel increases

in marginal mean of cr_score between different levels of advertising against increasing

stars number. This behaviour is indicative of a low effect interaction between the two

variables towards cr_score.

While it was previously shown that there was a significant relationship between

advertisestars, this relationship does not seem to influence cr_score.

Figure 7 Graph presenting advertising*star interaction effect

4.2 Regression Model I (RM1)

The previous section was about the analysis of the interaction effect between two variables.

The following two section analyses the interaction of multiple variables, conducted through

linear regression analysis.

Within our model, there are two quantitative variables and six qualitative variables, of which

28 dummy variables are formed. Using SPSS linear regression analysis, the following model

could be determined:

19






Validity and Explanatory Power Analysis

The model has a high validity (F=110.451, sig.=.000), with the

most significant variables being price and emp_sat. The model

also has a high explanatory power, as can be seen from a

moderately high correlation of determination (R2=0.696). In

addition, a graph of cr_score to Regression Standardised

Predicted Value (RSPV) indicates that the relationship seems

linear. The fact that most data points are concentrated around

the linear regression line indicates to us that produced model

is of good fit, thus high validity.

Variables Analysis

It should first be noted that SPSS omitted several variables due to very low correlations with

cr_score. These variables include property_type3, property_type4, and property_type6. The

SPSS analysis also indicates that there are three variables which are significant at 5%

significance level: price, emp_sat and property_type1.

The model shows the price variable has a strong relation to cr_score (t=4.77 Sig=0.00),

and while holding all the other variables constant, increasing one unit in price will add

0.001 in cr_score, hence indicating a positive correlation. As we have tested under the

bivariate analysis, when there is on average a higher priced hotel, there is also relatively

better services and general hotel quality (see starsprice bivariate test). In addition to

confirming price is correlated with cr_score in this regression analysis, we have also

demonstrated in the previous pricecr_score bivariate test there is a significant

relationship between the two variables. Note that while it was previously established that

the pricecr_score relationship was non-linear, this variable was still eventually included to

test its multivariate characteristics.

The same goes for the influence of emp_sat to cr_score (t=37.649, sig=0.00). If employee

satisfaction could increase by one unit, the cr_score will be 0.819 higher given the rest

variables constant. This is a surprising result, given that cr_score, a customer-orientated

scoring system, is highly influenced by employee satisfaction. Despite this, we have already

demonstrated in the emp_satcr_score bivariate test that these two variables are

correlated with each other, thus providing further evidence that the regression analysis is

valid

The only property_type variable which has a significant p-value is property_type1, the full

service hotels. (t=2.691, sig.=.000). In being a full service hotel, the cr_score of the hotel

increases by 0.151. We know already from the property_typecr_score analysis that these

20






two variables are correlated. Furthermore, we have also demonstrated that full service

hotels receive a higher cr_score rating than most other property types. We believe that this

is due to the fact that because full service hotels are better equipped to handle different

customer groups (e.g. travellers and businessmen) than other property types. This service

compatibility thus increases its overall cr_score rating.

One of the more surprising results within the model is the chain variable. When the

chaincr_score bivariate analysis was conducted, we have demonstrated that there is very

little difference between chained and non-chained hotels (ie. low significance). In contrast

with the regression analysis, the significance value of chain is not only much lower (t=1.893,

sig.=.059), but on average the difference between chained and unchained hotels is also

significantly bigger (on average, being in a hotel chain leads to a cr_score increase of 0.053.

This value was 0.013 when conducting the chaincr_score test)

Multicollinearity Analysis

While there most variables within the regression analysis are insignificant in nature, there

are no signs of multicollinearity within our model.

The price variable has the highest VIF value (3.053), but this value is still low enough to rule

out the possibility of multicollinearity. Nevertheless, further analysis of this variable reveals

that it is highly multicollinear to the dummy variables luxury1 and luxury3 (variance

proportions are 0.51 and 0.41 respectively). Removals of these variables, however, do lead

to a slightly lower VIF value, and instead of being highly multicorrelated with the

aforementioned variables, price now becomes highly multicorrelated with property_type7

and property_type8.

The conclusion that can be drawn from this is the price is multicorrelated with all dummy

variables. This result seems reasonable, as these variables and factors all logically have an

impact on the price variable to a certain extent (as demonstrated in some of bivariate tests

involving price). While we should note that such influences exist, it is still a significant and

predictive variable within our model.

Another variable with, on average, higher VIF value are dest_id dummy variables, which all

have a VIF value of 2.0 to 2.6. Most of the multicollinearity exists between each dest_id

dummy variable, this indicates to us that dest_id is perhaps not a very feasible variable to

consider within our model.

All other variables have low VIF values of 2 or lower.

Conclusion

Overall, the model I is both valid and has a high explanatory power, but because it only has

three significant variables, so therefore has a low predictive power.

21






4.3 Regression Model II (RM2)

For our second regression model, we decided to include some variables which did not make

it through our selection for the proposed causal relationship scheme such as the amount of

customer and employee satisfaction reviews. In addition, we were interested in the

qualitative dummy variable green. Finally, we wanted to include our other two bonus

variables as we were highly interested in the outcome of those two.

Validity and Explanatory Power Analysis

There is a similarly high coefficient of determination (R2=0.694)

for a second model, as well as very high significance value

(F=256.244, sig. =.000). This means that for our second model, it

is also has a high explanatory power and is of relatively good fit. In

plotting RSPV versus cr_score, it can be seen that signs of linearity

exists with the majority of the data points centred on the

regression line.

Variables Analysis

Given the rest variables, we found four variables that are significantly relevant to the

central dependent variable, including price, emp_review, dTallestB4 and hotrec0.

Not surprisingly, a positive relationship between price and employee satisfaction to

cr_score are still valid in this model, a result reconfirmed within with our conclusion in

bivariate tests and previous regression model. This level of validity reveals that these two

variables are essential for hotels when predicting the cr_score.

Hotrec0 is the only hotrec variable significantly relating to the cr_score (t=2.60, sig=0.01),

and it can be demonstrated that a hotels cr_score be negatively influenced, specifically,

decreasing by 0.11 if the hotel does not offer the facilities of a restaurant, safe, business

centre and minibar. However, it is unexpected that rest hotrec variables are not significantly

relevant for cr_score.

DTallestB4 is the last significant variable relating to cr_score (sig.=0.03) Specifically, a

hotel that is 15 km to 20km away from the tallest building will increase its cr_score by 0.07.

This result contradicts logic, however, a long distance to the citys largest business and

retail district should negatively influence the hotels cr_score due to its inconvenience for

travel. An alternative hypothesis is that in being located within the 15 to 20km from the

central business districts, the hotel is able to avoid overcrowding and congestion issues,

22






thus providing extra comfort for the hotels customers. The relations between the other

dTallest variables and central variable are not significant.

SPSS indicated that it does not support our assumption about the relation between the

variable of green, employee review and customer review with cr_score at 5% significance

level.

Multicollinearity Analysis

The coefficients table shows that the emp_reviews and cr_reviews variables VIF values

(5.14 and 4.63, respectively) are significantly higher, indicating there is a problem of

multicollinearity within this model. To investigate for the possible reasons, the highest VIF

value variable, employee reviews, is removed from the independent variables list

temporarily. After performing the test again, the new result demonstrates that cr_reviews

VIF values significantly decreased to 1.226. It can therefore be concluded that employee

satisfaction is the cause to cr_reviews high multicorrelation. We, however, do not know the

reason for the existence of this relationship.

Two other variables with relatively high VIF values, namely 2.19 for hotrec3 and 2.53 for

hotrec4, are also worthy for further analysis. It turns out, however, that these variables are

also influenced by emp_reviews, as their respective VIF values dropped to 1.393 and 1.583

respectively upon the removal of emp_reviews from the regression analysis. Again, we are

not able to explain the cause of this relationship.

Besides from the aforementioned instances, all the rest of the variables within this model

have acceptable VIF levels. This mirrors the result we have within the first regression model,

where our variables have low multicollinearity but are also insignificant.

Conclusion

The results of the second regression model yielded similar results to the first one in terms

of validity, explanatory power, variable significance and multicollinearity levels. While this

regression analysis yielded two more significant variables, it does demonstrate that there

are only a few variables that are determinant in relation to cr_score.

4.4 Comparison of Model I and Model II

Both models are very similar in nature: both contain few significant variables of low

multicollinearity, but still have a high explanatory power and validity. Nevertheless, a

combination of the results of the two models leads to the creation of the final regression

model which still holds some degree of predictive power.

23



Conclusion and Evaluation



Chapter 5 CONCLUSION AND EVALUATION

Basing on the conclusions from the two regression models, we are able to identify five

significant variables for our final regression model:

( )

In answering the focus question What Determines Hotel Customer-Review Scores?, this

model effectively states that customer satisfaction is determined by room price, employee

satisfaction, hotel property type, the number of HOTREC facilities within the hotel, and the

distance from the tallest building in the city.

More specifically, for the last three qualitative variables, there is only a significant relation

with cr_score when the property type is of a full service hotel, has none of the HOTREC

facilities and is located within 15-20km of the tallest building within the city.

5.1 Interpretation and Significance of Results

An increase in price or emp_sat, while keeping all other variables constant, increases the

cr_score by a 0.001 and 0.841 respectively. If the hotel fulfils the criterions of

propertype_type1 and dTallestB4, then there is cr_score increase of 0.134 and 0.054

respectively. However, should the hotel in question fulfil the hotrec0 criteria, then it

decreases the cr_score by 0.142.

24






We can conclude from this analysis that emp_sat is the most influential variable within this

model, while price has the least. These two variables are more controllable by hotel

managers in relation with other variables, and it is for this reason that managers should

place the most emphasis on when looking into improving customer satisfaction. In addition,

these variables have more predictive ability than the qualitative variables, as they are more

applicable to a wider variety of hotel types and situations.

It should be noted with caution, however, that simply increasing the price of hotels does not

mean that it will directly lead to higher levels of customer satisfaction. The increase in price

has to be justified with an equal increase in the quality and services of the hotel, otherwise

it could instead have a negative effect on satisfaction rating. Customers are more

concerned with the value for money of the hotel, rather than the price of the hotel rooms.

Managers are advised to avoid using price as a director predictor of cr_score, and should

instead look for ways to improve the price of the hotel through increasing the service quality

of the hotel.

The reader can gain a rough perspective of price determinants within our bivariate analysis,

where there is a significant number of relationships concerning price. Essentially, however,

a more in-depth research would have to be conducted to accurately find the determinants

of price, something which goes beyond the scope of this report.

Caution must also be exercised when utilising the price variable due to its non-linear

behaviour in relation to cr_score. Future analysis and research should look into non-linear

regression models to incorporate this variable more effectively. 5.2 Recommendations for Hotel Managers and Owners

Our recommendation for hotel owners and managers is to prioritise on improving employee

satisfaction and value for money of the hotels.

Increase value for money, invest in HOTREC facilities

Firstly, as mentioned previously, manager and owners should attempt to invest in more

value-adding services within the hotel to increase value more money for customers, which

in turn could potentially increases price. Particularly, investments into facilities listed under

the HOTREC variables will prove to be more essential, as the neglect of the implementation

of the said facilities will significantly lower customer review score. However, the hotel can

also choose to invest in sports-related luxury facilities and increased customer services as

means to increase the perceived value for money.

Improve employee satisfaction and implement data within operations

Secondly, hotels should look into ways to improving as well as implementing schemes and

operations involving employee satisfaction. Managers should first implement greater

25






measurability of employee satisfaction through both qualitative (e.g. increased meetings

and employee feedback) and quantitative means (e.g. balanced scorecard). After

establishing these measures, managers should set improvement targets based on the

employees perceived unsatisfactory points, and should act accordingly to fulfil these needs.

In addition to a reactive strategy, managers should also implement a proactive strategy of

improving employee satisfaction. This includes making sure that employees are provided

with the tools and ability to conduct their work effectively, as well as investing more into

human resource related areas. The establishment of an effective human resource

management (HRM) department is an important factor to consider.

Consider geographical position of hotel

With regards to hotel owners, owners may wish to consider geographical factors relating to

the hotel. If a relocation of the hotel or the opening of a new hotel is considered, owners are

advised to look into locations which are not overly congested with offices and businesses,

but should still contain relevant service facilities (e.g shopping malls) for customers to enjoy.

Locations which fulfil these criterions are typically located about 15 to 20km away from the

tallest building in the city

Lastly, if a hotel owner should look into serving customers groups so as to remain

competitive with other hotels. Full service hotels are most capable to fulfilling this

requirement.

5.3 Evaluation of Data and Research

While this research has not yielded many determinants of customer satisfaction scores, it

does demonstrate the complexity of human nature, something that will always be the basis

for further research for social scientists.

The biggest shortfall of the data collected within this report is the uneven distribution of

samples for certain independent variables such as property_type and advertisement; the

lack of samples for certain treatments within these variables often led to weak or false

conclusions within tests. An increase in the number of sample size would overcome such

an issue.

The second shortfall within this research is the high number of qualitative variables that

lower the predictive value of the research. An increase in quantitative variables such as the

number of operational years could add further depth into the research, and could us to

make more relevant recommendations for hotel owners.

Another shortfall is that we could not use all variables to test the research hypothesis.

Although it would become highly complicated, it does in fact mean that we had to make a

choice which variables to include and omit. We could bridge this partially by creating the

26






bonus variables luxury and hotrec which were built on some variables which we were not

able to include.

Regardless, we were able to obtain a satisfactory goodness of fit (R2) score for both

regression analyses therefore we do think that the variables which were in fact significant

did have good explanatory power for the central variable.

Overall, this field of study is still relatively open, and further research in the future will most

certainly be welcomed.

27



Appendix A: Bivariate Analysis Tests



APPENDIX A: BIVARIATE ANALYSIS TESTS

Chi-Squared (2) Test Unequal Variances T-Test

Variance F-Test One-way ANOVA

F-Test

Correlation

Coefficient

Demonstrated

Example advertisestars chainscore chainscore advertiseprice pricescore

Step 1:

Formulate

H0 and H1

H0: advertise and stars

independent

H1: advertise and stars

dependent

H0: 1-2=0

H1: 1-20 H0: 21/ 22 = 1

H1: 21/ 22 1

H0: 1= 2= n H1: At least two

means differ

H0: =0

H1: 0

Step 2:

Determine test

statistic

( )

Step 3:

Determine test

stat distribution 2obs ~ 2(r-1)(c-1) t ~ tn(welch) F ~ F(n1-1,n2-1) Fobs ~ F(k-1, n-k) T ~ t(n-2)

Step 4:

Assess intuitive

rejection area 2obs >> 0

tobs >> 0

tobs 1 Fobs >> 0

T 0

Step 5:

Determine

significance

= 0.05 = 0.05 = 0.05 = 0.05 = 0.05

Step 6:

Look up critical

value 2(4) = 9.49 t467.759 t468 =1.96 F(341,1219) = 1.15 F(4, 1557) = 2.37 T1520 = 1.96

Step 7: Perform

the test

( )

Conclusion

Because 2obs is bigger than2 (112.237>9.49)

therefore reject H0

Because tobs is

smaller thant

(0.271

28



Appendix B: Two-way ANOVA Tests



APPENDIX B: TWO-WAY ANOVA TESTS

Treatment A Treatment B Treatment A*B

Demonstrated

Example stars advertise stars*advertise

Step 1:

Formulate

H0 and H1


means differ


means differ

H0: 1= 2= n H1: At least two means

differ

Step 2:

Determine test

statistic

( )

( )

( )

Step 3:

Determine test

stat distribution Fobs ~ F(k-1, n-k) Fobs ~ F(k-1, n-k) Fobs ~ F(k-1, n-k)

Step 4:

Assess intuitive

rejection area Fobs >> 0 Fobs >> 0 Fobs >> 0

Step 5:

Determine

significance = 0.05 = 0.05 = 0.05

Step 6:

Look up critical

value F(4, 1488) = 2.37 F(4, 1488) = 2.37 F(12, 1488) = 1.76

Step 7: Perform

the test

Conclusion

Because Fobs is bigger thanF (25.444

29



Appendix C: Multivariate Analysis Tests



APPENDIX C: MULTIVARIATE ANALYSIS TESTS

T-Test for Regression Coefficient F-Test for Regression Model

Demonstrated

Example RM1 emp_sat RM1

Step 1: Formulate

H0 and H1

H0: 2=0

H1: 20 H0: 1= 2= n

H1: Not all i equal to zero

Step 2:

Determine test

statistic

Step 3:

Determine test stat

distribution Tobs ~ T(n-k-1) Fobs ~ F(k, n-k-1)

Step 4:

Assess intuitive

rejection area

Tobs > 0

Fobs >> 1

Step 5:

Determine

significance = 0.05 = 0.05

Step 6:

Look up critical

value T(1477-30-1) = 1.96 F(30, 1446) = 1.47

Step 7: Perform the

test

Conclusion

Because Tobs is bigger thanT (37.649>1.96) therefore reject H0

Because Fobs is bigger than F (110.451

30



Appendix D: Bonus Variables



APPENDIX D: BONUS VARIABLES

Description of bonus variables

dAirport

A citys airport is an important contributor of business and tourism aspects within a city.

This important infrastructure could have an effect on variables tested within this report.

This variable measures the distance to the nearest international airport, and hotels are

subsequently classed into five distance categories: 20km.

dTallestB

Distance to the tallest building within the city (dTallestB) is a variable chosen to reflect on

the hotels distance from the citys largest business and retail district, of which the citys

tallest building is normally located. Originally, we wanted to measure the distance between

hotels and city centres, but as there is no universally definition of a city centre, such a

variable was difficult to compute. Thus, the distance to the tallest building was chosen. The

distance categories are the same as dAirport.

luxury

The luxury scale has been arbitrarily determined by the authors through the data that has

been available to them. These are facilities which are defined as facilities which provide

recreational services to customers, but are not a perquisite towards the successful running

daily hotel operations. In this instance, the chosen variables are fitness centre (fit),

swimming pool (pool), tennis court (tenn) and sauna (saun). The number of listed facilities

that the hotel possesses also equates to the luxury score.

hotrec

The hotrec scale are variables which the Hotels, Restaurants & Cafs in Europa (HOTREC)

deems as important to a high quality hotel. The items within the hotrec score differ from the

luxury score by focusing on facilities which do not necessarily provide recreational service

for customers, but facilities which provide comfort and increased accessibility for

customers. These include of restaurant (res), safe (safe), business centre (bus) and minibar

(mini).

Calculation of distances between two coordinates (dAirport, dTallestB)

Calculations relating to these two variables incorporate the use of Haversine formula:

( ( ) ( ) ( ) ( ) ( ))

31



Appendix D: Bonus Variables



Where d is the distance between the two coordinates, latn being the latitude of coordinate

n, lonn being the longitude of coordinate n, and R being the radius of sphere in question

(6371km, the average radius of Earth, is used for this report).

Afterwards, the identification of the coordinates of each landmark must be determined.

Upon determining these coorindates, they have been first converted into degrees, and then

radians, before being calculated through the Haversine formula.

Name: City: Longitude: Latitude:

Hartsfield-Jackson Atlanta International Airport Atlanta 33.636667 -84.428056

Chicago O'Hare International Airport Chicago 41.978611 -87.904722

Chicago Midway International Airport Chicago 41.786111 -87.7525

Los Angeles International Airport Los Angeles 33.9425 -118.408056

McCarran International Airport Las Vegas 36.08 -115.152222

John F. Kennedy International Airport New York City 40.639722 -73.778889

LaGuardia Airport New York City 40.777222 -73.8725

Orlando International Airport Orlando 28.429444 -81.308889

Figure 8 Table listing chosen international airports and their coordinates

An example would be calculating the distance between the Inn at the Peachtrees hotel,

located in Atlanta, and the Hartsfield-Jackson Atlanta International Airport. The coordinates

of both location are (33.7636, -84.3878) and (33.6367, -84.4281) respectively. Converting

to radians, these coordinates become (0.5893, -1.4278) and (0.5871, -1.4736)

respectively. From here, the distance can then be calculated:

( ( ) ( ) ( ) ( )

( ))

Determination of luxury and HOTREC scales (luxury, hotrec)

Both scales are ranked from zero to four, and are cumulative number of each facility

present within each hotel.

In the instance of Sheraton Hotel Atlanta, we observe that this hotel has a business centre,

restaurant, safe and minibar. These four facilities count towards the hotrec scale, thus this

hotel has a hotrec score of four.

This hotel, however, only has a fitness centre and swimming pool, thus only fulfilling two

criterions within the luxury scale. Thus, Sheraton Hotel Atlanta only has a luxury score of

two.

applied business methods report

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mike lien table of contents

mike lien table of figures

multivariate analysis

bivariate analysis tests

contingency table test

research hypothesis

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anova tests