Applied Business Methods Report
description
Transcript of 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)
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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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Table of Figures
Applied Business Methods: Research Project
Erasmus University Rotterdam
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/)
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Introduction
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Introduction
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Introduction
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Univariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Univariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Bivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Multivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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).
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Multivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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:
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Multivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Multivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Multivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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,
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Multivariate Analysis
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Conclusion and Evaluation
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Conclusion and Evaluation
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Conclusion and Evaluation
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Conclusion and Evaluation
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Appendix A: Bivariate Analysis Tests
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Appendix B: Two-way ANOVA Tests
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
H0: 1= 2= n H1: At least two
means differ
H0: 1= 2= n H1: At least two
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Appendix C: Multivariate Analysis Tests
Applied Business Methods: Research Project
Erasmus University Rotterdam
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
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Appendix D: Bonus Variables
Applied Business Methods: Research Project
Erasmus University Rotterdam
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:
( ( ) ( ) ( ) ( ) ( ))
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What Determines Hotel Customer-Review Scores?
Dennis Johannisse, Zhengchen Wei, Mike Lien
Appendix D: Bonus Variables
Applied Business Methods: Research Project
Erasmus University Rotterdam
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.