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Due Date – 05th April 2023 Topic - Impact of service quality on customer satisfaction in hotel industry · Sample - Guests in Hotels in Colombo District. · Should gathered data by using...

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Due Date – 05th April 2023
Topic - Impact of service quality on customer satisfaction in hotel industry
· Sample - Guests in Hotels in Colombo District.
· Should gathered data by using multiple-choice questions. (Pls prepare questions )
· Pls used Demographic Analysis questioner and five-point scales Questioner (Aprox 04 questions per variable)
· Population - 160
· Sample Size – 113
· Conceptual Framework
Dependent Variable
Independent Variables
    
    
Customer Satisfaction
· Data analysis Tool – SPSS
· Demographic Analysis – Gender, Age, work experience, Educational Qualification, etc….
· Word Count – 18,000 excluding abstract
· Turnitin – Below 15%
· Reference – Harvard Reference
· SPSS data sheet also need

Table of Contents
Abstract    
CHAPTER ONE - INTRODUCTION    
1.1.    Introduction    
1.1.1.    Background of the Study    
1.1.2.    Industry Overview    
    (Hotel Industry Overview in Sri Lanka, Pls select several hotels in Sri Lanka as a sample)
1.2.    Statement of the Problem    
1.3.    Objectives of the Study    
1.3.1.    Main Objective    
1.3.2.    Specific Objectives    
1.4.    Research Questions    
1.5.    Significance of the Study    
1.6.    Scope of study    
1.7.    Limitation of the study
1.8.    Chapter Summary
CHAPTER TWO – LITERATURE REVIEW    
    
CHAPTER THREE - RESEARCH METHODOLOGY    
3.1.    Introduction    
3.2.    Conceptual framework    
3.3.    Operationalization    
3.4.    Hypothesis    
3.5.    Theoretical stance (The Research ‘Onion’)
3.6.    Research Design    
3.7.    Sampling framework    
3.7.1.    Population    
3.7.2.    Sample    
3.8.    Data Collection methods and Techniques    
3.9.     Data Analysis Tool
3.10.    Reliability, Validity and Generalizability    
CHAPTER FOUR : DATA ANALYSIS
4.1.    Introduction    
4.1.1.    Constraints and Ethical Consideration    
4.2.    Reliability Testing    
4.3.    Demographic Characteristics    
4.4.    Descriptive Analysis    
4.5.    Cronbach’s alpha
4.6.    Co
elation Analysis
4.7.    Multiple Regression Analysis
4.8.    Bivariate Analysis – (Model Summary, ANOVAa , Coefficientsa)
4.9.    Hypotheses Testing    
CHAPTER FIVE – CONCLUSION    
5.1.    Introduction    
5.2.    Summary of the Study    
5.3.    Conclusion of the Study    
5.4.    Recommendations    
5.5.    Suggestion for Further Research    
References    
Bibialogy
Appendix ( Questionnaire)
Tangibility
Reliability
Responsiveness
Assurance
Empathy

Due Date – 05th April 2023
Topic - Impact of service quality on customer satisfaction in hotel industry
· Sample - Guests in Hotels in Colombo District.
· Should gathered data by using multiple-choice questions. (Pls prepare questions )
· Pls used Demographic Analysis questioner and five-point scales Questioner (Aprox 04 questions per variable)
· Population - 160
· Sample Size – 113
· Conceptual Framework
Dependent Variable
Independent Variables
    
    
Customer Satisfaction
· Data analysis Tool – SPSS
· Demographic Analysis – Gender, Age, work experience, Educational Qualification, etc….
· Word Count – 18,000 excluding abstract
· Turnitin – Below 15%
· Reference – Harvard Reference
· SPSS data sheet also need

Table of Contents
Abstract    
CHAPTER ONE - INTRODUCTION    
1.1.    Introduction    
1.1.1.    Background of the Study    
1.1.2.    Industry Overview    
    (Hotel Industry Overview in Sri Lanka, Pls select several hotels in Sri Lanka as a sample)
1.2.    Statement of the Problem    
1.3.    Objectives of the Study    
1.3.1.    Main Objective    
1.3.2.    Specific Objectives    
1.4.    Research Questions    
1.5.    Significance of the Study    
1.6.    Scope of study    
1.7.    Limitation of the study
1.8.    Chapter Summary
CHAPTER TWO – LITERATURE REVIEW    
    
CHAPTER THREE - RESEARCH METHODOLOGY    
3.1.    Introduction    
3.2.    Conceptual framework    
3.3.    Operationalization    
3.4.    Hypothesis    
3.5.    Theoretical stance (The Research ‘Onion’)
3.6.    Research Design    
3.7.    Sampling framework    
3.7.1.    Population    
3.7.2.    Sample    
3.8.    Data Collection methods and Techniques    
3.9.     Data Analysis Tool
3.10.    Reliability, Validity and Generalizability    
CHAPTER FOUR : DATA ANALYSIS
4.1.    Introduction    
4.1.1.    Constraints and Ethical Consideration    
4.2.    Reliability Testing    
4.3.    Demographic Characteristics    
4.4.    Descriptive Analysis    
4.5.    Cronbach’s alpha
4.6.    Co
elation Analysis
4.7.    Multiple Regression Analysis
4.8.    Bivariate Analysis – (Model Summary, ANOVAa , Coefficientsa)
4.9.    Hypotheses Testing    
CHAPTER FIVE – CONCLUSION    
5.1.    Introduction    
5.2.    Summary of the Study    
5.3.    Conclusion of the Study    
5.4.    Recommendations    
5.5.    Suggestion for Further Research    
References    
Bibialogy
Appendix ( Questionnaire)
Tangibility
Reliability
Responsiveness
Assurance
Empathy
Answered 9 days After Mar 30, 2023

Solution

Banasree answered on Apr 09 2023
38 Votes
Population Pyramid of the Global Performance Data:
A population pyramid analysis involves examining the OCC%, ADR, and RevPAR distribution of a population using a graphical representation called a population pyramid. The population pyramid typically consists of two vertical bar charts, one for VAR 1 and one for VAR2, with each horizontal bar representing a specific Geographical group. The width of each bar co
esponds to the size of the population in that percentage of group, with less percentage groups at the bottom and moderate percentage groups at the top. By analyzing the shape of the pyramid, research can gain insight into the demographic characteristics of a Global Performance Data.
Automatic Linear Modeling
    Notes
    Output Created
    09-APR-2023 18:19:20
    Comments
    
    Input
    Data
    C:\Users\lenovo\Desktop\Global Performance.sav
    
    Active Dataset
    DataSet2
    
    Filte
        
    Weight
        
    Split File
        
    N of Rows in Working Data File
    20
    Syntax
    LINEAR
/FIELDS TARGET=V8 INPUTS=V2 V4 V5 V6 V7 V3 V1 ANALYSIS_WEIGHT=V9
/BUILD_OPTIONS OBJECTIVE=BOOSTING USE_AUTO_DATA_PREPARATION=TRUE CONFIDENCE_LEVEL=95 MODEL_SELECTION=FORWARDSTEPWISE CRITERIA_FORWARD_STEPWISE=AICC REPLICATE_RESULTS=TRUE SEED=54752075
/ENSEMBLES COMBINING_RULE_CONTINUOUS=MEAN COMPONENT_MODELS_N=10
/SAVE PREDICTED_VALUES(PredictedValue).
    Resources
    Processor Time
    00:00:01.26
    
    Elapsed Time
    00:00:02.31
    Variables Created or Modified
    Predicted Value
    PredictedValue
    Warnings
    During boosting some base models were not appropriate and have been removed from the ensemble
    Case Processing Summary
    Model
    N
    Percent
    Reference
    Included
    18
    90.0%
    
    Excluded
    2
    10.0%
    
    Total
    20
    100.0%
    Component 1
    Included
    18
    90.0%
    
    Excluded
    2
    10.0%
    
    Total
    20
    100.0%
    Component 2
    Included
    12
    90.0%
    
    Excluded
    8
    10.0%
    
    Total
    20
    100.0%
    Component 3
    Included
    14
    95.0%
    
    Excluded
    6
    5.0%
    
    Total
    20
    100.0%
    Component 4
    Included
    9
    100.0%
    
    Excluded
    11
    0.0%
    
    Total
    20
    100.0%
    Component 5
    Included
    7
    95.0%
    
    Excluded
    13
    5.0%
    
    Total
    20
    100.0%
    Component 6
    Included
    9
    95.0%
    
    Excluded
    11
    5.0%
    
    Total
    20
    100.0%
    Component 7
    Included
    12
    100.0%
    
    Excluded
    8
    0.0%
    
    Total
    20
    100.0%
    Component 8
    Included
    7
    100.0%
    
    Excluded
    13
    0.0%
    
    Total
    20
    100.0%
The output shows the results of an Automatic Linear Modeling process performed in SPSS on the data in the file "Global Performance.sav". Here is a
eakdown of the key elements of the output: The target variable for the linear model is "V8", and the input variables are "V2", "V4", "V5", "V6", "V7", "V3", and "V1". The analysis also uses a weighting variable "V9". The analysis uses boosting with 10 component models and a mean combining rule. The objective is to build the best linear model with a forward stepwise selection method using the AICC criterion and a confidence level of 95%. The predicted values of the linear model are saved in a new variable called "PredictedValue". The Case Processing Summary table indicates the number and percentage of cases that were included or excluded in each of the 8 component models that were built during the automatic linear modeling process. Such as, in the first component model, 18 out of 20 cases were included, and 2 were excluded. The warning message suggests that some of the base models used in the boosting process were not appropriate and were removed from the final ensemble model. Overall, the output indicates that a linear model was built using a forward stepwise selection method with boosting and 10 component models. The model includes 7 predictor variables and a weighting variable, and it predicts the value of the target variable "V8". The predicted values of the model can be found in the "PredictedValue" variable.
Analysis 2
Regression
    Notes
    Output Created
    09-APR-2023 18:21:32
    Comments
    
    Input
    Data
    C:\Users\lenovo\Desktop\Global Performance.sav
    
    Active Dataset
    DataSet2
    
    Filte
        
    Weight
        
    Split File
        
    N of Rows in Working Data File
    20
    Missing Value Handling
    Definition of Missing
    User-defined missing values are treated as missing.
    
    Cases Used
    Statistics are based on cases with no missing values for any variable used.
    Syntax
    REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) BCOV R ANOVA COLLIN TOL CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10) CIN(95)
/NOORIGIN
/DEPENDENT V8
/METHOD=ENTER V9 V2 V3 V4 V5 V6 V7 PredictedValue
/RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID)
/CASEWISE PLOT(ZRESID) OUTLIERS(3)
/SAVE PRED ZPRED ADJPRED SEPRED MAHAL COOK LEVER MCIN ICIN RESID ZRESID SRESID DRESID SDRESID
DFBETA SDBETA DFFIT SDFIT COVRATIO.
    Resources
    Processor Time
    00:00:00.02
    
    Elapsed Time
    00:00:00.26
    
    Memory Required
    7440 bytes
    
    Additional Memory Required for Residual Plots
    528 bytes
    Variables Created or Modified
    PRE_1
    Unstandardized Predicted Value
    
    RES_1
    Unstandardized Residual
    
    DRE_1
    Deleted Residual
    
    ADJ_1
    Adjusted Predicted Value
    
    ZPR_1
    Standardized Predicted Value
    
    ZRE_1
    Standardized Residual
    
    SRE_1
    Studentized Residual
    
    SDR_1
    Studentized Deleted Residual
    
    SEP_1
    Standard E
or of Predicted Value
    
    MAH_1
    Mahalanobis Distance
    
    COO_1
    Cook's Distance
    
    LEV_1
    Centered Leverage Value
    
    COV_1
    COVRATIO
    
    DFF_1
    DFFIT
    
    SDF_1
    Standardized DFFIT
    
    DFB0_1
    DFBETA for (Constant)
    
    DFB1_1
    DFBETA for V9
    
    DFB2_1
    DFBETA for V2
    
    DFB3_1
    DFBETA for V3
    
    DFB4_1
    DFBETA for...
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