customer satisfaction as a predictor of customer advocacy

8/14/2019 Customer Satisfaction as a Predictor of Customer Advocacy

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Customer Satisfaction as a Predictor ofCustomer Satisfaction as a Predictor of

Customer Advocacy and Negative WordCustomer Advocacy and Negative Wordof Mouth : A Hotel Industry Studyof Mouth : A Hotel Industry Study

Source: Business Journal From ForeSource: Business Journal From Fore

Presented by:Presented by:

Shreyas LasteShreyas Laste

Roll No : 28Roll No : 28


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IntroductionIntroduction

A satisfied person is going to tell 6 peopleA satisfied person is going to tell 6 people

and a dissatisfied person is going to tell 13and a dissatisfied person is going to tell 13

peoplepeople

Customer Advocacy has proved to be mostCustomer Advocacy has proved to be mostpowerful tools for marketing andpowerful tools for marketing and

companiescompanies

This study is to find relation of customerThis study is to find relation of customer

satisfaction with customer advocacy andsatisfaction with customer advocacy and

negative word of mouth in hotel industrynegative word of mouth in hotel industry


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ObjectivesObjectives

To develop and standardize aTo develop and standardize ameasure in hotel industry formeasure in hotel industry forcustomer satisfaction, customercustomer satisfaction, customer

advocacy and negative word ofadvocacy and negative word ofmouthmouth

To evaluate the underlying factor inTo evaluate the underlying factor in

hotel industry for customerhotel industry for customersatisfaction, customer advocacy andsatisfaction, customer advocacy andnegative word of mouthnegative word of mouth


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ContinuedContinued

To evaluate the relationship betweenTo evaluate the relationship between

customer satisfaction and customercustomer satisfaction and customer

advocacyadvocacy

To evaluate the relationship betweenTo evaluate the relationship between

customer dissatisfaction andcustomer dissatisfaction and

negative word of mouthnegative word of mouth

To open new vistas for furtherTo open new vistas for further

researchresearch


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MethodologyMethodology

ExploratoryExploratory

Population: The population is hotel industry ofPopulation: The population is hotel industry ofGwalior RegionGwalior Region

Sample size: 100 CustomerSample size: 100 Customer

Sampling Elements: The sampling element wasSampling Elements: The sampling element wasthe individual customer of hotel industries ofthe individual customer of hotel industries of

Gwalior RegionGwalior Region


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Tools for Data Collection: SelfTools for Data Collection: Self

designed questionnaire on scale ofdesigned questionnaire on scale of

1 to 5.1 to 5.

Tools for Data Analysis:Tools for Data Analysis:

> Internal consistency test> Internal consistency test

> Reliability and Validity test> Reliability and Validity test> Factor analysis> Factor analysis

> Regression test> Regression test


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Internal Consistency test:Internal Consistency test:

Iterative item to TC was applied onIterative item to TC was applied onthe response received fromthe response received from

consumer of hotel industryconsumer of hotel industry

Items having higher coefficient valueItems having higher coefficient valuethan the cut off value were retainedthan the cut off value were retained

for further analysisfor further analysis


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ITEMS COMPUTED CORRELATION VALUE

Lighting 0.34345

Furniture 0.46785

Dcor 0.36587

Impression 0.36217

Safety 0.43009

Delicious 0.50359

Hospitable 0.36784

Competence 0.42345

Politeness 0.54567

Interest 0.32409


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Reliability test:Reliability test:

> It was applied on customer satisfaction,> It was applied on customer satisfaction,

advocacy and negative word of mouthadvocacy and negative word of mouthmeasure using SSPS and value ofmeasure using SSPS and value of

Cronbach Alpha was found to be 0.0892Cronbach Alpha was found to be 0.0892

Validity test:Validity test:> Questionnaire was checked through face> Questionnaire was checked through face

validity method and was very highvalidity method and was very high


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Factor analysis:Factor analysis:

Principal factor analysis with VarimaxPrincipal factor analysis with Varimaxrotation and Kiser normalization wasrotation and Kiser normalization was

applied.applied.

The details is shown asThe details is shown as


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Factor Name Eigen value Variable convergence Loading Value

Total % of variance

Percieved substandar 8.052 22.366 Standard not mntd 0.88

Staff not trained 0.852

Difficult to access 0.774

Service inadequate 0.759

Below expection 0.682

Good ambience 7.570 21.027 Good furniture 0.857

Good dcor 0.797

Good lighting 0.781

Positive impression 0.607

Delicious food 0.509

Warm Hospitality 2.847 7.909 Willingness 0.739

Security 0.645

Respectful treatment 0.626


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Regression analysis:Regression analysis:

Satisfaction and advocacySatisfaction and advocacy

Its calculated by taking totalIts calculated by taking totalcustomer satisfaction and advocacycustomer satisfaction and advocacy

by using SSPS software.by using SSPS software.

CS is independent and advocacy isCS is independent and advocacy isdependent variable and regression isdependent variable and regression is

calculatedcalculated


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Model Unstandardized co-eff Standardized co-eff T Sig

B Std. Error Beta

(constant) 29.375 5.557 5.267 0

VAR0001 0.480 0.084 0.5 5.712 0


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Regression analysis:Regression analysis:

Satisfaction and negative word of mouthSatisfaction and negative word of mouth

Its calculated by taking total customerIts calculated by taking total customer

satisfaction and negative word of mouthsatisfaction and negative word of mouthby using SSPS software.by using SSPS software.

CS is independent and advocacy isCS is independent and advocacy is

dependent variable and regression isdependent variable and regression iscalculatedcalculated


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Model Unstandardized co-eff

Standardized

co-eff T Sig Collinearity Statistics

B Std.

Error Beta Tolerance VIF

(constant) 54.150 9.4227 5.744 0

VAR0001 -0.122 0.142 -0.086 -0.859 0.392 1 1


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THANK YOUTHANK YOU

customer satisfaction as a predictor of customer advocacy

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