Tourism Management 30 (2009) 629–637 A multi-criteria assessment of tourist farm service quality Authors: Crtomir Rozman, Majda Potocˇnik, Karmen Pazek, Andreja Borec, Darja Majkovic, Marko Bohanec

Introduction Methodology Multi-criteria Modeling methodology DEX DEX Model Support Tool DEXi v. 3.0 [2008 implementation] Data Collection, Results and Discussion Conclusions Questions

Introduction

Methodology

Multi-criteria Modeling methodology DEX

DEX Model Support Tool

DEXi v. 3.0 [2008 implementation]

Data Collection, Results and Discussion

Conclusions

Questions

Farm tourism is a significant means of supplementing farmers’ incomes Develop a tourist farm ranking system Service quality is a decisive factor for tourist farm success Previous methods have proven unsatisfactory A qualitative multi-criteria decision analysis methodology is developed, applied and evaluated

Farm tourism is a significant means of supplementing farmers’ incomes

Develop a tourist farm ranking system

Service quality is a decisive factor for tourist farm success

Previous methods have proven unsatisfactory

A qualitative multi-criteria decision analysis methodology is developed, applied and evaluated

Other methods of service quality assessment SERVQUAL Based on survey of customer (guest) perception using questionnaire Used in many assessments ECOSERVE Based on surveys of customer experience and expected levels of service quality Used in numerous applications Insufficient assessment methods due to data gathering methodology and limitations related to interview process

Other methods of service quality assessment

SERVQUAL

Based on survey of customer (guest) perception using questionnaire

Used in many assessments

ECOSERVE

Based on surveys of customer experience and expected levels of service quality

Used in numerous applications

Insufficient assessment methods due to data gathering methodology and limitations related to interview process

MCDA – Multi-Criteria Decision Analysis can be applied when the evaluation involves multiple variables which can not easily be transformed into quantitative units Quantitative models use numerical values Qualitative models use symbolic variables Well suited to soft decision problems with expert judgments using qualitative scales rather then quantitative scores

MCDA – Multi-Criteria Decision Analysis can be applied when the evaluation involves multiple variables which can not easily be transformed into quantitative units

Quantitative models use numerical values

Qualitative models use symbolic variables

Well suited to soft decision problems with expert judgments using qualitative scales rather then quantitative scores

Develop MCDA model Apply MCDA model to application Use DEX, a qualitative multi-criteria modeling methodology, to assess service quality 2008 DEX implementation DEXi v. 3.0 Applied to seven tourist farms using input from surveying operators and guests

Develop MCDA model

Apply MCDA model to application

Use DEX, a qualitative multi-criteria modeling methodology, to assess service quality

2008 DEX implementation DEXi v. 3.0

Applied to seven tourist farms using input from surveying operators and guests

MCDA model development process Decompose problem Create Tree of Attributes A hierarchical skeleton of attribute relationships Represent each qualitative attribute with a defined value scale A ordered list of states [words] representing values from worst to best Utility functions for each aggregate attribute are defined. DEXi uses decision rules represented in tabular form

MCDA model development process

Decompose problem

Create Tree of Attributes

A hierarchical skeleton of attribute relationships

Represent each qualitative attribute with a defined value scale

A ordered list of states [words] representing values from worst to best

Utility functions for each aggregate attribute are defined.

DEXi uses decision rules represented in tabular form

DEXi Model created in format suitable for the DEXi v3 decision management support tool Create TREE OF ATTRIBUTES Top Level Attribute: Tourist farm service quality Create Attribute Scales for each Attribute Create decision rules [tables]for all attribute nodes

DEXi Model created in format suitable for the DEXi v3 decision management support tool

Create TREE OF ATTRIBUTES

Top Level Attribute: Tourist farm service quality

Create Attribute Scales for each Attribute

Create decision rules [tables]for all attribute nodes

Leaf attributes – input data values Node attributes – computed from decision functions to build up to the Root node attribute: [Tourist farm service Quality]

Define value scale for each attribute - an ordered list of states [words] representing values from worst to best

Example for output attribute “Guest” Truth table represents the outcome: Guest based on all possible combinations [states] of input attributes.

Two questionnaires constructed to derive priorities and values for individual criteria Tourist farm operators Customers [guests] Each question corresponded exactly to one input attribute from the Tree of Attributes TOTAL SURVEYS: 103 Guests 7 tourist farm operators

Two questionnaires constructed to derive priorities and values for individual criteria

Tourist farm operators

Customers [guests]

Each question corresponded exactly to one input attribute from the Tree of Attributes

TOTAL SURVEYS:

103 Guests

7 tourist farm operators

Results did not reveal much differences between best and worst Authors point out all 7 tourist farms are of known high quality All farms in same quality range (the quality ranges from one to four apples established by the Slovene tourist farms associations) Ranking system uses: Tourist farm operators – could be useful to help determine where to invest for improved assessments Guests – could use system to help decide which tourist farm to visit

Results did not reveal much differences between best and worst

Authors point out all 7 tourist farms are of known high quality

All farms in same quality range (the quality ranges from one to four apples established by the Slovene tourist farms associations)

Ranking system uses:

Tourist farm operators – could be useful to help determine where to invest for improved assessments

Guests – could use system to help decide which tourist farm to visit

Notice that the multi-value result for Farm E can be displayed by showing both at “good” bar [blue] and a “very good” bar [green] for Farm E.

MCDA node attribute Guest[+] [+]Derived from lower level derived node attributes Premises[++] Services [++] Additional services [+] Repeat visits [*] [*]Derived from direct Survey Data Input Attributes

MCDA node attribute

Guest[+]

[+]Derived from lower level derived node attributes

Premises[++]

Services [++]

Additional services [+]

Repeat visits [*]

[*]Derived from direct Survey Data Input Attributes

DEXI Display level 2 attributes [1]Guest [2]Premises [2]Services [2]Additional services [2]Repeat visits [1]Farm Operator [2]Plans for the future [2]Satisfaction [?]Farm E has no weak points at this level.

DEXI Display level 2 attributes

[1]Guest

[2]Premises

[2]Services

[2]Additional services

[2]Repeat visits

[1]Farm Operator

[2]Plans for the future

[2]Satisfaction

[?]Farm E has no weak points at this level.

Qualitative analysis only This may be a very useful and valid method for supporting soft decisions in social science framework No use of quantitative factors even when possible: No method to give more value or higher confidence to large survey responses vs. low survey numbers Note: individual farm guest responses varied from 2 to 29 Radar charts could use radius size to indicate strength of information Define an average level of all service attributes to allow standard of comparison

Qualitative analysis only

This may be a very useful and valid method for supporting soft decisions in social science framework

No use of quantitative factors even when possible:

No method to give more value or higher confidence to large survey responses vs. low survey numbers

Note: individual farm guest responses varied from 2 to 29

Radar charts could use radius size to indicate strength of information

Define an average level of all service attributes to allow standard of comparison

Will the model work for finding realistic data for below standard tourist farms? Will guests answer survey questions with honest opinions? Many cultures would not want to give bad reports. How would a quantitative Multi-Criteria analysis differ?

Will the model work for finding realistic data for below standard tourist farms?

Will guests answer survey questions with honest opinions?

Many cultures would not want to give bad reports.

How would a quantitative Multi-Criteria analysis differ?

MCDA Methods – Qualitative vs Quantitative Qualitative models Qualitative models may prove best at displaying complex systems to a general audience Subtle word meanings may make the scalar ranges difficult to understand or translate in different languages Attributes represented as scalar enumerated types have clear applications to computer programming implementations Quantitative models More familiar to technically trained professionals More readily understood in different cultures due to common applied math

MCDA Methods – Qualitative vs Quantitative

Qualitative models

Qualitative models may prove best at displaying complex systems to a general audience

Subtle word meanings may make the scalar ranges difficult to understand or translate in different languages

Attributes represented as scalar enumerated types have clear applications to computer programming implementations

Quantitative models

More familiar to technically trained professionals

More readily understood in different cultures due to common applied math