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

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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

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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

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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

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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

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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

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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

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Leaf attributes – input data valuesNode attributes – computed from decision functions to buildup 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

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Example for output attribute “Guest”

Truth table represents the outcome: Guest based on all possible combinations [states] of input attributes.

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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

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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

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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.

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MCDA node attribute •Guest[+]

[+]Derived from lower level derived node attributes

•Premises[++]•Services [++]•Additional services [+]•Repeat visits [*]

[*]Derived from direct Survey Data Input Attributes

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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.

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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

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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?

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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

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