This article describes the development of a new online tool for collecting data about customer preferences. The tool was created to produce comprehensive data on the connections between important variables, while offering an interesting and easy-to-use tool for respondents and researchers. The tool uses the principle of a mind-map. It was applied to two cases concerning housing preferences, and to one of municipal construction. Consumer preferences are complex and the authors thought that a visual tool could prove helpful in data collection. We present examples of network analysis, which give good insights into the complexity of preferences. Ways to improve the usability and analytical potential of the tool are also discussed. The mind-map tool shows potential for being a good data collection method, although further development is necessary to uncover the whole potential of the tool.
References
1.
AlbertR., & BarabásiA.-L. (2002) Statistical mechanics of complex networks.Reviews of Modern Physics, 74, pp. 47–97.
2.
AresG., VarelaP., RadoG., & GiménezA. (2011) Identifying ideal products using three different consumer profiling methodologies. Comparison with external preference mapping.Food Quality and Preference, 22, pp. 581–591.
3.
ArvolaA., LahtiP., LampilaP., TiilikainenA., KyröR., ToivonenS., ViitanenK., & KeskifranttiS. (2010). Asuinympäristön ominaisuudet ja asukkaan arvot. Kuluttajatutkimusnäkökulman sovellus asuinympäristön koetun laadun tutkimukseen (Attributes of residential environment and values of the resident. An application of consumer research vantage point into studying the perceived quality of residential environment). Espoo, Finland: VTT.
4.
AutioM., HeiskanenE., & HeinonenV. (2009) Narratives of ‘green’ consumers – the antihero, the environmental hero and the anarchist.Journal of Consumer Behaviour, 8, 1, pp. 40–53.
5.
BlondelV.D., GuillaumeJ.-L., LambiotteR., & LefebvreE. (2008) Fast unfolding of communities in large networks.Journal of Statistical Mechanics: Theory and Experiment, 10, pp. 1–12.
6.
BoddyC. (2005) Projective techniques in market research: valueless subjectivity or insightful reality? A look at the evidence for the usefulness, reliability and validity of projective techniques in market research.International Journal of Market Research, 47, 3, pp. 239–254.
7.
BrinS., & PageL. (1998) The anatomy of a large-scale hypertextual web search engine.Proceedings of the Seventh International Conference on the World Wide Web (www1998), pp. 107–117.
8.
ClineB.E., BrewsterC.C., & FellR.D. (2010) A rule-based system for automatically evaluating student concept maps.Expert Systems with Applications, 37, pp. 2282–2291.
9.
CurtinR., PresserS., & SingerE. (2005) Changes in telephone survey nonresponse over the past quarter century.Public Opinion Quarterly, 69, 1, pp. 87–98.
10.
DeckerR., & TrusovM. (2010) Estimating aggregate consumer preferences from online product reviews.International Journal of Research in Marketing, 27, pp. 293–307.
11.
DouglasM., & IsherwoodB. (1996) The World of Goods. Towards an Anthropology of Consumption.London: Routledge.
12.
DuffyB., SmithK., TerhanianG., & BremerJ. (2005) Comparing data from online and face-to-face surveys.International Journal of Market Research, 47, 6, pp. 615–639.
13.
EvrekliE., İnelD., & BalimA.G. (2010) Development of scoring system to assess mind-maps.Procedia Social and Behavioral Sciences, 2, pp. 2330–2334.
14.
FanW., & YanZ. (2010) Factors affecting response rates of the web survey: a systematic review.Computers in Human Behaviour, 26, pp. 132–139.
15.
FortunatoS., & BarthélemyM. (2007) Resolution limit in community detection.PNAS, 104, 1, pp. 36–41.
16.
GrunertK.G., & GrunertS.C. (1995) Measuring subjective meaning structures by the laddering method: theoretical considerations and methodological problems.International Journal of Research in Marketing, 12, pp. 209–225.
17.
JacksonM. C. (2006) Creative holism: a critical systems approach to complex problem situations.Systems Research and Behavioral Science, 23, 5, pp. 647–657.
18.
KuuselaV. (2009) Otantamenetelmä on surveytutkimuksen kulmakivi (The sampling method is the cornerstone of survey research).Hyvinvointikatsaus 4/2009, Tilastokeskus, Finland. Available online at: http://www.tilastokeskus.fi/artikkelit/2009/art_2009-12-14_003.html?s=0 (accessed 11 December 2012).
19.
McCrackenG. (1988) Culture and Consumption.Indianapolis: Indiana University Press.
20.
McLindenD. (2013) Concept maps as network data: analysis of a concept map using the methods of social network analysis.Evaluation and Program Planning, 36, pp. 40–48.
21.
MilesS., & RoweG. (2004) The laddering technique, in BreakwellG. M. (ed.) Doing Social Psychology Research.Oxford: British Psychological Society and Blackwell Publishing, pp. 305–343.
22.
NewmanM.E.J. (2006) Modularity and community structure in networks.Proceedings of the National Academy of Sciences of the United States of America, 103, 23, pp. 8577–8582.
PallaG., BarabásiA.-L., & VicsekT. (2007) Quantifying social group evolution.Nature, 446, pp. 664–667.
26.
ReynoldsT.J., & GutmanJ. (1988) Laddering theory, method, analysis, and interpretation.Journal of Advertising Research, 2, pp. 11–31.
27.
RookeyB.D., LeL., LittlejohnM., & DillmanD.A. (2012) Understanding the resilience of mail-back survey methods: an analysis of 20 years of change in response rates to national park surveys.Social Science Research, 41, pp. 1404–1414.
28.
SauermannH., & RoachM. (2013) Increasing web survey response rates in innovation research: an experimental study of static and dynamic contact design features.Research Policy, 42, pp. 273–286.
29.
SchauerteT. (2009) Investigating Consumer Perceptions by Applying the Extended Association Pattern Technique – a Study on Wooden Multistory Houses.Växjö, Sweden: Växjö University Press.
30.
SchnittkaO., SattlerH., & ZenkerS. (2012) Advanced brand concept maps: a new approach for evaluating the favorability of brand association networks.International Journal of Research in Marketing, 29, pp. 265–274.
SteenkampJ.-B.E.M., & van TrijpH.C.M. (1996) Task experience and validity in perception mapping. A comparison of two consumer-adaptive techniques.International Journal of Research in Marketing, 13, pp. 265–276.
