Online measurement of mental representations of complex spatial decision problems: comparison of CNET and hard laddering

Oliver Horeni, Theo A. Arentze, Benedict G.C. Dellaert, Harry J.P. Timmermans

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


This paper introduces the online Causal Network Elicitation Technique (CNET), as a technique for measuring components of mental representations of choice tasks and compares it with the more common technique of online 'hard' laddering (HL). While CNET works in basically two phases, one in open question format and one as guided linking of attributes and benefits, HL works completely structured with revealed attributes and benefits. Mental representations of two activity travel tasks were collected with both techniques among members of a nationwide Dutch household panel. The results confirm the hypothesis that the revealed format of variables in HL has an effect on the indication of variables as the elicited mental representations are almost twice as big for HL than for CNET. Furthermore, it turned out that CNET is more sensitive in measuring shifts among attributes in the mental representations for situational changes of the activity-travel task.

Original languageEnglish
Pages (from-to)170-183
Number of pages14
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Activity-travel scheduling
  • Causal Network Elicitation
  • Hard laddering
  • Mental representations
  • Online qualitative data collection

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