What drives people? Analyzing leisure-shopping trip decision making

Because of the strong increase in the number of leisure-shopping trips, a shift towards more sustainable leisure-shopping behaviour is desirable. This can be attained by having a better insight into people’s reasoning in choosing a transport mode and shopping location for this type of activities. Thus, this paper highlights individuals’ leisure-trip decision-making processes. The uniqueness of this study is the use of a large sample group, consisting of 221 respondents. A Computer-Based Causal Network Elicitation Technique is developed for this purpose, and participants’ responses are analyzed by means of Frequent Itemset Analysis. It appears that the complexity of the mental representation of the decision problem is very stable over different socio-demographic groups. However, clear differences appear between these groups concerning the content of the mental representation. The most remarkable findings are the limited importance of cost and environmental aspects in the transport mode choice. This has important implications for policy and marketing efforts to encourage sustainable transport modes for leisure-shopping. It is recommended to focus advertising campaigns and policy measures on aspects that are most important in people’s decision making process: flexibility, travel time, accessibility, easiness for parking and some practical concerns.

[1]  Harry J. P. Timmermans,et al.  Shopping Context and Consumers' Mental Representation of Complex Shopping Trip Decision Problems , 2008 .

[2]  R. Boyd,et al.  In Search of Homo Economicus: Behavioral Experiments in 15 Small- Scale Societies , 2001 .

[3]  G. Wets,et al.  Destination Choice in Daily Activity Travel , 2008 .

[4]  T. Hägerstrand.  What about people in Regional Science? , 1970 .

[5]  Davy Janssens,et al.  An interactive computer-based interface to support the discovery of individuals' mental representations and preferences in decisions problems: An application to travel behavior , 2011, Comput. Hum. Behav..

[6]  Davy Janssens,et al.  Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand , 2010 .

[7]  W. Black,et al.  Experiments in Interviewing Techniques , 1980 .

[8]  H. Plessner,et al.  Intuition in judgment and decision making , 2008 .

[9]  J. Scott.  Rational Choice Theory , 2007 .

[10]  Kevin Lynch,et al.  The Image of the City , 1960 .

[11]  T. Arentze,et al.  Modeling and Measuring Individuals' Mental Representations of Complex Spatio-Temporal Decision Problems , 2008 .

[12]  T. Chartrand,et al.  THE UNBEARABLE AUTOMATICITY OF BEING , 1999 .

[13]  Yoram Shiftan,et al.  Effect of Auto Restraint on Travel Behavior , 2005 .

[14]  Shichao Zhang,et al.  Association Rule Mining: Models and Algorithms , 2002 .

[15]  A. Boardman,et al.  Cost-Benefit Analysis: Concepts and Practice , 1996 .

[16]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[17]  B. Verplanken,et al.  Interventions to Break and Create Consumer Habits , 2006 .

[18]  M. Minsky.  The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind , 2006 .