The influences of individuals in joint decisions: recreation in the Alps in Italy

Normal 0 false false false IT X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:IT; mso-fareast-language:EN-US;} Modeling preference heterogeneity within outdoor recreation is known to be a relevant issue particularly in Alpine protected areas where visitors display increasing expectations for high-quality experiences. Parkland management agencies are interested in designing environmental policies aimed to provide services for outdoor recreation activities. Such activities are often performed by visitors jointly as a couple. Individuals within a couple may typically display different preferences towards a selection of environmental services. Nevertheless, joint decisions usually drive outdoor experiences. Investigating how decision making process works within joint choices in couples can be of help to better address environmental policy. The issue under scrutiny is to what extent joint deliberations are most affected by one of the two members. The influence patterns, when heterosexual couples make joint decisions regarding outdoor leisure activities, are recorded using three discrete choice experiments (DCE) with the same attributes and levels. The woman and man each independently complete a DCE and then jointly complete a similar DCE. Using links between latent variables and a new modelling technique for heterogeneity, known as structural choice modelling (SCM), the three DCEs are simultaneously modelled. The extent of influence of the woman and man on the joint decisions is measured for each attribute using R Square statistics. The probabilities of joint choice, conditional on the choices made by the woman and man, are used to evaluate the competing models. Different approaches for coding - dummy, effects, binary and continuous - are tested and evaluated. Coding is shown to have an impact on the modelling of influence. The analysis shows that there are strong, clear and relatively simple influence patterns in the data over the three DCEs. Influence can be modelled and understood using well identified and parsimonious models. Furthermore, policy implications can be explored using classic simulations but applied to all three DCEs at the same time. Mara Thiene Dept. LEAF University of Padua (Italy), Viale dell'Universita 16, 35020, Legnaro (Pd), mara.thiene@unipd.it Cam Rungie School of Marketing, Ehrenberg Bass Institute for Marketing Science, University of South Australia, 70 North Terrace, Adelaide, SA 5000, Australia, c am.rungie@unisa.edu. au Riccardo Scarpa, Department of Economics Waikato Management School, The University of Waikato, Private Bag 3105 Hamilton, 3240, New Zealand,