Extending Importance-Performance Analysis with Benefit-Based Segmentation

Abstract

For decades, the motives and benefits sought by outdoor recreation participants have been of interest to recreation managers and researchers. Understanding visitors’ socio-psychological recreation experience dimensions affords management opportunities to provide more satisfying recreational experiences. Two methodological and conceptual approaches to understand motives and benefits are benefit-based segmentation and Importance-Performance (I-P) analysis. In this project, both approaches were used to understand visitors and their preferences at a frequently visited USDA Forest Service recreation area. Over a one-year period, contacts were made with campers at developed and dispersed campsites. The contacts were followed by a mail questionnaire. These procedures resulted in a 67.0% response rate and 402 usable questionnaires. First, 15 recreation experience preferences (benefits sought) were used in a principal components analysis to establish user groups. Next, cluster analysis of four factors resulted in three clusters (a) Nature Relaxers, (b) Area Enthusiasts, and (c) Passive Visitors, which were compared with a multivariate analysis of variance. As a final analysis step, I-P measures were analyzed according to the groups to determine significant patterns. Results showed there was a significant omnibus MANOVA for the importance attributes, but not for the performance attributes. Importance attributes differing between the three clusters were areas for large groups, natural setting, signs posting general information, and signs posting regulations. Attribute distributions for the three I-P cluster grids were similar, but no two were identical. Adequate distance among campers, adequate parking, control of discourteous users, signs posting general information, signs posting regulations, and unable to see adjacent campers floated among the grids. In general, those attributes oriented toward site conditions were more likely to fall in the “concentrate here” quadrant than facility attributes. Results suggest that applying I-P measures to benefit-based segmentation can enhance analysis. This study supports previous research suggesting that I-P analysis requires statistics beyond placing attributes on grids. Further, managers can improve site design, planning, and visitor information with visitor benefit-based segmentation. Subtle differences among clusters may be ignored or altogether missed without attention to market segments and decisions based on homogeneous groups may be inappropriate.?