Brida, J.G., Disegna, M. and Scuderi, R., 2013. Visitors of two types of museums: A segmentation study. Expert Systems with Applications, 40 (6), 2224 - 2232.
Full text available as:
ESWA2013_BDS_PROOF.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Market segmentation comprises a wide range of measurement tools that are useful for the sake of supporting marketing and promotional policies also in the sector of cultural economics. This paper aims to contribute to the literature on segmenting cultural visitors by using the Bagged Clustering method, as an alternative and effective strategy to conduct cluster analysis when binary variables are used. The technique is a combination of hierarchical and partitioning methods and presents several advantages with respect to more standard techniques, such as k-means and LVQ. For this purpose, two ad hoc surveys were conducted between June and September 2011 in the two principal museums of the two provinces of the Trentino-South Tyrol region (Bolzano and Trento), Northern Italy: the South Tyrol Museum of Archaeology in Bolzano (ÖTZI), hosting the permanent exhibition of the "Iceman" Ötzi, and the Museum of Modern and Contemporaneous Art of Trento and Rovereto (MART). The segmentation analysis was conducted separately for the two kinds of museums in order to find similarities and differences in behaviour patterns and characteristics of visitors. The analysis identified three and two cluster segments respectively for the MART and ÖTZI visitors, where two ÖTZI clusters presented similar characteristics to two out of three MART groups. Conclusions highlight marketing and managerial implications for a better direction of the museums. © 2012 Elsevier Ltd. All rights reserved.
|Uncontrolled Keywords:||Bagged Clustering ; Logit models ; Motivation ; Museum ; Segmentation|
|Group:||Faculty of Management|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||21 Mar 2016 14:44|
|Last Modified:||21 Mar 2016 14:44|
Downloads per month over past year
|Repository Staff Only -|