{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:40:20Z","timestamp":1780105220778,"version":"3.54.0"},"reference-count":60,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Management Science"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>A fundamental issue faced by experience providers\u2014ranging from retail to culture\u2014is displaying a collection of items for physical and digital interactions. The arrangement of the exhibits in different locations, which we call the layout, affects the visitors\u2019 choices over time and space, thereby driving their engagement with the offered experience. In a collaboration with the Van Gogh Museum (Netherlands), we develop a predict-then-optimize framework to inform such operational decisions. First, we propose a sequential choice model, called pathway multinomial logit, that represents visitor activity as a sequence of conditional logit outcomes influenced by the layout. Estimation on large-scale visitor activity logs recorded on multimedia guides reveals that increase in spatial distances and search distances on the multimedia guide interface are strongly correlated with a reduction of transition propensity between artworks, while also uncovering relationships with artwork characteristics and contextual features. Counterintuitively, in response to more congestion, visitors may interact with more exhibits, including less prominent artworks. Our model predicts the next visitor transition with an out-of-sample accuracy of 63%. We test the predictive accuracy of our model against several benchmarks and modified layouts. Finally, we formulate the layout optimization problem, where the goal is to assign artworks to different locations to maximize the expected length of visitors\u2019 paths. We establish a strong inapproximability result for this new optimization setting. Our simulations suggest that optimized layouts might lift visitor engagement by improving proximity and retention exerted by the layout.<\/jats:p>\n                  <jats:p>This paper was accepted by David Simchi-Levi, operations management.<\/jats:p>\n                  <jats:p>Funding: A. Aouad acknowledges the financial support provided by the UK Research and Innovation Engineering and Physical Sciences Research Council [Grant EP\/Y003721\/1]. V. Mart\u00ednez-de-Alb\u00e9niz acknowledges the financial support provided by the Agencia Estatal de Investigaci\u00f3n from the Spanish Ministry of Science and Innovation [Project PID2020-116135GB-I00 MCIN\/ AEI \/ 10.13039\/501100011033].<\/jats:p>\n                  <jats:p>Supplemental Material: The online appendix and data files are available at https:\/\/doi.org\/10.1287\/mnsc.2022.02024 .<\/jats:p>","DOI":"10.1287\/mnsc.2022.02024","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T15:09:52Z","timestamp":1756134592000},"page":"3528-3549","source":"Crossref","is-referenced-by-count":4,"title":["Designing Layouts for Sequential Experiences: Application to Cultural Institutions"],"prefix":"10.1287","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-6140","authenticated-orcid":false,"given":"Ali","family":"Aouad","sequence":"first","affiliation":[{"name":"Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-8518","authenticated-orcid":false,"given":"Abhishek","family":"Deshmane","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Scheller College of Business, Atlanta, Georgia 30308"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8391-2205","authenticated-orcid":false,"given":"Victor","family":"Mart\u00ednez-de-Alb\u00e9niz","sequence":"additional","affiliation":[{"name":"IESE Business School, University of Navarra, 08034 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1007\/s10288-015-0302-y"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.45.1.1"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2020.3664"},{"key":"B4","doi-asserted-by":"crossref","unstructured":"Arya V, Garg N, Khandekar R, Meyerson A, Munagala K, Pandit V (2001) Local search heuristic for k-median and facility location problems.\n                      Proc. 33rd Annual ACM Sympos. 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