Missing Impact of Ratings on Platform Participation in India: A Call for Research in GREAT Domains

Karhade, P and Kathuria, A (2020) Missing Impact of Ratings on Platform Participation in India: A Call for Research in GREAT Domains. Communications of the Association for Information Systems, 47: 17. pp. 364-381. ISSN 1529-3181

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In this study, we propose that research conducted in Western, educated, industrialized, rich, and democratic (WEIRD) domains does not necessarily generalize to the rest of the world. Growing, rural, eastern, aspirational, transitional (GREAT) domains now account for a significant proportion of world economic output and, thereby, warrant special attention. We submit that a tolerant stance under which scholars investigate GREAT domains with an open mind that allows for theoretical plurality will likely enrich IS theories. To exemplify this stance, we consider how online ratings affect ratee decisions to participate in financial transactions on a digital platform in a GREAT economy. The production and consumption of food affects every strata of society, and, thus, we choose to investigate our research question in the context of platform-enabled food delivery. We applied decision tree induction on a population-level dataset that included restaurants, their features, online ratings, and financial participation decisions from a major food discovery and delivery platform in India. Tree induction makes no distributional assumptions and makes no a priori assumptions on the combinations of factors, which allows one to put forth the most lenient test for uncovering any impact that online ratings have on the decisions that rates make tacitly. After conducting multiple computational experiments, we consistently found that online restaurant ratings did not have a significant bearing on their decision to participate on the food-delivery platform. Our counterintuitive finding serves as an example WEIRD domain logic that does not generalize to a GREAT domain and forms a credible basis for our call for additional research in GREAT domains.

Item Type: Article
Subjects: Information Systems
Date Deposited: 15 Apr 2021 09:12
Last Modified: 11 Jul 2023 16:37
URI: https://eprints.exchange.isb.edu/id/eprint/1445

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