Exploratory data science for discovery and ex‐ante assessment of operational policies: Insights from vehicle sharing

Abstract

The proliferation of mobile devices and the emergence of the Internet of Things are leading to an unprecedented availability of operational data. In this article, we study how leveraging this data in conjunction with data science methods can help researchers and practitioners in the development and evaluation of new operational policies. Specifically, we introduce a two‐stage framework for exploratory data science consisting of a policy identification stage and an ex‐ante policy assessment stage. We apply the framework to the context of free‐floating carsharing—a novel mobility service that is an example of data‐rich smart city services. Through data exploration, we identify a novel preventive user‐based relocation policy and provide an ex‐ante assessment regarding the feasibility of its implementation. We discuss practical implications of our approach and results for shared‐mobility providers as well as the relationship between data science and operations management research.

Do Emotions Sell? The Impact of Emotional Expressions on Sales in the Space‐Sharing Economy

“…2019, Choi et al. 2020, Tian and Jiang 2018), with few empirical studies focusing on designing efficient market and pricing strategies and reducing discriminations in the sharing economy (e.g., Brandt and Dlugosch 2020, Cui et al. 2020, Ming et al.…”

Distributed service with proximal capacity and pricing on a two‐sided sharing economy platform

“…Our paper also contributes to the recent body of work that employs a multimethod approach to overcome some of the limitations of standalone empirical research designs (Brandt & Dlugosch, 2021;Chandrasekaran et al, 2016Chandrasekaran et al, , 2018Choi et al, 2016). While our empirical analysis enabled us to test our hypotheses on the relationship between PCE, PPE and surge prices, we were bound by the range of observed values during our study's time range and by the parameter space due to the available platform data.…”

“…We round out the paper by pointing to multiple sharing-economy settings where the core decisions involving platform's management of these trade-offs are relevant. Methodologically, our paper answers the call for more multimethod studies combining empirical and simulation models (e.g., Brandt & Dlugosch, 2021;Chandrasekaran et al, 2016Chandrasekaran et al, , 2018Choi et al, 2016) by testing hypotheses via empirical analysis on ridesharing data and then employing welfare analysis via an empirically informed simulation model. Managerially, our paper offers platform-management firms profit and/or welfare guidance on tracking demand-and supply side price elasticity levels as well as price spread.…”

“…For system performance, we consider welfare implications related to a platform's profit, and levels of labor and consumer surplus. Based on recent research (e.g., Brandt & Dlugosch, 2021;De Vries et al, 2018;Park et al, 2018), our pairing of methodologies can augment and address research questions that rely on traditional empirical or simulation methods alone. Our results are as follows:…”

Data Science: Exploring Effective Strategies and Operations for Maximizing Insights and Impact

“…Building upon this foundation, exploratory data science can be leveraged in the first stage for identification. [1] Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high-value business performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context.…”