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- W4293353048 abstract "With the proliferation of mobile communication technology, many firms, including retailers and consumer banks, are making efforts to develop mobile platforms to better engage with their customers and reduce operational costs. However, the cost advantage of these mobile technologies will not yield fruition unless the mobile channel accounts for a significant share of total transactions. In this article, we explore potential options for firms to promote usage of the mobile channel. To achieve this, we propose a hidden Markov model to both model customers’ decisions and assess different managerial strategies through simulations. While we apply our proposed model to data from a leading financial institution, our approach is equally applicable to other retail settings. The results show that customers’ choices of services and channels are sensitive to process time by type of channel. A 10% increase in process time through non-mobile channels leads to a nearly 14% increase in utilization of mobile banking channel. Therefore, retailing firms could potentially influence customers’ choice of different channels by changing channel-specific process times. Motivated by these findings, we present and compare performances across five different policies, in promoting the usage of mobile channel. Drawing on our simulation results, we also make personalized policy recommendations to firms facing different levels of financial constraints. For those with financial constraints, we recommend a policy of increasing process time for a selected subset of service requests through non-mobile channels. Firms not constrained by the resources might be better off improving process time with the mobile channel by targeting customers identified as having a better relationship with the firm." @default.
- W4293353048 created "2022-08-27" @default.
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- W4293353048 date "2023-03-01" @default.
- W4293353048 modified "2023-10-18" @default.
- W4293353048 title "Evaluating strategies for promoting retail mobile channel using a hidden Markov model" @default.
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- W4293353048 doi "https://doi.org/10.1016/j.jretai.2022.07.001" @default.
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