Searching for Rewards
SSRN Electronic Journal
2024
- 500Usage
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Loyalty programs are pervasive across numerous markets, offering members rewards based on their past purchases for future benefits. This study explores the dynamics of loyalty programs within a repeated ordered search framework, where consumers sequentially search for the optimal product across multiple firms over two periods. Our findings reveal that firms strategically use price discounts and rewards to influence consumer behaviors. Price discounts discourage further search in the current shopping period, while rewards encourage consumer loyalty by inducing prominence in subsequent visits. As search costs increase, firms tend to offer lower price discounts but higher rewards. This strategy increases industry profit but reduces consumer surplus. Compared with its absence, loyalty programs decrease both industry profit and consumer welfare, leading to a lose-lose outcome. Moreover, we demonstrate that when the market is heterogeneous, high-type firms, with larger networks, offer lower rewards but achieve higher second-period prices and greater consumer loyalty, contrasting with low-type firms that compensate with higher rewards for their smaller networks. This study offers new insights into the strategic use of loyalty programs and their impact on market competition.
Bibliographic Details
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