2019
DOI: 10.1287/mnsc.2017.2924
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Corporate Innovation Along the Supply Chain

Abstract: In this paper, we document a positive effect of supplier-customer geographic proximity on supplier innovation.To establish causality, we explore plausibly exogenous variation in proximity caused by customer relocations.The positive effect of supplier-customer proximity on supplier innovation is stronger when customers are more innovative themselves, when suppliers and customers are closer in technological space, and when customers' demand accounts for a larger fraction of suppliers' total sales. These findings… Show more

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Cited by 280 publications
(117 citation statements)
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References 55 publications
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“…Due to the nature of the supplier-customer data, the sample usually consists of plenty of dyads but a rather short "T" (i.e., a small number of observations per dyad). For example, the average number of observations per dyad in Chu et al (2018) is 4.0 and in Lee et al, (2018) it is 3.7. These numbers are considerably smaller (lower than a 1:2000 ratio) than the number of dyads in their samples (8,645 and 7,274).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Due to the nature of the supplier-customer data, the sample usually consists of plenty of dyads but a rather short "T" (i.e., a small number of observations per dyad). For example, the average number of observations per dyad in Chu et al (2018) is 4.0 and in Lee et al, (2018) it is 3.7. These numbers are considerably smaller (lower than a 1:2000 ratio) than the number of dyads in their samples (8,645 and 7,274).…”
Section: Discussionmentioning
confidence: 99%
“…Relatedly, several recent studies in the operations management literature have used the customer segment data from Compustat and modeled the supplier‐customer dyads as fixed effects in their analysis (e.g., Chu, Tian, & Wang, ; Lee et al, ). The fixed‐effect specification used in these studies has one important limitation.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, supply chains identified in Compustat are limited to those among public firms, and the coverage is low (as mentioned in Section 3.1, the average number of firms per quarter in Barrot and Sauvagnat (2016) is 550). Moreover, users of Compustat often report difficulties in identifying ID number of suppliers and customers from their names (Barrot and Sauvagnat 2016, Chu, Tian, and Wang 2018). The coverage in data for global supply chains is even lower.…”
Section: Discussionmentioning
confidence: 99%
“…Based on the information, Compustat, a database compiled by Standard & Poor's, provides supplychain information for public firms. Accordingly, growing literature examines effects of supply chains on firm performance and decisions (Carvalho 2014, Chu, Tian, andWang 2018).…”
Section: Intranational Propagationmentioning
confidence: 99%
“…In turn, these relationship-specific investments are made to offer unique products to the need of the buyer, thus making it costly and infeasible for the buyer to switch to substitute products [26]. A number of studies including Tan et al [27], Chu et al [28], Nunn [29], Kale and Shahrur [30] claim that innovation is necessary to meet customer's specific needs. Thanks to a long relationship with customers, the seller is able to receive more constant feedback and be able to identify trends as guidelines to the innovation to offer better services/products to the customer.…”
Section: Contract Length and Firm Innovationmentioning
confidence: 99%