Browsing by Author "Cheng, Zhuo (June)"

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    Industry Level Supplier-Driven IT Spillovers
    (Informs, 2007-08) Nault, Barrie R; Cheng, Zhuo (June)
    We model and estimate the effects to downstream productivity from information technology (IT) investments made upstream. Specifically, we examine how an industry’s productivity is affected by the IT capital stock of its suppliers. These supplier-driven IT spillovers occur because, due to competition in the supplying industry, quality benefits from suppliers’ IT investments can pass downstream. If the output deflators of supplying industries (consequently the intermediate input deflator of the using industries) do not capture the quality improvement from IT, then the output productivity of the supplying industries is mismeasured or misassigned. We develop and empirically test a model capturing these supplier-driven effects using data on 85 manufacturing industries at the three-digit SIC code level. We find that for a 10.5% increase in suppliers’ IT capital, the suppliers’ output increases by 0.63%–0.70%, which is more than covering the cost of the increase in suppliers’ IT capital. In addition, this increase in suppliers’ IT capital increases the average downstream industry’s output by $66–$72 million, thereby confirming substantial supplier-driven IT spillovers downstream. We also infer the magnitude of the measurement error of the price deflator of the intermediate input resulting from the failure to account for IT-related quality improvement, finding that the measured price deflator overestimates the true deflator by approximately 30% at the mean level of IT capital.
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    Information technology substitution revisited
    (2014-12-26) Zhang, Dawei; Cheng, Zhuo (June); Mohammad, Hasan A. Qurban H.; Nault, Barrie R.
    Taking advantage of the opportunities created by the price adjusted performance improvement in IT depends in part on the ability of IT capital to substitute for other inputs in production. Studies in the IS literature as well as most economics training that examine substitution of IT capital for other inputs use the Allen elasticity of substitution (AES). We present a less-well-known measure for the elasticity of substitution, the Morishima Elasticity of Substitution (MES). In contrast to the AES which is misleading when there are three or more inputs – such as non-IT capital, labor and IT capital – the MES provides a substitution measure where the scale is meaningful, and the measure differs depending upon which price is changing. This is particularly important for IT capital as prices have been declining and there is evidence that IT capital can substitute for non-IT capital or labor in a qualitatively different way than non-IT capital and labor substitute for each other. Methodologically we also show the impact of imposing local regularity – for example, monotonicity of output from increases in inputs – that we do through Bayesian methods employed to estimate the underlying functions that are used to calculate various measures of substitution. We demonstrate the importance of the MES as an under-recognized measure of substitution and the impact of imposing local regularity using an economy-wide industry-level dataset covering 1998-2009 at the three-digit NAICS level. Our MES results show that reductions in the price of IT capital increase the quantity of IT capital in use but are unlikely to change the input share of IT capital – the value of IT capital as a proportion of the value of all inputs, in contrast to major studies using the AES. In addition, estimates for both elasticities of substitution are more stable after imposing local regularity. Both of these advances – that is, the MES and imposing local regularity – have potential to impact future work on IT productivity, IT pricing, IT cost estimation and any type of analysis that posits the substitution of IT capital for non-IT capital or labor.
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    Internet Channel Entry: Retail Coverage and Entry Cost Advantage
    (Springer, 2007-06) Nault, Barrie R; Cheng, Zhuo (June)
    In this research we study how existing market coverage affects the outcome of the Internet channel entry game between an existing retailer and a new entrant. A market is not covered when some consumers with low reservation prices are priced out by existing retailers and do not purchase. In a model with multiple existing retailers and a potential new entrant, we demonstrate that when entry costs are equal, one of the existing retailers enters the Internet channel first. However, if the market is covered by existing retailers before entry, then because of the threat of Internet channel entry by the potential new entrant, retailer entry cannibalizes existing retail profits—cannibalizing at a loss. In addition, if a potential new entrant has a slight advantage in Internet channel entry costs and the market is not covered by existing retailers, then the new entrant enters the Internet channel first. If the market is covered by existing retailers, then the new entrant must have a larger Internet channel entry cost advantage to be first to enter the Internet channel.
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    Relative Industry Concentration and Customer-Driven IT Spillovers
    (INFORMS, 2012-06) Nault, Barrie R; Cheng, Zhuo (June)
    We examine how one industry’s productivity is affected by the IT capital of its customers and how this effect depends on industries’ relative concentration. These customer-driven IT spillovers result from customers’ IT investments in various information systems that reduce transaction costs through information sharing and coordination and lead to more efficient production and logistics upstream. The magnitude of IT spillovers depends on relative industry concentration because customers in more concentrated industries relative to those of their suppliers are better able to retain the benefits from their IT investments. We model customer-driven effects based on production theory and empirically test the model using two industry-level data sets covering different and overlapping time periods (1987–1999 and 1998–2005), different scopes of the economy (manufac- turing only versus all industries), and different levels of industry aggregation. We find that, given an increase in a downstream industry’s IT capital, there is a significant increase in downstream industry output as well as significant increases in upstream industry output. Moreover, the magnitude of IT spillovers is related to relative industry concentration: A 1% decrease in a customer’s relative industry concentration increases spillovers by roughly 1%. Thus, further increases in IT capital can be justified along the supply chain, and an industry’s relative concentration—which can reflect market power—in part determines the distribution of productivity benefits.