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Relative Industry Concentration and Customer-Driven IT Spillovers

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  • Zhuo (June) Cheng

    (School of Accounting and Finance, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • Barrie R. Nault

    (Haskayne School of Business, University of Calgary, Calgary, Alberta T2N 1N4, Canada)

Abstract

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 (manufacturing 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.

Suggested Citation

  • Zhuo (June) Cheng & Barrie R. Nault, 2012. "Relative Industry Concentration and Customer-Driven IT Spillovers," Information Systems Research, INFORMS, vol. 23(2), pages 340-355, June.
  • Handle: RePEc:inm:orisre:v:23:y:2012:i:2:p:340-355
    DOI: 10.1287/isre.1100.0345
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    1. Belal Fallah & Mark D. Partridge & Dan S. Rickman, 2014. "Geography and High-Tech Employment Growth in US Counties," Journal of Economic Geography, Oxford University Press, vol. 14(4), pages 683-720.
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    3. Hua Zhang & Qiwang Zhang, 2023. "How Does Digital Transformation Facilitate Enterprise Total Factor Productivity? The Multiple Mediators of Supplier Concentration and Customer Concentration," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    4. Ye Shi & Layth C. Alwan & Srinivasan Raghunathan & Yugang Yu & Xiaohang Yue, 2021. "Mobile Consumer Scanning Technology: A Replacement for Interorganizational Information Systems for Demand Information Learning in Supply Chains?," Information Systems Research, INFORMS, vol. 32(4), pages 1431-1449, December.
    5. Fengmei Gong & Barrie R. Nault & Mohammad S. Rahman, 2016. "Research Note—An Internet-Enabled Move to the Market in Logistics," Information Systems Research, INFORMS, vol. 27(2), pages 440-452, June.
    6. Prasanna Tambe & Lorin M. Hitt, 2014. "Job Hopping, Information Technology Spillovers, and Productivity Growth," Management Science, INFORMS, vol. 60(2), pages 338-355, February.
    7. Dawei Zhang & Zhuo (June) Cheng & Hasan A. Qurban H. Mohammad & Barrie R. Nault, 2015. "Research Commentary—Information Technology Substitution Revisited," Information Systems Research, INFORMS, vol. 26(3), pages 480-495, September.
    8. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    9. Dawei (David) Zhang & Barrie R. Nault & Xueqi (David) Wei, 2019. "The Strategic Value of Information Technology in Setting Productive Capacity," Information Systems Research, INFORMS, vol. 30(4), pages 1124-1144, December.
    10. Sulin Ba & Barrie R. Nault, 2017. "Emergent Themes in the Interface Between Economics of Information Systems and Management of Technology," Production and Operations Management, Production and Operations Management Society, vol. 26(4), pages 652-666, April.
    11. Peng Huang & Marco Ceccagnoli & Chris Forman & D.J. Wu, 2022. "IT Knowledge Spillovers, Absorptive Capacity, and Productivity: Evidence from Enterprise Software," Information Systems Research, INFORMS, vol. 33(3), pages 908-934, September.
    12. Dennis O. Kundisch & Neeraj Mittal & Barrie R. Nault, 2014. "Research Commentary —Using Income Accounting as the Theoretical Basis for Measuring IT Productivity," Information Systems Research, INFORMS, vol. 25(3), pages 449-467, September.
    13. Lynn Wu & Fujie Jin & Lorin M. Hitt, 2018. "Are All Spillovers Created Equal? A Network Perspective on Information Technology Labor Movements," Management Science, INFORMS, vol. 64(7), pages 3168-3186, July.

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