Modeling supply-side dynamics of it components, products, and infrastructure

An empirical analysis using vector autoregression

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Prior IS research on technological change has focused primarily on organizational information systems and technology innovation; however, there is a growing need to understand the dynamics of supply-side forces in the introduction of new technologies. In this paper we investigate how the interdependencies among information technology components, products, and infrastructure affect the release of new technologies. Going beyond the ad hoc heuristic approaches applied in previous studies, we empirically validate the existence of several patterns of supply-side technology relationships in the context of wireless networking. We use vector autoregression (VAR) to model the comovements of new component, product, and infrastructure introductions and provide evidence of strong Granger-causal interdependencies. We also demonstrate that substantial improvements in forecasting can be gained by incorporating these cross-level effects into models of technological change. This paper provides some of the first research that empirically demonstrates these cross-level effects and also provides an exposition of VAR methodology for both analysis and forecasting in IS research.

Original languageEnglish (US)
Pages (from-to)397-417
Number of pages21
JournalInformation Systems Research
Volume23
Issue number2
DOIs
StatePublished - Jan 1 2012

Fingerprint

infrastructure
supply
technological change
Information technology
new technology
information technology
networking
heuristics
information system
Information systems
Innovation
innovation
methodology
evidence
Modeling
Empirical analysis
Vector autoregression
Supply side
Level effect
Technological change

Keywords

  • Information systems and technology trends
  • Supply-side forces
  • Technological change
  • Technology ecosystems
  • Technology forecasting
  • Time series analysis
  • Vector autoregression

Cite this

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title = "Modeling supply-side dynamics of it components, products, and infrastructure: An empirical analysis using vector autoregression",
abstract = "Prior IS research on technological change has focused primarily on organizational information systems and technology innovation; however, there is a growing need to understand the dynamics of supply-side forces in the introduction of new technologies. In this paper we investigate how the interdependencies among information technology components, products, and infrastructure affect the release of new technologies. Going beyond the ad hoc heuristic approaches applied in previous studies, we empirically validate the existence of several patterns of supply-side technology relationships in the context of wireless networking. We use vector autoregression (VAR) to model the comovements of new component, product, and infrastructure introductions and provide evidence of strong Granger-causal interdependencies. We also demonstrate that substantial improvements in forecasting can be gained by incorporating these cross-level effects into models of technological change. This paper provides some of the first research that empirically demonstrates these cross-level effects and also provides an exposition of VAR methodology for both analysis and forecasting in IS research.",
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author = "Gediminas Adomavicius and Jesse Bockstedt and Alok Gupta",
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AU - Gupta, Alok

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N2 - Prior IS research on technological change has focused primarily on organizational information systems and technology innovation; however, there is a growing need to understand the dynamics of supply-side forces in the introduction of new technologies. In this paper we investigate how the interdependencies among information technology components, products, and infrastructure affect the release of new technologies. Going beyond the ad hoc heuristic approaches applied in previous studies, we empirically validate the existence of several patterns of supply-side technology relationships in the context of wireless networking. We use vector autoregression (VAR) to model the comovements of new component, product, and infrastructure introductions and provide evidence of strong Granger-causal interdependencies. We also demonstrate that substantial improvements in forecasting can be gained by incorporating these cross-level effects into models of technological change. This paper provides some of the first research that empirically demonstrates these cross-level effects and also provides an exposition of VAR methodology for both analysis and forecasting in IS research.

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