Forecasting organizational adoption of high-technology product innovations separated by impact: Are traditional macro-level diffusion models appropriate?

Sean McDade, Terence A. Oliva, Ellen Thomas

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

This study examines forecasting accuracy when applying macro-level diffusion models to high-tech product innovations among organizational adopters. In addition, it explores whether the accuracy of macro-level diffusion models differs according to the impact of the new product. As a benchmark for comparison, three types of basic diffusion models are compared to three simple trend extrapolation models. The role of innovation impact in explaining forecasting accuracy is also considered. These issues are addressed by empirically testing organizational adoption data for 39 new high-tech products. Results indicate that for radical innovations the Bass model is best while for incremental innovations an external influence model is best. However, simple trend extrapolation models produced the most accurate overall forecasts. The purpose of the study is to reintroduce an important topic and give practitioners better insight into forecasting the organizational adoption of high-tech products once initial sales data becomes available.

Original languageEnglish (US)
Pages (from-to)298-307
Number of pages10
JournalIndustrial Marketing Management
Volume39
Issue number2
DOIs
StatePublished - Feb 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Marketing

Keywords

  • Diffusion models
  • Forecasting
  • High-tech products
  • Organizational adopters

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