Forecasting Technology Diffusion

Forecasting Technology Diffusion is a theoretical framework within the domain of innovation adoption research that specifically models the temporal and social dynamics through which novel technologies propagate across populations, distinguishing itself by integrating predictive analytics to anticipate the rate and extent of uptake over time.

96

Publications

6.6K

Citations

234

Authors

123

Institutions

Publications per year

2017–2026

17

Authors

234

  1. CH
    Christos H. Skiadas

    Technical University of Crete, Greece

    3 publications · 122 citations

    3

    h-index

  2. JB
    Jess Benhabib

    New York University, United States

    3 publications · 571 citations

    3

    h-index

  3. MM
    Mark M. Spiegel

    Federal Reserve Bank of San Francisco, United States

    3 publications · 571 citations

    3

    h-index

  4. FM
    Frank M. Bass

    The University of Texas at Dallas, United States

    2 publications · 145 citations

    2

    h-index

  5. ML
    Mary Lou Githens

    SMART Reading, United States

    2 publications · 145 citations

    2

    h-index

1–5 of 234

Venues

  1. Technological Forecasting and Social Change

    21 publications · 2.3K citations

    18

    h-index

  2. IEEE Transactions on Engineering Management

    3 publications · 77 citations

    3

    h-index

  3. Journal of Product Innovation Management

    2 publications · 109 citations

    2

    h-index

  4. Journal of Marketing Research

    2 publications · 85 citations

    2

    h-index

  5. Expert Systems with Applications

    2 publications · 97 citations

    2

    h-index

1–5