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The challenges of pre-launch forecasting of adoption time series for new durable products

Paul Goodwin, Sheik Meeran, Karima Dyussekeneva

International Journal of Forecasting, Volume: 30, Issue: 4, Pages: 1082 - 1097

Swansea University Author: Karima Dyussekeneva

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DOI (Published version): 10.1016/j.ijforecast.2014.08.009

Abstract

The successful introduction of new durable products plays an important part in helpingcompanies to stay ahead of their competitors. Decisions relating to these products can beimproved by the availability of reliable pre-launch forecasts of their adoption time series.However, producing such forecasts...

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Published in: International Journal of Forecasting
ISSN: 01692070
Published: 2014
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URI: https://cronfa.swan.ac.uk/Record/cronfa43558
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spelling 2020-07-15T12:25:10.8085111 v2 43558 2018-08-24 The challenges of pre-launch forecasting of adoption time series for new durable products 159ce7d6be8f1aff521f126f9699bb6d Karima Dyussekeneva Karima Dyussekeneva true false 2018-08-24 BBU The successful introduction of new durable products plays an important part in helpingcompanies to stay ahead of their competitors. Decisions relating to these products can beimproved by the availability of reliable pre-launch forecasts of their adoption time series.However, producing such forecasts is a difficult, complex and challenging task, mainly becauseof the non-availability of past time series data relating to the product, and the multiplefactors that can affect adoptions, such as customer heterogeneity, macroeconomicconditions following the product launch, and technological developments which may leadto the product’s premature obsolescence. This paper provides a critical review of the literatureto examine what it can tell us about the relative effectiveness of three fundamental approachesto filling the data void : (i) management judgment, (ii) the analysis of judgmentsby potential customers, and (iii) formal models of the diffusion process. It then shows thatthe task of producing pre-launch time series forecasts of adoption levels involves a set ofsub-tasks, which all involve either quantitative estimation or choice, and argues that thedifferent natures of these tasks mean that the forecasts are unlikely to be accurate if a singlemethod is employed. Nevertheless, formal models should be at the core of the forecastingprocess, rather than unstructured judgment. Gaps in the literature are identified, and thepaper concludes by suggesting a research agenda so as to indicate where future researchefforts might be employed most profitably. Journal Article International Journal of Forecasting 30 4 1082 1097 01692070 new product forecasting, diffusion model, forecasting methods, Bass diffusion, judgmental forecasting, time series, quantitative forecasting, analogous products 30 4 2014 2014-04-30 10.1016/j.ijforecast.2014.08.009 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2020-07-15T12:25:10.8085111 2018-08-24T10:59:09.5793397 Faculty of Humanities and Social Sciences School of Management - Business Management Paul Goodwin 1 Sheik Meeran 2 Karima Dyussekeneva 3
title The challenges of pre-launch forecasting of adoption time series for new durable products
spellingShingle The challenges of pre-launch forecasting of adoption time series for new durable products
Karima Dyussekeneva
title_short The challenges of pre-launch forecasting of adoption time series for new durable products
title_full The challenges of pre-launch forecasting of adoption time series for new durable products
title_fullStr The challenges of pre-launch forecasting of adoption time series for new durable products
title_full_unstemmed The challenges of pre-launch forecasting of adoption time series for new durable products
title_sort The challenges of pre-launch forecasting of adoption time series for new durable products
author_id_str_mv 159ce7d6be8f1aff521f126f9699bb6d
author_id_fullname_str_mv 159ce7d6be8f1aff521f126f9699bb6d_***_Karima Dyussekeneva
author Karima Dyussekeneva
author2 Paul Goodwin
Sheik Meeran
Karima Dyussekeneva
format Journal article
container_title International Journal of Forecasting
container_volume 30
container_issue 4
container_start_page 1082
publishDate 2014
institution Swansea University
issn 01692070
doi_str_mv 10.1016/j.ijforecast.2014.08.009
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description The successful introduction of new durable products plays an important part in helpingcompanies to stay ahead of their competitors. Decisions relating to these products can beimproved by the availability of reliable pre-launch forecasts of their adoption time series.However, producing such forecasts is a difficult, complex and challenging task, mainly becauseof the non-availability of past time series data relating to the product, and the multiplefactors that can affect adoptions, such as customer heterogeneity, macroeconomicconditions following the product launch, and technological developments which may leadto the product’s premature obsolescence. This paper provides a critical review of the literatureto examine what it can tell us about the relative effectiveness of three fundamental approachesto filling the data void : (i) management judgment, (ii) the analysis of judgmentsby potential customers, and (iii) formal models of the diffusion process. It then shows thatthe task of producing pre-launch time series forecasts of adoption levels involves a set ofsub-tasks, which all involve either quantitative estimation or choice, and argues that thedifferent natures of these tasks mean that the forecasts are unlikely to be accurate if a singlemethod is employed. Nevertheless, formal models should be at the core of the forecastingprocess, rather than unstructured judgment. Gaps in the literature are identified, and thepaper concludes by suggesting a research agenda so as to indicate where future researchefforts might be employed most profitably.
published_date 2014-04-30T03:54:47Z
_version_ 1763752739273179136
score 11.036378

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