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New product entry success : an examination of variable, dimensional and model evolution

By John Boyd Green


This thesis examines the evolution of antecedents, dimensions and initial screening\ud models which discriminate between new product success and failure. It advances on\ud previous empirical new product success/failure comparative studies by developing a\ud discrete simulation procedure in which participating new product managers supply\ud judgements retrospectively on new product strategies and orientations for two distinct\ud time periods in the new product program: (1) the initial screening stage and (2) a\ud period approximately 1 year after market entry. Unique linear regression functions\ud are derived for each event and offer different, but complimentary, temporally\ud appropriate sets of determining factors. Model predictive accuracy ascends over time\ud and conditional process moderators alter success factors at both time periods. Whilst\ud the work validates and synthesises much from the new product development\ud literature, is exposes probable measurement timing error when single retrospective\ud models assess success dimension rank at the initial screen.\ud Six of seven hypotheses are accepted and demonstrate that:\ud 1. Many antecedents of success and measures of objective attainment are perceived\ud by NPD (new product development) managers to differ significantly over time.\ud 2. Reactive strategy, NPD multigenerational history and a superior product are the\ud most important dimensions of success through one year post launch.\ud 3. Current linear screening models constructed using retrospective methods produce\ud average prescriptive dimensions which exhibit measurement timing error when\ud used at the initial screen.\ud 4. Success dimensions evolve from somewhat deterministic to more stochastic over\ud time with model forecasting accuracy rising as launch approaches based on better\ud data availability.\ud 5. Product market PiLC (the life expectancy of an introduction before modification\ud is necessary calculated in years and months) and its order of entry and level of\ud innovation alter aggregate success model accuracy and dimension rank.\ud 6. Proper initial dimensional alignment and intra-process realignment based on\ud changing environments is critical to a successful project through one year post\ud launch.\ud The work cautions practitioners not to wait for better models to be developed but\ud immediately: (1) benchmark reasons for their current product market success, failure\ud and kill historical "batting average"; (2) enhance and/or replace\ud contributing/offending processes and systems based on these history lessons; (3)\ud choose or reject aggregate or conditional success/failure models based on team\ud forecasting ability; (4) concentrate on the selected model's time specific dimensions\ud of success and (5) provide/reserve adequate resources to adapt strategically over time\ud to both internal and external antecedent changes in the NPD environment.\ud Finally, it recommends new research into temporal, conditional and strategic tradeoffs\ud in internal and external antecedents/dimensions of success. Best results should\ud come from using both linear and curvilinear methods to validate more complex yet\ud statistically elegant NPD simulations

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