Predictors of the Survival of Innovations^*

Abstract

This article examines the impact of key success factors on the survival of innovations that have reached the market and were developed by inventors outside of established organizations. It is of interest to learn which characteristics predict, at an early stage, the duration of the innovation's length of sales, because this duration is important to the financial success of new products. A focus on survival also can contribute conceptual clarity to the study of new product development. This study uses the Inventor's Assistance Program (IAP) at the Canadian Innovation Centre (CIC) in Waterloo, Canada, as the source of data. The CIC is a not-for-profit agency that provides various services to foster business development involving innovative products and services. Analysts in the IAP evaluate a specific product idea or invention on 37 dimensions before it has reached the market. The data for the present study involved these 37 variables evaluated each with a three-point linguistic scale. As the evaluations of the criteria are subjective, they might be argued to contain inaccuracies compared to objective data. On the other hand, the analysts use multiple related measures of concepts that have been shown to increase predictive accuracy. The use of experts who are unrelated to the projects avoids decisionmaking biases potentially associated with project managers' assessment of their own projects, such as unrealistic optimism. The recording of the expert evaluations of the ideas before they reached the market and independent of the measure of success, rather than using post-project completion evaluations, eliminates measurement biases such as hindsight bias and common method variance bias. Identifying information was used in these records to conduct a telephone survey of the inventors. An exploratory method of data analysis is identified and used that distinguishes research-appropriate constructs and their indicators in these data. Cluster analysis was performed, and survival regression correlated cluster scores with survival.

The Social Lives of Products:Analyzing Product Demography for Management Theory and Practice

“…Indeed, high product entry rates associate empirically with high demand (Connor, 1981) and with large market size (Ingram & Roberts, 1999). Likewise, low product disappearance rates correspond with high demand (de Figueiredo & Kyle, 2006), low demand uncertainty (Astebro & Michela, 2005;Hitsch, 2006), high initial revenues (Chisholm & Norman, 2006), and high sales and shipments in complementary and symbiotic industries (Cottrell & Nault, 2004;de Figueiredo & Kyle, 2006;Khessina & Carroll, 2008).…”

“…Researchers theorize and find that products with performance close to the technological frontier appeal more to customers and, therefore, stay on the market longer (Astebro & Michela, 2005;de Figueiredo & Kyle, 2006;Khessina & Carroll, 2008;Requena-Silvente & Walker, 2009;Ruebeck, 2005;Stavins, 2005). In the laser printer industry, (Bayus, 1998).…”

“…The impact of product characteristics on product vital rates also depends on their congruence with the market, especially consumer demand. Theorists reason that demand is positively related to product entry (Connor, 1981) and negatively related to product disappearance (Astebro & Michela, 2005). Indeed, high product entry rates associate empirically with high demand (Connor, 1981) and with large market size (Ingram & Roberts, 1999).…”

“…For example, product entry increases when entry barriers are low and market opportunities exist (Putsis & Bayus, 2001), competition is low and threat from substitutes is small (Greenstein & Wade, 1998), sales are moderately concentrated and advertising intensity is high (Connor, 1981). Moreover, products tend to stay on the market longer when average price in the industry is high (Astebro & Michela, 2005) and when densities of competing and substitute products are low (Greenstein & Wade, 1998).…”

“…The core insight here holds that competitive pressures create a shortage of consumer attention for uncompetitive products, driving them out of the market and preventing the entry of new ones. Empirically, the main finding shows that competition, measured by the number of all products made by all firms on the market, decreases product entry (Greenstein & Wade, 1998;Ingram & Roberts, 1999) and increases product exit (Astebro & Michela, 2005;Cottrell & Nault, 2004;Greenstein & Wade, 1998;Khessina & Carroll, 2008;Requena-Silvente & Walker, 2009). …”