Long Term Sales Forecasts of Innovations - An Empirical Study of the Consumer Electronic Market

This paper empirically examines models of replacement sales for six electronic consumer durables – TVs, VCRs, DVD players, Digital Cameras, personal and notebook computers – using data from a large survey of 8077 German households. A new replacement model is developed that fits the empirical "lifetables" better than existing models. This said, fitting to replacement sales data was not substantially improved as these fits are not particularly sensitive to mis-specification of the shape of the underlying distribution. Since many product innovations can be targeted at replacement rather than first purchase buyers – this improved understanding of replacement behaviour helps entrepreneurs identify new opportunities

Drivers of adoption for successive generations of high tech products

High technology consumer products such as notebooks, digital cameras and DVD players are not introduced into a vacuum. Consumer experience with related earlier generation technologies, such as PCs, film cameras and VCRs, and the installed base of these products strongly impacts the market diffusion of the new generation products. Yet technology substitution has received only sparse attention in the diffusion of innovation literature. Research for consumer durables has been dominated by studies of (first purchase) adoption (c.f. Bass 1969) which do not explicitly consider the presence of an existing product/technology. More recently, considerable attention has also been given to replacement purchases (c.f. Kamakura and Balasubramanian, 1987). Only a handful of papers explicitly deal with the diffusion of technology/product substitutes (e.g. Norton and Bass, 1987: Bass and Bass, 2004). They propose diffusion-type aggregate-level sales models that are used to forecast the overall sales for successive generations. Lacking household data, these aggregate models are unable to give insights into the decisions by individual households - whether to adopt generation II, and if so, when and why. This paper makes two contributions. It is the first large-scale empirical study that collects household data for successive generations of technologies in an effort to understand the drivers of adoption. Second, in comparision to traditional analysis that evaluates technology substitution as an ''adoption of innovation'' type process, we propose that from a consumer's perspective, technology substitution combines elements of both adoption (adopting the new generation technology) and replacement (replacing the generation I product with generation II). Based on this proposition, we develop and test a number of hypotheses. Methodology/Key Propositions In some cases, successive generations are clear ''substitutes'' for the earlier generation, in that they have almost identical functionality. For example, successive generations of PCs Pentium I to II to III or flat screen TV substituting for colour TV. More commonly, however, the new technology (generation II) is a ''partial substitute'' for existing technology (generation I). For example, digital cameras substitute for film-based cameras in the sense that they perform the same core function of taking photographs. They have some additional attributes of easier copying and sharing of images. However, the attribute of image quality is inferior. In cases of partial substitution, some consumers will purchase generation II products as substitutes for their generation I product, while other consumers will purchase generation II products as additional products to be used as well as their generation I product. We propose that substitute generation II purchases combine elements of both adoption and replacement, but additional generation II purchases are solely adoption-driven process. Extensive research on innovation adoption has consistently shown consumer innovativeness is the most important consumer characteristic that drives adoption timing (Goldsmith et al. 1995; Gielens and Steenkamp 2007). Hence, we expect consumer innovativeness also to influence both additional and substitute generation II purchases. Hypothesis 1a) More innovative households will make additional generation II purchases earlier. 1 b) More innovative households will make substitute generation II purchases earlier. 1 c) Consumer innovativeness will have a stronger impact on additional generation II purchases than on substitute generation II purchases. As outlined above, substitute generation II purchases act, in part like a replacement purchase for the generation I product. Prior research (Bayus 1991; Grewal et al 2004) identified product age as the most dominant factor influencing replacements. Hence, we hypothesise that: Hypothesis 2: Households with older generation I products will make substitute generation II purchases earlier. Our survey of 8,077 households investigates their adoption of two new generation products: notebooks as a technology change to PCs, and DVD players as a technology shift from VCRs. We employ Cox hazard modelling to study factors influencing the timing of a household's adoption of generation II products. We determine whether this is an additional or substitute purchase by asking whether the generation I product is still used. A separate hazard model is conducted for additional and substitute purchases. Consumer Innovativeness is measured as domain innovativeness adapted from the scales of Goldsmith and Hofacker (1991) and Flynn et al. (1996). The age of the generation I product is calculated based on the most recent household purchase of that product. Control variables include age, household size and income , primary decision-maker's age and education.. Results and Implications Our preliminary results confirm both our hypotheses. Consumer innovativeness has a strong influence on both additional purchases (exp a = 1.11) and substitute purchases (exp a = 1.09). Exp a is interpreted as the increased probability of purchase for an increase of 1.0 on a 7-point innovativeness scale. Also consistent with our hypotheses, the age of the generation I product has a dramatic influence for substitute purchases of VCR/DVD (exp a = 2.92) and a strong influence for PCs/notebooks (exp a = 1.30). Exp a is interpreted as the increased probability of purchase for an increase of 10 years in the age of the generation I product. Yet, also as hypothesised, there was no influence on additional purchases. The results lead to two key implications. First, there is a clear distinction between additional and substitute purchases of generation II products, each with different drivers. Treating these as a single process will mask the true drivers of adoption. For substitute purchases, product age is a key driver. Hence, implications for marketers of high technology products can utilise data on generation I product age (e.g. from warranty or loyalty programs) to target customers who are more likely to make a purchase

Long term sales forecasts of innovations - an empirical study of the consumer electronic market

Entrepreneurial action, whether undertaken by firms small or large, new or old, involves the ability to recognise and exploit opportunities. This is a risky activity. The consumer electronic industry (the focus of this study) is characterized by high development and launch costs of technological innovations as well as high failure rates. Moreover, technological innovation tends to be an ongoing pursuit of consumer electronic companies. Accurate long term forecasting of sales, and understanding the drivers of those sales, assists managers to better recognise and exploit opportunities over the life cycle of a product category. In durable product categories such as consumer electronics, accurate long term forecasting models need to incorporate first and repeat purchases. This is important both because the latter represents a significant share of sales for durables over time, and because consumer requirements for product innovation develops as they become experienced, sophisticated users of the product. While traditional aggregate diffusion models for durables mostly consider first purchases only (e.g., Bass 1969 and its many extensions), less attention has been devoted to diffusion models considering repeat purchases. This is due to the fact that this research tradition has largely relied on secondary data which do not offer a distinction between first and repeat purchases. This makes it very difficult to assess the validity of any model incorporating repeat purchase. Therefore,our study undertakes a large primary data collection to address this issue. In the diffusion literature, there is no consensus regarding which repeat purchase model is most suitable for forecasting purposes. Most of these models are developed on the basis of aggregate annual sales data which do not provide any information on the individual buying behaviour. One weakness of the repeat purchase models published so far is the failure to measure heterogeneity of consumer's repeat purchasing behaviour. Thus, modelling this component adequately might lead to a better overall forecasting performance. For this reason, individual consumer data, rather than aggregate sales data, was collected in this study

Regression Analysis

Linear regression analysis is one of the most important statistical methods. Itexamines the linear relationship between a metric-scaled dependent variable (alsocalled endogenous, explained, response, or predicted variable) and one or moremetric-scaled independent variables (also called exogenous, explanatory, control, orpredictor variable). We illustrate how regression analysis work and how it supportsmarketing decisions, e.g., the derivation of an optimal marketingmix.We also outlinehow to use linear regression analysis to estimate nonlinear functions such as amultiplicative sales response function. Furthermore, we show how to use the resultsof a regression to calculate elasticities and to identify outliers and discuss in detailsthe problems that occur in case of autocorrelation, multicollinearity and heteroscedasticity.We use a numerical example to illustrate in detail all calculations anduse this numerical example to outline the problems that occur in case of endogeneity

Repeat Purchasers and the Diffusion of Electronic Products: Does Consumer Innovativeness Matter?

Despite the enormous economic importance of consumer durable repeat purchases they have been largely neglected in the diffusion of innovations research. Only a handful of studies have examined the drivers of replacement timing of durables. We extend this research in two important ways. Based on a large survey of 8,077 households, we demonstrate that consumer innovativeness, the most prominent driver of adoption, also influences repeat purchase timing. Further, we investigate for the first time the drivers of additional unit purchase timing

Repeat Purchasers and the Diffusion of Electronic Products: Does Consumer Innovativeness Matter?

Despite the enormous economic importance of consumer durable repeat purchases they have been largely neglected in the diffusion of innovations research. Only a handful of studies have examined the drivers of replacement timing of durables. We extend this research in two important ways. Based on a large survey of 8,077 households, we demonstrate that consumer innovativeness, the most prominent driver of adoption, also influences repeat purchase timing. Further, we investigate for the first time the drivers of additional unit purchase timing

Too close for comfort : the strategic implications of getting close to the customer

While getting close to the customer is widely recommended as an obvious way of serving the customer better, there is no clear demarcation between being close to the customer and being customer-driven. No longer content with the ability to anticipate customer demand, many suppliers are seeking to influence this demand, moving from being reactive to gently proactive. Increasing involvement in the customer's business may help lock in the customer, but it also leads to increasing involvement of the customer in the supplier's business, locking in the supplier as much as the customer. This incremental integration has important strategic implications that firms must examine carefully