Abstract
This article proposes and develops the concept of technological complexity (TC) as a useful and simple tool for grouping key attributes that give added value to a product. In addition, it reports an empirical application of this concept to two different food products (cured ham and cured sausage). The authors used a mixed-effects multi-nomial logistic regression model and show that in the cured pork product agribusiness, a low frequency of consumption favours the acceptance of high TC products. The results also confirm that marketing high TC products in stores with a large assortment decreases the chances of success for agribusiness companies that produce cured pork food products. These finding can be used by the managers for designing complementary attributes that improve their product portfolio. Besides, advertising expenditures associated with introducing new products could be reduced if companies strengthened their presence in specialty stores.
Highlights
Managers must make decisions with high levels of uncertainty, especially in increasingly competitive environments in which product differentiation represents a common strategy
We ask ourselves; how grouping key attributes that give value added to a product in a simple way? how some consumer features can influence on the decision to buy high value-added products? We propose focusing on a product’s technological complexity (TC) as a means to study the factors that affect consumer preferences for products with high TC
In terms of explaining a preference for more complexity, more household members between 7 and 17 years of age reduces the chances that the household chooses a product with intermediate TC, but we found only moder
Summary
Managers must make decisions with high levels of uncertainty, especially in increasingly competitive environments in which product differentiation represents a common strategy. Such differentiation enables companies to market higher value-added products that provide economic benefits and improve the welfare of consumers of these products. SKU choice models have emerged with increasing frequency in marketing research (Bell et al 2005; Ho, Chong 2003; Inman et al 2008; Singh et al 2005), with theoretical foundations in economics (Lancaster 1975, 1991) and psychology (Simon 1956). From the results of these analyses, we derive some conclusions and managerial implications
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