This article presents lessons from the rich adoption literature for the nascent research on adaptation. Individuals' adoption choices are affected by profit and risk considerations and by credit and biophysical constraints. New technologies spread gradually, reflecting heterogeneity among potential adopters, processes of learning and technological improvement, and policies and institutions. Adaptation is the response of economic agents and societies to major shocks. We distinguish between reactive and proactive adaptation. The latter is important in the context of climate change and consists of mitigation, reassessment, and innovation that aim to affect the timing and location of shocks. Adaptation strategies also include adoption of innovation and technology transfer across locations, insurance and international trade, and migration and invasions. Recent research emphasizes multidisciplinary collaborations; historical analysis; and the roles of returns to scale of key technologies, social networks, behavioral economics, path dependency, and ex ante adjustment in explaining patterns of adoption and adaptation.
Coronavirus disease 2019 and related lockdown policies in 2020 shocked food industry firms’ supply chains in developing regions. Firms “pivoted” to e‐commerce to reach consumers and e‐procurement to reach processors and farmers. “Delivery intermediaries” copivoted with food firms to help them deliver and procure. This was crucial to the ability of the food firms to pivot. The pandemic was a “crucible” that induced this set of fast‐tracking innovations, accelerating the diffusion of e‐commerce and delivery intermediaries, and enabling food industry firms to redesign, at least temporarily, and perhaps for the long term, their supply chains to be more resilient, and to weather the pandemic, supply consumers, and contribute to food security. We present a theoretical model to explain these firm strategies, and then apply the framework to classify firms’ practical strategies. We focus on cases in Asia and Latin America. Enabling policy and infrastructural conditions allowed firms to pivot and copivot fluidly.
Firms use samples to increase the sales of almost all consumable goods, including food, health, and cleaning products. Despite its importance, sampling remains one of the most under-researched areas. There are no theoretical quantitative models of sampling behavior other than the pioneering work of Jain et al. (1995), who modeled sampling as an important factor in the diffusion of new products. In this paper we characterize sampling as having two effects. The first is the change in the probability of a consumer purchasing a product immediately after having sampled the product. The second is an increase in the consumer's cumulative goodwill formation, which results from sampling the product. This distinction differentiates our model from other models of goodwill, in which firm sales are only a function of the existing goodwill level. We determine the optimal dynamic sampling effort of a firm and examine the factors that affect the sampling decision. We find that although the sampling effort will decline over a product's life cycle, it may continue in mature products. Another finding is that when we have a positive change in the factors that increase sampling productivity, steady-state goodwill stock and sales will increase, but equilibrium sampling can either increase or decrease. The change in the sampling level is indeterminate because, while increased sampling productivity means that firms have incentives to increase sampling, the increase in the equilibrium goodwill level indirectly reduces the marginal productivity of sampling, thus reducing the incentives to sample. We discuss managerial implications, and how the model can be used to address various circumstances.Diffusion, Learning, Product Sampling, Goodwill, Forgetting, Experimenting
One of the major ways that manufacturers decrease the uncertainty that is involved in making a choice between competing brands of experience goods is through demonstrations of their new products. The authors investigate the issue of the length of a demonstration and, in particular, provide answers to the following three questions: What is the optimal length of a demonstration? Does the optimal length vary between different products? Does the optimal length vary between different consumers? They analyze 225 cases from the motor vehicle industry and 46 cases from the computer industry with regard to number and length of demonstrations offered and relate the findings to their theoretical model. The empirical results yield strong support for their theory. They also find that most firms offered a longer demonstration than needed and failed to use an appropriate segmentation technique for optimizing the demonstration with respect to different consumer groups.
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