Food Trend Adoption (Network Thresholds)
This article covers the topic of product trends within the food market. It considers adoption and persistence of food trends. The author observes that not all trends persist (calling it these temporary trends “fads”). Citing a workshop they attended, the author tells of the prediction models made for the adoption of several types of food products that abide by the rules of specific food trends (keto, gluten-free, vegan…).
This article is particularly relevant to this class in terms of adoption thresholds in a network. For instance, a keto and gluten free pizza were predicted to perform well in a model market due to the projected individual adoption of keto and vegan food trend adherence in the market which would cause people to seek out such products. Low-FODMAP pizza was not because the low-FODMAP trend was not predicted to have enough weight to convince consumers to abide by it and seek out products related to it. The author takes a very high-level view of the issue, speaking about trends overall in the real or hypothetical markets. However, beneath this high-level view are the network nodes which show the progression and adoption of food trends in the population. These individual interactions within the population network are critical to being able to say anything at a high level concerning the trends; the high-level models couldn’t be made without the use of the lower-level concepts of individual node “trend catching” which we learned in class.