Purpose This research aimed to understand and compare the behavior of Brazilian and North American coffee consumers to obtain insights and identify potential trends in the domestic and international markets. Design/methodology/approach The “X” platform was used as the source of information; posts containing the keywords “café” (Brazil) or “coffee” (USA) were retrieved, and automation and manual content analysis were performed. The regional diversity among each country was also verified: five regions (Southeast, South, Midwest, Northeast and North) were considered for Brazil and four (West, Midwest, Northeast and South) for the USA. Findings Five categories of posts were identified: “moment and side dish,” “brewing,” “quality,” “health and disposition” and “socialization and location.” “Moment and side dishes” was the category that was highly mentioned in both countries, with a stronger association with Brazil (40%). There is interest in “brewing” (around 18%), but the citation frequency did not differ between the two countries. The gathered posts also revealed different behaviors: Brazilians were more likely to discuss coffee “quality” (16%) and “health and disposition” (15%), and North Americans more frequently mentioned “socialization and location” (24%). The findings suggest Brazil has higher internal diversity and is experiencing the third coffee wave. In the USA, the behavior appears more homogeneous and well-established in the third wave, potentially transitioning toward the fourth coffee wave. Practical implications Findings showed the distinct coffee consumption cultures in Brazil and the USA, providing valuable insights that can drive strategies in the coffee chain to market from each country and capitalize on emerging trends. Originality/value Information on coffee consumption patterns using “X” has been relatively unexplored, and no previous research has used this data source to analyze potential trends and compare the behavior of the two major world coffee consumers (Brazil and the USA). An original approach was also proposed for data treatment: a combination of automation and manual analysis to ensure the accuracy and reliability of the conclusions.
Portela et al. (Wed,) studied this question.