Purpose: The concept of an innovation ecosystem, based on the idea of business ecosystem, has increasingly grown in the literature on strategy, innovation, and entrepreneurship. However, not all innovation ecosystems have the same architectural models or internal collaboration, and existing research rarely deconstructs an ecosystem of innovation and examines its structure. The objective of this article is to systematize the discussion about the structure of an innovation ecosystem and offer a foundation for future research.Design/methodology/approach: Using the Web of Science database as the source for the articles, this paper presents a systematic review of the literature on the structure of the innovation ecosystems. The period of analysis spanned from January 1993 to August 2019. Two methods, bibliometric analysis and content analysis, were used to structure the systematic review.Findings: The results of the content analysis showed that the main classifications related to the structure of an innovation ecosystem are the ecosystem life cycle (birth, expansion, leadership, and self-renewal), the classification according to the ecosystem level (macroscopic, medium, and microscopic), and the layered structure (core–periphery structure, triple-layer structure, triple-layer core–periphery structure, and framework 6C). The results also showed that studies in the field are concentrated around a small group of authors, and few studies have discussed the structure of an ecosystem.Research limitations/implications: This study includes only peer-reviewed articles from the Web of Science database.Originality/value: This article contributes to innovation ecosystem theory by exploring the characteristics that influence ecosystem structure. In addition to the theoretical contribution, the triple-layer core–periphery framework and the 6C framework set a benchmark for future research on innovation ecosystems.

This study aims to determine whether the results obtained by applying sentiment analysis to data extracted from social media can reveal the political preferences of citizens to a greater degree of accuracy than traditional public opinion surveys. The researcher collected and analyzed 92,441 tweets related to the Brazilian presidential candidates during the second round of elections in 2014. The analysis results were compared with six voter preference polls conducted by the Datafolha Research Institute. The results showed that sentiment analysis may indicate voter preferences at an accuracy that was only 1% to 8% different than that of traditional research, which has an average accuracy of 81.05%.