Business intelligence is the most developed subject in strategic management research; however the connection to startup performance has not been much conducted in research. This research examines the model for startups in Indonesia, considering that investment for startups in Indonesia is one of the largest in the region of Asia Pacific. The research used SEM-PLS to analyze the relationship between business intelligence, innovation, network learning, and startup performance. The unit of analysis used was the startup registered in the Indonesian startup database, published by the Indonesian Creative Economy Agency (Badan Ekonomi Kreatif). It was 992 startups registered, and there were sent to 885 emails to startups that included emails. Just 31 startups replied to the research questionnaire, resulting in a 3.5 percent response rate. The findings of this study show that while business intelligence does not appear to have an impact on startup success, it does have an impact on network learning. Innovativeness has been shown to have an effect on startup success in Indonesia. The paper helps to explain the position of business intelligence. Of these, only 31 startups filled out the research questionnaire or a response rate of 3.5%. Result of this study find that business intelligence is not proven to have an influence on startup performance, but it does affect network learning. What is proven to have an influence on startup performance in Indonesia is innovativeness. The paper allows a better understanding of the role of business intelligence, network learning, and innovativeness for startups. This improved understanding can help executives or managers of startup in making their decisions. In contrast to the results of previous research on the effect of business intelligence on startup performance, the results obtained from this study do not support this relationship. This research paves the way for the need for confirmation of the effect of business intelligence on startup performance, as well as further understanding of how the mechanisms are going on in it.
Competitive intelligence, also known as business intelligence, market intelligence, customer intelligence, business intelligence & analytics, is the most developed subject in strategic management research (Wheelen, Thomas et al., 2017). But the connection to startup performance has not been much researched (Caseiro & Coelho, 2019; Hoppe et al., 2009).
From the perspective of resource-based view (RBV), knowledge is one of the assets, and even then it becomes the main asset to win the current competition, giving birth to knowledge-based view (KBV). In KBV, the main asset for a company is knowledge in formulating its competitive advantage (Villar et al., 2014). Knowledge enhancement can result from business intelligence, because the processes involved in knowledge production are search and recombination (Colombelli et al., 2013). Aside from business intelligence, the process of acquiring and utilizing knowledge can also be obtained from network learning, related to the condition that startups rely a lot on external sources to obtain their knowledge (Weerawardena et al., 2014). Moreover, related to performance, innovation is one of the keys to improving company performance in a rapidly changing era (R. Calantone et al., 2003; Vnoučková, 2018; Z. Wang & Wang, 2012).
In general, the three variables studied were able to explain well to startup performance, so it is worth further investigation, especially considering that research in the context of startup is still not much done.
The research has several limitations. The first is a small sample size. To overcome this problem, the cooperation with various business hubs, incubators or business accelerators, especially those owned by universities. The second is the characteristics of startups studied, most of them are startups that have never received external funding so that the scale is still small. Therefore, it is suspected that their business intelligence & analytics activities are still not effective. Future studies need to consider researching largesized startups, for example startups that have received at least a title of centaur (have received funding of more than 500 million USD). Third, the model tested is the same model from reference  research. For further research, it can add several other variables such as absorption capacity, entrepreneurial orientation to organizational culture.