Context factors have lasting impacts on people's sentiments. Exploring impacts that different contexts have on sentiments can be crucial for managing the increasing number of communications companies nowadays maintain with customers via social media channels. To help companies prevent impacts of negative word of mouth, we provide an overview about sentiment-influential contexts for tweets as one kind of social media texts previously discussed within the literature. We collected an overall amount of 358.923.210 tweets and performed analysis to uncover the effects of continents, mobile devices' operating systems (OS) and the combination of both on sentiments expressed within tweets. Our results show remarkable differences for tweets originating from North America and Apple devices, which turned out to be the tweets with the lowest sentiments compared to the other continents and the mobile OS Android.

Lead users are often established in an organizational innovation process to attenuate the difficulties a company faces, such as high costs or the obscurity of customers’ needs. But to benefit from these lead users a major challenge is to characterize and identify them especially in the fast-moving world of social media. Therefore, we aim to design a tool to identify lead users automatically for the two innovation phases (“Idea generation” and “Development”) by combining different approaches such as social network analysis, topic modeling and sentiment analysis. Thus, we consulted the design science approach and applied our artifact to 11,481 contributions of an online digital platform. The technical realization of the six different characteristics and their respective weighting according to the different phases of the innovation process resulted in different lead users and showed the necessity of distinguishing between them. Our results were evaluated and confirmed by the identified lead users and an expert. Hence, our investigation contributes to both practice and theory (kernel theories and design theory) alike.

Incorporating product trends into innovation processes is imperative for companies to meet customers' expectations and to stay competitive in fiercely opposing markets. Currently, aspect-based sentiment analysis has proven an effective approach for investigating and tracking towards products and corresponding features from social media. However, existing trend analysis tools on the market that offer aspect-based sentiment analysis capabilities, do not meet the requirements regarding the use case Product Development. Therefore, based on these requirements, we implemented an automated artifact by following the design science research. We applied our tool to real-world social media data (37,638 Yelp reviews) from one major fast-food restaurant in the US, and thereby demonstrated that our tool is capable of identifying remarkable and fine-grained product trends.