Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

A Contingent Perspective of Open Innovation in New Product Development Projects

Studies of open innovation are predominantly concerned with firm-level strategy development. The result is that the literature has largely ignored the multiple contingencies that influence the implementation of an open strategy at the level of the NPD project. In this paper, we develop a conceptual framework of inbound open innovation at the NPD project level to assess factors that help determine the degree of openness along three dimensions. We argue that the margin of managerial action is not only constrained to the decision to open up the NPD project to a wide range of different types of external parties (breadth dimension), but that it is equally important to consider the depth of the relationships with different types of external parties (depth dimension) and the balance between the development of new and longstanding relationships (ambidexterity dimension). The calibration of these three dimensions represents the levers when managing an inbound open innovation strategy during an NPD project. Finally, we identify a range of contingencies, which potentially have a bearing on the appropriate calibration of the breadth, depth and ambidexterity dimensions of an open innovation strategy. We argue that appropriate calibration of the three dimensions of inbound open innovation is determined by the type of innovation (radical versus incremental), product complexity (discrete versus complex) and the appropriability regime (tight versus weak)