The Not-Invented-Here (NIH) syndrome describes a negative attitude towards knowledge (ideas, technologies) derived from an external source. Despite being one of the most cited constructs in the literature on knowledge transfer, previous research has not provided a clear understanding of the antecedents, underlying attitudes, and behavioral consequences of NIH. The objective of our paper is to open the black box of NIH by providing an in-depth analysis of this frequently mentioned, yet rarely understood phenomenon. Building on recent research in psychology and an extensive review of the management literature on NIH, we first develop a framework of different sources classifying knowledge as "external" which might trigger a rejection of such inputs.Differentiating various functions of an attitude, we then identify possible trajectories linking NIH with individual behavior and decision-making. We apply this understanding to develop an extensive agenda for future research.

Service innovation is intertwined with service design, and knowledge from both fields should be integrated to advance theoretical and normative insights. However, studies bridging service innovation and service design are in their infancy. This is because the body of service innovation and service design research is large and heterogeneous, which makes it difficult, if not impossible, for any human to read and understand its entire content and to delineate appropriate guidelines on how to broaden the scope of either field. Our work addresses this challenge by presenting the first application of topic modeling, a type of machine learning, to review and analyze currently available service innovation and service design research (n ¼ 641 articles with 10,543 pages of written text or 4,119,747 words). We provide an empirical contribution to service research by identifying and analyzing 69 distinct research topics in the published text corpus, a theoretical contribution by delineating an extensive research agenda consisting of four research directions and 12 operationalizable guidelines to facilitate cross-fertilization between the two fields, and a methodological contribution by introducing and demonstrating the applicability of topic modeling and machine learning as a novel type of big data analytics to our discipline.

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This paper adds to the emerging literature stream advocating a contingency view on open innovation. Drawing on the relational view of the firm, this study sheds light on the complex interplay among collaboration partner types (market‐ and science‐focused innovation partners), governance modes (informal, self‐enforcing and formal, contractual collaboration governance), and internal research and development(R&D). More specifically, it is proposed that the use of governance modes tailored to both the characteristics of each innovation partner type and the specific innovation objectives pursued by the focal firm (incremental and radical new product development) can increase the payoff from innovation collaboration. Moreover, appropriate collaboration governance is expected to reduce the focal firm's vulnerability to possible negative side effects often assumed to be associated with the simultaneous pursuit of external collaboration and internal R&D, among which most notably the not‐invented‐here (NIH) syndrome. Cross‐industry evidence from 2502 German firms underlines the critical role of collaboration governance—a contingency factor that is at the heart of the relational view, yet has remained surprisingly absent from the open innovation debate so far.

During the three decades since its inception in 1984, the JPIM has shaped the evolution of innovation research as a scientific field. It helped create a topic landscape that is not only more diverse and rich in insights, but also more complex and fragmented in structure than ever before. We seek to map this landscape and identify salient development trajectories over time. In contrast to prior citation-based studies covering the first two decades of JPIM research, we benefit from recent advances in natural language processing and rely on a topic modeling algorithm to extract 57 distinct topics and the corresponding most common words, terms, and phrases from the entire full-text corpus of 1008 JPIM articles published between 1984 and 2013. Estimating the development trajectory of each topic based on yearly publication counts in JPIM allows us to identify "hot," "cold," "revival," "evergreen," and "wall-flower" topics. We map these topics onto the Product Development and Management Association (PDMA) Body of Knowledge categories and discover that these categories differ significantly not only in terms of their internal topic diversity and relative prevalence, but also-and arguably more importantly-in terms of their publication and citation trajectories over time. For instance, the PDMA category "Codevelopment and Alliances" exhibits only moderate topic diversity (7 out of 57 topics) and prevalence in JPIM (161 out of 1008 articles). That said, it is among the most dynamic categories featuring two evergreen topic ("Users and Innovation" and "Tools and Systems for Technology Transfer") and three hot topics ("Open Innovation," "Alliances and Cooperation," and "Networks and Network Structure") as well as a sharply growing annual number of citations received. Our findings are likely to be of interest to all those who are keen to (re)discover JPIM's topic landscape in search of hidden structures and development trajectories. Practitioners Points• We provide a map of the topic landscape in JPIM that enables practitioners and researchers to navigate the field more intuitively. • Using this map, practitioners can also identify experts in specific areas of innovation management. • We identify five articles per innovation management topic that are most strongly associated with the respective topic to provide a fast and efficient way to dive into a topic.• We show how to apply text mining methods to structure large collections of text documents and analyze their content automatically.