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 = 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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