Idea Generation, Creativity, and Prototypicality

Abstract

We explore the use of big data tools to shed new light on the idea generation process, automatically "read" ideas to identify promising ones, and help people be more creative. The literature suggests that creativity results from the optimal balance between novelty and familiarity, which can be measured based on the combinations of words in an idea. We build semantic networks where nodes represent word stems in a particular idea generation topic, and edge weights capture the degree of novelty versus familiarity of word stem combinations (i.e., the weight of an edge that connects two word stems measures their scaled co-occurrence in the relevant language). Each idea contains a set of word stems, which form a semantic subnetwork. The edge weight distribution in that subnetwork reflects how the idea balances novelty with familiarity. Based on the "beauty in averageness" effect, we hypothesize that ideas with semantic subnetworks that have a more prototypical edge weight distribution are judged as more creative. We show this effect in eight studies involving over 4,000 ideas across multiple domains. Practically, we demonstrate how our research can be used to automatically identify promising ideas and recommend words to users on the fly to help them improve their ideas. History: Fred Feinberg served as the senior editor and Gerard Tellis served as associate editor for this article.

Topic Modeling in Management Research: Rendering New Theory from Textual Data

“…A second topic in this subject area that is closely related to explaining shifts in patent citations is the use of texts more generally as a means to measure innovation and creativity. Toubia and Netzer (2016) proposed that creative and novel ideas should have some type of structural signature that can be found in cognitive representations. Drawing on literature related to cognitive creative processes in science (i.e., Rothenberg, 2014;Uzzi et al, 2013), they explored this proposition as an optimal balance of familiarity and novelty.…”

“…Drawing on literature related to cognitive creative processes in science (i.e., Rothenberg, 2014;Uzzi et al, 2013), they explored this proposition as an optimal balance of familiarity and novelty. Toubia and Netzer (2016) primarily adopted a semantic network analysis approach to explore the structural argument of familiarity, showing how co-occurrences of word stems can constitute a common substructure, what they called a "structural prototype." In turn, they argued that creativity is a function of a semantic network structure with a core substructure corresponding to a familiar prototype, and novelty dimensions reflected as sufficient semantic distance in the overall structure.…”

“…They used LDA as a robustness check to show that creativity was not simply a function of semantic distance. Interestingly, both Toubia and Netzer (2016) and Kaplan and Vakili (2015) featured in this topic: in different domains, the authors leveraged topic modeling techniques to theorize how to identify innovation in documents through the direct measurement of cognitive representations.…”

“…The process of generating provisional knowledge by iterating between selecting and trimming raw textual data, applying algorithms and fit criteria (#25: patent, technology, knowledge, technological, citation, identify, path, base, cite, highly) Kaplan & Vakili 2015Provides a means to disentangle the cognitive content of novel innovations from the outcomes associated with innovations Measuring topics to understand innovation (#24: idea, weight, distribution, edge, measure, base, node, combination, average, semantic) Toubia & Netzer (2016) Provides a means of empirically measuring different theoretical dimensions of creativity to develop new understandings of idea generation Using topic models to understand managerial cognition through technology problems, search and attention (#1: problem, search, structure, attention, concept, process, exist, unit, create, general) Wilson & Joseph 2015Provides a way for researchers to understand the dynamics of managerial attention relative to background knowledge.…”

Collective firm-internal online idea development

“…Digital front-end innovation and ideation research tends to focus either on the generation of novel ideas (e.g. Björk, 2012;Christensen et al, 2017;Toubia and Netzer, 2016;Zhu et al, 2019) or the selection/evaluation of ideas (e.g. Beretta, 2019;Chan et al, 2018;Magnusson et al, 2016), while the intermediate process where ideas are nurtured is often neglected (Beretta, 2019;Hoornaert et al, 2017).…”