This article addresses gaps about abductive reasoning—widely considered key to design-thinking but rarely detailed in design-thinking and innovation literatures—by examining two types of abduction; identifying impediments to it; and proposing the promise of Artificial Intelligence (AI) to mitigate those impediments. Contrasting with the deductive and inductive approaches that dominant problem-solving, we distinguish and elucidate explanatory abduction and innovative abduction in problem finding, where the problem to solve is itself uncertain. We argue these are appropriate for generating innovative problem-finding ideas. Focusing thenceforth on problem finding alone, the heart of the article proposes a comprehensive conceptual model of innovative idea generation in that more ambiguous, complex, under-researched but exciting problem space. The model details three chief stages: (1) problem search frame, combining leadership’s vision and innovators’ knowledge; (2) generating abductive hypotheses from often-surprising observations and their synthesis into insights; and (3) evaluating abductive hypotheses, against the novel quality criteria of plausibility and relevance. Among cognitive impediments we show how the downsides of mental model, limited cognitive load and exemplifying heuristics and cognitive biases, such as confirmation bias, can hinder each stage. Conversely, we examine how support from AI can help human innovators improve the quantity, speed, and quality of their innovative idea generation.
|Number of pages||25|
|Journal||Journal of Product Innovation Management|
|Early online date||28 Sep 2021|
|Publication status||Published - Nov 2021|
- artificial intelligence
- innovative idea generation
- problem finding