Cognitive computing on unstructured data for customer co-innovation

Abstract

Purpose This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation. Design/methodology/approach The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory. Findings The paper identifies a suite of methodologies for facilitating company co-innovation via engaging with customers and external data with cognitive computing technologies. It helps to expand marketing researchers and practitioners’ understanding of using unstructured data. Research limitations/implications This paper provides a conceptual framework that divides co-innovation process into three stages, ideas generation, ideas integration and ideas evaluation, and maps cognitive computing methodologies and technologies to each stage. This paper makes the theoretical contributions by developing propositions from both customer and firm perspectives. Practical implications This paper can be used for companies to engage consumers and external data for co-innovation activities by strategically select appropriate cognitive computing techniques to analyze unstructured data for better insights. Originality/value Given the lack of systematic discussion regarding what is possible from using cognitive computing to analyze unstructured data for co-innovation. This paper makes first attempt to summarize how unstructured data can be analyzed with cognitive computing techniques. This paper also integrates complexity theory to the framework from a novel perspective.