Systematic problem-specification in innovation science using language
Authors: Sheth Ananya , Sinfield Joseph Victor 
Published: January 2021

Purpose

Problem specification is a key front-end step in the innovation process. This paper aims to introduce ‘purpose-context’ – a conceptual framework to systematically explore problem-specification across mapped contexts. The framework’s logic is operationalized by the inherent structure of language – its syntax/grammar, which enables the systematic exploration of problem-specification. The method showcases two approaches to structurally explore the vast textual databases available to us today for problem-specification in innovation science, thereby furthering the pursuit of innovation through its foundational elements.

Design/methodology/approach

The conceptualization of the purpose-context framework was guided by logic and the scholarship of integration applied to bodies of work including innovation, design and linguistics. Further, the key elements of the conceptual framework were unpacked and structured using the syntax of language. Two approaches to operationalize the method were developed to illustrate the systematicity of the process. The construct was then validated by using it to systematically specify problems in the technical context of Raman spectroscopy and in the socio-technical context of international development. Overall, this paper is a work of relational scholarship of integration that bridges academic-practitioner gaps.

Findings

The purpose-context framework is well-suited for application in the innovation process with applicability across several abstraction levels. One key contribution is the recognition that a broader problem-specification exercise covering one-one, one-many, many-one, many-many problem-context mappings expands the range of potential solutions (innovations) to address the problem-space. Additionally, the work finds that it is possible to provide structure to the cognitive elements of the innovation process by drawing inspiration from the structure inherent in other cognitive processes such as language (e.g., parts-of-speech, phrase composition). Drawing from language is particularly appropriate as language mediates communication in any collective pursuit of the innovation process and furthermore because a large amount of information exists in textual form. Finally, this paper finds that there is merit in approaching innovation science from its foundational elements – i.e. data, information and knowledge.

Research limitations/implications

While the purpose-context framework is broadly applicable, the methodical approach to provide structure to the front-end cognitive process is ‘one’ fruitful approach. We suspect other approaches exist.

Practical implications

The purpose-context framework is simple in its framing yet provides innovators, scholars and thought leaders, the ability to specify the problem space with greater coverage and precision. Further, in the solution-space, it provides them the ability to choose the breadth of solution scope (e.g. targeted solution addressing a single problem, targeted solution addressing a set of problems, the combination of solutions addressing a single problem and combination of solutions addressing a combination of problems). In addition, by pairing the creative front-end innovation process with machine power, this study provides a formal method to scale-up the coverage of creativity (and potentially that of solutions to those problems) and reduces the chances of missed/blind-spots in problem-specification. Finally, evaluating purpose-contexts leads to ‘capability-contexts’ – a capability-oriented viewpoint informing capability development decisions such as the focus of R&D programs and related resource allocation decisions.

Originality/value

The paper uses logic to connect multiple bodies of research with a goal to provide systematicity to problem-specification – problem-specification, which is an under-addressed part of the innovation process. The use of data to systematically explore problem-space lends it systematicity (repeatability and measurability) and is therefore, valuable to innovation science. The proof-of-concept demonstrates the conversion of concept into a method for practical application.