The challenges of coming up with fresh ideas and realizing profits from them are different for every company. One firm may excel at finding good ideas but may have weak systems for bringing them to market. Another organization may have a terrific process for funding and rolling out new products and services but a shortage of concepts to develop. In this article, Hansen and Birkinshaw caution executives against using the latest and greatest innovation approaches and tools without understanding the unique deficiencies in their companies' innovation systems. They offer a framework for evaluating innovation performance: the innovation value chain. It comprises the three main phases of innovation (idea generation, conversion, and diffusion) as well as the critical activities performed during those phases (looking for ideas inside your unit; looking for them in other units; looking for them externally; selecting ideas; funding them; and promoting and spreading ideas companywide). Using this framework, managers get an end-to-end view of their innovation efforts. They can pinpoint their weakest links and tailor innovation best practices appropriately to strengthen those links. Companies typically succumb to one of three broad "weakest-link" scenarios. They are idea poor, conversion poor, or diffusion poor. The article looks at the ways smart companies - including Intuit, P&G, Sara Lee, Shell, and Siemens- modify the best innovation practices and apply them to address those organizations' individual needs and flaws. The authors warn that adopting the chain-based view of innovation requires new measures of what can be delivered by each link in the chain. The approach also entails new roles for employees "external scouts" and "internal evangelists," for example. Indeed, in their search for new hires, companies should seek out those candidates who can help address particular weaknesses in the innovation value chain.

Researchers can expect to perform analytic actions repeatedly; that this iteration is required is a common observation. Yet, how researchers engage in iteration to progress their theorizing is not articulated. Our analysis provides new insight into what it means to iterate in the service of driving analysis. We examine iteration through the lens of the analytic process of coding in specific research projects. Using a relational definition of coding, we identify the reported coding actions of several studies with rich descriptions of their analytical processes. By doing this, we show that it is useful to understand these coding actions in the context of coding moments that relate to how researchers use the coding actions as their project develops. The moments we identify are making codes, organizing to code, and putting patterns together. To show iteration, we trace the reported coding practices in exemplar articles. These tracings indicate that the reported process is not a fixed or consistent sequence. Rather, iterativity is organized by what is the next needed analytic input required to progress a given situated study.

For all its richness and potential for discovery, qualitative research has been critiqued as too often lacking in scholarly rigor. The authors summarize a systematic approach to new concept development and grounded theory articulation that is designed to bring “qualitative rigor” to the conduct and presentation of inductive research.