Multidisciplinary evidence suggests that people often make evaluative judgments by monitoring their feelings toward the target. This article examines, in the context of moderately complex and consciously accessible stimuli, the judgmental properties of consciously monitored feelings. Results from four studies show that, compared to cold, reason-based assessments of the target, the conscious monitoring of feelings provides judgmental responses that are (a) potentially faster, (b) more stable and consistent across individuals, and importantly (c) more predictive of the number and valence of people's thoughts. These findings help explain why the monitoring of feelings is an often diagnostic pathway to evaluation in judgment and decision making.

This article considers the contribution that qualitative software can make to 'opening up' Open-Ended Question ('OEQ') data from surveys. While integrating OEQ data with the analysis of fixed response items is a challenge, it is also an endeavour for which qualitative software offers considerable support. For survey researchers who wish to derive more analytic value from OEQ data, qualitative software can be a useful resource. We profile the systematic use of qualitative software for such purposes, and the procedures and practical considerations involved. The discussion is illustrated by examples derived from a survey dataset relating to environmental risk in the UK. Keywords OPEN ENDED QUESTIONS SURVEY METHODS QUALITATIVE SOFTWARE1

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Product developers understand the difficulties of trying to hit a moving target from atop a runaway train. Competitors come and go, technological change occurs at an ever‐increasing rate, customer wants and needs are constantly shifting, and a product's life cycle may be shorter than its development time. We can't meet these challenges with a methodical, step‐by‐step approach to product development. In such a fast‐paced environment, product development must be transformed into a continuous, iterative, learning process focused on customer value. G. David Hughes and Don C. Chafin describe one means for making this transformation: the value proposition process (VPP). The objectives of this development approach are continuous learning, identifying the certainty of knowledge used for decision‐making, building consensus, and focusing on adding value. The VPP consists of a framework of continuous planning cycles, called the value proposition cycle (VPC), and an integrated screening methodology, called the value proposition readiness assessment (VPRA). The VPC comprises four iterative loops, addressing the following activities: Capturing the market value of the proposition (Does the customer care?); Developing the business value (Do we care?); Delivering a winning solution (Can we beat the competition?); and, Applying project and process planning (Can we do it?). Somewhat akin to stage‐gate methods (but with the added dimension of continuous cycling), the VPRA involves screens along each loop in the VPC. This screening methodology summarizes the company's critical success factors, allowing the project team to assess the success potential of a new product idea. Each screen involves a structured set of questions. For each question, respondents provide three measures: their evaluation of that success factor's favorability to the company's position, the certainty of their evaluation, and their estimate of the relative importance of that success factor. Rather than building a model that computes a weighted‐sum score for predicting the success of a new idea, the VPRA serves as a consensus facilitator, allowing team members to see how closely they agree. The certainty measures help the team identify knowledge gaps, and the importance measures help the team set priorities for successive iterations of the VPRA.