Food choices are being implicated as a key driver in the rising rates of obesity, as well as associated with other health problems that impact both individuals and society as a whole (Cawley and Meyerhoefer). Calorie listings, whether provided on menus or packaged goods, increase consumer's awareness of the calories in potential food choices and, as a result, make consumers more likely to evaluate a product relative to others in the choice set based on this attribute (e.g. choosing a higher or lower calorie option). In this research, we explore whether the provision of calorie information, when presented in the context of other food options, will result in (1) compromise effects, whereby individuals select intermediate (or middle) options in a choice set, and (2) attraction effects, whereby individuals gravitate towards items that are similar to others but also dominate these items in the choice set. In two experimental studies we find evidence for the compromise effect and the attraction effect. These findings extend work demonstrating that the context in which food decisions occur can impact choice, and builds on existing knowledge regarding the consequences of providing calorie information for food items. Although most work has shown that making calories salient has a beneficial impact on the accuracy of consumer calorie estimates and food choice, in this work we show that the compromise and attraction effects -two well-established findings in decision-making literature -can actually shift consumers to either higher or lower calorie options. This carries important implications for consumers making choices in information-rich choice environments.

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Out-of-stock options, Reason-based choice, Justification of choices,

Purpose Uncertainty in the early development of digital business startups can benefit from data-driven testing of hypotheses. Startups face uncertainty not only in product development, but also over the structure of the business model and the nature of the customer or market to address. The authors conceptualize a new model, the Lean Discovery Process (LDP), which focuses on market-based testing from the early business idea through to fully realized product stages of an innovation. The purpose of this paper is to highlight a methodology to help digital business reduce uncertainty and apply lean principles as early as possible in the development of a business concept. Design/methodology/approach Examining literature in lean startups, lean user experience and lean software development, the authors highlight underlying assumptions of existing lean models. The authors then examine the LDP using the case of raiserve, a social entrepreneurship startup that focuses on the management of cause-based voluntary service. Findings Existing literature focuses on product development against an assumed customer base. Early hypothesis testing can be applied to business concept development to substantially reduce cost and time to market. Research limitations/implications Further investigation of different forms of uncertainty in digital startups can open up opportunities to further apply lean methodologies. A more extensive empirical study to measure the potential impact is warranted. Originality/value The authors conceptualize the minimum viable customer and support early testing with concepts from market research and collective intelligence. The authors demonstrate early opportunities to apply lean principles and rigorous hypothesis testing in an LDP that results in significant reductions in time and expense of product development.