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Corvallis, OR, United States

Chung S.-J.,Seoul Womens University | McDaniel M.R.,Oregon State University | Lundahl D.S.,InsightsNow Inc
Food Science and Biotechnology | Year: 2010

As the global market expands, it is critical that researchers understand how cultural factors influence the expectations and liking for a food product. The objectives of this study were to identify the optimal sweetness level for a sports-drink for consumers originating from different countries and to investigate the factors affecting the optimal sweetness level. In the first study, respondents (n=372) originated from 5 Asian countries and US tasted and evaluated the sports-drink at 4 sweetener levels. Consumers (n=256) from 3 Asian countries and US participated in the second study. Consumers evaluated the concept and sensory expectations for the 'lemon-lime flavored' sports-drink and other beverages. The optimal sweetness level for the sports-drink was lower for Americans than Asians. The familiarity to the product was a key factor affecting the optimal sweetener level. The results also suggested that information can differently influence the product acceptance depending on one's familiarity with the product. © KoSFoST and Springer 2010. Source

Plaehn D.,InsightsNow Inc
Food Quality and Preference | Year: 2012

A method of analyzing check-all-that-apply (CATA) data similar to penalty analysis is given. The approach gives a mean drop/increase relative to a "reference" variable such as overall liking. The method is demonstrated on "emotion" responses for five citrus flavored sodas. Six significance testing methods are compared. The effects of sample size, a CATA proportion threshold and covariance thresholds on significance are investigated in an appendix via simulations. © 2011 Elsevier Ltd. Source

Plaehn D.,InsightsNow Inc
Food Quality and Preference | Year: 2013

Traditional penalty analysis (TPA) is an application meant to help product developers better understand product strengths and weaknesses. TPA uses just-about-right (JAR) attributes in conjunction with a " reference" variable such as overall liking to assign " penalties" to a product for being " too low/weak" or " too high/strong" in some aspect. This article proposes an alternative method, Penalty Allocation Map (PAM), of estimating product penalties that allegedly gives a more " realistic" assessment of penalty balance and magnitude relative to TPA. In particular, generalized additive models are created for each individual using all products and, typically, multiple JAR attributes. These models are then combined after possibly culling " bad" models to estimate aggregate penalties. Monte Carlo cross-validation and so-called covariance penalties are used for model appraisal. Model stats are encouraging. An alternate approach is detailed in an appendix. © 2012 Elsevier Ltd. Source

InsightsNow Inc. | Date: 2003-12-09

Computer software for use in statistical analysis and user manuals sold therewith. Information management services, namely, statistical analysis. Educational services, namely, providing courses pertaining to information management.

InsightsNow Inc. | Date: 2011-01-05

computer software for creating and accessing searchable databases of information and data.

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