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Human Response to a question with multiple alternatives can be imprecise or fuzzy for various reasons. Part of this fuzziness can be attributed to the lack of sincerity and clarity in the thought process of the respondent. A formal measure of this aspect of fuzziness is formulated in this work on the basis of consistency of the respondent's response to the same question repeated in multiple scales. Discarding haphazard and insincere respondents can improve the quality of data resulting in more efficient survey analysis. This may be achieved in the framework of statistical testing of hypothesis using the probability distribution of the proposed fuzziness measure. Similarly, an attribute can be fuzzy, and it may generate inconsistent response from many respondents. The application of the proposed methodology extends to identifying such fuzzy or unclear attributes. The paper also proposes an algorithm for screening inconsistent respondents and fuzzy attributes simultaneously.

Kantar Health in Healthcare.

Saphetor: Leader of the Revolution in Clinical Diagnostics.

Healthcare Advancement: “Healthcare is a National Right, Not a Privilege”.

Data Showed Treatment Makes mCRPC Patients Live Longer.

This study focuses on high-tech startups in the Norwegian context and investigates the use of data and/or big data to support validation at the early stage of development. Early-stage startups often fail due to lack of validation and incur loss to all the stakeholders involved in the process. Early validation through a lean or agile approach can help these young companies to manoeuvre through a turbulent external environment. The results from the study show that big data and/or data can act as a support at this stage but is not necessarily the sole solution to the problem. There are various barriers that need to be addressed for successful data-based validation. Based on multi-case study research method, this study proposes an early validation user guide (EVU) to overcome these barriers and make data adoption easier. The EVU can provide startups support to show how and when to use data and/or big data depending on the market context. The study thus contributes to the current body of knowledge of innovation and entrepreneurship concerning validation for early stage startups and can also guide practitioners in validating their startup ideas.

This study considers the fruit juice market and investigates its preference and competitive structure by examining empirical survey data from Greece. Data on eight hundred individuals are collected through personal interviews and subsequently analyzed through multivariate methods in order to explore main product choice criteria and consumption patterns. The data reveal considerable category penetration and high individual consumption rates. Multi-criteria and multi-dimensional scaling analysis provides an insight into the structure of consumer preferences and brand competition in the examined market. The primary structure of the market lies in price and product form differences, which are also associated with specific characteristics of the examined brands.

In the modeling of market research data the so-called Gamma-Poisson model is very popular. The model fits the number of purchases of an individual product made by a random consumer. The model presumes that the number of purchases made by random households, in any time interval, follows the negative binomial distribution. The fitting of the Gamma-Poisson model requires the estimation of the mean m and shape parameter k of the negative binomial distribution. Little is known about the optimal estimation of parameters of the Gamma-Poisson model. The primary aim of this paper is to investigate the efficient estimation of these parameters.