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Ensuring data quality is central to the digital transformation in industry. Business processes such as predictive maintenance or condition monitoring can be implemented or improved based on the available data. In order to guarantee high data quality, a single data validation system are usually used to validate the production data for further use. However, using a single system allows an attacker only to perform one successful attack to corrupt the whole system. We present a new approach in which a data validation system using multiple different validators minimizes the probability of success for the attacker. The validators are arranged in clusters based on their properties. For a validation process, a challenge is given that specifies which validators should perform the current validation. Validation results from other validators are dropped. This ensures that even for more than half of the validators being corrupted anomalies can be detected during the validation process.
With information on corporate ethical behavior now more accessible than ever, consumers have become increasingly socially and environmentally aware, which has translated into a growing demand for ethically made products. For ethically minded consumers, certification labels such as fair trade or organic are simple indicators of whether a product meets their ethical standards. For companies that wish to become certified, which is a lengthy and sometimes expensive process, there are several pertinent questions to consider, such as how much customers really value particular labels and whether multiple labels yield significant added competitive benefits. One should also consider how best to collect this information, because simply asking customers via surveys isn’t guaranteed to return results that actually reflect or predict real-life behavior (Carrington et al. 2010). For this paper, we collected information on consumers’ willingness to pay for products with the organic and fair trade labels (both individually and in combination) using two different methods: a traditional questionnaire and a reaction-time based electronic research method designed to reveal subconscious value perceptions. The factors involved were product type and number of labels. We found little evidence to suggest that additional ethical labels significantly increase willingness to pay.