The sustainable development of human activities is directly related to the protection of the environment by lowering the anthropogenic stress. Pharmaceuticals – due to their growing consumption (use in medicine, veterinary, animal production, cosmetics) and their incomplete removal in wastewater treatment plants – are classiﬁed as a group of new and rapidly emerging pollutants which have been proven to have a negative impact onto water organisms. In order to ensure the proper protection of human health and the environment there is an urgent necessity of determining pharmaceuticals in clinical, cosmetic, food and environmental samples. Gas (GC) and high performance liquid chromatography (HPLC) are valuable techniques for such determination, especially when they are coupled with mass spectrometry (GC-MS; LC-MS) or tandem mass spectrometry (GC-MS/MS; LC-MS/MS). The purpose of this paper is to present an analysis of sustainability features of analytical techniques in the light of necessity to determine trace amounts of pharmaceuticals in the aforementioned different matrices. Using the Delphi method we performed an analysis of the key sources of the competitive advantages of the application of GC and GC-MS techniques for determining the pharmaceutical residue in clinical, cosmetic, food and environmental samples – compared to techniques based on HPLC or LC-MS. The analysis covered the following areas: (i) the features of the technique, (ii) the price, and (iii) the applicability in various sectors of economy.
The main focus of this paper is to propose a method for prioritizing knowledge and technology factor of firms towards sustainable competitive advantage. The data has been gathered and analyzed from two high tech start-ups in which technology and knowledge play major role in company’s success. The analytical hierarchy model (AHP) is used to determine competitive priorities of the firms. Then knowledge and technology part of sense and respond questionnaire is used to calculate the variability coefficient i.e. the uncertainty caused by technology and knowledge factor. The proposed model is tested in terms of two start-ups. Based on the initial calculation of uncertainties, some improvement plan is proposed and the method is applied again to see if the uncertainty of knowledge and technology decreases. In both cases, the proposed model helped to have a clear and precise improvement plan and led in reduction of uncertainty.