Sustainable business models and eco-innovation: A life cycle assessment
مدل های تجاری پایدار و نوآوری در محیط زیست: ارزیابی چرخه زندگی-2020
Eco-innovative business models are prominent elements of the development of sustainable production and consumption systems in organizations of all sizes, especially for small and medium enterprises, where a key challenge is to direct eco-innovation strategies toward the goals of their business model. Therefore, using product life cycle assessment, this research analyzed the alignment between the sus- tainable business model and the eco-innovative strategies of a Brazilian company in the veterinary homeopathy pharmaceutical industry. Disregarding the controversial discussion about homeopathy, this activity has shown signiﬁcant growth, having a representative economic importance in the animal protein production chain. The research adopted a case study method for one of Brazil’s leading com- panies in this activity. Data were collected through interviews, process analysis and company records. The results were built through quantitative and qualitative techniques that demonstrated that the eco- innovations developed by the company are directed toward the creation of new production methods and, above all, new products. The management model was framed in the “adopt a management role” archetype, in accordance with the literature. It was found that eco-innovation strategies are important for the development of the company’s business model and that this alignment is possible only when there is a management system and investments in the company’s ability to eco-innovate in product, process and organizational structure.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Agribusiness | Sustainable development indicators | Organizational innovation | Veterinary medicine
Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK
اعتبارسنجی تکنیک های استخراج متن و تجزیه و تحلیل محتوا با استفاده از داده های جمع آوری شده از سیستم های نرم افزار مدیریت دامپزشکی در انگلستان-2019
Electronic patient records from practice management software systems have been used extensively in medicine for the investigation of clinical problems leading to the creation of decision support frameworks. To date, technologies that have been utilised for this purpose such as text mining and content analysis have not been employed significantly in veterinary medicine. The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be able to validate this approach for future employment across a number of practices for the purposes of practice based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR) were employed to identify specific occurrences of data across the records. Two different datasets were interrogated, a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning veterinary clinic dataset that had been extracted from one veterinary practice. Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting in a sensitivity of between 85.3–100%, a specificity of between 99.1–99.7%, a positive predictive value between 93.5–95.8% and a negative predictive value between 97.7–100%. The KR search, based on machine learning, was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis. This study is the first to demonstrate the application of content analysis and text mining software for validation purposes across a number of different datasets for the purpose of search and recall of specific information across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological research for the purposes of large scale analysis for practice-based research. Extension of this work to investigate more complex diseases across larger populations is required to fully explore the use of this approach in veterinary practice.
Keywords: Text mining | Content analysis | Veterinary practice | Practice based research