Certify or not? An analysis of organic food supply chain with competing suppliers

Customers expect companies to provide clear health-related information for the products they purchase in a big data environment. Organic food is data-enabled with the organic label, but the certification cost discourages small-scale suppliers from certifying their product. This lack of a label means that product that satisfies the organic standard is regarded as conventional product. By considering the trade-off between the profit gained from organic label and additional costs of certification, this paper investigates an organic food supply chain where a leading retailer procures from two suppliers with different brands. Customers care about both the brand-value and quality (more specifically, if food is organic or not) when purchasing the product. We explore the organic certification and wholesale pricing strategies for suppliers, and the supplier selection and retail pricing strategies for the retailer. We find that when two suppliers adopt asymmetric certification strategy, the retailer tends to procure the product with organic label. The supplier without a brand name can compensate with organic certification, which leads to more profits than the branded rival. As the risk of being abandoned by the retailer increases, the supplier without a brand name is more eager than the rival to obtain the organic label. If both suppliers certify the product, however, they will fall into a prisoner’s dilemma under situation with low health utility from organic label and high certification cost

A scoring approach for the assessment of study skills and learning styles

This paper presents the application of a scoring method and algorithm, adapted from the domain of financial risk management, for the computer-based assessment of study skills and learning styles of university students. The goal is to provide a single score that summarizes the overall intensity of a student’s study skills and, in effect, develop a deeper understanding of the relation between learning styles and study skills. The dimensionality reduction obtained through the scoring algorithm also enables comparing the single-dimensional study skill scores of students for various learning styles. The algorithm computes a weight for each study skill to measure its linear contribution to the overall study skill score, also providing a natural ranking of various study skills with respect to impact on total score. Statistical tests have been conducted to measure the differences in scores for various styles in Kolb’s four-region and nine-region models. The results suggest that students with different learning styles can have statistically significant differences in their overall study skill scores. The primary contribution of the study is illustrating how a scoring approach, based on unsupervised machine learning, can enable a deep understanding of learning styles and development of educational strategies. © 2020 by the authors

An Augmented Dataset of Unicorn Companies and their Graph Visualizations

Firstly, the repository includes a tabular dataset of unicorn companies around the world, which is originally obtained from a Kaggle repository with CC0 license (https://www.kaggle.com/datasets/deepcontractor/unicorn-companies-dataset). The source tabular dataset was then methodologically enriched with additional data for missing cells, and represented in a format that can be read into yEd graph analytics software (http://www.yworks.com/products/yed). The steps are described in the paper and the presentation.Secondly, the repository includes multiple yEd-readable *.graphml files, that were constructed using various layout algorithms.Thirdly, the repository includes a zip file that contains the Full Supplement to the paper. This zip file contains the dataset, graphml files, the presentation of the research, zoomable complete graphs as pdf files, a graphml drawing of the applied graph analysis methodology, and a 30+ page Supplement document. The 30+ page Supplement document (within the Full Supplement zip file) contains 50+ references, steps of the methodology (including the steps to produce the data), settings and parameters for the graph layout algorithms, and additional analyses that present a spectrum of different types of insights that can be derived from the data.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Survey Data on European Consumers' Adoption Attitude Towards Electric Vehicles (EVs)

A survey data on European Consumers' adoption attitude towards electric vehicles (EVs), derived from an earlier research and its dataset posted under Mendeley Data under https://data.mendeley.com/datasets/85nz9k5tf5/4. Specifically, the new derived dataset in this repository is derived from “(6)_Transnational_EVsurvey_datavalues.csv” of the source data. The dataset consists of 69 predictor features and one binary target feature, which is Question 16 (Q16), which asks the adoption intention regarding EVs. The dataset is suitable for classification analysis, especially with continuization, as done in the research paper, yet can also be analyzed with association mining, as it contains many binary attributes. The Full Supplement also includes a presentation of the paper, the Orange *.ows model for classification and ranking analysis, and a Supplement document file that contains detailed Literature review.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Social Media Metrics for E-Commerce Companies in the United Arab Emirates (UAE)

A dataset of the top 10,000 e-commerce Companies in the United Arab Emirates (UAE) and their social media metrics, obtained on November 2023. Both the full source dataset (Dataset A.zip) and the augmented dataset (Dataset B.xlsx) are provided.Furthermore, the file "Decision Support Systems.xlsx" in the repository is the developed Decision Support System (DSS). The DSS enables the benchmarking of e-commerce companies with the 10,000 e-commerce companies in the UAE, with respect to social media performance.The Full Supplement zip file contains the full source dataset, the augmented dataset, the DSS, a presentation of the research, the source graphml model for the Methodology workflow, and a Supplement document file that contains detailed Literature Review and additional analysis.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Wind Turbine Accident News (1980-2013)

This data sets includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research paper: Asian, S., Ertek, G., Haksoz, C., Pakter, S., Ulun, S. “Wind Turbine Accidents: A Data Mining Study”. IEEE Systems Journal, vol: PP, issue: 99, Pages: 1 - 12, 2016. DOI: 10.1109/JSYST.2016.2565818. Please refer to the following web page for detailed explanation of the data, together with images: http://ertekprojects.com/wind-turbine-accidents/data

Wind Turbine Accident News (1980-2013)

This data sets includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research paper: Asian, S., Ertek, G., Haksoz, C., Pakter, S. and Ulun, S., 2017. Wind turbine accidents: A data mining study. IEEE Systems Journal, 11(3), pp.1567-1578. As of now, the most extensive data available on the Internet on wind turbines accidents is published by the Caithness Windfarm Information Forum (CWIF), a UK-based grassroots organization opposing wind turbine installations. While the Caithness list is impressive in magnitude, the quality and reliability of the list is open to discussion because of the following reason: * Many of the web links to the news sources are not valid, and some of the accidents appear in multiple lines of the data. In spite of containing much more magnitude of data, the data available in other online sources also exhibit similar deficiencies. So, there are problems when it comes to using the Caithness data or other data in research studies. To this end, we collected data on wind turbine accidents ourselves, also using the data from Caithness and we share our collected data on this page (please click the link at the top of the page to download the data). The data we collected consists of three folders, and a MS Excel file. The folder News.txt contains the accident news, with each news in a separate text file: The folder News.doc contains news, with each news in a separate MS Word file: Finally, the folder News.doc.with.notes contains news, with each news in a separate MS Word file, but with extensive comments, explaining how the database in the MS Excel file was constructed: The MS Excel file News.Database.xlsx contains the structured data created based on the detailed reading of the accident news text: The MS Excel file is the file that was analyzed in our research paper