The amount of health-related data is increasing. Recent advancements in Big Data analytics and related statistical and computational tools raised interest in data-driven healthcare services. Data-driven healthcare (DHC) service supports healthcare by providing data and analytics to create value for the customer. DHC has different business characteristics from conventional healthcare services in terms of scope, target, and player covered by the service. Despite the increasing importance of DHC, there is still limited research on DHC business models. As a result, it’s hard to know in detail how to use valuable health-related data. For companies, there is a need for definite comprehension of the properties of DHC services to develop an appropriate strategy and thus exploit new opportunities. Therefore, this study was conducted to find the main elements to be examined when designing and evaluating business models of DHC services. Therefore, the objective of this study is to produce a taxonomy to find key dimensions and their corresponding items of business models of the DHC service. This study employs an iterative taxonomy development method for taxonomy development to systematically derive a taxonomy that reflects both literature research and DHC service cases. This study derives nine dimensions and 42 corresponding items. For researchers, the proposed taxonomy derives dimensions that should be considered mainly in business model research of DHC. For practitioners, our taxonomy serves as a strategic management tool for designing and benchmarking existing DHC services business models.
In the semiconductor manufacturing process, wafers consist of multiple chips that are tested for quality before packaging. The data collected during this wafer test, which measures the electrical direct current voltage and characteristics of each chip, is known as wafer test data. However, missing values often occur in wafer test data due to factors such as faulty data acquisition sensors and intentional test skipping. This study presents a missing value imputation method that takes into account the spatial similarity among chips and the correlation between test items in wafer test data. The proposed method incorporates chip location information to capture the spatial tendencies of chips and modifies the loss functions of Generative Adversarial Imputation Nets to preserve correlations between test items before and after imputation. The effectiveness of the proposed method is demonstrated through the application of real-world wafer test data from a domestic semiconductor company, resulting in an improvement in imputation accuracy for over 80% of test items compared to five existing methods. This improved imputation method has the potential to increase wafer yield and efficiency in manufacturing quality management.
Advanced metering infrastructure (AMI) is an integrated system of smart meters, communication networks, and data management systems. AMI allows the automatic and remote measurement and monitoring of electricity consumption. It also provides important information for managing peak demand and power quality. Such information can be useful for both electric power companies and customers (e.g., households and building managers). Electric power companies can use the information to make intelligent decisions for the efficient operation of power plants. Customers can receive feedback about electricity price signals and projected monthly bill, which can support them make more informed decisions about their usage. According to the enormous benefits of AMI, much efforts have been made to promote AMI and to utilize the data collected by AMI. This research has two objectives. The first objective is to develop an AMI acceptance model. Despite many benefits of AMI, several obstacles to penetrating AMI still remain. Among the obstacles, this research focuses on information privacy concerns (IPC) and perceived bad electricity usage habits (PEUH) of households. The electricity usage