Internet Based Self Service Systems for Customer Oriented Processes in Public Administration

Self service systems in public administration can lead to a more efficient organization as well as to an improved customer orientation. The objective is to offer high-quality services and to involve the service recipient in the administrative process to a greater extent. Re-engineering public service processes is necessary to promote a shift from the supplier-dominated push principle to a demand-oriented pull principle. A self service infrastructure allows direct access to IT supported public services. The transactions between service suppliers and service recipients are based on Internet communication. A smartcard represents a powerful element to identify and authenticate the user and offers value-added functions such as data storage, data encryption or electronic payment

Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry

Cost estimation methods are crucial to support inter- and intraorganizational cost management. Despite intense research on machine learning and deep learning for the prediction of costs, the acceptance of such models in practice remains unclear. The aim of this study is to evaluate the acceptance of an implemented deep learning-based cost estimation system. In an empirical study at a large Bavarian automotive manufacturer we use surveys to collect opinions and concerns from experts who regularly use the system. The evaluation is framed by the basic theories of the Technology Acceptance Model. The results from 50 questionnaires and qualitative participant observations show further development potentials of intelligent cost estimation systems in terms of perceived usefulness and user-friendliness. Building on our empirical findings we provide implications for both research and practice

Conceptualising and Understanding User Behaviour in Enterprise Social Networks: A Qualitative Analysis

Social commerce greatly changes buyers’ purchase behavior nowadays. Understanding the psychological mechanism of consumers’ purchase behavior is important for both buyers and sellers in social commerce platform. This study will examine the nature and role of psychological contract (PC) that is an utmost important variable in social commerce context. The psychological contract comprises a consumer’s beliefs about the reciprocal obligations that exist between him/her and sellers or other social members. Specifically, we propose that consumers may make purchase decisions by two processes, in which two types of PC--transactional contract and relational contract are the key mediators. In addition, we further discuss the antecedents (platform website ethics, the reliability of social elements) and consequences (trust and satisfaction in the platform) of PC, and their impacts on purchase intention. We plan to empirically test our model by collecting survey data from the users from the largest social commerce website in China—Taobao

Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy

Unplanned hospital readmissions are a key indicator of quality in healthcare and can lead to high unnecessary costs for the hospital due to additional required resources or reduced payments by insurers or governments. Predictive analytics can support the identification of patients at high-risk for readmission early on to enable timely interventions. In Australia, hysterectomies present the 2nd highest observed readmission rates of all surgical procedures in public hospitals. Prior research so far only focuses on developing explanatory models to identify associated risk factors for past patients. In this study, we develop and compare 24 prediction models using state-of-the-art sampling and ensemble methods to counter common problems in readmission prediction, such as imbalanced data and poor performance of individual classifiers. The application and evaluation of these models are presented, resulting in an excellent predictive power with under- and oversampling and an additional slight increase in performance when combined with ensemble methods

Helper, Sharer or Seeker? - A Concept to Determine Knowledge Worker Roles in Enterprise Social Networks

In order to manage knowledge work, companies need to understand how knowledge is shared, integrated, translated and transformed in organisa-tional practice. However, knowledge work often happens in informal organisa-tional structures, thus, making it difficult to identify and understand the occurring knowledge practices and participating actors. Enterprise Social Networks (ESN), i.e. internally accessible social networking services, have evolved as important platforms for knowledge work. Facilitating knowledge interactions between us-ers, the analysis of ESN data might be well suited for characterising and identi-fying knowledge actions and different knowledge worker roles. Drawing on an existing knowledge worker role typology as well as findings from social media research, this paper develops a conceptual basis that serves as starting point for determining knowledge worker roles using ESN data. The next steps of this re-search involve the empirical testing of the typology using data obtained from a real case scenario

Interorganizational Information System Deployment in Supply Chain Triads

Interorganizational information systems (IOIS) are valuable tools providing platforms for information flow enabling more efficient and reliable collaboration in digitalized supply chains. An IOIS is subject to influencing factors originating in the company and environment. Inspired by complex adaptive system theory, an agent-based simulation model is designed, exploring factors affecting the integration and efficiency of IOIS. These factors are derived from resource-based view and dynamic capabilities theory. The influence on information system deployment is assessed by merging these factors into exogenous, intercompany climate, and operational dimensions. First, the results indicate that product-specific factors have a greater influence than the environment when deploying an IOIS. Second, deliberate design of IC relationships should be considered during the development of an IOIS. Third, extensive information exchange between supply chain partners might be disadvantageous for IOIS utilization. Fourth, the advantages of IOIS can be lost when completely open systems are used

Predictive Cost Analytics of Vehicle Assemblies Based on Machine Learning in the Automotive Industry

Due to the high pace of development in the automotive industry, there is a need for innovating cost engineering. A methodology for intelligent cost estimation in the early stages of the product life cycle is introduced. In a first step it is shown how significant economic and technical parameters for cost prediction can be prepared and filtered from historical calculation data. Subsequently, it is shown how cost prediction models can be developed using machine learning algorithms. Learning data and practical use cases come from a large automotive manufacturer in Germany. The models predict the costs of car parts and assemblies of increasing complexity. Seven different machine learning models are trained and optimized. Based on the test data of the use cases these models are assessed and compared. Finally, the prediction results obtained are evaluated from different perspectives, demonstrating the practical applicability of the most suitable methods explored

Scientific Approaches and Methodology to Determine the Value of Data as an Asset and Use Case in the Automotive Industry

From a theoretical perspective data does not constitute a traditional business asset. Existing valuation approaches are either sector specific or still unexplored. In modern businesses the value-adding use and monetization of existing “big data” represents one of the greatest potentials in the context of digital transformation. This paper aims at reviewing methods and developing an integrated methodology for the value determination of data in general and for use in the manufacturing industry in particular. Therefore, the general state of research in data value assessment is investigated by a broad literature analysis. Based on the identified general principles, methodological requirements for data value determination are compiled. A new methodology for data evaluation is developed and applied to four use cases coming from the automotive industry. The results show that the methodology can be used in different contexts and thus enables managers to explore the most promising use cases for data-driven business