OFFERING KNOWLEDGE AS A SERVICE - A TAXONOMY OF KNOWLEDGE-INTENSIVE BUSINESS SERVICES

2023 | European Conference On Information Systems | Citations: 0

Authors: Ksouri-Gerwien, Christoph; Ebel, Martin; Bittner, Kendra; Poeppelbuss, Jens

Abstract: Servitization describes the transformation of a manufacturer to a customer-cent ... Expand

Abstract: Servitization describes the transformation of a manufacturer to a customer-centric service and solution provider. Providing customer solutions requires the integration of knowledge from different domains, e.g., engineering, software, and service, and usually also entails that more knowledge-intensive business services (KIBS) become part of the manufacturer's overall business model. Against this background, this article investigates how manufacturing and software firms leverage knowledge in KIBS and corresponding business models. We developed a taxonomy that systematizes KIBS along the three metadimensions of value proposition, value creation, and value capture. The application of our taxonomy to exemplary cases from both industries shows different strategies of knowledge usage across these industries. Our findings can support the development of KIBS and help practitioners to understand different ways of utilizing knowledge as a strategic resource. Implications for research point to the need for better understanding the collaboration of multiple actors from a knowledge-based perspective. Collapse

Topics: business model value creation innovation management knowledge sharing website

Methods: case study theory development triangulation qualitative interview design artifact

Theories: knowledge based theory of the firm

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor

2020 | HICSS | Citations: 0

Authors: Kaczor, Eric E; Carreiro, Stephanie; Chapman, Brittany

Abstract: Physician stress, and resultant consequences such as burnout, have become increa ... Expand

Abstract: Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on selfreported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data were continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other highstress professionals. Collapse

Topics: smart wearable device job satisfaction biometric lean manufacturing Health Insurance Portability and Accountability Act

Methods: machine learning wearables based research electrocardiography computational algorithm self reported survey

Advancing social media derived information messaging and management: A multi-mode development perspective

2020 | International Journal of Information Management | Citations: 0

Authors: Martin, Nigel; Rice, John; Arthur, Damien

Abstract: With global reach of over 2 billion active users, the evolution of Social Media ... Expand

Abstract: With global reach of over 2 billion active users, the evolution of Social Media (SM) systems has provided organizations with sophisticated tools and technologies for delivering business objectives. Importantly, while marketers and public relations experts have taken leading positions in promotion and advancement of SM, project managers are often tasked with delivering SM systems. In this study, a sample of 127 project managers were asked to evaluate and recommend modes of SM development for six diverse firms using a four-part taxonomy. The results show that firms of varying size can employ narrowly focused and low cost SM development modes to meet their business objectives, with well-resourced firms able to use experimental modes to deliver widespread and higher cost ‘listen and learn’ SM systems. Alternatively, in addition to achieving groundswell promotions and broader business marketing and sales influencing objectives, firms that engage in large scale SM developments can document and implement SM best practices and apply multi-organizational collaborations required for information exchange, customer feedback and experience sharing. These managerial perspectives expose the intrinsic connections between SM systems and information messaging and management within firms. The article builds further into cumulative studies directed at SM systems construction, deployment, and firm capability affordances. Collapse

Topics: systems development information management Facebook WeChat affordance

Methods: qualitative coding theory development survey qualitative content analysis literature study

Theories: theory of affordance

Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

2020 | HICSS | Citations: 1

Authors: Yin, Tianzhixi; Zang, Xiaoqin; Hou, Z. Jason; Jacobson, Paul; Mueller, Robert; Deng, Zhiqun

Abstract: An automatic system that utilizes data analytics and machine learning to identif ... Expand

Abstract: An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities. Collapse

Topics: analytical information system

Methods: field experiment laboratory experiment convolutional neural network experiment machine learning

An algebra of temporal faults

2016 | Information Systems Frontiers | Citations: 1

Authors: Didier, André; Mota, Alexandre

Abstract: Faults modelling is essential to anticipate failures in critical systems. Tradit ... Expand

Abstract: Faults modelling is essential to anticipate failures in critical systems. Traditionally, Static Fault Trees are employed to this end, but Temporal and Dynamic Fault Trees are gaining evidence due to their enriched power to model and detect intricate propagation of faults that lead to a failure. In previous work, we showed a strategy based on the process algebra CSP and Simulink models to obtain fault traces that lead to a failure. Although that work used Static Fault Trees, it could be used with Temporal or Dynamic Fault Trees. In the present work we define an algebra of temporal faults (with a notion of fault propagation) and prove that it is indeed a Boolean algebra. This allows us to inherit Boolean algebra’s properties, laws and existing reduction techniques, which are very beneficial for faults modelling and analysis. We illustrate our work on a simple but real case study supplied by our industrial partner EMBRAER. Collapse

Topics: computer hardware digital service Pandora

Methods: case study simulation

Service Ratio-Optimal, Content Coherence-Aware Data Push Systems

2016 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Liaskos, Christos; Tsioliaridou, Ageliki

Abstract: Advertising new information to users via push is the trigger of operation for ma ... Expand

Abstract: Advertising new information to users via push is the trigger of operation for many contemporary information systems. Furthermore, passive optical networks are expected to extend the reachability of high-quality push services to thousands of clients. The efficiency of a push service is the ratio of successfully informed users. However, pushing only data of high popularity can degrade the thematic coherency of the content. The present work offers a novel, analysis-derived, tunable way for selecting data for push services. The proposed scheme can maximize the service ratio of a push system with regard to data coherence constraints. Extensive simulations demonstrate the efficiency of the scheme compared to alternative solutions. The proposed scheme is the first to tackle the problem of data coherence-aware, service ratio optimization of push services. Collapse

Methods: computational algorithm cluster analysis simulation k-means clustering DBSCAN

A Decision Support System for market-driven product positioning and design

2015 | Decision Support Systems | Citations: 0

Authors: Lei, Ningrong; Moon, Seung Ki

Abstract: This paper presents a Decision Support System (DSS) for market-driven product po ... Expand

Abstract: This paper presents a Decision Support System (DSS) for market-driven product positioning and design, based on market data and design parameters. The proposed DSS determines market segments for new products using Principal Component Analysis (PCA), K-means, and AdaBoost classification. The system combines the data integrity, security, and reliability of a database with the unparalleled analytical capability of the Matlab tool suite through an intuitive Graphical User Interface (GUI). This GUI allows users to explore and evaluate alternative scenarios during product development. To demonstrate the usefulness of the proposed system, we conducted a case study using US automotive market data. For this case study, the proposed DSS achieved classification accuracies in a range from 76.1% to 93.5% for different scenarios. These high accuracy levels make us confident that the DSS can benefit enterprise decision makers by providing an objective second opinion on the question: To which market segment does a new product design belong? Having information about the market segment implies that the competition is known and marketing can position the product accurately. Furthermore, the design parameters can be adjusted such that (a) the new product fits this market segment better or (b) the new product is relocated to a different market segment. Therefore, the proposed system enables enterprises to make better informed decisions for market-driven product positioning and design. Collapse

Topics: decision support system decision making decision support database system IT work

Methods: computational algorithm case study AdaBoost machine learning cluster analysis

Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network

2013 | Decision Support Systems | Citations: 1

Authors: Xu, M.; Wong, T. C.; Chin, K. S.

Abstract: Emergency Department (ED) plays a critical role in healthcare systems by providi ... Expand

Abstract: Emergency Department (ED) plays a critical role in healthcare systems by providing emergency care to patients in need. The quality of ED services, measured by waiting time and length of stay, is significantly affected by patient arrivals. Increased patient arrivals could undermine service timeliness, thus putting patients in severe conditions at risk. These factors lead to the following research questions that have rarely been studied before: What are the variables directly associated with patient arrivals in the ED? What is the nature of association between these variables and patient arrivals? Which variable is the most influential and why? To address the above questions, we proposed a three-stage method in this paper. First, a data-driven method is used to identify contributing variables directly correlated with the daily arrivals of Categories 3 and 4 patients (i.e., non-critical patients). Second, the association between contributing variables and daily patient arrival is modeled by using artificial neural network (ANN), and the modeling ability is compared with that of nonlinear least square regression (NLLSR) and multiple linear regression (MLR) in terms of mean average percentage error (MAPE). Third, four types of relative importance (RI) of input variables based on ANN are compared, and their statistical reliability is tested by the MLR-based RI. We applied this three-stage method to one year of data of patient arrivals at a local ED. The contribution of this paper is twofold. Theoretically, this paper emphasizes the importance of using data-driven selection of variables for complex system modeling, and then provides a comprehensive comparison of RI using different computational methods. Practically, this work is a novel attempt of applying ANN to model patient arrivals, and the result can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations. Collapse

Topics: hurricane decision support system decision making

Methods: sensitivity analysis correlation analysis descriptive statistic parametric test artificial neural network

A DSS for planning of soil-sensitive field operations

2012 | Decision Support Systems | Citations: 0

Authors: Bochtis, Dionysis D.; Sørensen, Claus G.; Green, Ole

Abstract: The current increased size of agricultural vehicles aggravates the problem of so ... Expand

Abstract: The current increased size of agricultural vehicles aggravates the problem of soil compaction causing increased energy requirements, increased CO2 emissions, and reduced yields. The aim of this paper was to develop a DSS for optimize route planning in terms of minimized risk for soil compaction for agricultural vehicles carrying time-depended loads. The developed system uses as input field and operational characteristics, including a potential risk indicator map based on specific measure of distributed soil physical–chemical properties. It provides the optimal field-work tracks traversal sequence which can be executed using state-of-the-art auto-steering and navigation-aiding systems available on modern agricultural vehicles. The system has been demonstrated and tested for heavy application units used for organic fertilizer. The risk factor was reduced up to 61% by using the corresponding optimal plans instead of the non-optimal conventional ones that an operator would follow. Collapse

Topics: productivity decision support system agriculture information system systems implementation programming language

Methods: case study field study

A DSS Design Model for complex problems: Lessons from mission critical infrastructure

2007 | Decision Support Systems | Citations: 5

Authors: Klashner, Robb; Sabet, Sameh

Abstract: This paper presents a new DSS Design Model for complex, mission critical decisio ... Expand

Abstract: This paper presents a new DSS Design Model for complex, mission critical decision-making situations and its technical, conceptual, and partial empirical evaluation. The new model was derived from conceptual design research and through a deep qualitative field research study at an electric power utility control center—a typical arena for “wicked” problems. The model suggests an iterative process of theory, simulation, and decision-making interactions. The DSS Design Model is validated using a tool instantiation, microgrid mini-case, and current research of a KMDSS for telecommunications. Main findings suggest that broader and more integrated approaches are necessary to design DSS for complex domains. Collapse

Topics: decision support system enterprise architecture communication service infrastructure systems development knowledge management

Methods: simulation design science field study qualitative interview design artifact

Theories: complex adaptive system