FACTORS INFLUENCING THE ACCEPTANCE OF SELF-SERVICE BIG DATA PREPARATION AND ANALYTICS TOOLS

2023 | European Conference On Information Systems | Citations: 0

Authors: Zaki, Mohamed; Fischer, Svenja; Woodall, Philip; Leischnig, Alexander

Abstract: Although self-service data preparation and analytics tools (SSPATs) have become ... Expand

Abstract: Although self-service data preparation and analytics tools (SSPATs) have become increasingly popular and can provide major benefits to users, barriers associated with the technology and its usage may prevent adoption. Based on prior work on technology acceptance and related theories, this study develops a net effects model and a configurational effects model to further the understanding of SSPAT acceptance. Using a configurational lens, this study shows how a fresh methodological approach from a different epistemological stance applied to a well-established theoretical basis can offer novel insights into effects and patterns that may complement exisiting findings. The results of our empirical study based on a sample of 339 firms confirm insights from previous studies and advance them by providing vision for compound antecedents of SSPAT acceptance and complementarity effects among antecedent conditions. Collapse

Topics: ease of use fuzzy logic technology adoption perceived usefulness user experience

Methods: survey partial least squares path modeling theoretical contribution meta analysis exploratory data analysis

Theories: fuzzy set theory technology acceptance model unified theory of acceptance and use of technology

An Ontology of Emotion Process to Support Sentiment Analysis

2022 | Journal of the Association for Information Systems | Citations: 1

Authors: Storey, Veda; Park, Eun Hee

Abstract: Sentiment analysis is used to mine text data from many sources, including blogs ... Expand

Abstract: Sentiment analysis is used to mine text data from many sources, including blogs, support forums, and social media, in order to extract customers' opinions and attitudes. The results can be used to make important assessments about a customer's attitude toward a company and if and how a company should respond. However, much research on sentiment analysis uses simple classification, where the polarity of a text that is mined is classified as positive, negative, or neutral. This research creates an ontology of emotion process to support sentiment analysis, with an emphasis on obtaining a more fine-grained assessment of sentiment than polarity. The ontology is grounded in a theory of emotion process and consists of concepts that capture the generation of emotion all the way from the occurrence of an event to the resulting behaviors of the person expressing the sentiment. It includes two lexicons: one for affect and one for appraisal. The ontology is applied to posts obtained from customer support forums of large companies to show its applicability in a multilevel evaluation. Doing so provides an example of a complete ontology assessment effort. Collapse

Topics: knowledge representation online review Bing evaluation criteria website

Methods: ontological modelling design artifact sentiment analysis experiment aspect-based sentiment analysis

Data analytics for the sustainable use of resources in hospitals: Predicting the length of stay for patients with chronic diseases

2022 | Information & Management | Citations: 0

Authors: Zolbanin, Hamed M.; Davazdahemami, Behrooz; Delen, Dursun; Zadeh, Amir Hassan

Abstract: Various factors are behind the forces that drive hospitals toward more sustainab ... Expand

Abstract: Various factors are behind the forces that drive hospitals toward more sustainable operations. Hospitals con­ tracting with Medicare, for instance, are reimbursed for the procedures performed, regardless of the number of days that patients stay in the hospital. This reimbursement structure has incentivized hospitals to use their re­ sources (such as their beds) more efficiently to maximize revenues. One way hospitals can improve bed utili­ zation is by predicting patients’ length of stay (LOS) at the time of admission, the benefits of which extend to employees, communities, and the patients themselves. In this paper, we employ a data analytics approach to develop and test a deep learning neural network to predict LOS for patients with chronic obstructive pulmonary disease (COPD) and pneumonia. The theoretical contribution of our effort is that it identifies variables related to patients’ prior admissions as important factors in the prediction of LOS in hospitals, thereby revising the current paradigm in which patients’ medical histories are rarely considered for the prediction of LOS. The methodo­ logical contributions of our work include the development of a data engineering methodology to augment the data sets, prediction of LOS as a numerical (rather than a binary) variable, temporal evaluation of the training and validation data sets, and a significant improvement in the accuracy of predicting LOS for COPD and pneumonia inpatients. Our evaluations show that variables related to patients’ previous admissions are the main driver of the deep network’s superior performance in predicting the LOS as a numerical variable. Using the assessment criteria introduced in prior studies (i.e., ±2 days and ±3 days tolerance), our models are able to predict the length of hospital stay with 86 % and 91 % accuracy for the COPD data set, and with 74 % and 85 % accuracy for the pneumonia data set. Hence, our effort could help hospitals serve a larger number of patients with a fixed amount of resources, thereby reducing their environmental footprint while increasing their revenue, as well as their patients’ satisfaction. Collapse

Topics: analytical information system missing data electronic health record healthcare data data warehouse

Methods: deep learning decision tree classification Keras experiment convolutional neural network

A Predictive Analytics-Based Decision Support System for Drug Courts

2020 | Information Systems Frontiers | Citations: 0

Authors: Zolbanin, Hamed M.; Delen, Dursun; Crosby, Durand; Wright, David

Abstract: This study employs predictive analytics to develop a decision support system for ... Expand

Abstract: This study employs predictive analytics to develop a decision support system for the prediction of recidivism in drug courts. Based on the input from subject matter experts, recidivism is defined as the violation of the treatment program requirements within three years after admission. We use two data processing methods to improve the accuracy of predictions: synthetic minority oversampling and survival data mining. The former creates a balanced data set and the latter boosts the model’s performance by adding several new, informative variables to the data set. After running several tree-based machine learning algorithms on the input data, random forest achieved the best performance (AUROC = 0.884, accuracy = 80.76%). Compared with the original data, oversampling and survival data mining increased AUROC by 0.068 and 0.018, respectively. Their combined contribution to AUROC was 0.088. We present a simplified version of decision rules and explain how the decision support system can be deployed. Therefore, this paper contributes to the analytics literature by illustrating how date/time variables - in applications where the response variable is defined as the occurrence of some event within a certain period - can be used in data management to improve the performance of predictive models and the resulting decision support systems. Collapse

Topics: decision support system data management decision making analytical information system missing data

Methods: machine learning random forest classification computational algorithm hazard function survival analysis

Recombinant Service Systems Engineering

2018 | Business & Information Systems Engineering | Citations: 69

Authors: Beverungen, Daniel; Lüttenberg, Hedda; Wolf, Verena

Abstract: Although many methods have been proposed for engineering service systems and cu ... Expand

Abstract: Although many methods have been proposed for engineering service systems and customer solutions, most of these approaches give little consideration to recombinant service innovation. Recombinant innovation refers to reusing and integrating resources that were previously unconnected. In an age of networked products and data, we can expect that many service innovations will be based on adding, dissociating, and associating existing value propositions by accessing internal and external resources instead of designing them from scratch. The purpose of this paper is to identify if current service engineering approaches account for the mechanisms of recombinant innovation and to design a method for recombinant service systems engineering. In a conceptual analysis of 24 service engineering methods, the study identified that most methods (1) focus on designing value propositions instead of service systems, (2) view service independent of physical goods, (3) are either linear or iterative instead of agile, and (4) do not sufficiently address the mechanisms of recombinant innovation. The paper discusses how these deficiencies can be remedied and designs a revised service systems engineering approach that reorganizes service engineering processes according to four design principles. The method is demonstrated with the recombinant design of a service system for predictive maintenance of agricultural machines. Collapse

Topics: innovation management predictive maintenance value creation business process management systems analysis

Methods: machine learning design principle literature study design methodology field study

Theories: socio technical theory core competency theory dominant logic

Designing Predictive Maintenance for Agricultural Machines

2018 | European Conference On Information Systems | Citations: 1

Authors: Lüttenberg, Hedda; Bartelheimer, Christian; Beverungen

Abstract: The Digital Transformation alters business models in all fields of application, ... Expand

Abstract: The Digital Transformation alters business models in all fields of application, but not all industries transform at the same speed. While recent innovations in smart products, big data, and machine learning have profoundly transformed business models in the high-tech sector, less digitalized industries—like agriculture—have only begun to capitalize on these technologies. Inspired by predictive maintenance strategies for industrial equipment, the purpose of this paper is to design, implement, and evaluate a predictive maintenance method for agricultural machines that predicts future defects of a machine’s components, based on a data-driven analysis of service records. An evaluation with 3,407 real-world service records proves that the method predicts damaged parts with a mean accuracy of 86.34%. The artifact is an exaptation of previous design knowledge from high-tech industries to agriculture—a sector in which machines move through rough territory and adverse weather conditions, are utilized extensively for short periods, and do not provide sensor data to service providers. Deployed on a platform, the prediction method enables co-creating a predictive maintenance service that helps farmers to avoid resources shortages during harvest seasons, while service providers can plan and conduct maintenance service preemptively and with increased efficiency. Collapse

Topics: predictive maintenance expert system enterprise resource planning open source mobile application

Methods: machine learning computational algorithm design artifact random forest classification cluster analysis

Evolutionary Software Requirements Factors and their Effect on Open Source Project Attractiveness

2017 | HICSS | Citations: 0

Authors: Vlas, Radu; Robinson, William; Vlas, Cristina

Abstract: Successful projects effectively manage their requirements. How the mix of differ ... Expand

Abstract: Successful projects effectively manage their requirements. How the mix of different requirements evolves throughout a successful project life-cycle is poorly understood. Moreover, requirements practices may be changing, according to the authors of the New RE—a model of six critical requirements factors. The New RE focuses on leveraging existing components to create new functionality. This practice is also central to open-source development. Thus, to understand the proposed New RE model and its relationship to open-source development, in this study, we analyze over 200 projects from GitHub.com and compare them with a prior analysis of 31 projects from SourceForge. The results show that many of the proposed New RE factors are related to project attractiveness, which is important for open-source project success. Collapse

Topics: open source open source software project V model Java requirements analysis

Methods: design methodology longitudinal research Poisson regression data modeling machine learning process

Sentiment Analysis Meets Semantic Analysis: Constructing Insight Knowledge Bases

2015 | International Conference on Information Systems | Citations: 0

Authors: Qi, Zirun; Storey, Veda; Jabr, Wael

Abstract: Numerous Web 2.0 applications collect user opinions, and other user-generated co ... Expand

Abstract: Numerous Web 2.0 applications collect user opinions, and other user-generated content in the form of product reviews, discussion boards, and blogs, which are often captured as unstructured data. Text mining techniques are important for analyzing users’ opinions (sentiment analysis) and identifying topics of interest (semantic analysis). However, little work has been carried out that combines semantics with user’s sentiments. This research proposes a Sentiment-Semantic Framework that incorporates results from both semantic and sentiment analysis to construct a knowledge base of insights gained from integrating the information extracted from each type of analysis. To evaluate the framework, a prototype is developed and applied to two different domains (e-commerce and politics) and the resulting insight knowledge bases constructed. Collapse

Topics: knowledge base user-generated content knowledge representation online review cloud storage

Methods: design artifact sentiment analysis part of speech tagging ontological modelling product review

Theories: affect theory

Detection of financial statement fraud and feature selection using data mining techniques

2011 | Decision Support Systems | Citations: 24

Authors: Ravisankar, P.; Ravi, V.; Raghava Rao, G.; Bose, I.

Abstract: Recently, high profile cases of financial statement fraud have been dominating t ... Expand

Abstract: Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. Collapse

Topics: fraud detection

Methods: support vector machine machine learning experiment decision tree classification discriminant analysis

Theories: statistical learning theory