What Makes Digital Technology? A Categorization Based on Purpose

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Baier, Marie-Sophie; Berger, Stephan; Kreuzer, Thomas; Oberländer, Anna; Röglinger, Maximilian

Abstract: Digital technology (DT) is creating and shaping today's world. Building on its ... Expand

Abstract: Digital technology (DT) is creating and shaping today's world. Building on its identity and history of technology research, the Information Systems discipline is at the forefront of understanding the nature of DT and related phenomena. Understanding the nature of DT requires understanding its purposes. Because of the growing number of DTs, these purposes are diversifying, and further examination is needed. To that end, we followed an organizational systematics paradigm and present a taxonomic theory for DT that enables its classification through its diverse purposes. The taxonomic theory comprises a multi-layer taxonomy of DT and purpose-related archetypes, which we inferred from a sample of 92 real-world DTs. In our empirical evaluation, we assessed the reliability, validity, and usefulness of the taxonomy and archetypes. The taxonomic theory exceeds existing technology classifications by being the first that (1) has been rigorously developed, (2) considers the nature of DT, (3) is sufficiently concrete to reflect the diverse purposes of DT, and (4) is sufficiently abstract to be persistent. Our findings add to the descriptive knowledge on DT, advance our understanding of the diverse purposes of DT, and lay the ground for further theorizing. Our work also supports practitioners in managing and designing DTs. Collapse

Topics: knowledge base perceived usefulness competitive advantage decision making technology adoption

Methods: cluster analysis theory development longitudinal data analysis focus group computational algorithm

Theories: information systems theory sociomaterialism theory

Explainable artificial intelligence in information systems: A review of the status quo and future research directions

2023 | Electronic Markets | Citations: 4

Authors: Brasse, Julia; Broder, Hanna Rebecca; Förster, Maximilian; Klier, Mathias; Sigler, Irina

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an ... Expand

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an emerging phenomenon of global interest for academia, business, and society and brought about the rise of the research field of explainable artificial intelligence (XAI). With its pluralistic view, information systems (IS) research is predestined to contribute to this emerging field; thus, it is not surprising that the number of publications on XAI has been rising significantly in IS research. This paper aims to provide a comprehensive overview of XAI research in IS in general and electronic markets in particular using a structured literature review. Based on a literature search resulting in 180 research papers, this work provides an overview of the most receptive outlets, the development of the academic discussion, and the most relevant underlying concepts and methodologies. Furthermore, eight research areas with varying maturity in electronic markets are carved out. Finally, directions for a research agenda of XAI in IS are presented. Collapse

Topics: explainable artificial intelligence artificial intelligence user requirement electronic market user experience

Methods: literature study literature sample cluster analysis synthesis machine learning

Theories: information systems theory activity theory agency theory attribution theory cognitive fit theory

Doing good by going digital: A taxonomy of digital social innovation in the context of incumbents

2023 | Journal of Strategic Information Systems | Citations: 0

Authors: Buck, Christoph; Krombacher, Anna; Röglinger, Maximilian; Körner-Wyrtki, Katrin

Abstract: Digital social innovation (DSI) offers incumbents a strategic field of action to ... Expand

Abstract: Digital social innovation (DSI) offers incumbents a strategic field of action to leverage the op­ portunities of digital technologies to address pressing societal challenges. By proposing a tax­ onomy and 12 clusters of incumbents’ DSI initiatives based on a sample of 296 real-world objects, we develop a unified understanding of DSI and its characteristics. This lays the foundation for further theorising on DSI from an incumbent perspective and for researchers to shape the DSI field. The taxonomy provides incumbents with an orientation to realise DSI’s rich strategic po­ tentials throughout the DSI ideation process and in assessing DSI types. Collapse

Topics: innovation management societal challenge database system value creation digital innovation

Methods: literature sample cluster analysis agglomerative hierarchical clustering structured literature research theory development

Theories: information systems theory

Clustering and Topological Data Analysis: Comparison and Application

2023 | HICSS | Citations: 0

Authors: Combs, K.; Bihl, T.

Abstract: Clustering is common technique used to demonstrate relationships between data a ... Expand

Abstract: Clustering is common technique used to demonstrate relationships between data and information. Of recent interest is topological data analysis (TDA), which can represent and cluster data through persistent homology. The TDA algorithms used include the Topological Mode Analysis Tool (ToMATo) algorithm, Garin and Tauzin's TDA Pipeline, and the Mapper algorithm. First, TDA is compared to ten other clustering algorithms on artificial 2D data where it ranked third overall. TDA had the second-highest performance in terms of average accuracy (97.9%); however, its computation-time performance ranked in the middle of the algorithms. TDA ranked fourth on the qualitative "visual trustworthiness" metric. On realworld data, TDA showed promising classification results (accuracy between 80-95%). Overall, this paper shows TDA is a competitive algorithm performancewise, though computationally expensive. When TDA is used for visualization, the Mapper algorithm allows for unique alternative views especially effective for visualizing highly dimensional data. Collapse

Topics: Python cloud computing missing data

Methods: computational algorithm cluster analysis DBSCAN agglomerative hierarchical clustering BIRCH

Disentangling Human-AI Hybrids

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Fabri, Lukas; Häckel, Björn; Oberländer, Anna Maria; Rieg, Marius; Stohr, Alexander

Abstract: Artificial intelligence (AI) offers great potential in organizations. The path t ... Expand

Abstract: Artificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate. To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates a solid foundation for the understanding and analysis of human-AI hybrids, the archetypes illustrate the range of roles that AI-enabled systems can play in those interworking scenarios. Collapse

Topics: artificial intelligence human computer interaction human AI collaboration sensemaking system development method

Methods: cluster analysis qualitative interview design artifact design methodology literature study

Theories: sociomaterialism theory co-evolution theory social practice theory

Process and Strategy Mining in Real-Time Strategy Games

2023 | International Conference on Business Informatics | Citations: 0

Authors: Elbert, Nico; Stein, Nikolai; Flath, Christoph M.

Abstract: In this study, we introduce a data-driven approach for process mining in real-t ... Expand

Abstract: In this study, we introduce a data-driven approach for process mining in real-time strategy (RTS) games, which serve as a proxy for corporate decision processes. By analyzing decision sequences in gaming environments, we uncover strategic behaviors impacting performance and match outcomes. Our methodology leverages game data, enriching process mining research and providing implications for strategic planning in business process management. Collapse

Topics: process mining strategic management business process management decision support decision making

Methods: cluster analysis Petri net modeling language XGBoost event log

What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media

2023 | International Conference on Information Systems | Citations: 0

Authors: Kuang, Junwei; Xie, Jiaheng; Yan, Zhijun

Abstract: Depression is the most prevalent and serious mental illness, which induces grav ... Expand

Abstract: Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few depression detection studies attempt to explain the decision, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses show that MSTPNet outperforms state-of-the-art depression detection methods. This result also reveals new symptoms that are unnoted in the survey approach. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media. Collapse

Topics: social media healthcare analytics

Methods: design artifact deep learning computational algorithm machine learning ablation analysis

Analyzing Energy Consumption of Appliances in Smart Grids

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Lee, Jonathan; Gupta, Ashish

Abstract: Modern power grids are undergoing many changes to meet the growing energy deman ... Expand

Abstract: Modern power grids are undergoing many changes to meet the growing energy demand. A major limitation of traditional power grids is that they only allow for understanding the overall household's total consumption, which is typically measured with high latency over the entire billing period. Smart grids and smart meters, on the other hand, enable access to timely household data. When such grids are equipped with the ability to read device-level data then they can be used to provide deeper analytic insights into energy utilization patterns at a very granular level. In this study, we propose using Dynamic Time Warping as a distance metric for clustering to examine consumers' device-level energy consumption for four different geographic regions. Additional insight into device-level usage patterns for consumers is likely to lead us to develop persuasive IS systems that can improve energy efficiency and bring benefits to both power suppliers and power consumers. Collapse

Topics: persuasive information system Python database system accounting

Methods: longitudinal research time series analysis linear regression analysis cluster analysis agglomerative hierarchical clustering

Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings

2023 | HICSS | Citations: 0

Authors: Otudi, H.; Mohamed, T.; Kezunovic, M.; Hu, Y.; Obradovic, Z.

Abstract: Utilizing data analytics and machine learning (ML) on phasor measurement units ... Expand

Abstract: Utilizing data analytics and machine learning (ML) on phasor measurement units (PMUs) data to analyze faults automatically is the focus of this paper. Insufficient labels and natural uneven distribution of different types of line fault events found in fieldrecorded PMU data make supervised ML model development challenging. To address this issue, we train off-the-shelf Support Vector Machine (SVM) ML models for line fault classification using simulated PMU data obtained from a combination of 12 physical and virtual PMUs placed on a synthetic IEEE 14-bus system as well as using this simulated data unified with field recordings. A conducted sensitivity study is focused on three factors, 1) the number of PMUs used to train the ML model, 2) the voltage level at which the model is trained, and 3) the vicinity of PMUs to transmission line faults. The ML models trained with simulated and field data are evaluated on one-year field-recorded data collected from 38 PMUs sparsely located in the US Western interconnection. We demonstrate that when training ML models with only simulated data, the performance varies significantly with different number of PMUs, voltage level, and PMU placement in separate areas of the synthetic grid (F1 score of 0.78 to 0.92). We obtained an F1 score of 0.94 using the simulated dataset integrated with field recordings. The performance of a ML model developed using simulated data is also evaluated on the three-phase voltage signals extracted from 188 PMUs in the Eastern interconnection accompanied by imprecise labels, where the majority of the labels do not identify the fault type. On this extremely challenging task, we achieved 77% accuracy solely using synthetic data for ML training. Collapse

Topics: Apache Spark analytical information system computer hardware

Methods: simulation machine learning support vector machine experiment event log

Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual Data Quality Enrichment

2023 | DESRIST | Citations: 0

Authors: Reinhard, Philipp; Li, Mahei Manhai; Dickhaut, Ernestine; Peters, Christoph; Leimeister, Jan Marco

Abstract: AI-based recommendation systems to augment working conditions in the field of IT ... Expand

Abstract: AI-based recommendation systems to augment working conditions in the field of IT service management (ITSM) have attracted new attention. However, many IT support organizations possess high volumes of tickets but are confronted with low quality, to which they train the underlying models of their AI systems. In particular, support tickets are documented insufficiently due to time pressure and lack of motivation. Following design science research, we design and evaluate an analytics pipeline to address the data quality issue. The pipeline can be applied to assess and extract high-quality support tickets for subsequent model training and operation. Based on a data set of 60.000 real-life support tickets from a manufacturing company, we develop the artifact, instantiate a recommender system and achieve a higher prediction performance in comparison to naïve enrichment methods. In terms of data management literature, we contribute to the understanding of assessing textual ticket data quality. By deriving a pipeline reference model, we move towards a general approach to designing machine learning-driven data quality analytics pipelines for attached recommender systems. Collapse

Topics: data quality recommender system system support shapley additive explanation database system

Methods: machine learning reference modelling design artifact cluster analysis topic model