Business-IT alignment as a coevolution process: An empirical study

2023 | Journal of Strategic Information Systems | Citations: 0

Authors: Amarilli, Fabrizio; Van Den Hooff, Bart; Van Vliet, Mario

Abstract: In this paper, we provide a detailed insight into the complex coevolution dynami ... Expand

Abstract: In this paper, we provide a detailed insight into the complex coevolution dynamics that shape the alignment process by analyzing how different mechanisms and factors are mutually related in complex networks of feedback loops. We combine insights from the literature on alignment as a (coevolution) process with literature on alignment as a state to identify the different components of the organization’s socio-technical system that influence alignment, the relationships between these components, and the role that different factors play. In our empirical analysis (based multiple case studies) we then focus on the actual interplay between relevant factors. Using a causal loop diagramming approach - based on system dynamics – we analyze how these factors mutually influence each other through various feedback loops and thus shape the alignment process. We extend previous literature on the alignment process by identifying the way that the complex interplay between different factors shapes this process. By identifying the feedback loops between relevant factors, we also provide more insight into the complex bottom-up and top-down dynamics that shape the process, and that provide explanations for why this process is charac­ terized by transitions between alignment and misalignment. For practice, our paper provides a deeper understanding of the alignment process, which is a precondition for improving alignment practices in organisations. Collapse

Topics: Chief Information Officer IT alignment strategic management IT project enterprise information system

Methods: qualitative interview case study multiple case study theoretical contribution system dynamics

Theories: co-evolution theory socio technical theory complex adaptive system

DataOps as a Prerequisite for the Next Level of Self-Service Analytics – Balancing User Agency and Central Control

2023 | International Conference on Business Informatics | Citations: 0

Authors: Baars, Henning

Abstract: The area of Business Intelligence and Analytics (BIA) has repeatedly oscillated ... Expand

Abstract: The area of Business Intelligence and Analytics (BIA) has repeatedly oscillated between more central, efficiency-oriented, professionalized approaches and decentral, agility-oriented, user-driven ones. This paper investigates whether and how to alleviate that tradeoff by combining an agility-oriented self-service analytics approach with the professionalization-driven DataOps concept: DataOps aims at transferring ideas from DevOps to the realm of analytics, namely a mutual integration of Development and Operations and a high degree of professionalization and automation. A case study with a series of interviews and a workshop led to insights into the viability of such a combination. These inspire a theoretical concept for capturing the economics behind the approaches that is considering the (opportunity) costs of the components "user agency" and "central control". The concept has been evaluated with representatives from the case study. Based on the results, I argue that the discussed combination can push BIA solutions towards fine-tuned federated environments. Collapse

Topics: devops artificial intelligence mlops data management data integration

Methods: qualitative interview case study literature study machine learning survey

COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Bartenschlager, Christina C.; Ebel, Stefanie S.; Kling, Sebastian; Vehreschild, Janne; Zabel, Lutz T.; Spinner, Christoph D.; Schuler, Andreas; Heller, Axel R.; Borgmann, Stefan; Hoffmann, Reinhard; Rieg, Siegbert; Messmann, Helmut; Hower, Martin; Brunner, Jens O.; Hanses, Frank; Römmele, Christoph

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map ... Expand

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approximately 4,000 patients and an extraordinary high ratio of SARS-CoV-2-positive patients. We analyze the influence of data preparation, flexibility in optimization targets, as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities, and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times, and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model. Collapse

Topics: artificial intelligence mobile application pandemic logistics management data integration

Methods: computational algorithm machine learning normalization data transformation support vector machine artificial neural network

From Full-fledged ERP Systems Towards Process-centric Business Process Platforms

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Böhme, Lukas; Wuttke, Tobias; Bender, Benedict; Teusner, Ralf; Baltes, Sebastian; Matthies, Christoph; Perscheid, Michael

Abstract: Enterprise Resource Planning (ERP) systems are critical to the success of enter ... Expand

Abstract: Enterprise Resource Planning (ERP) systems are critical to the success of enterprises, facilitating business operations through standardized digital processes. However, existing ERP systems are unsuitable for startups and small and medium-sized enterprises that grow quickly and require adaptable solutions with low barriers to entry. Drawing upon 15 explorative interviews with industry experts, we examine the challenges of current ERP systems using the task technology fit theory across companies of varying sizes. We describe high entry barriers, high costs of implementing implicit processes, and insufficient interoperability of already employed tools. We present a vision of a future business process platform based on three enablers: Business processes as first-class entities, semantic data and processes, and cloud-native elasticity and high availability. We discuss how these enablers address current ERP systems' challenges and how they may be used for research on the next generation of business software for tomorrow's enterprises. KeywordsERP system, enterprise system, business process platform. Collapse

Topics: enterprise resource planning business process management enterprise information system cloud computing semantic data model

Methods: qualitative interview personal interview data modeling business process modeling literature study

Theories: theory of task technology fit

Enabling Sustainable Logistics for Fresh Produce SMBEs: A Case Study Perspective on Local Retail Markets in Sydney

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Chaudhry, Maria; Karimi, Faezeh; Khalilpour, Kaveh

Abstract: In the recent era of "Internet of things" (IoT), giant logistics enterprises ac ... Expand

Abstract: In the recent era of "Internet of things" (IoT), giant logistics enterprises achieve their sustainability goals comparatively easier than "Small to Medium Business Entrepreneurs" (SMBEs). Currently, IoT connected trucks generate massive data such as locations, cabin environmental conditions, vehicle movements, engine status variables, driver behavioral patterns, etc. Large-scale enterprises process this information in real time to maintain green compliance and waste reduction. However, SMBEs often lack resources to afford smart vehicles and tracking systems for critical sustainability parameters to become visible in time, henceforth, failing to comply with green standards. This research proposes a cost effective, lightweight cloud-based enabler i.e., "Green Tracking as a Service" (GTaaS) based on sustainability eco-system, backing the need of such service through case studies conducted in retail markets of Sydney. By performing state and barrier analysis of multiple fresh produce SMBEs, we aim to help them go green, through continuous monitoring and predictive maintenance techniques, implemented via GTaaS. Collapse

Topics: logistics management mobile system internet of things digital dashboard decision making

Methods: case study machine learning conceptual modelling survey mathematical model

Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

2023 | European Conference On Information Systems | Citations: 0

Authors: Gunklach, Jonas; Nadj, Mario

Abstract: While the data amount grows exponentially, the number of people with analytical ... Expand

Abstract: While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues. Collapse

Topics: analytical information system data visualization data quality business intelligence artificial intelligence

Methods: exploratory data analysis machine learning literature sample time series analysis longitudinal research

Introducing a Data Perspective to Sustainability: How Companies Develop Data Sourcing Practices for Sustainability Initiatives

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

Authors: Krasikov, Pavel; Legner, Christine

Abstract: ... Expand

Abstract: Collapse

Topics: green IS sensemaking environmental sustainability data management data quality

Methods: case study focus group qualitative interview data modeling theory development

Theories: institutional theory

Laboratory Forensics for Open Science Readiness: an Investigative Approach to Research Data Management

2023 | Information Systems Frontiers | Citations: 0

Authors: Lefebvre, Armel; Spruit, Marco

Abstract: Recently, the topic of research data management has appeared at the forefront of ... Expand

Abstract: Recently, the topic of research data management has appeared at the forefront of Open Science as a prerequisite for preserving and disseminating research data efficiently. At the same time, scientific laboratories still rely upon digital files that are processed by experimenters to analyze and communicate laboratory results. In this study, we first apply a forensic process to investigate the information quality of digital evidence underlying published results. Furthermore, we use semiotics to describe the quality of information recovered from storage systems with laboratory forensics techniques. Next, we formulate laboratory analytics capabilities based on the results of the forensics analysis. Laboratory forensics and analytics form the basis of research data management. Finally, we propose a conceptual overview of open science readiness, which combines laboratory forensics techniques and laboratory analytics capabilities to help overcome research data management challenges in the near future. Collapse

Topics: data management data quality digital forensics digital dashboard digital transformation

Methods: experiment digital trace data case study design artifact exploratory data analysis

Theories: semiotic theory theory of information information systems theory

Compatibility promotion for standard development within shared platforms: A rising tide does not lift all boats

2023 | Electronic Markets | Citations: 0

Authors: Lindgren, Rikard; Saadatmand, Fatemeh; Schultze, Ulrike

Abstract: Selective promotion entails the exploitation of resources offered by individual ... Expand

Abstract: Selective promotion entails the exploitation of resources offered by individual complementors to improve a proprietary platform’s competitive position. However, as governance decisions to endorse a particular complementor are made unilaterally, such promotion is less suitable for shared platforms that are ultimately owned and managed by an ecosystem of heterogeneous, autonomous complementors. We explore compatibility promotion, which involves screening a shared stock of infrastructural resources and making a choice as to which complementor to promote in light of the capabilities the platform seeks to develop. Based on a longitudinal study of a shared platform that evolved around a new technology standard in the Swedish road haulage industry, we explicate how elevating one complementor over others unsettled the governance of the platform and denied the promoted complementors the opportunity to generate the kind of value that their elevated status implicitly promised. This made the platform better off at the expense of the promoted complementor. Our surprising insight lets us theorize why compatibility promotion in shared platforms renders outcomes opposite to those of selective promotion in proprietary platforms. Collapse

Topics: information systems standardization digital ecosystem sharing platform digital platform intellectual property

Methods: theory development qualitative interview action research longitudinal data analysis case study

Reidentification Risk in Panel Data: Protecting for k-Anonymity

2023 | Information Systems Research | Citations: 0

Authors: Li, Shaobo; Schneider, Matthew J.; Yu, Yan; Gupta, Sachin

Abstract: We consider the risk of reidentification of panelists in marketing research data ... Expand

Abstract: We consider the risk of reidentification of panelists in marketing research data that are widely used to obtain insights into buyer behavior and to develop marketing strategy. We find that 17%–94% of the panelists in 15 frequently bought consumer goods categories are subject to high risk of reidentification through a potential record linkage attack based on their unique purchasing histories even when their identities are anonymized. We first demonstrate that the risk of reidentification is vastly understated by unicity, the conventional measure. Instead, we propose a new measure of reidentification risk, termed sno-unicity, which accounts for the longitudinal nature of panel data, and show that it is much larger than unicity. To protect the privacy of panelists, we consider the well-known privacy notion of k-anonymity and develop a new approach called graph-based minimum movement k-anonymization (k-MM) that is designed especially for panel data. The proposed k-MM approach can be formulated as an optimization problem in which the objective is to minimally distort variables in the original data based on weights that users prespecify corresponding to their use case. We further show how our approach can be extended to achieve l-diversity. We apply the k-MM approach to two different panel data sets that are widely used in marketing research. To achieve a given privacy level, compared with several benchmark protection methods, the protected data from our method result in the least distortion in inferences about key marketing metrics, such as brand market shares, share of category requirements, brand switching rates, and marketing-mix parameters estimated from a hierarchical Bayesian brand choice model. History: Param Singh, Senior Editor and Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1169. Collapse

Topics: privacy marketing management differential privacy data protection record linkage

Methods: longitudinal research optimization model descriptive statistic simulation cluster analysis