A taxonomy of data governance decision domains in data marketplaces

2023 | Electronic Markets | Citations: 0

Authors: Abraham, Rene; Schneider, Johannes; vom Brocke, Jan

Abstract: Commercializing data and data-related services has gained in importance in recen ... Expand

Abstract: Commercializing data and data-related services has gained in importance in recent years. Driven by digitalization and the Internet-of-Things (IoT), companies and individuals continuously generate vast amounts of data. Data marketplaces have emerged to support these data providers in selling their data to different data consumers. However, data marketplaces face challenges in different data governance decision domains that inhibit their adoption. To get a better understanding of how data marketplaces counteract these challenges, this paper develops a taxonomy of data governance decision domains in data marketplaces. We used a taxonomy development method to inspect 13 data marketplaces from eight countries. The resulting taxonomy shows an overview of mechanisms concerning data quality, data security, data architecture, metadata, data lifecycle, data storage, and data pricing. We discuss common instantiation patterns, highlight gaps, and propose possible solutions. The taxonomy sets a foundation for further research and theory-building on data marketplaces. Practitioners can use the taxonomy to develop customized data governance strategies for data marketplaces. Collapse

Topics: data governance price management system development method computer data storage website

Methods: design artifact data modeling design methodology design science company material

Editor's Comments: Producing Significant Research

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Burton-Jones, Andrew; Gray, Peter; Majchrzak, Ann

Abstract: ... Expand

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Topics: data quality artificial intelligence filter bubble social media citizen science

Methods: field study machine learning computational algorithm field experiment applicability check

How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing

2023 | European Conference On Information Systems | Citations: 0

Authors: Mehler, Maren; Vetter, Oliver

Abstract: Machine Learning (ML) technologies have become the foundation of a plethora of ... Expand

Abstract: Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers' incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choicebased conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research. Collapse

Topics: willingness to pay ease of use quality management artificial intelligence software developer

Methods: conjoint analysis literature study delphi study survey computational algorithm

A Taxonomy and Archetypes of Business Analytics in Smart Manufacturing

2023 | ACM SIGMIS Database | Citations: 0

Authors: Wanner, Jonas; Wissuchek, Christopher; Welsch, Giacomo; Janiesch, Christian

Abstract: Fueled by increasing data availability and the rise of technological advances fo ... Expand

Abstract: Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its multidisciplinary complexity, both researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field, as there is a lack of a holistic conceptualization. To address this issue, we performed an extensive structured literature review, yielding 904 relevant hits, to develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing. The taxonomy comprises the following meta-characteristics: application domain, orientation as the objective of the analysis, data origins, and analysis techniques. Collectively, they comprise eight dimensions with a total of 52 distinct characteristics. Using a cluster analysis, we found six archetypes that represent a synthesis of existing knowledge on planning, maintenance (reactive, offline, and online predictive), monitoring, and quality management. A temporal analysis highlights the push beyond predictive approaches and confirms that deep learning already dominates novel applications. Our results constitute an entry point to the field but can also serve as a reference work and a guide with which to assess the adequacy of one's own instruments. Collapse

Topics: production management smart manufacturing analytical information system industry 4.0 database system

Methods: cluster analysis literature study survey literature sample machine learning

Theories: fuzzy set theory

Offline to online: The impacts of offline visit experience on online behaviors and service in an Internet hospital

2023 | Electronic Markets | Citations: 0

Authors: Ye, Qing; Wu, Hong

Abstract: Only the online-to-offline process has been explored, the literature is largely ... Expand

Abstract: Only the online-to-offline process has been explored, the literature is largely silent on the offline-to-online process, which restricts the online service quality and the development of Internet hospitals. We approach this issue by investigating the impact of patients’ offline visit experience on their online engagement, and then how patients’ online engagement affects the quality and efficiency of physicians’ online services. Using archival data with 69,078 online consultation records from a large tertiary hospital and its Internet hospital in China, we developed several regression models to analyze the impact of offline visit experience on patients’ online information provision and the effect of online information provision on service quality and efficiency. We identified that offline visit experience has heterogeneous effects on patients’ online information provision, and patients’ online information provision positively influences physicians’ online service quality, while negatively influencing online service efficiency. Moreover, online information provision mediates the relationship between offline visit experience and online service quality. This study contributes to our understanding of the mechanisms by which the offline visit experience influences online service quality in the online-offline channel integration context. Collapse

Topics: service quality data quality internet technology healthcare data electronic health

Methods: regression analysis method robustness check descriptive statistic cross sectional research statistical hypothesis test

Theories: information integration theory

Enabling Efficient Deduplication and Secure Decentralized Public Auditing for Cloud Storage: A Redactable Blockchain Approach

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

Authors: Mishra, Rahul; Ramesh, Dharavath; Kanhere, Salil S.; Edla, Damodar Reddy

Abstract: Public auditing and data deduplication are integral considerations in providing ... Expand

Abstract: Public auditing and data deduplication are integral considerations in providing efficient and secure cloud storage services. Nevertheless, the traditional data deduplication models that support public auditing can endure the enormous waste of storage and computation resources induced through data redundancy and repeated audit work by multiple tenants on trusted third-party auditor (TPA). In this work, we introduce blockchain-based secure decentralized public auditing in a decentralized cloud storage with an efficient deduplication model. We employ blockchain to take on the task of centralized TPA, which also mitigates the implications of malicious blockchain miners by using the concept of a decentralized autonomous organization (DAO). Specifically, we employ the idea of redactability for blockchain to handle often neglected security issues that would adversely affect the integrity of stored auditing records on blockchain in decentralized auditing models. However, the proposed model also employs an efficient deduplication scheme to attain adequate storage savings while preserving the users from data loss due to duplicate faking attacks. Moreover, the detailed concrete security analysis demonstrates the computational infeasibility of the proposed model against proof-of-ownership, duplicate faking attack (DFA), collusion attack, storage free-riding attack, data privacy, and forgery attack with high efficiency. Finally, the comprehensive performance analysis shows the scalability and feasibility of the proposed model. Collapse

Topics: blockchain data encryption outsourcing cloud computing cloud storage

Methods: experiment computational algorithm mixed method business process modeling

AN INVESTIGATION INTO THE ROLE OF DATA GOVERNANCE IN IMPROVING DATA QUALITY: A CASE STUDY OF THE OMANI BANKING SECTOR

2022 | European Conference On Information Systems | Citations: 0

Authors: Al Wahshi, Jihad; Foster, Jonathan; Abbott, Pamela

Abstract: In the era of big data analytics, data is widely recognised as a valuable asset ... Expand

Abstract: In the era of big data analytics, data is widely recognised as a valuable asset that can enable organisations to achieve their strategic objectives. Despite that, banks are still struggling to maintain high-quality data. Prior studies show that a data governance programme can play a critical role in improving data quality. It can provide data quality professionals with a holistic approach to formally define policies, procedures and decision rights required for managing data quality in a more systematic manner. However, few empirical studies were conducted in this area. Therefore, the present paper aims to close this gap by investigating the data quality problem in the Omani banking industry to understand how various data governance mechanisms can address this issue. The study adopted a qualitative case study with semi-structured interviews and document reviews being used to collect data. A theoretical framework by Abraham et al. (2019) was adopted to guide the collection and analysis of the data. A thematic analysis (TA) by Braun and Clark was followed for data analysis. Findings of the study suggest that the data governance mechanisms, namely 'performance measurement', 'compliance monitoring' and 'training', have positively contributed to mitigating data quality issues in the Omani banking sector. Collapse

Topics: data quality data governance business intelligence Sarbanes–Oxley Act data management

Methods: qualitative interview case study personal interview field study qualitative content analysis

Alternative Futures for the Digital Transformation: A Macro-Level Schumpeterian Perspective

2022 | Organization Science | Citations: 0

Authors: Bodrožić, Zlatko; S. Adler, Paul

Abstract: This paper develops and deploys a theoretical framework for assessing the prospe ... Expand

Abstract: This paper develops and deploys a theoretical framework for assessing the prospects of a cluster of technologies driving what is often called the digital transformation. There is considerable uncertainty regarding this transformation’s future trajectory, and to understand and bound that uncertainty, we build on Schumpeter’s macro-level theory of economy-wide, technological revolutions and on the work of several scholars who have extended that theory. In this perspective, such revolutions’ trajectories are shaped primarily by the interaction of changes within and between three spheres—technology, organization, and public policy. We enrich this account by identifying the critical problems and the collective choices among competing solutions to those problems that together shape the trajectory of each revolution. We argue that the digital transformation represents a new phase in the wider arc of the information and communication technology revolution—a phase promising much wider deployment—and that the trajectory of this deployment depends on collective choices to be made in the organization and public policy spheres. Combining in a 2 × 2 matrix the two main alternative solutions on offer in each of these two spheres, we identify four scenarios for the future trajectory of the digital transformation: digital authoritarianism, digital oligarchy, digital localism, and digital democracy. We discuss how these scenarios can help us trace and understand the future trajectory of the digital transformation. Collapse

Topics: digital transformation problem solving quality management armed conflict volcanic eruption

Methods: synthesis theory development

Quality 4.0 – An Analysis from the Perspective of Method Engineers in Quality Management

2022 | International Conference on Business Informatics | Citations: 0

Authors: Johannsen, Florian

Abstract: Quality 4.0” is a central research field of quality management in times of indus ... Expand

Abstract: Quality 4.0” is a central research field of quality management in times of industry 4.0. However, the topic is rather new, and a profound theoretical foundation of the concept does not yet exist. Hence, it is not clear whether existing quality management methods can be smoothly transferred to industry 4.0 settings or not. Moreover, companies often lack the time to design quality management approaches for this field and to critically scrutinize the skills needed by quality managers for quality 4.0. Based on a structured literature review, this study takes the perspective of method engineers in quality management. It provides an overview of those quality management methods and quality techniques that are frequently discussed in quality 4.0 literature and seem promising for quality projects in industry 4.0 settings. In this way, insights for method engineers are derived for the purpose of method construction for quality 4.0. Collapse

Topics: quality management production management industry 4.0 Six Sigma

Methods: literature study literature sample structured literature research

Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence

2022 | International Journal of Information Management | Citations: 0

Authors: Johnson, Marina; Albizri, Abdullah; Harfouche, Antoine; Fosso-Wamba, Samuel

Abstract: Artificial intelligence (AI) has been gaining significant attention in various f ... Expand

Abstract: Artificial intelligence (AI) has been gaining significant attention in various fields to reduce costs, increase rev­ enue, and improve customer satisfaction. AI can be particularly beneficial in enhancing decision-making pro­ cesses for complex and ill-structured problems that lack transparency and have unclear goals. Most AI algorithms require labeled datasets to learn the problem characteristics, draw decision boundaries, and generalize. However, most datasets collected to solve complex and ill-structured problems do not have labels. Additionally, most AI algorithms are opaque and not easily interpretable, making it hard for decision-makers to obtain model insights for developing effective solution strategies. To this end, we examine existing AI paradigms, mainly symbolic AI (SAI) guided by human domain knowledge and data-driven AI (DAI) guided by data. We propose an approach called informed AI (IAI) by integrating human domain knowledge into AI to develop effective and reliable data labeling and model explainability processes. We demonstrate and validate the use of IAI by applying it to a social media dataset comprised of conversations between customers and customer support agents to construct a so­ lution – IAI defect explorer (I-AIDE). I-AIDE is utilized to identify product defects and extract the voice of cus­ tomers to help managers make decisions to improve quality and enhance customer satisfaction. Collapse

Topics: artificial intelligence social media Twitter decision making shapley additive explanation

Methods: computational algorithm support vector machine design science data transformation machine learning