Acceptance of AI for delegating emotional intelligence: Results from an experiment

Detecting emotions of other humans is challenging for us humans. It is however important in many social contexts so that many individuals seek help in this regard. As technology is evolving, more and more AI-based options emerge that promise to detect human emotions and support decision making. We focus on the full delegation of detecting emotions to AI to contribute to our understanding how such AI is perceived and why it is accepted. For this, we conduct an online scenario-based experiment in which participants have the choice to delegate emotion detection to another human in one group and to an AI tool in the other group. Our results show that the delegation rates are higher for a human, but surprisingly high for AI. The results provide insights that should be considered when designing AI-based emotion-detection tools to build trustworthy and accepted designs

Critical Success Factors in Data Analytics Projects: Insights from a Systematic Literature Review

Various data analytics applications are increasingly used by organizations to extract insights from data. There are numerous studies exploring the critical success factors (CSFs) in different data analytics fields including BusinessIntelligence, Artificial Intelligence, Machine Learning, Data Science, and Big Data. Despite the extensive body of research, there remains a gap in identifying a structured CSFs list that offers a holistic view across all these fields. This study addresses this gap by conducting a systematic literature review to investigate CSFs in data analytics projects, aiming to create a comprehensive list that is applicable across various fields. We have categorized CSFs into six key themes: People, Strategy, Technology & Data, Organizational Culture, Process Design and External Factors derived from 28 research papers. By presenting these CSFs comprehensively, this paper seeks to provide a structured approach that will enhance the success rates of data analytics projects, facilitating better strategic alignment and operational efficiency across multiple fields

A Capability Maturity Model for Developing and Improving Advanced Data Analytics Capabilities

Background: Advanced data analytics (ADA) is increasingly used in organizations to enhance decision-making, improve operational efficiency, and gain a competitive advantage. Yet, there is limited guidance available on the capabilities an organization should develop and improve on to effectively leverage ADA. To address this gap this study develops a capability maturity model answering the research question: “What are the key components of a capability maturity model that can effectively guide organizations in assessing and enhancing their advanced data analytics capabilities?”Methods: A capability maturity model for advanced data analytics (ADA-CMM) was developed through a Delphi study using the design science research paradigm. To evaluate ADA-CMM for its utility interviews with practitioners were conducted on the use of ADA-CMM for assessing the maturity of a large company. To evaluate ADA-CMM effectiveness a nomological model was developed and tested using PLS-SEM based on a multi-company surveyResults: A comprehensive ADA capability maturity model prescribing necessary capabilities was presented. The model is deemed useful and effective and offers a method to assess ADA capabilities. The findings provide evidence supporting that ADA-CMM encompasses essential capabilities for creating value from ADA initiatives and can effectively measure an organization’s ADA capability maturity.Conclusion: This paper emphasizes the growing importance of ADA in enhancing business operations and competitiveness. Despite technological advancements, many organizations struggle to translate analytics efforts into tangible benefits. To address this, the paper proposes a Capability Maturity Model, ADA-CMM, to guide organizations in developing and improving ADA capabilities. This study contributes to literature by providing a well-structured and thoroughly evaluated capability maturity model for ADA, and to practice for navigating the challenges of ADA adoption and use

Elements of Blockchain-based Circular Business Models in Manufacturing: A Synthesis of the Literature

The manufacturing industry faces barriers to transitioning to a circular economy. Blockchain technology can help manufacturing supply chains overcome barriers and achieve core principles of circular economy, for example, through increased traceability of materials among network partners. However, the current literature lacks an overview of the contribution of blockchain to circular business models (CBMs) that can be used as a reference to facilitate the implementation of blockchain-based circularity solutions in manufacturing. In this study, we performed a systematic literature review to identify the studies that provide blockchain applications and use cases for CBMs in the manufacturing industry. We classified the selected articles according to the elements of networked business models, as such solutions involve multiple businesses that collaborate tightly. Our results show traceability and transparency as the central value propositions of CBM networks. We provide a classification of network actors and roles, their coproduction activities, and common benefits they gain and costs they incur to achieve the value propositions. Our results provide a better understanding of the body of knowledge on the use of blockchain for CBMs and highlight understudied points. Manufacturing companies can leverage our comprehensive classification and enumeration of CBM elements to inform and optimize the design of their own CBMs. In future work, we aim to provide more assistance to companies by developing reference blockchain CBM blueprints and applying them to CBM design to evaluate their effectiveness and utility

Process Mining Guidelines for Greenhouse Gas Emission Management in Production Processes

Despite the urgent need for becoming more sustainable and enhancing sustainability reporting induced by, e.g., the Corporate Sustainability Reporting Directive effective from January 2024, there exists a lack in research and industry efforts for integrating sustainability metrics into business processes. One particular reporting requirement entails that large EU companies must disclose their sustainability metrics for greenhouse gas (GHG) emissions across their supply chains. To address this challenging task, this paper presents the Process Mining Guidelines for Greenhouse Gas Emission Management (PMG3), helping companies implement process mining to meet GHG emissions targets in production processes. Thereby, the PMG3 provides detailed steps for defining business and data requirements, analyzing inefficiencies, and formulating recommendations to enhance sustainability reporting. To validate PMG3, a detailed demonstration was conducted using real-world data from a business case in the production process within the consumer goods industry. The utility evaluation revealed high approval for the PMG3's usefulness, ease of use, and practitioners' intention to use it in industry settings. Overall, this paper contributes a structured and applied approach for organizations to report GHG emissions and improve sustainability performance through process mining

A Reflection on the Interrelations Between Business Process Management and Requirements Engineering with an Agility Perspective

The paper points out some aspects of the interrelations between business process management, agility, flexibility, and requirements engineering. It shows some possibilities for agile development of business processes and for the development of flexible processes for changing requirements