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TOWARDS DESIGN PRINCIPLES FOR TEACHING INDUSTRY 4.0 COMPETENCIES TO CS/IS STUDENTS
AI Processed

How can educators effectively teach modern industrial software skills without expensive physical equipment?

Felix Salcher
TOWARDS DESIGN PRINCIPLES FOR TEACHING INDUSTRY 4.0 COMPETENCIES TO CS/IS STUDENTS
ECIS 2026 · 2026
Key findings
  • Eight design principles were established, emphasizing virtualization, containerization, and problem-based learning cases as alternatives to expensive learning factories.\n- Preliminary course evaluation yielded positive descriptive feedback, with a mean rating of 4.43 out of 5 across satisfaction categories.\n- No formal statistical significance tests were performed due to the pilot nature of the study and the small, specific evaluation sample.\n- The pilot is limited by a very small sample size (9 respondents from a single cohort) and relies on subjective student perceptions rather than objective competency-based assessments.\n- Students noted difficulty adapting to the course's fast pace and programming requirements, highlighting a need for aligned prerequisites.
What it means for you
  • CIO / IT Executive: Direct your enterprise architecture team to draft a transition plan to replace physical IT hardware training labs with virtualized, containerized sandbox environments (e.g., Docker/Kubernetes) to reduce infrastructure costs and align onboarding with modern Industry 4.0 software architectures.
  • IT Manager: Review the programming prerequisites of your junior developers and assign them a structured, hands-on troubleshooting task using a containerized microservice mock-up on Monday morning to assess their readiness for complex Industry 4.0 integration projects.
  • Business Strategist: Reallocate a portion of next quarter's talent development budget away from high-cost physical equipment training and toward scalable, software-based virtual simulators to cost-effectively build digital twin and data architecture competencies.
  • Researcher: Draft a research proposal to conduct a multi-institution comparative study on these eight design principles, incorporating objective pre- and post-intervention technical testing rather than relying on subjective student feedback to measure competency gains.
  • Policymaker: Create a state-level educational grant initiative that incentivizes higher education institutions to update computer science curricula with virtualization-based Industry 4.0 frameworks, shifting funding priority away from physical learning factories.
Design Principles, Design Science Research, Industry 4.0, Smart Manufacturing
THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION
AI Processed Human Approved

How should teams structure initial challenges to successfully launch AI projects?

Franziska Anna Röckel, Benjamin van Giffen, Andreas Janson, Jennifer Hehn
THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION
ECIS 2026 · 2026
Key findings
  • The study empirically validated a structural anatomy for the Design Challenge consisting of four robust elements (verb, object of innovation, target group, context) and three plastic elements (internal data, technology, potential), which was supported across all 42 cases analyzed.
  • The robust elements were validated to successfully create a stable, shared identity and common direction among diverse team members, acting as a center of authority.
  • The plastic elements were shown to successfully absorb technological uncertainty, providing a flexible framework for teams to negotiate and adapt to AI's emergent capabilities over time.
  • The qualitative analysis identified three distinct nuances in shaping the 'object of innovation' (modular, abstract, and space) that guide teams toward either incremental or more radical innovation pathways.
What it means for you
  • CIO / IT Executive: Mandate that all upcoming AI project proposals use a standardized 'Design Challenge' charter template structured around the 4 robust elements (verb, object of innovation, target group, context) and 3 plastic elements (internal data, technology, potential), explicitly forbidding teams from locking in a rigid technical architecture in their initial pitch.
  • IT Manager: Gather your interdisciplinary project team for a 60-minute workshop on Monday morning to co-create a 'Design Challenge' statement, explicitly defining the stable 'robust' elements to align the business and tech members, while leaving the 'plastic' elements (like specific data sources and AI models) open as flexible targets to be negotiated as the project progresses.
  • Business Strategist: Review your company's AI project portfolio on Monday morning and intentionally rewrite the 'object of innovation' for each project: frame them as 'modular' if you need predictable, incremental improvements, or rewrite them as 'space-based' (e.g., focused on broad capabilities) if you want to push the team toward radical, disruptive business model innovation.
  • Researcher: Update your qualitative coding scheme for ongoing field studies of AI adoption to track how frequently and at what project stages teams modify the 'plastic' elements (internal data, technology, potential) versus the 'robust' elements of their project charters to measure the empirical threshold where technological uncertainty triggers project misalignment.
  • Policymaker: Revise the application guidelines for the upcoming public AI innovation grant cycle to replace rigid 'technical specifications' requirements with a mandatory 'Design Challenge' framework, scoring applicants on the clarity of their robust elements (target group, context) and the adaptive flexibility of their plastic elements (data and technology integration plans).
Design Challenge, AI Innovation, Boundary Object, Anatomy, Interdisciplinary Collaboration, Problem Formulation
UNPACKING THE ROLE OF INDIVIDUALS IN PROCESS MINING: A LITERATURE REVIEW AND RESEARCH FRAMEWORK
AI Processed

How do individual user behaviors and attitudes impact the business value generated from process mining?

Julian Dyong, Sandro Franzoi, Jan vom Brocke
UNPACKING THE ROLE OF INDIVIDUALS IN PROCESS MINING: A LITERATURE REVIEW AND RESEARCH FRAMEWORK
ECIS 2026 · 2026
Key findings
  • The review identifies and establishes user behavior, usage patterns, and affect (such as uncertainty and trust) as the three core elements constituting the individual level of process mining, supported by the synthesized literature.
  • The analysis reveals that technical, individual, and organizational contingency factors systematically influence user behavior and affect; for example, data inaccuracies undermine user trust, while formal training and methodological guidance support correct execution.
  • Individual behaviors and affective states are shown to directly impact value outcomes, with structured behaviors like hypothesis testing decreasing analysis duration and increasing the quality of insights.
  • Key limitations of the study include its reliance on a limited sample of 23 reviewed papers, a static framework that does not capture sequential or temporal dynamics, and a lack of true inter-coder reliability during the qualitative coding process.
What it means for you
  • CIO / IT Executive: Initiate a data audit of the source pipelines feeding your enterprise process mining tools to eliminate data discrepancies, directly addressing the research finding that data inaccuracies severely undermine user trust and derail tool adoption.
  • IT Manager: Add a mandatory 'Hypothesis-First' template to your team's process mining workflow on Monday morning, requiring analysts to document their specific diagnostic hypothesis before running queries to enforce structured behavior and reduce analysis duration.
  • Business Strategist: Redesign the process mining ROI dashboard to measure actual business value outcomes (e.g., cost savings from process changes) rather than user log-in frequency, linking individual analytical insights directly to operational KPIs.
  • Researcher: Draft a research design for a longitudinal study tracking 20 process mining analysts over six months to capture the sequential and temporal dynamics of user affect and trust, directly addressing the static limitations of current literature.
  • Policymaker: Establish an internal organizational standard mandating that no department may deploy process mining software without integrating a formal training and methodological guidance program, ensuring compliant and high-quality data execution.
Process Mining, Individual Level, Literature Review, Research Framework, Value Creation, User Behavior
Corporate Nomads: Working at the Boundary Between Corporate Work and Digital Nomadism
AI Processed Human Approved

How can companies and employees successfully negotiate a nomadic lifestyle alongside permanent corporate employment?

Julian Marx, Milad Mirbabaie, Stefan Stieglitz
Corporate Nomads: Working at the Boundary Between Corporate Work and Digital Nomadism
Journal of the Association for Information Systems · 2025
Key findings
  • The study develops a three-phase process model (splintering, calibrating, and harmonizing) that explains how corporate nomads and their organizations successfully negotiate this new work arrangement.
  • The integration of corporate nomads is not a one-sided decision but a mutual process of 'boundary work' requiring engagement, negotiation, and trade-offs from both the employee and the company.
  • Corporate nomads operate as individual outliers who change their personal work boundaries (e.g., location and time) without transforming the entire organization's structure.
  • Information Technology (IT) is crucial in managing the inherent tensions of this lifestyle, helping to balance organizational control with employee autonomy and enabling integration from a distance.
Corporate Nomads, Digital Nomads, Boundary Work, Digital Work, Information Systems
Capturing the “Social” in Social Networks: The Conceptualization and Empirical Application of Relational Quality
AI Processed

How do activity levels and connection counts shape the quality of employee interactions on corporate social networks?

Christian Meske, Iris Junglas, Matthias Trier, Johannes Schneider, Roope Jaakonmäki, Jan vom Brocke
Capturing the “Social” in Social Networks: The Conceptualization and Empirical Application of Relational Quality
Journal of the Association for Information Systems · 2025
Key findings
  • Relational quality is a distinct and relevant dimension that complements traditional structural social network analysis (SNA), which typically only focuses on network structure.
  • The study identifies and measures four key facets of relational quality: being personal, being curious, being polite, and sharing.
  • Different types of users exhibit distinct patterns of relational quality; for instance, 'connectors' (users with many connections but low activity) are the most personal, while 'broadcasters' (users with high activity but few connections) share the most resources.
  • As a user's activity (e.g., number of posts) increases, their interactions tend to become less personal, curious, and polite, while their sharing of resources increases.
  • In contrast, as a user's number of connections grows, their interactions become more personal and curious, but they tend to share fewer resources.
Enterprise Social Network, Social Capital, Relational Quality, Social Network Analysis, Linguistic Analysis, Computational Research
REGULATING EMERGING TECHNOLOGIES: PROSPECTIVE SENSEMAKING THROUGH ABSTRACTION AND ELABORATION
AI Processed

How can governments and industry leaders create effective rules for emerging technologies without stifling innovation?

Stefan Seidel, Christoph J. Frick, Jan vom Brocke
REGULATING EMERGING TECHNOLOGIES: PROSPECTIVE SENSEMAKING THROUGH ABSTRACTION AND ELABORATION
MIS Quarterly · 2025
Key findings
  • Creating effective regulation for new technologies is a process of 'collective prospective sensemaking,' where diverse stakeholders build a shared understanding over time.
  • This process relies on two interrelated activities: 'abstraction' and 'elaboration'. Abstraction involves generalizing the essential properties of a technology to create flexible, technology-neutral rules that encourage innovation.
  • Elaboration involves specifying details and requirements to provide legal certainty and protect users.
  • Through this process, the regulatory target can evolve significantly, as seen in the case study's shift from regulating 'blockchain/cryptocurrency' to a broader, more durable law for the 'token economy' and 'trustworthy technology'.
Technology regulation, prospective sensemaking, sensemaking, institutional construction, emerging technology, blockchain, token economy
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