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On the Potential of Business Process Management for Digital Entrepreneurship: Findings from a Literature Review
AI Processed Human Approved

How can growing digital startups balance structured business processes with the flexibility needed to scale?

Tobias Wuttke, Thomas Haskamp, Michael Perscheid, Falk Uebernickel
On the Potential of Business Process Management for Digital Entrepreneurship: Findings from a Literature Review
Forty-Fourth International Conference on Information Systems · 2023
Key findings
  • Reconciles BPM and digital entrepreneurship literature by highlighting synergies such as process modularity, agility, and customer orientation.
  • Identifies key tensions between traditional process structures and the dynamic operational flexibility required by digital ventures.
  • Proposes three main research avenues: applying ambidextrous BPM in digital ventures, treating digital venturing as a business process, and developing capabilities to balance flexibility and structure.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of our current core digital processes (e.g., customer onboarding, product deployment) to identify specific components that could be modularized to allow for independent updates and scaling without disrupting the entire workflow.
  • IT Manager: On Monday morning, schedule a 30-minute meeting with a key team member from sales or customer support to identify one friction point in their daily workflow that could be addressed by a small, agile process improvement, rather than a large, monolithic system change.
  • Business Strategist: On Monday morning, dedicate an hour to mapping out the critical decision-making points in our growth strategy and outline potential process 'switches' or 'modes' that can be activated or deactivated based on market feedback or scaling needs, embodying ambidextrous BPM.
  • Researcher: On Monday morning, begin by identifying three specific digital venture growth challenges (e.g., rapid customer acquisition, international expansion) and conceptualize how treating the *process of venturing itself* as a business process (i.e., defining, measuring, and iteratively improving the venture creation and growth cycle) could be studied.
  • Policymaker: On Monday morning, draft a brief (one-page) outline of potential policy levers (e.g., startup support programs, digital skills training) that could foster the development of 'capabilities to balance flexibility and structure' within digital entrepreneurial ecosystems, focusing on practical implementation.
business process management, digital entrepreneurship, digital venturing, ambidextrous BPM, process innovation, literature review
Building the Processes Behind the Product: How Digital Ventures Create Business Processes That Support Their Growth
AI Processed Human Approved

How do fast-growing digital ventures build and adapt their internal business processes to support growth?

Tobias Wuttke, Thomas Haskamp, Michael Perscheid, Falk Uebernickel
Building the Processes Behind the Product: How Digital Ventures Create Business Processes That Support Their Growth
Business & Information Systems Engineering · 2024
Key findings
  • Identified four distinct patterns of business process change in digital ventures: creating minimum viable processes, creating encapsulated processes, creating centralized control flow integrations, and creating centralized data flow integrations.
  • Revealed that digital ventures typically start with flexible, human-orchestrated processes supported by low-specificity tools before migrating to specialized enterprise systems to boost efficiency.
  • Showed how proxy communication tools and centralized analytics systems are used to reduce system complexity and eliminate data silos in maturing ventures.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of our current proxy communication tool usage and explore options for centralizing our analytics systems to proactively address potential data silos as we scale.
  • IT Manager: On Monday morning, identify one key operational process (e.g., sales lead qualification) that is currently human-orchestrated and explore if a 'minimum viable process' could be documented and supported by a low-specificity tool for immediate iteration.
  • Business Strategist: On Monday morning, map out the current flow of data for our top 2 growth-driving products and identify one area where centralized data flow integration could significantly reduce friction and improve speed to market.
  • Researcher: On Monday morning, begin compiling a list of digital ventures that have recently experienced rapid growth and categorize their initial process creation patterns based on the four distinct patterns identified in the research (MVP, encapsulated, centralized control, centralized data).
  • Policymaker: On Monday morning, task a working group to investigate how current regulatory frameworks might be hindering the adoption of flexible, technology-enabled business processes in rapidly scaling digital ventures.
Business process management, Digital entrepreneurship, Digital ventures, Business process change, Enterprise systems
MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS: A DYNAMIC CAPABILITIES PERSPECTIVE
AI Processed Human Approved

How can public sector organizations effectively adopt and manage artificial intelligence?

Isabella Urban, Manfred Baer, Ralf Plattfaut
MANAGING ARTIFICIAL INTELLIGENCE IN PUBLIC SECTOR ORGANIZATIONS: A DYNAMIC CAPABILITIES PERSPECTIVE
ECIS 2026 · 2026
Key findings
  • The study empirically supports that AI adoption in public organizations is hindered by high levels of technological, regulatory, and organizational uncertainties, which require proactive management.
  • The research empirically identifies and supports a framework of specific organizational microfoundations across three key dimensions: sensing (e.g., tracing employee expectations and monitoring regulatory landscapes), seizing (e.g., establishing AI governance and launching pilot projects), and transforming (e.g., active change management and promoting AI literacy).
  • The findings empirically show that employee representation bodies (such as staff councils) often play a critical, gatekeeping role that can delay or hinder AI implementation decisions.
  • The study acknowledges limitations, including its geographical focus on the German public sector and a limited observation timeframe from April 2024 to October 2025.
What it means for you
  • CIO / IT Executive: Send an email calendar invite to the chair of your organization's Employee Representation Body (Staff Council) to establish a joint, bi-weekly 'AI Governance and Alignment Committee' to co-create deployment guidelines and prevent approval bottlenecks.
  • IT Manager: Draft a 1-page project charter for a highly constrained, 4-week pilot project using a low-risk internal AI tool (e.g., automated document summarizing) to test technical capability and establish baseline performance metrics.
  • Business Strategist: Design and launch a 5-question MS Forms survey to all department heads to map their teams' current AI literacy levels and identify specific anxieties, using the results to structure next month's targeted change management plan.
  • Researcher: Draft a research proposal and reach out to two international public sector research partners to replicate this qualitative dynamic capabilities study in a non-German jurisdiction (e.g., the US or UK) to address the geographical limitations of the original study.
  • Policymaker: Create and distribute a standardized 'Public Sector AI Regulatory Compliance Checklist' to provide local agency leaders with clear, step-by-step guidance on data privacy and municipal regulations, directly reducing their regulatory uncertainty.
Artificial Intelligence, Dynamic Capabilities, Microfoundations, Multiple Case Study, Public Sector
TIME TO TURN OFF THE LIGHTS: A SUCCESS FACTOR FRAMEWORK FOR ADVANCING HYPERAUTOMATION IN ORGANIZATIONS
AI Processed Human Approved

What are the key factors needed to successfully scale automation across an organization?

Lisa Straub, Christian Zeiß, Ulvi Ibrahimli, Christoph Tomitza, Benedikt Wirsing, Axel Winkelmann
TIME TO TURN OFF THE LIGHTS: A SUCCESS FACTOR FRAMEWORK FOR ADVANCING HYPERAUTOMATION IN ORGANIZATIONS
ECIS 2026 · 2026
Key findings
  • The survey results statistically supported all 15 success factors within the framework, achieving a high average agreement score of 6.05 out of 7, with 93.2% of respondents selecting positive agreement.
  • 'Leadership Commitment' (SF3) and 'Communication and Trust Building' (SF4) received the highest levels of overall agreement, indicating strong statistical support.
  • 'Strategic Partner Collaboration' (SF15) and 'Customer Integration' (SF13) received statistically supported relevance, although they had comparatively lower overall agreement scores.
  • The study's generalizability is limited by the German context of the initial qualitative interviews, a modest quantitative survey sample size (n = 103), and its reliance on subjective assessments rather than objective performance metrics.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute alignment meeting with business unit heads to establish a joint 'Hyperautomation Governance Board' to audit all active shadow-IT automation tools (like RPA and low-code apps) and map them against your long-term IT integration roadmap to prevent fragmented technical debt.
  • IT Manager: On Monday morning, block out two hours to audit your current automation deployment backlog and draft a standardized 'API-First Integration Checklist' that your development team must use to ensure new automations do not bypass APIs and create system incompatibilities.
  • Business Strategist: On Monday morning, select one high-volume customer-facing workflow and schedule a scoping call with a key strategic partner to co-design an integrated, automated data-sharing process, directly applying the 'Customer Integration' (SF13) and 'Strategic Partner Collaboration' (SF15) success factors.
  • Researcher: On Monday morning, draft a research proposal and outreach email to target three non-German, US-based enterprises to secure pilot sites for a comparative case study that measures objective operational KPIs (such as transaction error rates and processing speed) to validate the subjective findings of the 15-factor framework.
  • Policymaker: On Monday morning, draft an amendment to your organization's IT Procurement Policy mandating that any new business unit purchasing low-code or RPA tools must submit a 'Systemic Compatibility and Trust Impact Assessment' addressing how the tool aligns with the enterprise IT architecture and employee trust-building guidelines.
Hyperautomation, Transformation Management, Organizational Change, Success Factor Framework, Affordance Theory
FROM BROWN TO GREEN: HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION
AI Processed Human Approved

How can businesses use artificial intelligence to transition their business models toward sustainability?

Elaine Mosconi, Marie-Claude Boudreau
FROM BROWN TO GREEN: HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION
ECIS 2026 · 2026
Key findings
  • The AI-enabled platform qualitatively supported a business model shift from transactional, time-based billing to recurring, outcome-based services focused on asset durability and lifecycle performance.
  • A strategic alignment gap was identified: executives framed the AI and sustainability transformation as radical, whereas operational managers experienced it as incremental.
  • Successful scaling of the AI initiative qualitatively depended on adaptive governance (looser decision-making, rapid experimentation), pre-requisite capability building in data engineering, and strong leadership support.
  • Key organizational trade-offs and barriers emerged, specifically related to skills gaps, legacy system integration, high energy consumption of AI models, and delayed financial return on investment.
  • Limitation: The findings of this study are based on a single qualitative case study of a Canadian engineering firm, which may limit the direct generalizability of the results to other industries or regions.
What it means for you
  • CIO / IT Executive: Initiate an audit of legacy data pipelines to identify bottlenecks for AI integration, and establish a 'green sandbox' environment with relaxed governance rules to allow developers to experiment with asset-durability algorithms while tracking model energy consumption.
  • IT Manager: Hold a 30-minute team sync to map out existing data engineering skill gaps and assign one developer to clean and structure a single historical dataset of asset lifecycle performance to prepare it for future AI model training.
  • Business Strategist: Draft a pilot proposal to transition one high-trust client from hourly billing to a recurring, outcome-based pricing model focused on extending their asset lifecycle, and schedule a meeting with operational managers to align on realistic, incremental milestones.
  • Researcher: Design a survey instrument targeting business and IT leaders across multiple industries to quantitatively test whether the perceptual gap between executive (radical) and operational (incremental) views on AI-driven green transitions exists outside of the engineering sector.
  • Policymaker: Draft a policy brief proposing a targeted grant program that subsidizes data-engineering upskilling and offsets energy-efficiency auditing costs for mid-sized firms adopting AI-enabled, sustainable business models.
Twin Transition, Artificial Intelligence, Sustainability, Business Model, Dynamic Capabilities
THE AI-ENABLED CIRCULAR ECONOMY: EXTRACTING AND SYNTHESIZING DESIGN KNOWLEDGE
AI Processed

How can organizations design and use artificial intelligence to support circular economy practices?

Linda Sagnier Eckert, Christoph Hoppe-Ludwig, Timo Strohmann, Anne Ixmeier, Daniel Heinz
THE AI-ENABLED CIRCULAR ECONOMY: EXTRACTING AND SYNTHESIZING DESIGN KNOWLEDGE
ECIS 2026 · 2026
Key findings
  • Synthesized design knowledge from 99 empirically grounded studies supported four distinct meta-principles for AI-enabled circular economies.
  • Meta-Principle 1 (AI for slowing resource loops) is supported by 15 empirical studies showing predictive AI models successfully forecast asset degradation to trigger life-extending actions.
  • Meta-Principle 2 (AI for closing resource loops) is supported by 29 empirical studies showing integrated computer vision and robotic sorting systems reliably classify and separate materials.
  • Meta-Principle 3 (AI for narrowing resource loops) is supported by 18 empirical studies showing hybrid optimization models successfully reduce resource and energy consumption.
  • Meta-Principle 4 (AI for socio-technical orchestration) is supported by 37 empirical studies demonstrating that AI can drive the organizational dynamic capabilities needed to coordinate circular workflows.
  • A recognized limitation is that the corpus is restricted to English-language, peer-reviewed publications from the last five years, and the synthesized principles have not been longitudinally tested in new field environments.
What it means for you
  • CIO / IT Executive: Schedule a 9:00 AM workshop with your Enterprise Architecture and Sustainability teams to map your organization's existing data infrastructure against the study's two-layered framework, explicitly linking your micro-level AI model choices with macro-level organizational capabilities to support circular workflows.
  • IT Manager: Initiate a pilot project on your team's backlog to deploy a predictive maintenance model on a single class of high-wear manufacturing assets, using telemetry data to forecast asset degradation and automatically trigger life-extending maintenance tickets.
  • Business Strategist: Conduct a value-chain audit to identify where materials are lost, and draft a business case for integrating computer vision and robotic sorting systems at your primary waste-handling facility to automate material classification and close resource loops.
  • Researcher: Draft a research proposal for a 12-month longitudinal field study in partnership with a local electronics manufacturer to empirically validate the paper's four synthesized meta-principles in a real-world operational environment, addressing the literature's lack of longitudinal testing.
  • Policymaker: Draft a policy memo proposing a targeted grant program that provides R&D tax credits to industrial recycling facilities that implement integrated computer vision and automated sorting systems to improve regional material recycling rates.
Circular economy, Artificial intelligence, Design knowledge, Meta-Synthesis, Design science research, Dynamic capabilities
ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS
AI Processed Human Approved

What competing tensions and trade-offs do leaders face when guiding national quantum computing ecosystems?

Jori Taipale, Olli Tyrväinen, Tuure Tuunanen, Joel Mero, Teiko Heinosaari
ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS
ECIS 2026 · 2026
Key findings
  • Organizing tensions were identified as the most frequent paradox, specifically focusing on the trade-offs between collaboration vs. resource competition and guidance vs. flexibility.
  • Performance tensions emerged regarding resource allocation between software vs. hardware development and building a shared vision vs. individual firm survival.
  • Belonging tensions highlighted a 'godfather' paradox, where the ecosystem must balance the state's role as a major supportive funder and customer with the ultimate goal of corporate independence.
  • Learning tensions were characterized by the challenge of managing multi-talent knowledge development versus narrow technology specialization.
  • The study is limited by its single qualitative case context in Finland with a small sample of 15 informants, requiring caution when applying these findings to other national contexts.
What it means for you
  • CIO / IT Executive: Schedule a 9:00 AM meeting with your lead enterprise architect to audit your current quantum-readiness roadmap, establishing a formal policy that divides software development and hardware access investments 50/50 to hedge against the performance tension of committing prematurely to a single unproven hardware stack.
  • IT Manager: Create a bi-weekly 1-hour 'Quantum Cross-Training' slot in your team's calendar starting this Friday, pairing your specialized quantum physics PhDs with your generalist cloud engineers to address the learning tension between narrow technical specialization and broad-based systems integration.
  • Business Strategist: Perform a revenue dependency audit to calculate the percentage of your quantum business unit's funding derived from state grants versus commercial clients, and draft a pitch deck for two private-sector pilots to actively transition away from 'godfather' state-funding reliance.
  • Researcher: Draft a pre-competitive collaboration agreement template to send to a peer researcher at a competing institution, outlining a shared framework for baseline algorithm testing while clearly defining IP boundaries to manage the tension of collaboration versus resource competition.
  • Policymaker: Draft a revision for the upcoming national quantum funding round guidelines that introduces a 'co-investment requirement,' mandating that state-funded consortia secure at least 25% of their budget from private corporate buyers to counter the 'godfather' dependency paradox.
Ecosystem orchestration, Paradox theory, Quantum computing innovation ecosystem, Explorative case study, Interpretive qualitative research
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
Designing and Implementing Digital Twins in the Energy Grid Sector
AI Processed Human Approved

How can energy grid companies successfully design and implement digital twins?

Christian Meske, Karen S. Osmundsen, Iris Junglas
Designing and Implementing Digital Twins in the Energy Grid Sector
MIS Quarterly Executive · 2021
Key findings
  • The digital twin provides real-time and historical insights into the grid's status, enabling proactive maintenance, prediction of component failures, and more efficient management of power loads.
  • It serves as a powerful simulation tool to model future scenarios, such as the impact of increased electrification from electric ferries, allowing for better long-term planning and investment.
  • Successful implementation requires a strong focus on organizational learning, innovative co-creation with technology partners, and continuous feedback from end-users throughout the project.
  • The project highlighted the critical importance of evolving data governance, forcing the company to tackle complex issues of data security, integration, and standardization to unlock the full potential of the digital twin.
Digital Twin, Energy Sector, Grid Management, Digital Transformation, Organizational Learning, Co-creation, Data Governance
Managing IT Challenges When Scaling Digital Innovations
AI Processed

How can managers overcome IT challenges when scaling digital innovations from initial exploration to profitable growth?

Sara Schiffer, Martin Mocker, Alexander Teubner
Managing IT Challenges When Scaling Digital Innovations
MIS Quarterly Executive · 2023
Key findings
  • Prepare for a significant cultural shift: Management must explicitly communicate the change in focus from creative exploration and prototyping to efficient and profitable operations to align the team and manage expectations.
  • Rearchitect IT systems for scalability: Systems built for speed and flexibility in the exploration phase must be redesigned or replaced with robust, efficient, and reliable platforms capable of handling a large user base.
  • Adjust team composition and skills: The transition to scaling requires different expertise, shifting from IT generalists who explore new technologies to specialists focused on process automation, data analytics, and stable operations. Companies must be prepared to bring in new talent and restructure teams accordingly.
digital innovation, scaling, IT management, organizational change, case study, insurtech, innovation lifecycle
EMERGENCE OF IT IMPLEMENTATION CONSEQUENCES IN ORGANIZATIONS: AN ASSEMBLAGE APPROACH
AI Processed Human Approved

Why does the same technology succeed in one organization but fail in another?

Abdul Sesay, Elena Karahanna, and Marie-Claude Boudreau
EMERGENCE OF IT IMPLEMENTATION CONSEQUENCES IN ORGANIZATIONS: AN ASSEMBLAGE APPROACH
MIS Quarterly · 2025
Key findings
  • The process through which technology creates new behaviors and practices is complex and non-linear, occurring in three distinct phases (individuation, composition, and actualization).
  • Successful implementation is not guaranteed; it depends on the careful alignment of the technology itself (material components) with policies, training, and user adoption (expressive components) at each stage.
  • The study found that of the three police departments, only one successfully implemented body cameras because it carefully selected high-quality equipment, developed specific policies for its use, and ensured officers were trained and held accountable.
  • The other two departments experienced failure or delays due to poor quality equipment, generic policies, and inconsistent use, which prevented new, positive practices from taking hold.
  • The model shows that outcomes emerge over time and may require continuous adjustments, demonstrating that success is an ongoing process, not a one-time event.
IT implementation, Assemblage theory, body-worn camera, organizational change, police technology, process model
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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