AIS Research Insights

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New

AI Processed Human Approved

How can digital platforms help city leaders coordinate local experiments and adapt policies for long-term sustainability?

S.-V. Rehm, S. McLoughlin, G. Maccani

COPING WITH THE DYNAMICS OF URBAN SUSTAINABILITY: POSITIONING URBAN EXPERIMENTATION PLATFORMS AS TOOLS FOR POLICY MAKING

2021

Key findings

Urban experimentation platforms act as vital socio-technical bridges that align municipal policy-making, business ecosystems, and grassroots innovation.
A policy structure diagram illustrates key dynamic feedback loops where data and digital services directly inform and adapt local policy interventions.
Digital capabilities such as urban data observatories, open APIs, and challenge management portals empower collective intelligence and sustained civic engagement.

What it means for you

CIO / IT Executive: Allocate Q3 budget to architect an open API gateway and a centralized urban data observatory that connects municipal databases with external innovation platforms.
IT Manager: Draft technical requirements for a digital challenge management portal to ingest grassroots sensor data and track civic pilot projects.
Business Strategist: Schedule a meeting with local startup incubators and civic tech leaders to co-design a framework for integrating commercial solutions into municipal sustainability pilots.
Researcher: Map out the specific feedback loops between grassroots civic innovations and municipal policy outcomes using the study's System Dynamics approach as a baseline.
Policymaker: Draft a mandatory review checkpoint in the upcoming sustainability policy cycle that requires incorporating real-time data feeds from local urban experimentation platforms.

Digital Platform, Urban Experimentation, Urban Governance, System Dynamics, Urban Sustainability, Collective Intelligence

New

AI Processed Human Approved

How do combinations of different tools and routines help partner companies coordinate when developing new products?

Sven-V. Rehm, Lakshmi Goel

Composite Boundary Objects In Inter-Organizational Innovation Activities

2013

Key findings

Demonstrates that coordination across organizational boundaries is rarely driven by single, isolated tools, but rather through combinations of artifacts and routines termed composite boundary objects (CBOs).
Shows that effective collaboration emerges from the combined interplay of material objects (such as prototypes and central IT platforms), abstract concepts (such as shared product visions), and informal work routines.
Finds that cross-organizational coordination can emerge organically through everyday interactions, even when such coordination was not deliberately planned in advance.
Reveals that composite boundary objects are temporary and evolve across project milestones, requiring managers to support flexible environments of communication rather than focusing on standalone software applications.

What it means for you

CIO / IT Executive: Stop investing in standalone software silos and schedule a meeting with cross-company project leads on Monday morning to audit how your central IT platforms, shared physical prototypes, and communication routines are currently integrated to support external partners.
IT Manager: On Monday morning, map out the digital tools and informal communication channels your team uses with external partners, and set up a shared digital workspace that links these tools directly to ongoing physical prototyping milestones.
Business Strategist: On Monday morning, draft a flexible collaboration framework for your new product development project that combines shared product visions with routine check-ins, rather than relying on rigid, pre-planned coordination structures.
Researcher: On Monday morning, design a qualitative study protocol to trace how digital platforms, physical prototypes, and informal routines co-evolve across specific milestones in inter-organizational innovation projects.
Policymaker: On Monday morning, revise your innovation grant evaluation criteria to reward collaborative consortia that demonstrate flexible, multi-layered coordination mechanisms—such as combined digital and material artifact routines—rather than funding isolated software tools.

boundary objects, composite boundary objects, inter-organizational innovation, new product development, knowledge management, collaboration

New

AI Processed Human Approved

How can networked businesses quickly and cost-effectively integrate IT systems to support collaborative planning?

Sven-Volker Rehm

Architectural Design for Service-Supported Collaborative Work in Extended Enterprises

Americas Conference on Information Systems (AMCIS) · 2009

Key findings

A model-driven integration platform enables collaborative business applications to be built from reusable service components without modifying core internal systems.
Representing services as database models rather than custom software code drastically simplifies workflow modification and inter-firm data mapping.
A pilot implementation in a manufacturing network demonstrated that onboarding and integration time per partner could be reduced from four weeks to just a few days.
Shared service repositories allow networks of small and medium-sized enterprises to achieve significant economies of scale and extend enterprise planning capabilities to partners without advanced software.

What it means for you

CIO / IT Executive: Initiate an audit of all current custom point-to-point supply chain integrations and direct your architecture team to evaluate adopting a model-driven service platform to replace them.
IT Manager: Catalog your team's existing workflow and data integration tasks with external partners to identify which processes can be standardized into reusable database-backed service components.
Business Strategist: Draft a pilot project proposal to test a shared service repository with two key external manufacturing partners to evaluate reductions in onboarding time.
Researcher: Design a study to measure the exact time-to-onboard and transaction cost differentials between database-modeled services and traditional custom-code integrations in multi-firm networks.
Policymaker: Draft a grant funding framework that subsidizes shared service repositories and model-driven integration platforms specifically for small and medium-sized enterprise manufacturing networks.

Service-Oriented Architecture, Extended Enterprise, Enterprise Architecture, Enterprise Resource Planning, Business Process Modeling, Collaborative Networks

New

AI Processed Human Approved

How do remote teams collaborate and make complex decisions inside 3D virtual environments?

Lakshmi Goel, Sven-Volker Rehm

How teams complete tasks in virtual worlds

Americas Conference on Information Systems (AMCIS) · 2017

Key findings

Teams spend an extensive initial phase engaging in spatial and social sense-making rather than adopting an immediate task stance.
Progress occurs through multiple internal disruptions driven by hands-on tinkering rather than a single midpoint turnaround.
Virtual 3D objects serve as critical anchors that help members point, align perspectives, and reconcile individual viewpoints into shared team models.
Teams do not enter a second period of inertia, but rather cycle continuously through adjustments until reaching alignment.

What it means for you

CIO / IT Executive: Procure and provision enterprise-grade 3D virtual workspace software featuring interactive digital objects to serve as collaborative anchors for distributed teams.
IT Manager: Allocate the first 15 minutes of Monday's virtual team meeting to unguided spatial exploration and hands-on tinkering with the digital project board rather than jumping straight into the agenda.
Business Strategist: Restructure project timelines to eliminate expectations of a single midpoint review, replacing them with a framework that budgets time for continuous, iterative adjustments.
Researcher: Design a longitudinal study to observe how distributed teams utilize specific 3D visual anchors to reconcile divergent mental models over time.
Policymaker: Draft procurement standards for digital collaboration tools that mandate support for interactive 3D modeling and shared spatial environments to enhance remote worker accessibility and alignment.

Punctuated equilibrium, Boundary objects, Mental models, virtual worlds, team work, decision-making

New

AI Processed Human Approved

How can teams across an organization collaboratively map and visualize their business ecosystem to make better decisions?

Anne Faber, Adrian Hernandez-Mendez, Sven-Volker Rehm, Florian Matthes

Visualizing Business Ecosystems: Applying a Collaborative Modelling Process in Two Case Studies

2018

Key findings

Validated a six-step collaborative modeling process consisting of scoping, model instantiation, data collection, visualization, feedback adaptation, and storytelling.
Identified four critical collaboration roles that drive modeling success: the gatekeeper, facilitator, modeler, and recorder.
Discovered that ecosystem models evolve primarily at the attribute level rather than the entity level once visualization views are established.
Demonstrated that providing early, interactive visual layouts is vital to keeping stakeholders motivated during tedious data collection phases.
Showed that participants' prior modeling familiarity significantly affects progress, underscoring the need for hands-on preparatory training.

What it means for you

CIO / IT Executive: Procure and provision an agile, collaborative visualization software framework to support multi-stakeholder ecosystem mapping sessions this quarter.
IT Manager: Assign team members to specific collaboration roles—specifically gatekeeper, facilitator, modeler, and recorder—ahead of Monday's stakeholder workshop.
Business Strategist: Design an interactive, early visual layout of your target market ecosystem to present on Monday to maintain stakeholder engagement during data collection.
Researcher: Investigate how ecosystem models evolve specifically at the attribute level rather than the entity level during longitudinal case studies.
Policymaker: Develop hands-on preparatory training modules focused on modeling familiarity to better equip regional industry participants for collaborative workshops.

Business Ecosystem, Collaborative Modelling, Information Visualization, Action Research, Case Studies, Knowledge Management

New

AI Processed Human Approved

What different types of business ecosystems exist and how do they connect with one another?

Anne Faber, Maximilian Riemhofer, Sven-Volker Rehm, Gloria Bondel

A Systematic Mapping Study on Business Ecosystem Types

Americas Conference on Information Systems (AMCIS) · 2019

Key findings

Identifies 12 distinct business ecosystem types across academic literature, including platform, innovation, knowledge, software, IoT, and entrepreneurial ecosystems.
Maps ecosystems across two primary viewpoints: an organizational perspective centered on the enterprise and a product or service perspective centered on core value creation.
Highlights innovation ecosystems and platform business ecosystems as the most prominently discussed forms in literature.
Reveals that specialized types, such as IoT and customer-centric ecosystems, operate at the intersections of platform, knowledge, and innovation domains.
Demonstrates that software ecosystems uniquely bridge both organizational and product perspectives depending on their focus.

What it means for you

CIO / IT Executive: Review your current technology architecture on Monday morning to identify whether your software initiatives operate purely from an organizational or product perspective, and explicitly map them to one of the 12 recognized ecosystem types to align your IT investments with your enterprise's core value creation.
IT Manager: Audit your active platform and IoT projects on Monday morning by checking if they effectively bridge knowledge, innovation, and platform domains as outlined in the literature taxonomy, and flag any isolated technical silos for integration.
Business Strategist: Pull your strategic partnership portfolio on Monday morning and categorize each external alliance using the study's distinction between organization-centric and product-centric ecosystem types to identify gaps in your collaborative networks.
Researcher: Cross-reference your current literature review matrix on Monday morning against the 12 identified business ecosystem types to ensure your scope accounts for intersections between specialized domains like IoT and customer-centric ecosystems.
Policymaker: Examine your regional economic development grants on Monday morning to verify whether funding criteria adequately distinguish between broad innovation ecosystems and narrower platform or software ecosystems, ensuring targeted support for local tech clusters.

business ecosystem, innovation ecosystem, platform ecosystem, digital business ecosystem, systematic mapping study, software ecosystem

New

AI Processed Human Approved

What stops carmakers and software developers from creating shared data platforms, and what pushes them forward?

Gloria Bondel, Sven-Volker Rehm, Florian Matthes

JUST LINKING THE DOTS? BARRIERS AND DRIVERS TO CREATING VALUE FROM APPLICATION PROGRAMMING INTERFACES

2020

Key findings

External software developers drive early data access through workarounds like diagnostic hardware dongles and sustained pressure on carmakers.
Vehicle manufacturers implement APIs largely due to regulatory and standardization pressures rather than voluntary ecosystem growth, while maintaining tight gatekeeping.
The absence of proven commercial value propositions and fears of legal liability significantly delay manufacturer participation in open data sharing.
Intense competition over platform ownership and vehicle operating system control prevents manufacturers from standardizing on a common platform.
Emerging mobility trends, particularly autonomous driving, create future expectations that incentivize gradual steps toward open API ecosystems.

What it means for you

CIO / IT Executive: Initiate a legal risk-assessment workshop with compliance and legal counsel to draft clear liability frameworks for third-party API data sharing, removing internal roadblocks to manufacturer participation.
IT Manager: Audit all current vehicle diagnostic APIs and technical boundary resources to ensure they meet existing regulatory standards while safely accommodating third-party developer workarounds.
Business Strategist: Schedule a stakeholder alignment meeting to model a low-risk, shared-value commercial pilot focusing on autonomous driving data use cases, bypassing platform ownership deadlocks.
Researcher: Design a comparative interview study tracking how external software developers leverage diagnostic hardware dongles to pressure incumbent automakers into data access.
Policymaker: Draft standardized data-sharing guidelines that reduce legal liability fears for automotive manufacturers while mandating fair API access for third-party mobility start-ups.

Application Programming Interfaces, Boundary Resources, Digital Platforms, Automotive Industry, Connected Vehicles, Business Ecosystems

New

AI Processed Human Approved

How can digital experimentation platforms help city leaders make better, more adaptive urban sustainability policies?

Sven Volker Rehm, Shane McLoughlin, Brian Donnellan

HOW TO COPE WITH THE DYNAMICS OF URBAN SUSTAINABILITY: URBAN EXPERIMENTATION PLATFORMS AS TOOLS FOR ADAPTIVE POLICY-MAKING

2022

Key findings

Urban experimentation platforms function both as vehicles for implementing policy and as essential instruments for adaptive, feedback-driven policymaking.
Municipal decision-makers currently demonstrate low awareness of platform data, largely using platforms to broadcast priorities rather than using trial results to update policy.
Establishing structured feedback loops between urban business ecosystems, city authorities, and digital platforms substantially improves collaborative problem-solving and long-term sustainability.

What it means for you

CIO / IT Executive: Audit current urban data platforms to ensure they include incoming feedback channels from local experimentation trials, rather than functioning solely as one-way broadcast tools.
IT Manager: Configure dashboard alerts to aggregate and flag real-time stakeholder feedback and usage metrics from ongoing urban sustainability pilots.
Business Strategist: Initiate a meeting with municipal authorities and local business ecosystem partners to design a recurring framework for collaborative problem-solving using platform data.
Researcher: Apply system dynamics modeling to map the existing stakeholder feedback loops within your city's active urban experimentation platforms.
Policymaker: Review the latest data reports from active local sustainability trials and draft a plan to update an existing municipal policy based on the trial results.

Urban Sustainability, Urban Experimentation Platforms, Digital Platforms, Urban Governance, System Dynamics, Case Study

New

AI Processed Human Approved

What causes organizational inertia during digital transformation, and how does it block successful change?

Thomas Haskamp, Christian Dremel, Carolin Marx, Falk Uebernickel

Understanding Inertia in Digital Transformation: A Literature Review and Multilevel Research Framework

International Conference on Information Systems (ICIS) · 2021

Key findings

Conceptualizes inertia through five key attributes: paradoxical duality (vital for efficiency yet resistant to change), rigidity of social/material entities, relational character, time-dependency, and misalignment with the environment.
Identifies nine distinct dimensions of inertia spanning individual (behavioral, cognitive, affective), organizational (structural, cultural, socio-technical, political, economic), and industry levels.
Maps 12 antecedents—ranging from individual cognitive flexibility to organizational routines, legacy IT, culture, and external uncertainty—that give rise to inertia.
Outlines 12 critical consequences of inertia, including active employee resistance, biased management decision-making, project failures, compromised agility, and failure to renew business models.
Pinpoints 15 moderating factors, such as dynamic capabilities, agility, absorptive capacity, and managerial cognition, that influence how inertia translates into operational impacts.

What it means for you

CIO / IT Executive: Audit your portfolio for socio-technical legacy IT misalignments by scheduling a joint review with department heads to identify legacy systems anchoring obsolete business routines.
IT Manager: Map your team's current project workflows against the five inertia attributes to pinpoint where cognitive and behavioral rigidity is slowing down daily software deployment.
Business Strategist: Review the 12 documented inertia antecedents against your current transformation roadmap to identify which cultural and structural barriers threaten your upcoming business model renewal.
Researcher: Formulate a new empirical study focusing on the interactions between individual-level cognitive inertia and organizational-level structural inertia during enterprise cloud migrations.
Policymaker: Draft guidelines for state-backed digital grants that require organizations to explicitly account for socio-technical and cultural inertia mitigation strategies before receiving funding.

digital transformation, organizational inertia, socio-technical inertia, change resistance, dynamic capabilities, literature review

AI Processed Human Approved

How can collaborative software be designed so domain experts can configure it for their specific needs?

Sven-Volker Rehm, Thomas Reschenhofer, Klym Shumaiev

IS Design Principles for Empowering Domain Experts in Innovation: Findings From Three Case Studies

International Conference on Information Systems (ICIS) · 2014

Key findings

Identified key design principles for domain expert configurable software, including iterative modeling, content-context relation, information re-use, function re-use, and context alignment.
Highlighted the essential role of 'facilitators' as mediating agents who help bridge technical configuration mechanisms with business domain requirements.
Demonstrated that domain expert configuration provides a flexible, cost-effective method to align IT support with dynamic inter-organizational innovation processes.

What it means for you

CIO / IT Executive: On Monday morning, schedule a brief meeting with your innovation network leads to discuss the potential of 'domain expert configuration' for upcoming projects, specifically asking them to identify one current pain point related to rigid software that could be addressed by such a system.
IT Manager: On Monday morning, review your current internal documentation for any existing examples of 'facilitators' – individuals who successfully bridge technical and business needs – and plan a quick chat with one of them to understand their approach.
Business Strategist: On Monday morning, draft a one-page concept document outlining how a more flexible, domain expert-configurable information system could accelerate a specific strategic innovation initiative, focusing on the benefits of rapid adaptation to diverse partner needs.
Researcher: On Monday morning, identify three publicly available collaborative information systems (e.g., wikis, content management systems) that exhibit elements of 'content-context relation' or 'information re-use' and briefly document how they do so.
Policymaker: On Monday morning, review existing procurement guidelines for IT systems to identify any clauses that might hinder the adoption of flexible, configurable solutions, and consider proposing a pilot program to test the effectiveness of domain expert configuration for inter-organizational projects.

Domain expert configuration, design science, innovation networks, domain knowledge, design principles, case study

AI Processed Human Approved

How can organizations define and select performance indicators for collaborative business networks?

Montijn van de Ven, Paola Lara Machado, Banu Aysolmaz, Oktay Turetken

Managing Performance in Collaborative Networks: A Method for Defining Key Performance Indicators for Collaborative Business Models

Business & Information Systems Engineering · 2026

Key findings

Introduced a three-step method covering KPI specification, multi-criteria KPI selection, and attribute operationalization.
Differentiated KPI scopes into network, subnet, and actor levels to balance network alignment with individual flexibility.
Demonstrated practical utility through business scenarios including car navigation services, urban indoor farming, and sustainable households.

What it means for you

CIO / IT Executive: On Monday morning, initiate a pilot project to apply the three-step KPI definition method to one specific collaborative network your organization participates in, focusing on identifying and documenting initial network-level KPIs using your existing data infrastructure.
IT Manager: On Monday morning, schedule a working session with a key business stakeholder from a collaborative project to begin the 'KPI specification' step of the research method, focusing on articulating the desired outcomes for that specific collaboration.
Business Strategist: On Monday morning, review the research's findings on differentiating KPI scopes and draft a preliminary list of potential network, subnet, and actor-level KPIs for a newly forming collaborative initiative.
Researcher: On Monday morning, begin designing a case study to apply and further refine the proposed KPI definition method within a new collaborative business scenario, focusing on the 'multi-criteria KPI selection' and 'attribute operationalization' steps.
Policymaker: On Monday morning, identify a specific public-private collaborative initiative and begin exploring how the research's approach to KPI definition could be adapted to measure its network-wide alignment and individual actor contributions effectively.

Business models, Collaborative business models, Collaborative networks, Key performance indicators, Performance management, Design science research

AI Processed Human Approved

How can organizations turn digital activity data into actionable solutions for improving their business processes?

Jan vom Brocke, Sandro Franzoi, Sophie Hartl, Thomas Grisold

The Process of Process Science – Generating Actionable Insights from Digital Trace Data

2026

Key findings

Defines three core methodological principles for process science: contextual grounding, iterative reasoning, and the integration of computational and human sense-making.
Proposes three interconnected, recursive cycles of inquiry: a descriptive cycle (from scope via data to description), an explanatory cycle (from analysis via pattern to explanation), and a prescriptive cycle (from intervention via evaluation to prescription).
Demonstrates the practical execution of these cycles through a real-world case study of a financial institution's digital customer onboarding process.
Provides concrete guidance for researchers and practitioners on combining qualitative insights with computational techniques to move from descriptive analytics to actionable organizational interventions.

What it means for you

CIO / IT Executive: Schedule a 30-minute meeting with your IT Manager for Monday morning to initiate the 'descriptive cycle' by identifying a specific digital process (e.g., customer support ticket resolution) and confirming the availability of its digital trace data for analysis.
IT Manager: On Monday morning, begin by downloading and organizing the raw digital trace data logs (e.g., timestamps, event names, user IDs) for the chosen IT process from your systems and prepare to import them into a process mining tool.
Business Strategist: On Monday morning, prepare a concise list of 3-5 key business questions you want to understand about a specific digital process (e.g., 'Why is our customer onboarding taking so long?') to inform the 'explanatory cycle'.
Researcher: On Monday morning, review the provided case study from the research paper and begin to outline a potential scope for a process science study based on a digital process you have access to data for, focusing on defining the initial research questions.
Policymaker: On Monday morning, request a brief overview from your IT Executive and Business Strategist regarding the current state of understanding and identified inefficiencies within a key digital customer-facing process, preparing to guide future intervention strategies.

Process Science, Methodology, Iteration, Process Mining, Digital Trace Data, Socio-Technical Processes

AI Processed Human Approved

How does adopting smart speakers change how consumers search for, buy, and consume digital content?

Yoonseock Son, Wonseok Oh, Il Im

THE VOICE OF COMMERCE: HOW SMART SPEAKERS RESHAPE DIGITAL CONTENT CONSUMPTION AND PREFERENCES

MIS Quarterly · 2023

Key findings

Smart speaker adoption led to a 21.5% increase in monthly VOD content expenditures, alongside a 3.0% decrease in average content completion rates.
The increase in purchases is driven by lowered search friction, resulting in more search incidences, higher conversion rates, and less labor-intensive evaluation of content.
Using smart speakers for conversational purposes beyond simple informational queries amplifies the positive impact of device adoption on sales.

What it means for you

CIO / IT Executive: On Monday morning, task your team with investigating integration points between existing VOD platforms and major smart speaker voice assistant APIs (e.g., Alexa, Google Assistant) to enable seamless content discovery and playback via voice commands.
IT Manager: On Monday morning, analyze the current VOD platform's data infrastructure to identify how to track and log voice-initiated search queries and content playback events, distinguishing them from traditional remote control interactions.
Business Strategist: On Monday morning, initiate a brainstorm session to explore new content promotion strategies that leverage voice-activated search, considering how to make titles more discoverable through natural language queries and conversational recommendations.
Researcher: On Monday morning, begin outlining a follow-up study to quantify the correlation between specific conversational voice interactions (beyond simple search) and increased VOD content consumption, perhaps through A/B testing of voice-guided discovery features.
Policymaker: On Monday morning, convene a working group to assess the potential implications of increased voice-activated content consumption on digital accessibility standards and to explore how to ensure equitable access to content across all user interfaces.

Voice channel, search, smart speaker, digital content, econometrics

AI Processed Human Approved

How can practitioner researchers overcome the divide between academia and industry to create real-world impact?

Stephen McCarthy, Hendrik Scholta, Geir Inge Hausvik, Peter André Busch

Boundary Spanning and Practical Impact in IS Research: A Bourdieusian Analysis

Information Systems Journal (ISJ) · 2025

Key findings

IS practitioner doctorates facilitate knowledge exchange through boundary-spanning activities including research design adaptation, multi-channel dissemination, and creating symbolic discourse.
Practical impact progresses through proof-of-concept, proof-of-value, and proof-of-use stages, each requiring different combinations of social, cultural, economic, and symbolic capital.
Environmental forces such as academic vs. practice doxa (institutional norms) and habitus (professional dispositions) create positional conflicts that require active negotiation and tailored incentives to sustain engagement.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your direct reports to initiate a 'practice impact review' focused on one ongoing IS project. Ask each person to identify one specific academic insight or research finding from a recent external study or internal research initiative that could be directly applied to improve that project's current proof-of-concept or proof-of-value stage, and assign them to explore this application by end of week.
IT Manager: On Monday morning, identify one specific, recent academic paper or conference proceeding relevant to a current team challenge. Forward this paper to your team with a concise note asking for a brief, 1-2 sentence summary of its most practical takeaway and how it *might* apply to your current proof-of-value stage objective by Tuesday afternoon.
Business Strategist: On Monday morning, review the key performance indicators (KPIs) of one strategic business initiative currently underway. Identify which KPIs are not meeting expectations and, using the 'proof-of-value' framework, draft a one-page hypothesis on how a novel IS application or process, potentially informed by academic research, could demonstrably improve these specific KPIs within the next quarter.
Researcher: On Monday morning, select one recent academic publication where you are the lead author and identify its 'proof-of-use' stage potential. Draft a single-slide executive summary that articulates the core problem addressed, the practical solution proposed, and one tangible benefit for a specific industry practitioner role (e.g., IT manager, business analyst), and share it with three relevant practitioner contacts for feedback by Wednesday.
Policymaker: On Monday morning, review the current grant application criteria for technology innovation funding. Identify if and how 'demonstrable practical impact' is explicitly assessed, and draft a brief proposal to incorporate a specific requirement for applicants to detail their strategy for achieving 'proof-of-value' or 'proof-of-use' within a defined timeframe, alongside theoretical contributions.

academia-practice gap, boundary spanning, design science research, engaged IS scholarship, practical impact, theory of practice

AI Processed Human Approved

How can digital data be used to understand and guide ongoing changes involving people and technology?

Jan vom Brocke, Wil M. P. van der Aalst, Nicholas Berente, Boudewijn van Dongen, Thomas Grisold, Waldemar Kremser, Jan Mendling, Brian T. Pentland, Maximilian Roeglinger, Michael Rosemann, Barbara Weber

Process science: the interdisciplinary study of socio-technical change

Process Science · 2024

Key findings

Defines process science as an interdisciplinary domain aimed at capturing, understanding, and intervening in socio-technical change across multiple levels of abstraction.
Categorizes key activities of process science into discovery (detecting patterns via event data), explanation (identifying cause-effect relations), and intervention (shaping process trajectories).
Advocates for shifting from an 'entity-first' to a 'process-first' perspective to identify analogies across diverse domains and support computationally intensive theory building.
Introduces a research framework that integrates digital trace data from practice with theoretical foundations to solve real-world problems and drive societal impact.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of your organization's current digital infrastructure and data collection capabilities to identify which systems generate 'digital trace data' relevant to socio-technical processes. Prioritize understanding the flow of this data and its potential for analysis.
IT Manager: On Monday morning, investigate your team's current methods for logging and tracking user interactions with key IT systems. Identify specific 'events' that could be captured and analyzed to reveal patterns of socio-technical change within your department.
Business Strategist: On Monday morning, schedule a brainstorming session with your core team to identify one specific business process currently undergoing significant change (e.g., customer onboarding, product development). Brainstorm what digital 'trace data' this process generates and how understanding its dynamics could inform strategy.
Researcher: On Monday morning, start cataloging existing datasets within your research domain that represent 'digital traces' of human-technology interaction. Begin exploring computational tools that can analyze event sequences rather than just static entities.
Policymaker: On Monday morning, identify a specific public policy area (e.g., digital inclusion, citizen engagement) that is experiencing rapid socio-technical evolution. Begin researching existing digital platforms and services that generate trace data related to this policy area.

Process Science, Socio-Technical Processes, Digital Trace Data, Process Mining, Organizational Change, Interdisciplinary Research

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

ARE THE REGULATORS WINNING THE FIGHT WITH ANONYMITY-ENHANCING COINS? STUDYING BINANCE’S EUROPEAN DELISTING OF MONERO AS A NATURAL EXPERIMENT

AI Processed Human Approved

How does delisting a privacy coin from a major exchange impact its price over time?

Robin Renwick, Rob Gleasure

ARE THE REGULATORS WINNING THE FIGHT WITH ANONYMITY-ENHANCING COINS? STUDYING BINANCE’S EUROPEAN DELISTING OF MONERO AS A NATURAL EXPERIMENT

ECIS 2026 · 2026

Key findings

Hypothesis 1 (delisting will cause an immediate negative price impact) was STATISTICALLY SUPPORTED: Monero's price was significantly lower (estimated at 63% to 72% lower) post-delisting compared to its synthetic counterfactual.\n- Hypothesis 2 (the negative price impact will diminish over time) was NOT SUPPORTED: Rather than recovering, the negative price impact intensified when longer time lags of up to 200 days were examined.\n- Limitations: The findings are restricted to a single delisting event of one specific privacy coin (Monero) and use trading price as an imperfect proxy for perceived value.

What it means for you

CIO / IT Executive: Audit our organization's digital asset holdings and treasury to identify any exposure to privacy coins like Monero, and immediately initiate a migration plan to convert these assets to compliant, liquid cryptocurrencies to avoid permanent valuation loss from regulatory delistings.
IT Manager: Update network monitoring tools and firewall rules to detect and block any Monero nodes, mining scripts, or wallet connections on company-owned infrastructure to prevent compliance violations arising from tightening centralized exchange regulations.
Business Strategist: Review our payment gateway integrations and product roadmap to remove Monero or other privacy coins as accepted payment methods, and pivot our privacy-focused initiatives toward compliant layer-2 scaling solutions (like Zero-Knowledge rollups) that satisfy regulators.
Researcher: Draft a new research proposal to apply the synthetic control method to other privacy-centric assets (like Zcash or Dash) across different geographic regions, incorporating decentralized exchange (DEX) trading volumes to test if the value shifted off centralized order books.
Policymaker: Draft an amendment to the national crypto-asset regulatory framework to mandate that all registered centralized exchanges delist anonymity-enhancing coins, citing this empirical evidence that exchange-level bans are highly effective at permanently reducing these assets' market value and liquidity.

Cryptocurrencies, Monero, Privacy, Regulation, Natural Experiment, Synthetic Control Method

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

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