AIS Research Insights

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AI Processed Human Approved

How can managers use generative AI to turn expert insights into practical strategies for digital platforms?

Sven-Volker Rehm

Turning Tacit Knowledge into Dynamic Strategies Through Collective Inferencing

MIS Quarterly Executive · 2025

Key findings

Formulates a four-step Collective Inference Method involving differential analysis, landscape mapping, dynamic modeling, and scenario synthesis.
Demonstrates how generative AI can structure messy stakeholder narratives into functional modules while mitigating hallucination risks.
Validates the approach within the cplace platform ecosystem, resulting in 59 actionable management measures that effectively address governance, customization, and user empowerment.

What it means for you

CIO / IT Executive: On Monday morning, initiate a pilot project with your innovation team to test the 'differential analysis' step of the Collective Inference Method on a small, contained platform ecosystem. Gather 2-3 key internal stakeholders with diverse operational roles and feed their raw, unstructured operational challenges into a generative AI tool to identify initial tacit knowledge patterns.
IT Manager: On Monday morning, schedule a 30-minute informal discussion with two of your most experienced support technicians. Ask them to describe in detail a recent complex troubleshooting scenario where they had to 'go beyond the documentation' to resolve the issue. Record their responses, focusing on the 'why' and 'how' of their intuitive decisions.
Business Strategist: On Monday morning, identify one critical strategic decision your organization is facing that has significant ambiguity or conflicting stakeholder perspectives. Document the perceived goals and constraints of three different key stakeholder groups involved in this decision in their own words, without trying to structure them yet.
Researcher: On Monday morning, select one publicly available case study of a failed platform strategy and manually extract all unstructured stakeholder feedback, forum discussions, or public comments related to its challenges. Categorize these snippets into broad areas of operational friction or unmet needs.
Policymaker: On Monday morning, identify a specific public service delivery challenge where fragmented stakeholder input leads to inefficient or contradictory policy outcomes. Gather and print out the last 5 public consultation submissions from different community groups on this issue, without any initial analysis.

Collective Inferencing, Tacit Knowledge, Generative AI, Platform Governance, Ecosystem Management, Dynamic Modeling

AI Processed Human Approved

How can business schools integrate location analytics into their curriculum and research to meet industry demand?

Michael A. Erskine, James B. Pick, Asish Satpathy, Andrés Díaz López, Avijit Sarkar, and Namchul Shin

Location Analytics in Information Systems: Opportunities for Research and Teaching

Communications of the Association for Information Systems · 2024

Key findings

Highlights key research opportunities where location analytics can address complex, multidisciplinary problems such as climate change, network disruptions, and location privacy.
Proposes a stepwise framework for business schools to integrate location analytics into IS courses, concentrations, and interdisciplinary programs.
Recommends action steps to strengthen industry-academic partnerships, train faculty, and promote spatial methodologies in leading IS journals.

What it means for you

CIO / IT Executive: On Monday morning, task your IT strategy team to identify one current business challenge (e.g., supply chain optimization, customer segmentation) that could be significantly improved by incorporating location data, and schedule a brief exploratory meeting to discuss potential pilot projects using spatial analysis tools.
IT Manager: On Monday morning, identify one existing data set within your organization that has inherent spatial components (e.g., customer addresses, store locations, sensor readings) and initiate a conversation with a data analyst about how to visualize and query this data using basic geospatial techniques.
Business Strategist: On Monday morning, review your current strategic plan and identify two key business objectives where understanding 'where' things happen or are located could provide a competitive advantage or unlock new opportunities, then brainstorm potential data sources for that 'where'.
Researcher: On Monday morning, identify one of your ongoing or future research projects that currently lacks a spatial dimension and draft a brief proposal outline for how integrating location analytics could address a key limitation or open new avenues of inquiry, referencing specific spatial methods mentioned in the literature.
Policymaker: On Monday morning, reach out to a representative from a local university's Information Systems or Geography department to inquire about potential collaborations or pilot programs for integrating location analytics into business curricula, focusing on areas relevant to local economic development or public services.

Location Analytics, Spatial Analysis, IS Curriculum, Research Methodologies, Geographic Information Systems, Spatial Business

AI Processed Human Approved

How does making conversational agents appear more human affect customer reactions when errors occur?

Alfred Benedikt Brendel, Sascha Lichtenberg, Fabian Hildebrandt & Alan R. Dennis

Artificial Normality: How Conversational Agents’ Perceived Humanness Inhibits Error Attribution and Preserves Satisfaction

Journal of Management Information Systems · 2026

Key findings

Greater perceived humanness in conversational agents preserves perceptions of situational normality when minor errors occur.
Preserved situational normality reduces users' tendency to attribute error cause to the agent, thereby safeguarding overall service satisfaction.
Errors made by humanlike conversational agents are processed similarly to errors made by human service agents, demonstrating that humanlike design can alleviate algorithm aversion.

What it means for you

CIO / IT Executive: On Monday morning, allocate budget to pilot the integration of more human-centric design principles (e.g., nuanced language, empathetic phrasing) into our core customer-facing conversational agent, focusing on areas where process errors are most common, to preemptively reduce user error attribution and maintain satisfaction.
IT Manager: On Monday morning, task your development team to review our current conversational agent's dialogue logs for instances of process errors and identify the top 3 most frequent error types to begin redesigning their responses with more humanlike cues (e.g., slight pauses, conversational fillers) to normalize the situation for users.
Business Strategist: On Monday morning, initiate a cross-functional workshop with marketing and product development to explore how to strategically position our conversational agents' perceived humanness in customer communications, emphasizing its role in creating a seamless experience even when minor technical glitches occur.
Researcher: On Monday morning, begin designing a follow-up study that experimentally manipulates specific humanlike conversational features (e.g., varying levels of politeness, acknowledgment of difficulty) within a simulated error scenario to quantify their direct impact on error attribution and situational normality, building upon the findings of Artificial Normality.
Policymaker: On Monday morning, convene a small working group to draft guidelines for the ethical development of conversational agents, recommending that AI design standards encourage the incorporation of humanlike qualities to enhance user experience and mitigate negative reactions to inevitable system errors, rather than solely focusing on pure efficiency.

Chatbot, Chatbot errors, situational normality, anthropomorphism, humanness, error attribution, user satisfaction

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

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

AI Processed Human Approved

How well does ChatGPT perform on information systems course questions and professional certification exams?

Daniel E. O’Leary, Veda C. Storey, Aaron M. French, Joseph R. Buckman, Cecil Chua, Andrew W. Green, Grace Gu, Fred Niederman, Francis Pereira, Gary Templeton, Linda Wallace

What Does ChatGPT Know About Information Systems?

Communications of the Association for Information Systems · 2025

Key findings

ChatGPT answered 65% to 85% of Information Systems queries correctly across all core framework areas, performing roughly at the level of an average student.
The model performed significantly better on university course exams than on professional certification exams, failing to reach passing scores for the PMP and Microsoft Azure Administrator tests.
ChatGPT achieved higher relative accuracy against class averages on closed-book exams compared to open-book or online exam environments where students had external resource access.
While ChatGPT displayed broad coverage across diverse IS topics, its responses favored listing structured information over demonstrating expert-level critical thinking or complex problem-solving.

What it means for you

CIO / IT Executive: On Monday morning, task your IT training lead to pilot a small internal workshop for a select group of IT professionals focusing on the PMP and Microsoft Azure Administrator certifications, using ChatGPT as a supplementary learning tool to identify specific knowledge gaps and assess its effectiveness in preparing for these professional exams.
IT Manager: On Monday morning, instruct your team members responsible for generating internal documentation or knowledge base articles to compare the accuracy and completeness of ChatGPT's initial drafts on at least two core Information Systems topics (e.g., database management, network security) against your existing, verified internal resources.
Business Strategist: On Monday morning, task a market analyst to explore potential use cases for ChatGPT in rapidly generating initial drafts of business process documentation or competitive analysis reports within your IS domain, noting its tendency to provide structured information over deep critical thinking.
Researcher: On Monday morning, design a controlled experiment to compare ChatGPT's performance on retrieving specific, factual information about IS concepts in a closed-book scenario versus an open-book scenario using publicly available academic papers, to validate the finding about its performance in different exam environments.
Policymaker: On Monday morning, request a briefing from your education and technology policy advisors on the implications of AI tools like ChatGPT achieving 'average student' performance in IS education, and explore potential guidelines for incorporating such tools responsibly in curriculum development and assessment.

ChatGPT, Large Language Models, Generative AI, Information Systems Competency, Artificial Intelligence in Education, Knowledge Assessment

GENAI AS A ROMANTIC RELATIONSHIP ADVISOR: EXPLORING HUMAN–AI COLLABORATION IN EMOTIONAL SUPPORT CONTEXTS

AI Processed Human Approved

What are the benefits and risks of using generative artificial intelligence for romantic relationship advice?

Anna Schätzle, Peter Buxmann

GENAI AS A ROMANTIC RELATIONSHIP ADVISOR: EXPLORING HUMAN–AI COLLABORATION IN EMOTIONAL SUPPORT CONTEXTS

ECIS 2026 · 2026

Key findings

Qualitative evidence supported that GenAI helps users self-clarify emotions, organize thoughts, and prepare communication strategies prior to interpersonal discussions.
Qualitative evidence supported that GenAI use entails risks of automation and confirmation bias, as the system tends to validate the user's one-sided, potentially biased framing of relationship conflicts.
Qualitative evidence supported that users often attribute unintended authority to GenAI, potentially misinterpreting its structured, polished responses as clinical diagnoses.
Qualitative analysis supported that GenAI is a useful complementary or preparatory tool but cannot replace professional therapy because it lacks emotional intuition, nonverbal communication perception, and dyadic mediation capabilities.
No statistical hypotheses were tested due to the study's exploratory qualitative nature; findings are limited by a small sample size of 18 interviewees, retrospective self-reports, a German-speaking context, and homogeneous use of ChatGPT.

What it means for you

CIO / IT Executive: Configure the enterprise GenAI gateway to detect personal counseling or emotional-support-seeking queries and automatically inject a system-level disclaimer redirecting employees to the company's official Employee Assistance Program (EAP) to prevent reliance on GenAI as an authoritative clinical resource.
IT Manager: Create and distribute a 'Balanced Prompting' template to the team, instructing them to explicitly prompt GenAI to 'act as a devil's advocate and challenge my assumptions' when using it to prepare for difficult workplace or interpersonal conversations, directly mitigating the tool's confirmation bias.
Business Strategist: Review the product roadmap for our wellness and conversational AI features to pivot the UX away from direct 'advice-giving' toward 'communication preparation and self-reflection' tools, integrating mandatory disclaimers that clarify the AI has no clinical diagnostic capabilities.
Researcher: Draft a research proposal for a pre-registered quantitative study ($N > 300$) comparing how different LLM models (e.g., ChatGPT, Claude, Gemini) handle relationship conflict prompts, specifically measuring the rate of user agreement (automation bias) versus the model's level of validation.
Policymaker: Draft a regulatory policy mandate requiring developers of consumer-facing GenAI tools to implement prominent 'non-clinical tool' labels and active-consent pop-ups warning users of the risks of automation bias and the lack of professional diagnostic authority in emotionally sensitive domains.

Generative Artificial Intelligence, Relationship, Counseling, Therapy, Human-AI Interaction, HAII-TIME

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

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

HARNESSING AND REDUCING THE GENERATIVE AND WORKFLOW AUTONOMY OF GENERATIVE AI SYSTEMS: A MULTIPLE-CASE STUDY

AI Processed Human Approved

How can businesses manage generative AI autonomy after deployment to reduce risks without losing value?

Vincent Heimburg, Manuel Wiesche

HARNESSING AND REDUCING THE GENERATIVE AND WORKFLOW AUTONOMY OF GENERATIVE AI SYSTEMS: A MULTIPLE-CASE STUDY

ECIS 2026 · 2026

Key findings

The study conceptualizes AI autonomy as consisting of two dimensions: generative autonomy (content creation) and workflow autonomy (process and system interaction), which is qualitatively supported by the case analysis.
The research identifies 20 distinct post-deployment practices categorized under either harnessing value-adding autonomy (such as real-time auditing and semantic resource enrichment) or reducing unproductive autonomy (such as architectural weaving and resource circuit breakers), which is qualitatively supported by the case analysis.
The analysis identifies 'averting effects' where efforts to manage one dimension of autonomy require maintaining the other, which is qualitatively supported by the case analysis.
The study identifies a 'potential' relationship where reducing workflow autonomy creates a structural foundation that allows providers to further restrict generative autonomy, which is qualitatively supported by the case analysis.
The findings are limited by the exploratory, qualitative nature of the seven cases, which does not quantitatively measure the operational efficiency, safety, or economic impacts of the individual practices, as well as the limited temporal autonomy of current systems.

What it means for you

CIO / IT Executive: On Monday morning, audit your organization's active GenAI integrations and mandate the immediate implementation of 'resource circuit breakers'—hard programmatic limits on API calls and database writes—to restrict workflow autonomy and prevent runaway system actions.
IT Manager: On Monday morning, set up a 'real-time auditing' log pipeline for your team's customer-facing GenAI application to flag and review outputs where the system's confidence score drops below 80%, immediately routing those queries to human-in-the-loop agents.
Business Strategist: On Monday morning, map your company's GenAI product features onto a 2x2 grid of Generative Autonomy vs. Workflow Autonomy to identify features where you can reduce risky workflow autonomy (e.g., auto-sending emails) without degrading the value of the generated content.
Researcher: On Monday morning, draft a research proposal for a quantitative empirical study that measures the operational latency, safety rates, and economic impacts of 'architectural weaving' vs. 'semantic resource enrichment' across B2B GenAI deployments.
Policymaker: On Monday morning, draft an update to your agency's AI procurement guidelines requiring vendors to prove they have 'averting effects' controls in place, specifically demonstrating how their systems constrain generative output risks when workflow autonomy is elevated.

Artificial intelligence, GenAI, Agentic AI, Large Language Models, Case Study, Autonomy

ACTOR-NETWORKS IN CORPORATE DIGITAL SUSTAINABILITY INITIATIVES

AI Processed

How do IT and sustainability departments interact to shape corporate digital sustainability initiatives?

David Stanlein, Lea Püchel, Enni Alina Juntunen, Tobias Brandt

ACTOR-NETWORKS IN CORPORATE DIGITAL SUSTAINABILITY INITIATIVES

ECIS 2026 · 2026

Key findings

The case study supported that sustainability management acts as a stabilizer by establishing structural frameworks, securing senior executive backing, and fostering interdepartmental knowledge sharing.
The analysis supported that IT/IS management often acts as a destabilizing force by imposing resource limitations, prioritizing short-term operational goals, and withholding full active engagement.
The research demonstrated that the corporate digital sustainability actor-network remains unstable due to unresolved conflicts between sustainability goals and IT efficiency metrics.
A limitation of these findings is their reliance on a single-case study of a German insurance company with an interview sample dominated by IT-department stakeholders.

What it means for you

CIO / IT Executive: Schedule a 30-minute meeting with the Head of Sustainability to co-design and integrate a 'sustainability impact score' directly into the IT department's project prioritization framework, ensuring green metrics are evaluated alongside traditional cost and efficiency KPIs.
IT Manager: Review your team's current project backlog and formally assign a senior systems engineer as the dedicated 'Green IT Liaison' to attend the sustainability working group's bi-weekly meetings, ensuring technical resource constraints are communicated early.
Business Strategist: Draft a charter for a joint IT-Sustainability Steering Committee that outlines a shared governance model, specifically defining a conflict-resolution protocol for when short-term IT cost-cutting clashes with long-term carbon reduction targets.
Researcher: Draft a research proposal for a multi-case study targeting non-financial sectors (such as manufacturing or retail) that intentionally oversamples sustainability managers to address the IT-biased, single-case limitation of the existing literature.
Policymaker: Initiate a draft for a voluntary corporate digital sustainability reporting template that requires firms to disclose how their IT infrastructure procurement policies align with national green transition targets.

digital sustainability, actor-network theory, case study, IT/IS alignment, IT/IS management

STRUCTURING READINESS FOR AI INNOVATION: THE ROLE OF THE DESIGN CHALLENGE BRIEF AS A COORDINATION AND GATEKEEPING ARTEFACT

AI Processed Human Approved

How can teams evaluate whether an artificial intelligence project is ready to launch?

Jennifer Hehn, Franziska Anna Röckel, Benjamin van Giffen

STRUCTURING READINESS FOR AI INNOVATION: THE ROLE OF THE DESIGN CHALLENGE BRIEF AS A COORDINATION AND GATEKEEPING ARTEFACT

ECIS 2026 · 2026

Key findings

The study outlines a preliminary three-phase qualitative model for establishing AI project readiness: divergent exploration, negotiation/translation, and convergent alignment, though these findings are descriptive and lack statistical testing.
The Design Challenge Brief is shown to act as a gatekeeping artifact that structures readiness reviews and allows teams to selectively revisit earlier project assumptions without resetting the entire process.
The study is limited by its small qualitative sample of five projects and its setting within a structured, university-led innovation lab, which may restrict generalizability to less-structured corporate settings.

What it means for you

CIO / IT Executive: Draft and mandate a standard 'Design Challenge Brief' template that all AI project champions must complete and get approved before receiving formal project launch funding.
IT Manager: Gather your AI project team for a 1-hour workshop to map your current pre-project planning stage to the three phases of divergent exploration, negotiation/translation, and convergent alignment, explicitly documenting how you will handle fluid data availability.
Business Strategist: Audit your pipeline of proposed AI initiatives and flag any project that has not yet completed a collaborative 'Design Challenge Brief' to prevent launching projects with high uncertainty around non-deterministic outcomes.
Researcher: Draft a research proposal and survey instrument to test the generalizability of the three-phase AI readiness model across a quantitative sample of 100+ corporate AI projects outside of academic lab settings.
Policymaker: Integrate a 'Design Challenge Brief' gatekeeping requirement into your public sector AI procurement and funding frameworks to ensure public agencies evaluate data readiness and outcome uncertainties before awarding AI contracts.

AI innovation, readiness work, design challenge brief, boundary objects, gatekeeping

SCHNITZEL-PREDICTION: DESIGNING HUMAN–AI COLLABORATION FOR CAFETERIA DEMAND FORECASTING

AI Processed

How does artificial intelligence compare to experienced human planners when forecasting cafeteria meal demand?

Justus Cappel, Timo Strohmann, Mara Burger, Marleen Voß, Jan vom Brocke

SCHNITZEL-PREDICTION: DESIGNING HUMAN–AI COLLABORATION FOR CAFETERIA DEMAND FORECASTING

ECIS 2026 · 2026

Key findings

The algorithmic machine learning model achieved a 30% reduction in forecasting errors compared to a naive 7-day lag baseline.
The hypothesis that the algorithmic model would statistically outperform experienced human planners across all menu items was not supported, as direct statistical comparisons showed no overall significant difference (p = 0.454).
Experienced human planners statistically outperformed the algorithmic model for novel menu items with limited historical data (MAE of 10.46 for humans versus 17.00 for the algorithm), supporting the hypothesis that human judgment remains superior for high-uncertainty contextual forecasting.
Acknowledgeable limitations of this research include its single-case design at a German firm, a small quantitative evaluation scope of 24 operating days, and qualitative insights derived from two think-aloud sessions.

What it means for you

CIO / IT Executive: Issue a directive to your enterprise architecture team to halt all pure-automation forecasting initiatives and mandate that any future demand-planning software deployments integrate a 'human-in-the-loop' interface, specifically reserving override capabilities for experienced frontline staff in high-uncertainty scenarios.
IT Manager: Create a task in your team's active development sprint to build an LLM-powered semantic tagging feature for your menu database, and pair it with a simple UI override slider on the kitchen manager's dashboard to let them manually adjust AI-generated forecasts for newly introduced dishes.
Business Strategist: Review your cafeteria's historical food waste logs and restructure the kitchen managers' weekly KPIs to incentivize them to manually audit and adjust automated algorithmic forecasts, focusing their human intervention specifically on novel menu items where historical data is sparse.
Researcher: Draft a grant proposal for a multi-site, longitudinal study that replicates the 'SCHNITZEL-PREDICTION' methodology across five different corporate catering firms over a six-month period to overcome the single-case and 24-day evaluation limitations of this study.
Policymaker: Draft an update to the public sector sustainable procurement guidelines requiring that any government-funded cafeteria software tenders mandate human-AI collaborative interfaces rather than fully automated solutions to actively prevent food waste caused by algorithmic 'cold-start' forecasting failures.

Human-AI Collaboration, Action Design Research, Sustainable IS, Demand Forecasting, Food Waste, Large Language Models

HOW GENERATIVE AI DRIVES FIELD-LEVEL CHANGE IN MANAGEMENT CONSULTING

AI Processed

How does using generative AI in everyday work change how management consulting firms operate and deliver value?

Robin Killewald, Thomas Haskamp

HOW GENERATIVE AI DRIVES FIELD-LEVEL CHANGE IN MANAGEMENT CONSULTING

ECIS 2026 · 2026

Key findings

The study identified four core mechanisms of change—negotiated legitimacy, provisionality of reliance, skill polarity, and contestation of value—which were qualitatively supported by empirical interview data.
Under the 'provisionality of reliance' mechanism, temporal pressures cause consultants to bypass validation routines, creating fragile outputs that risk client trust (supported qualitatively).
Under the 'skill polarity' mechanism, generative AI simultaneously expands short-term capabilities and erodes long-term analytical thinking and mentoring routines (supported qualitatively).
Under the 'contestation of value' mechanism, the commoditization of standardized tasks shifts the focus of consulting value toward human-centered contextual judgment and experience (supported qualitatively).
Quantitative hypothesis testing was not performed due to the study's qualitative, inductive methodology, which limits statistical generalizability to other contexts or regions.

What it means for you

CIO / IT Executive: Integrate an automated 'Validation Gateway' into the firm’s proprietary GenAI workspace that blocks users from exporting drafts into client-ready formats (like PPTX or PDF) until they complete a digital checklist verifying the accuracy and sources of the underlying data.
IT Manager: Set up a weekly automated audit report in your GenAI usage analytics platform to identify teams with high-volume AI prompt generations but low average document-editing times, and flag these teams for manual quality-assurance reviews.
Business Strategist: Redesign the firm's standard client engagement proposal template to phase out hourly billing for commoditized deliverables (like market research summaries) and replace it with value-based pricing focused on human-led strategic judgment workshops.
Researcher: Draft a quantitative survey instrument targeting 500 management consultants across North America and Asia to test if the qualitative mechanisms of 'skill polarity' and 'provisionality of reliance' negatively correlate with long-term client retention metrics.
Policymaker: Introduce an industry-standard certification framework for professional service firms that mandates documented 'human-in-the-loop' validation protocols and minimum structured mentoring hours to protect junior workforce development against generative AI skill erosion.

Generative AI, Professional Service Firms, Causal Mechanisms, Routines, Management Consulting

RETHINKING KNOWLEDGE WORK: DESIGNING LLM-BASED SYSTEMS FOR COMPLEXITY MANAGEMENT

AI Processed Human Approved

How effectively can artificial intelligence tools screen and extract information from complex procurement documents?

Moritz Diener, Simon Kaps, Philipp Spitzer, Robin Hirt, Michael Vössing, Gerhard Satzger

RETHINKING KNOWLEDGE WORK: DESIGNING LLM-BASED SYSTEMS FOR COMPLEXITY MANAGEMENT

ECIS 2026 · 2026

Key findings

The LLM-based system reduced initial document screening time by 59% (from an average of 3 minutes 42 seconds manually to 1 minute 30 seconds via the system), which was statistically supported.
The system achieved an overall content-match accuracy of 88.82% for text extraction, with highest accuracy for identifying the procuring entity and place of execution (94.10% each) and lowest for the type of service (79.40%).
For binary classification tasks, the system achieved an average accuracy of 83.85%, performing significantly better on detecting required certifications (91.20%) than on required references (76.50%).
Qualitative feedback supported the system's ability to lower perceived document, compliance, and process complexity, shifting human workflows from manual reading and extraction to output verification.
Limitations include testing within a single German SME in the data center sector, a short-term study duration, and a small evaluation dataset (34 documents), restricting direct generalizability to other industries.

What it means for you

CIO / IT Executive: On Monday morning, issue an IT directive mandating that any generative AI tools deployed for document screening must include a 'human-in-the-loop' verification interface, explicitly prohibiting automated final submissions due to the system's average 11.18% error rate in text extraction.
IT Manager: On Monday morning, configure your document-processing pipeline's UI to visually highlight and force manual validation of 'type of service' and 'required references' fields, as the research shows these have lower extraction accuracies (79.4% and 76.5%) compared to highly reliable fields like 'procuring entity' (94.1%).
Business Strategist: On Monday morning, calculate the potential ROI of deploying an LLM-based screening tool for your proposal-writing team, using the study's proven 59% reduction in initial document screening time (down to 1 minute 30 seconds per document) to project resource hours saved.
Researcher: On Monday morning, write a research proposal to extend this design science framework by testing it on a dataset larger than 34 documents across multi-industry and non-German settings, focusing specifically on optimizing prompts to improve the low 76.5% accuracy baseline for identifying required references.
Policymaker: On Monday morning, initiate a draft proposal for public procurement formatting standards that mandates standardized, machine-readable sections for 'required certifications' and 'references' to minimize AI extraction errors (currently at 8.8% and 23.5% respectively) and ensure fair bidding processes for SMEs.

large language models, generative AI, complexity management, knowledge work, design science research

DESIGNING FOR IMPERFECT PROMPTS: A SIMULATION-BASED EVALUATION OF AN LLM-BASED ENTERPRISE DATA ASSISTANT USING KNOWLEDGE GRAPHS

AI Processed Human Approved

Which approach best helps AI assistants translate imperfect user prompts into accurate database queries?

Niklas Wagner, Alexander Mdche

DESIGNING FOR IMPERFECT PROMPTS: A SIMULATION-BASED EVALUATION OF AN LLM-BASED ENTERPRISE DATA ASSISTANT USING KNOWLEDGE GRAPHS

ECIS 2026 · 2026

Key findings

The KG-based mediation statistically outperformed the schema-based mediation across Precision, Recall, and F1-Score metrics.\n- For GPT-4.1, the KG-based approach achieved a Precision of 90.67% (compared to 69.05% for schema-based) and an F1-Score of 89.47% (compared to 71.02% for schema-based).\n- For Claude Sonnet 4, the KG-based approach achieved a Precision of 87.23% (compared to 75.17% for schema-based) and an F1-Score of 90.29% (compared to 82.17% for schema-based), while Recall remained statistically comparable (93.52% vs 92.56%).\n- The study is limited by its focus on a single-enterprise setting within a CFO unit, a finite dataset of 158 interactions from 29 users, and reliance on a simulation-based evaluation instead of live field deployments.

What it means for you

CIO / IT Executive: Issue a directive to transition the enterprise's LLM-based data assistant architecture away from simple schema-based semantic similarity matching and onto a Knowledge Graph-based mediation layer constructed from database schemas to capture the demonstrated 12% to 21% precision improvements.
IT Manager: Instruct your database and engineering teams to begin constructing a Knowledge Graph from your current database schemas to serve as a semantic mediation layer for your LLM assistant, replacing manual prompt engineering.
Business Strategist: Launch a pilot program within the CFO business unit to deploy the Knowledge Graph-based data assistant, leveraging its verified ability to resolve incomplete, domain-specific prompts into accurate database queries for business users.
Researcher: Draft a research proposal to test the Knowledge Graph-based mediation layer in a live, real-world field deployment to gather empirical performance data beyond the current study's simulation of 158 interactions across 29 users.
Policymaker: Draft an internal data governance policy requiring that all natural-language LLM interfaces querying corporate databases must use a schema-derived Knowledge Graph mediation layer to ensure structural query alignment and prevent inaccurate data retrieval.

Large Language Models, Enterprise Data Assistants, Text-to-SQL, Knowledge Graphs, Design Science Research

NAVIGATING THE TWIN TRANSITION: A PARADOX PERSPECTIVE ON CONCURRENT DIGITAL AND SUSTAINABILITY CHANGE

AI Processed

What tensions arise when pursuing digital and sustainability goals together, and how do organizations manage them?

Daria L. Stumkat, Jan Stockhinger

NAVIGATING THE TWIN TRANSITION: A PARADOX PERSPECTIVE ON CONCURRENT DIGITAL AND SUSTAINABILITY CHANGE

ECIS 2026 · 2026

Key findings

The study qualitatively identifies four persistent tensions in the twin transition: cost efficiency vs. sustainability impact, IT as enabler vs. IT as concern, individual values vs. organizational inertia, and fast IT iteration vs. slow sustainability institutionalization, though these are exploratory and not statistically tested.\n- The findings reveal that actors manage these paradoxes through a portfolio of splitting, suppressing, adjusting, and opposing responses, which often remain fragmented rather than coordinated.\n- Successful navigation was shown to occur gradually through sequencing and formalizing responses (such as in UI/UX accessibility standards), indicating that twin transition synergies are an outcome of continuous paradox management rather than a starting condition.\n- The study's findings are limited by its exploratory nature and single-case, cross-sectional design at one European IT service provider, meaning they are not statistically generalizable.

What it means for you

CIO / IT Executive: On Monday morning, update your organization's 'Definition of Done' (DoD) for all software release pipelines to mandate that any major code deployment must include a documented estimate of its cloud carbon footprint (using tools like Cloud Carbon Footprint) alongside traditional speed and cost metrics.
IT Manager: On Monday morning, during your weekly sprint planning, introduce one concrete green-coding or UI accessibility standard (such as optimizing image compression to reduce data transfer energy) to the active backlog, demonstrating to your team how to integrate sustainability into fast-paced IT iterations.
Business Strategist: On Monday morning, review your active digital transformation roadmaps and replace any assumptions of automatic 'twin transition' synergies with a formal risk register that explicitly maps out where digital speed and carbon-reduction goals conflict, scheduling a sequenced timeline for which priority takes precedence at each milestone.
Researcher: On Monday morning, draft a research proposal for a longitudinal, multi-sector quantitative survey targeting CIOs across Europe to statistically test the generalizability of the four identified twin-transition tensions beyond the single-case IT service provider context.
Policymaker: On Monday morning, initiate the draft of a voluntary 'Green IT Integration' compliance guide that provides businesses with standardized templates to formalize digital-sustainability KPIs, helping them move from fragmented employee-led initiatives to structured, audit-ready corporate reporting.

Twin transition, Twin transformation, Paradox theory, Tensions, Paradox navigation, Digital transformation

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