AIS Research Insights

Browse the library 70 papers

All Artificial Intelligence & Automation Information Systems & Management Human-Computer Interaction Data Analytics & Big Data Digital Transformation & Strategy Cybersecurity & Privacy Cloud & Infrastructure E-Commerce & Digital Platforms Operations & Supply Chain Sustainability & Green IS

AI Processed Human Approved

How can artificial intelligence simplify complex privacy policies while maintaining their essential legal accuracy?

Al Maha Al Jabor, Shahad Adnan Astaneh, Manoranjan Mohanty, Chadi Aoun

Automated Privacy Policy Simplification through a GPT-Powered Chrome Extension

International Conference on Information Systems (ICIS) · 2025

Key findings

The audience-adjusted rewriting prompt ('rewrite so a 10th-grade student can understand while maintaining legal accuracy') produced the highest readability improvements while preserving core legal content.
Readability metrics improved substantially after simplification, with Flesch Reading Ease increasing significantly and complexity indices like Flesch-Kincaid Grade Level dropping sharply.
ROUGE content evaluation metrics (ROUGE-1 Precision = 0.8862, ROUGE-L Precision = 0.6025) confirmed strong textual overlap and high fidelity to original legal terms.
A prototype Chrome extension was created, enabling real-time policy summarization and context-aware Q&A capabilities grounded in the APCO theoretical framework.

What it means for you

CIO / IT Executive: On Monday morning, initiate a pilot program to evaluate the integration of a GPT-powered privacy policy simplification Chrome extension across a select department, focusing on user adoption and feedback regarding usability and perceived value.
IT Manager: On Monday morning, prepare a technical feasibility assessment for deploying the developed Chrome extension across the organization's standard user workstations, outlining any necessary infrastructure changes or security considerations.
Business Strategist: On Monday morning, analyze how the real-time privacy policy simplification and Q&A features of the Chrome extension can be leveraged to enhance user trust and potentially create a competitive differentiator for our online services.
Researcher: On Monday morning, begin designing an experiment to test the effectiveness of the audience-adjusted rewriting prompt ('rewrite so a 10th-grade student can understand while maintaining legal accuracy') with a new set of privacy policies from a different industry sector.
Policymaker: On Monday morning, review the research findings on improved readability and content preservation to draft talking points for discussions on enhancing consumer understanding of digital service terms and conditions.

AI Processed Human Approved

How do stakeholders' past negative experiences affect their trust and adoption of new technology?

Ranjan Vaidya

Yoga Sutra-Based Memory Analysis of Information Systems Implementation

Communications of the Association for Information Systems · 2026

Key findings

The Yoga Sutra framework demonstrates that subliminal impressions (Samskara) formed by past unfair trade practices deeply influence stakeholder behaviors and technology adoption.
Contextual factors and research interviews function as triggers (Alambana) that evoke unpleasant memories, reinforcing distrust between system users, government boards, and private vendors.
Information system failures in developing contexts often result from a lack of adherence to core ethical and behavioral values rather than culture acting as a barrier.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute discovery call with your Head of User Experience and your Chief Ethics Officer to brainstorm how to proactively identify and mitigate potential 'Samskara' (subliminal impressions) in upcoming system implementations by focusing on past negative stakeholder experiences.
IT Manager: On Monday morning, review the last three system user feedback reports for any recurring themes related to distrust or negative past experiences, and flag the top two recurring issues to investigate further for potential 'Alambana' (external triggers) in your current system.
Business Strategist: On Monday morning, draft a brief (one-page) internal memo to your team outlining the concept of 'Samskara' and its potential impact on technology adoption, and request them to identify one instance in recent projects where past negative impressions might have influenced user behavior.
Researcher: On Monday morning, begin drafting the introduction to your next research paper by clearly articulating the gap in current information systems research regarding the lack of focus on individual cognitive variations and Eastern philosophical perspectives on memory, using the Yoga Sutra framework as your proposed solution.
Policymaker: On Monday morning, initiate a discussion with your advisory board regarding the importance of embedding ethical and behavioral values into the design and implementation guidelines for all new government information systems, referencing the finding that failures often stem from a lack of adherence to these values, not culture.

Yoga, Information Systems, Memory, Behavioural Analysis, Samskara, ICT4D

AI Processed Human Approved

How do technology projects affect local communities, and what determines whether they succeed or fail?

Devinder Thapa

Ethics-based ontology in ICT4D

2025

Key findings

Establishes an ethics-based ontology in ICT4D that shifts focus from instrumental views of technology to lived human experience and moral responsibility.
Demonstrates through the OLE Nepal case study how embedding technology into local educational ecosystems, national curricula, and teacher practices leads to sustainable success.
Reveals through the OLPC Peru case study how top-down, context-unaware technological implementations erode local agency and lead to project failure.
Highlights key implementation challenges, including translating abstract philosophical concepts into practical design metrics and addressing Eurocentric biases in technology evaluation.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your ICT4D project leads to introduce the concept of an 'ethics-based ontology' and ask them to identify one project where technology is deeply integrated into user workflows and cultural practices. Assign them to bring initial thoughts on how the project's impact on lived experience and moral agency could be assessed in the next project review.
IT Manager: On Monday morning, review the support tickets and user feedback logs from the last quarter for your current ICT4D deployment. Specifically, look for recurring issues that are not purely technical but seem to relate to user frustration, resistance to adoption, or a perception of technology not fitting their daily routines. Flag these for further investigation with your team.
Business Strategist: On Monday morning, identify your organization's most critical ICT4D initiative. Draft a brief memo outlining how you plan to evaluate its success not just on efficiency metrics but also on its impact on the dignity, agency, and cultural integrity of the end-users. Prepare to discuss this revised evaluation framework with your team by the end of the week.
Researcher: On Monday morning, revisit the 'implementation challenges' section of the research paper and select ONE challenge (e.g., translating abstract concepts into practical design metrics). Begin drafting a short (1-page) proposal for how you might operationalize this concept for a future ICT4D project you are involved in, focusing on a tangible measurement.
Policymaker: On Monday morning, review existing national or organizational guidelines for technology procurement and deployment in development contexts. Identify sections that address user impact, cultural sensitivity, or long-term sustainability. Draft a short list of questions to investigate how these existing guidelines could be strengthened to incorporate considerations of lived experience and moral responsibility for technology's impact.

ICT4D, being-in-the-World, ethics of responsibility, ontology, development, philosophy

AI Processed Human Approved

What drives users to stay engaged with location-based games and build real-world communities?

Faisal Al Thani, Chadi Aoun, Savanid Vatanasakdakul

Gamification for Community Building: Click to Shake Hands

Pacific Asia Conference on Information Systems (PACIS) · 2025

Key findings

Formulates a novel theoretical framework connecting technology adoption, gamification mechanics, external social influences, and intrinsic motivations to app stickiness and community engagement.
Identifies key drivers such as location incentives, perceived ease of use, reward value, achievement, network influence, herd behavior, social motive, and sense of belonging.
Establishes an empirical research design targeting over 1,000 active Pokémon GO players using Partial Least Squares Structural Equation Modeling (PLS-SEM).
Provides preliminary theoretical grounding for designing mobile gamified systems that promote social sustainability and persistent physical-world social interactions.

What it means for you

CIO / IT Executive: On Monday morning, allocate budget to pilot a location-based gamified application in one of our company's facilities, specifically focusing on fostering cross-departmental collaboration by integrating 'location incentives' for shared project spaces.
IT Manager: On Monday morning, schedule a meeting with your development team to discuss how to integrate elements like 'reward value' and 'achievement' into our existing internal communication platform to encourage more active participation and knowledge sharing.
Business Strategist: On Monday morning, initiate a brainstorming session with your marketing and product teams to identify opportunities to leverage 'network influence' and 'herd behavior' in upcoming product launches or community outreach initiatives, perhaps by creating exclusive early-access groups.
Researcher: On Monday morning, refine the survey instrument to include questions specifically measuring 'sense of belonging' and 'social motive' as distinct constructs, and prepare to recruit participants from diverse community groups for your next study.
Policymaker: On Monday morning, direct your staff to research successful public policy initiatives that have used location-based incentives or gamification to encourage citizen engagement with local services or community events, and identify potential applications for our jurisdiction.

Community Engagement, Gamification, Location-based games, Stickiness, Technology Adoption, Social Interaction

AI Processed Human Approved

How can organizations effectively secure generative AI systems against unique threats under differing international regulations?

Vijay Kanabar and Kalinka Kaloyanova

Securing Generative AI Systems: Threat-Centric Architectures and the Impact of Divergent EU–US Governance Regimes

2026

Key findings

High-impact generative AI risks are structural properties inherent to architectural design rather than isolated, patchable bugs.
Effective risk mitigation requires a layered defense-in-depth model that combines traditional cybersecurity controls with AI-specific mechanisms such as context separation and tool sandboxing.
Transatlantic regulatory divergence establishes EU frameworks as the de facto technical baseline for multinational deployments, driving global architectural convergence.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of the current generative AI system architecture and identify which of the five layers (e.g., prompt engineering, model, data, tools, infrastructure) represent the highest structural risk based on the research's threat taxonomy. Prioritize a pilot project to implement context separation and tool sandboxing for a critical generative AI application.
IT Manager: On Monday morning, schedule a team meeting to map the specific adversarial techniques identified in the research (e.g., prompt injection, data poisoning, model evasion) to the current generative AI tools and integrations in use. Document which of these techniques pose the most significant structural risk to your deployed systems.
Business Strategist: On Monday morning, begin researching the EU's AI Act and its implications for generative AI deployments. Identify how the research's findings on divergent EU-US governance regimes suggest that adopting EU-like technical security baselines will be crucial for global market access and competitive advantage.
Researcher: On Monday morning, focus on replicating the research's methodology by attempting to map a specific novel adversarial technique against a widely used generative AI reference architecture, documenting any emergent failure modes observed and comparing them to the research's findings.
Policymaker: On Monday morning, analyze the comparative governance analysis presented in the research, specifically focusing on how the EU's framework is driving global architectural convergence. Draft a memo outlining potential legislative actions to align US generative AI security regulations with the EU's technical baseline to foster transatlantic interoperability and responsible innovation.

Generative AI Security, Cybersecurity Architecture, AI Governance, EU AI Act, NIST AI RMF, OWASP LLM Risks, MITRE ATLAS

AI Processed Human Approved

How can storytelling AI chatbots be used to engage students in learning about social engineering?

Mark Freeman

STORYTELLING IS NOT JUST FOR CHILDREN: USING AN AI CHATBOT TO ENGAGE UNIVERSITY STUDENTS

AIS SIGED International Conference on Information Systems Education and Research (SIGED) · 2025

Key findings

The study demonstrated that co-designing AI chatbot prompts with students created effective 'teachable moments' for understanding social engineering tactics and data governance.
Analysis of student interactions revealed diverse engagement approaches, ranging from friendly small talk to strategic IT troubleshooting and phishing tactics.
Key strategies were established for integrating a storytelling culture in higher education, including establishing a shared language, providing appropriate digital resources, and fostering collaborative environments.
Essential ethical guidelines were identified, emphasizing the necessity of robust data privacy, algorithmic bias mitigation, and content boundaries in educational AI applications.

What it means for you

CIO / IT Executive: On Monday morning, initiate a departmental meeting to discuss the potential integration of AI-powered scenario-based learning tools, like the one described in the research, to enhance student engagement in cybersecurity concepts and explore pilot program feasibility.
IT Manager: On Monday morning, schedule a brainstorming session with your team to identify specific social engineering concepts within your curriculum that could be effectively simulated using an interactive AI chatbot and begin researching available AI chatbot platforms or development frameworks.
Business Strategist: On Monday morning, analyze how the described AI chatbot's engagement model, which fosters 'teachable moments' through interactive storytelling, could be adapted to train employees on emerging cybersecurity threats or compliance protocols, identifying potential ROI.
Researcher: On Monday morning, review the ethical guidelines identified in the research (data privacy, bias mitigation, content boundaries) and begin designing a framework for evaluating the ethical implications of deploying AI-driven educational tools in your next study.
Policymaker: On Monday morning, draft a preliminary proposal for a grant or funding initiative that supports the development and implementation of innovative, AI-enhanced educational technologies in higher education, specifically mentioning the potential for improving cybersecurity education through interactive learning.

Storytelling, Artificial Intelligence Chatbot, Design Science Research, Scenario Based Learning, Social Engineering, Active Learning

AI Processed Human Approved

How can leaders set up and manage shared technology tools to support collaborative innovation across partner companies?

Sven-Volker Rehm, Lakshmi Goel, Iris Junglas

Role of Information Systems in Empowering Innovation Networks

MIS Quarterly Executive · 2015

Key findings

Identifies five key IS functionalities critical for innovation networks: collaborative work environments, knowledge maps, knowledge spaces, innovation procedure toolkits, and social media platforms.
Reveals that network success stems from pragmatically using a diverse set of accessible IS tools to form a shared infrastructure rather than relying on a single complex system.
Delivers nine practical guidelines for CIOs to manage IS infrastructure across the co-shaping, co-performing, and co-opting phases of open innovation networks.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of our existing IS tools to identify at least three readily accessible platforms (e.g., cloud-based project management, secure file sharing, internal wiki) that can be quickly configured to support a collaborative work environment for our next cross-company project, prioritizing ease of adoption for all partners.
IT Manager: On Monday morning, assess the user-friendliness and integration capabilities of our current knowledge management system and identify any gaps. Then, plan a quick training session for a pilot group of users demonstrating how to tag and categorize project-related documents to build a rudimentary 'knowledge map' for our innovation network.
Business Strategist: On Monday morning, draft a brief outline of key innovation challenges and opportunities for our next external collaboration, and then identify which of the five identified IS functionalities (collaborative work environments, knowledge maps, knowledge spaces, innovation procedure toolkits, and social media platforms) would be most impactful in addressing these specific challenges, focusing on pragmatic tool selection.
Researcher: On Monday morning, begin cataloging and categorizing the publicly available 'innovation procedure toolkits' or best practice guides that could be shared or adapted for our current project, focusing on those with clear, step-by-step instructions that can be easily integrated into a shared digital space.
Policymaker: On Monday morning, draft a short proposal for an initiative that encourages or provides resources for SMEs to adopt cloud-based collaborative platforms, specifically highlighting how these platforms can support cross-organizational knowledge sharing and innovation within their respective industries.

Open Innovation, Innovation Networks, Information Systems Infrastructure, Small and Medium Enterprises, Collaborative Work Environments, Medical Device Industry

AI Processed Human Approved

How can collaborating companies use shared information systems to align resources and develop new products?

Sven-Volker Rehm, Lakshmi Goel, Iris Junglas

Using Information Systems in Innovation Networks: Uncovering Network Resources

Journal of the Association for Information Systems · 2017

Key findings

Inter-organizational information systems are essential for enabling networking capabilities and constructing unique, shared resource constellations at the network level.
Four core informational purposes of information systems—representation, sharing, architecture, and reach—facilitate the cross-boundary resource alignment process.
Creating IS-embedded network resources enables collaborating firms to establish sustainable competitive advantages that extend beyond individual firm boundaries.

What it means for you

CIO / IT Executive: On Monday morning, schedule a meeting with key innovation network partners to initiate discussions on standardizing or enhancing inter-organizational information system capabilities for resource visibility and alignment. Identify one critical shared process in an ongoing innovation project and task your team with exploring how a new information system feature could better represent and share partner resources for that process.
IT Manager: On Monday morning, review the existing inter-organizational information systems used by your company in innovation networks. Identify one area where information sharing or representation of partner capabilities is currently fragmented or manual, and develop a concrete proposal for a pilot project to improve this using a specific IS feature (e.g., a shared dashboard for resource availability).
Business Strategist: On Monday morning, identify one ongoing collaborative innovation project and map out the key physical, human, and organizational resources currently being leveraged from your network partners. Then, identify specific gaps in visibility or alignment of these resources and brainstorm one practical way an inter-organizational information system could be adapted or enhanced to address these gaps.
Researcher: On Monday morning, begin drafting a qualitative interview guide for your next set of case studies, focusing on questions that explore how specific inter-organizational information system features (representation, sharing, architecture, reach) enable or hinder the alignment of physical, human, and organizational resources for new product development.
Policymaker: On Monday morning, research existing government grants or incentives that support the adoption and development of inter-organizational information systems for small and medium-sized enterprises involved in collaborative innovation. Draft a one-page summary of potential policy interventions to encourage such adoption for a discussion with relevant industry stakeholders.

Network Resource, Dynamic Capability, Networking Capability, Resource-based View, Innovation Network, New Product Development

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 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 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

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

Load More Showing 54 of 70