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AI Processed Human Approved

What roles can generative AI play in data quality management, and how should leaders govern them?

Karen Roht, Anastasija Nikiforova, Hippolyte Lefebvre

A Typology For Responsible Delegation Of Data Quality Management To Generative AI

ECIS 2026 · 2026

Key findings

Formulated a four-role typology for GenAI in DQM: Translator (natural language to rule logic), Explainer (narrative summaries of anomalies and metadata), Resolver (data correction suggestions), and Integrator (cross-system schema alignment).
Revealed that current GenAI adoption in commercial DQM tools remains nascent (~17%), functioning largely as an interpretive co-pilot rather than an autonomous decision-maker.
Proposed five theoretical governance guidelines highlighting task decomposability appraisal, mandatory human validation for high-impact actions, reverse delegation protocols, explainability standards, and retained human accountability.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of your organization's current DQM tool landscape to identify which, if any, are actively experimenting with or have integrated GenAI capabilities, and schedule a follow-up meeting with the IT Manager to discuss initial findings and potential pilot opportunities based on the 'Translator' role.
IT Manager: On Monday morning, dedicate an hour to review the existing data quality rulebooks and documentation within your team's purview, noting down specific areas where natural language descriptions of data quality issues or desired rules are prevalent, to prepare for a discussion with the CIO/IT Executive about the potential application of the 'Translator' GenAI role.
Business Strategist: On Monday morning, identify one critical business process that is currently hampered by data quality issues and then, using the 'Explainer' GenAI role as a conceptual framework, draft three potential narrative-style questions you would ask an AI about the root causes and impact of these data quality problems to present to stakeholders.
Researcher: On Monday morning, begin by outlining a research proposal to investigate the effectiveness and potential biases of GenAI 'Resolver' roles in correcting common data anomalies within a specific industry domain, drawing upon the governance guidelines related to mandatory human validation for high-impact actions.
Policymaker: On Monday morning, begin drafting a preliminary policy document that outlines the principle of 'retained human accountability' for any data quality decisions made or influenced by GenAI within the organization, focusing on the need for clear audit trails and oversight for 'Resolver' and 'Integrator' roles.

Data Quality Management, Generative AI, Human-AI Collaboration, AI Governance, Task Delegation, Agentic IS

AI Processed Human Approved

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

Yoonseock Son, Wonseok Oh, Il Im

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

MIS Quarterly · 2023

Key findings

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

What it means for you

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

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

AI Processed Human Approved

How does collaborative course redesign affect student performance, teaching consistency, and instructor confidence?

John Michael Muraski, Donald H. Heath, Michael A. Eierman, Michael Patton

Efficacy Beliefs and Outcomes: Socio-Cognitive Perspectives on a Faculty-Led coP

AIS SIGED International Conference on Information Systems Education and Research (SIGED) · 2023

Key findings

Student performance showed consistent, measurable gains across multiple semesters following the course redesign, as demonstrated by improved pre- and post-test assessment deltas.
Participation in the community of practice positively impacted instructors' self-efficacy beliefs, with vicarious learning and peer verbal persuasion emerging as key drivers of confidence.
The collaborative approach established high course consistency across sections while creating a durable, supportive framework for peer mentoring and ongoing curriculum enhancement.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your IT academic technology support lead to explore a pilot program implementing a Communities of Practice model for a high-enrollment introductory course, using the provided research as a framework for discussion on potential student outcome improvements and faculty development.
IT Manager: On Monday morning, gather the instructors for the introductory information systems course for a 45-minute session. Share the key findings of this research, specifically highlighting the gains in student performance and instructor self-efficacy. Then, collaboratively brainstorm one small, concrete change to the course syllabus or assessment that can be implemented next semester as a collective first step towards greater consistency.
Business Strategist: On Monday morning, analyze the current onboarding and ongoing professional development process for new faculty teaching foundational courses. Identify any gaps in collaborative support and informal learning opportunities, and consider how principles from this research, such as vicarious learning and peer persuasion, could be integrated into existing programs to improve faculty confidence and course consistency.
Researcher: On Monday morning, begin drafting a proposal for a follow-up study. Focus on investigating the specific mechanisms of vicarious learning and peer verbal persuasion within the identified Community of Practice, perhaps through targeted surveys or observation protocols, to quantify their impact on instructor self-efficacy and to better understand how to scale these findings.
Policymaker: On Monday morning, initiate a discussion with the Provost's office and the Faculty Senate leadership to present the findings of this research. Propose the development of a university-wide framework or grant program that incentivizes and supports the formation of faculty Communities of Practice for foundational courses, emphasizing the potential for improved student learning outcomes and faculty development.

Community of Practice, Self-Efficacy, Course Redesign, Social Cognitive Theory, Information Systems Education, Participatory Action Research

AI Processed Human Approved

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

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

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

Information Systems Journal (ISJ) · 2025

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

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

Process science: the interdisciplinary study of socio-technical change

Process Science · 2024

Key findings

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

What it means for you

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

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

AI Processed Human Approved

How do virtual worlds help people with lifelong disabilities move from social isolation to social inclusion?

Karen Stendal, Maung K. Sein, Devinder Thapa

Affordance-Based Pathway Model of Social Inclusion: A Case Study of Virtual Worlds and People With Lifelong Disability

Journal of the Association for Information Systems · 2026

Key findings

Identified three functional affordances (communicability, mobility, and personalizability) and two social affordances (engageability and self-actualizability) crucial for social inclusion.
Established that the actualization of functional affordances enables and dynamically interacts with social affordances to foster interpersonal relationships and community participation.
Discovered that outcomes achieved by actualizing specific affordances serve as facilitating conditions that support the perception and realization of additional affordances.
Extended affordance theory by demonstrating a feedback loop where social engagement refines and enhances the perception and use of basic functional capabilities.

What it means for you

CIO / IT Executive: On Monday morning, task your accessibility team to conduct an initial assessment of our current platform's 'communicability' features (e.g., real-time chat, group messaging, avatar-based interaction quality) against the research's identified functional affordances to understand potential barriers and opportunities for individuals with disabilities.
IT Manager: On Monday morning, schedule a 30-minute working session with your frontline support staff to review existing user feedback logs for mentions of difficulty with navigation, communication, or customization within our virtual environment, specifically looking for patterns that might align with limitations in perceived 'mobility' or 'personalizability' affordances.
Business Strategist: On Monday morning, initiate a competitive analysis focusing on how emerging virtual platforms or digital communities are leveraging 'engageability' and 'self-actualizability' affordances to foster user connection and content creation, particularly noting any strategies that might appeal to or benefit users with disabilities.
Researcher: On Monday morning, begin drafting a protocol for a pilot study within our organization's virtual world that focuses on measuring the impact of enhanced 'communicability' features on user-reported feelings of 'engageability' and 'self-actualizability' among a diverse user group, potentially including participants with disabilities.
Policymaker: On Monday morning, convene a brief meeting with your advisory committee to discuss how current digital inclusion policies might be strengthened by explicitly incorporating the research's identified 'functional affordances' (communicability, mobility, personalizability) and 'social affordances' (engageability, self-actualizability) as measurable criteria for assessing technological accessibility and support for social participation.

Social Inclusion, Virtual Worlds, People With Lifelong Disability, Affordances, Second Life, ICT

AI Processed Human Approved

How do people experience and make sense of their bodies in immersive virtual reality?

Amir Haj-Bolouri

The Philosopher’s Corner: The “Immersed Flesh”: A Phenomenological Conceptualization of Immersive Virtual Reality Embodiment

ACM SIGMIS Database: the DATABASE for Advances in Information Systems · 2025

Key findings

Conceptualizes IVR embodiment through the new concept of 'Immersed Flesh', defined as a dynamic matrix that merges physical and virtual worlds into a co-extensive reality.
Adapts Merleau-Ponty's ontology of the Flesh into key structural elements for IS, including Actual Grip, Apparent Grip, Circuit, Chiasma, Interworld, Measuring Body, and Reversibility.
Illustrates the practical and theoretical value of the framework through three distinct IVR scenarios: expressing the living body (e.g., IVR dancing), intersubjective perspective-taking (e.g., embodying a tree), and experiencing the objective body of passing entities (e.g., avatars of deceased relatives).
Generates research questions for future IS inquiry into user self-agency, bodily empathy, and ethical considerations surrounding virtual presence.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute briefing for your senior IT team to introduce the concept of 'Immersed Flesh' as a potential lens for evaluating our next-generation immersive technology investments, specifically focusing on how it moves beyond purely functional metrics to consider user experience and embodiment.
IT Manager: On Monday morning, gather your team responsible for VR development or deployment and ask them to review our current IVR platforms and identify one specific feature or interaction where users might be experiencing something akin to 'Actual Grip' or 'Apparent Grip' as described in the 'Immersed Flesh' concept.
Business Strategist: On Monday morning, dedicate one hour to brainstorm three hypothetical business use cases where fostering 'bodily empathy' or enhancing 'user self-agency' through IVR, inspired by the 'Immersed Flesh' framework, could provide a competitive advantage or solve a current business challenge.
Researcher: On Monday morning, draft a preliminary research proposal outline that incorporates at least two of the structural elements from the 'Immersed Flesh' framework (e.g., Chiasma, Interworld) to investigate user embodiment in a specific IVR application relevant to our field.
Policymaker: On Monday morning, request a brief (1-page) summary of the 'Immersed Flesh' concept from your policy research unit, specifically asking them to highlight any potential ethical implications or policy considerations related to virtual presence and user embodiment in immersive technologies.

Immersive Virtual Reality, Embodiment, Phenomenology, Information Systems, Merleau-Ponty

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 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 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

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

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

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

ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS

AI Processed Human Approved

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

Jori Taipale, Olli Tyrväinen, Tuure Tuunanen, Joel Mero, Teiko Heinosaari

ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS

ECIS 2026 · 2026

Key findings

Organizing tensions were identified as the most frequent paradox, specifically focusing on the trade-offs between collaboration vs. resource competition and guidance vs. flexibility.
Performance tensions emerged regarding resource allocation between software vs. hardware development and building a shared vision vs. individual firm survival.
Belonging tensions highlighted a 'godfather' paradox, where the ecosystem must balance the state's role as a major supportive funder and customer with the ultimate goal of corporate independence.
Learning tensions were characterized by the challenge of managing multi-talent knowledge development versus narrow technology specialization.
The study is limited by its single qualitative case context in Finland with a small sample of 15 informants, requiring caution when applying these findings to other national contexts.

What it means for you

CIO / IT Executive: Schedule a 9:00 AM meeting with your lead enterprise architect to audit your current quantum-readiness roadmap, establishing a formal policy that divides software development and hardware access investments 50/50 to hedge against the performance tension of committing prematurely to a single unproven hardware stack.
IT Manager: Create a bi-weekly 1-hour 'Quantum Cross-Training' slot in your team's calendar starting this Friday, pairing your specialized quantum physics PhDs with your generalist cloud engineers to address the learning tension between narrow technical specialization and broad-based systems integration.
Business Strategist: Perform a revenue dependency audit to calculate the percentage of your quantum business unit's funding derived from state grants versus commercial clients, and draft a pitch deck for two private-sector pilots to actively transition away from 'godfather' state-funding reliance.
Researcher: Draft a pre-competitive collaboration agreement template to send to a peer researcher at a competing institution, outlining a shared framework for baseline algorithm testing while clearly defining IP boundaries to manage the tension of collaboration versus resource competition.
Policymaker: Draft a revision for the upcoming national quantum funding round guidelines that introduces a 'co-investment requirement,' mandating that state-funded consortia secure at least 25% of their budget from private corporate buyers to counter the 'godfather' dependency paradox.

Ecosystem orchestration, Paradox theory, Quantum computing innovation ecosystem, Explorative case study, Interpretive qualitative research

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