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AI Processed Human Approved

How can AI governance be adapted to fit local cultural and ethical values around the world?

Dimitrios Symeonidis, Johannes Schneider, Anastasija Nikiforova

TOWARD CULTURAL–ETHICAL FIT IN AI GOVERNANCE: A GLOBAL TYPOLOGY OF GOVERNANCE PROFILES

ECIS 2026 · 2026

Key findings

Identified eight distinct ideal-type AI governance profiles reflecting specific cultural and ethical alignments: group fairness, individual fairness, market-led growth, value-led growth, human oversight fortress, autonomous growth, individual control, and collective security.
Demonstrated that global AI governance is structured by two key meta-tensions: social justice versus economic competitiveness, and autonomy versus security and control.
Formulated three theoretical propositions outlining how cultural alignment determines governance legitimacy, shapes patterns of societal resistance, and guides regulatory evolution over time.
Offered a diagnostic decision framework for policymakers to adapt international AI guidelines to local socio-cultural contexts without sacrificing core safety requirements.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute deep dive with your legal and ethics teams to identify which of the eight AI governance profiles identified in the research most closely aligns with your organization's current AI development and deployment practices, and flag any immediate misalignments.
IT Manager: On Monday morning, initiate a review of your team's current AI project documentation and development methodologies, specifically looking for instances where decisions about fairness, transparency, or accountability might have been implicitly influenced by a single cultural perspective (e.g., U.S.-centric) and note them for further discussion.
Business Strategist: On Monday morning, begin analyzing your organization's key international markets and identify the primary cultural value orientations that characterize them, then cross-reference this with the meta-tensions (social justice vs. economic competitiveness, autonomy vs. security) to anticipate potential governance friction for upcoming AI initiatives.
Researcher: On Monday morning, begin drafting a short literature review outline focusing on how cultural value orientations (e.g., individualism vs. collectivism) have historically shaped the adoption and regulation of other transformative technologies (e.g., the internet, biotechnology) to draw parallels with AI governance.
Policymaker: On Monday morning, review the 'diagnostic decision framework' mentioned in the research, and identify one specific AI guideline (e.g., data privacy regulations) that your jurisdiction currently applies universally, then brainstorm how to adapt its interpretation to better fit the dominant cultural value orientations of your primary constituent groups.

AI governance, cultural values, governance typology, ethical AI, regulatory design

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 can organizations effectively design and implement artificial intelligence to support business decision-making?

Savindu Herath Pathirannehelage, Yash Raj Shrestha, Georg von Krogh

Design principles for artificial intelligence-augmented decision making: An action design research study

European Journal of Information Systems · 2024

Key findings

Formulate a strategic data roadmap aligning AIADM use cases with business model requirements, resources, and capabilities.
Ensure synergy across input data, AI models, and visual outputs to demonstrate tangible business value.
Implement ethical AI governance frameworks and auditing mechanisms to ensure compliance and build stakeholder trust.
Maintain human involvement in design and execution to integrate domain expertise and retain decision accountability.
Embrace an iterative design mindset focused on continuous learning, adaptation, and system improvement over time.
Utilize open-source tools and external expert partnerships to mitigate talent and resource limitations.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute kickoff meeting with your heads of data science, infrastructure, and business intelligence to review the company's current AIADM initiatives and identify one high-priority business model requirement for AIADM to address within the next quarter.
IT Manager: On Monday morning, initiate a preliminary audit of existing data pipelines and AI model repositories, specifically noting any inconsistencies or gaps in documentation that hinder synergy between input data, AI models, and visual outputs.
Business Strategist: On Monday morning, draft a concise one-page document outlining at least two specific AIADM use cases that directly align with the company's current strategic objectives, identifying the key data inputs and desired decision outcomes for each.
Researcher: On Monday morning, review the internal documentation for the AIADM system currently in use, and identify one specific instance where human domain expertise could have significantly improved the AI's output or decision recommendation.
Policymaker: On Monday morning, begin drafting a preliminary checklist of essential components for an ethical AI governance framework, focusing on elements relevant to data privacy, bias detection, and clear lines of decision accountability for AI-augmented decisions.

AI-augmented decision making, artificial intelligence, decision support systems, design principles, action design research, human-AI ensemble

AI Processed Human Approved

How can organizations turn responsible artificial intelligence principles into practical governance practices?

Emmanouil Papagiannidis, Patrick Mikalef, Kieran Conboy

Responsible artificial intelligence governance: A review and research framework

2025

Key findings

Synthesizes prior research on responsible AI into seven core principles: accountability, diversity and fairness, human agency and oversight, privacy and data governance, technical robustness and safety, transparency, and social and environmental well-being.
Establishes a tripartite framework for responsible AI governance comprising structural (roles and authority), procedural (processes and evaluation), and relational (collaboration and literacy) practices.
Maps the contextual antecedents of AI governance, including evolving societal norms and organizational values, as well as its effects on business value and social assessment.
Formulates a detailed research agenda with key questions to guide future academic inquiry and practical implementation.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of your organization's current AI project lifecycle documentation to identify where the seven core responsible AI principles (accountability, diversity & fairness, human agency & oversight, privacy & data governance, technical robustness & safety, transparency, and social & environmental well-being) are explicitly addressed and where gaps exist, preparing to assign ownership for these areas.
IT Manager: On Monday morning, schedule a 30-minute meeting with your team responsible for AI development or deployment to collaboratively identify one specific AI project that could benefit from enhanced procedural practices (e.g., implementing a new risk assessment checklist based on transparency principles) and assign a team member to draft this enhancement by Friday.
Business Strategist: On Monday morning, dedicate an hour to researching how responsible AI governance (structural, procedural, and relational practices) could directly contribute to your organization's stated business objectives and competitive advantage, identifying at least one concrete opportunity to explore further.
Researcher: On Monday morning, select one of the seven core responsible AI principles that you find most underdeveloped in current practice and spend two hours identifying three specific research questions from the provided framework that directly address operationalizing this principle, preparing to draft a short proposal for investigation.
Policymaker: On Monday morning, dedicate an hour to identifying which of the seven core responsible AI principles your current regulatory framework or internal policies address most weakly, and then draft a one-page summary of potential areas for future policy development or amendment, focusing on bridging the gap between abstract principles and actionable practices.

Artificial intelligence, Responsible AI governance, Governance practices, AI implementation, AI lifecycle, Trustworthy AI

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 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

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