AIS Research Insights

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AI Processed Human Approved

How common is body-related cyberbullying against adolescent girls on TikTok, and what forms does it take?

Gordon Amidu

Body Image Distress and Cyberbullying Among Adolescent Girls in the U.S.

2025

Key findings

Cyberbullying constituted 7.4% of total analyzed interactions, whereas non-cyberbullying comments comprised 92.6%.
Body shaming was the most prevalent cyberbullying behavior (41.68%), followed by insults (24.78%) and teasing (21.26%).
Neutral sentiment predominated user responses, followed by positive sentiment, with negative sentiment representing the smallest proportion.
Topic modeling revealed five primary themes: Appearance Monitoring, Body Commentary, Positive Affirmations, Social Comparison, and Identity Confusion.

What it means for you

CIO / IT Executive: Initiate a review of existing content moderation algorithms and reporting mechanisms to assess their effectiveness in detecting and flagging body shaming, insults, and teasing comments related to appearance on platforms frequented by adolescent girls, aiming to prioritize enhancements for these specific issue types.
IT Manager: Schedule a technical deep-dive meeting with the content moderation team to present the research findings on the prevalence of body shaming and insults on TikTok, and collaboratively identify specific keyword filters and pattern recognition strategies to implement within current moderation tools to better catch these behaviors.
Business Strategist: Develop a proposal for a new product feature or marketing campaign that leverages positive affirmations and counter-narratives to combat social comparison and identity confusion related to body image on social media platforms, referencing the research's thematic analysis of positive affirmations as a potential intervention.
Researcher: Begin drafting a follow-up research proposal that specifically investigates the effectiveness of different types of content moderation (e.g., AI-driven vs. human) in mitigating body shaming and insult-based cyberbullying on TikTok, and proposes a quantitative analysis of user sentiment shifts post-intervention.
Policymaker: Draft a preliminary policy brief for the relevant government agency, highlighting the study's quantitative data on the percentage of cyberbullying and the specific themes of harassment (body shaming, insults), and outlining the urgent need for platform-specific regulations and youth mental health intervention funding to address appearance-centered online harassment.

Body shaming, Cyberbullying, Adolescent girls, TikTok, Online harassment

AI Processed Human Approved

How can universities better organize themselves to adapt and deliver effective online learning?

Indriani Noor Hapsari, Dana Indra Sensuse, Harry Budi Santoso, Indra Budi, Ryan Randy Suryono, Kautsarina

Improving Self-Organization Capability in e-Learning using Soft System Methodology: A Case from Indonesia

International Conference on Information Systems (ICIS) · 2024

Key findings

Identified four core dimensions influencing self-organization in higher education: strategic planning, personnel development, learning resource management, and IT capability.
Formulated seven actionable interventions, including establishing clear multi-year strategies, promoting open dialogue across faculties, visualizing workload allocations, and implementing collaborative feedback mechanisms for course design.
Highlighted the vital roles Information Systems play as enablers for group decision-making, performance tracking, knowledge sharing, and managing system dependencies.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of existing IT infrastructure and systems specifically identifying which ones are currently siloed and propose an initial phased approach (e.g., Phase 1: Integration of student information system with the learning management system) for breaking down these silos, with a preliminary timeline and resource estimate.
IT Manager: On Monday morning, schedule a 30-minute check-in with a representative from the academic affairs department to discuss how existing IT systems can better support collaborative feedback mechanisms for course design, identifying one specific tool or feature that could be enhanced or implemented in the next sprint.
Business Strategist: On Monday morning, draft a one-page outline for a multi-year e-learning strategy document, focusing on how to integrate the identified four core dimensions (strategic planning, personnel development, learning resource management, IT capability) into measurable objectives for the next academic year, and identify one key stakeholder group for initial feedback.
Researcher: On Monday morning, begin compiling a list of existing open-dialogue platforms or communication channels within the university and identify two specific instances where they could be leveraged to facilitate cross-faculty discussions on e-learning best practices or challenges.
Policymaker: On Monday morning, draft a memo to the academic senate proposing a pilot program for a collaborative feedback mechanism for course design, outlining the key benefits, the intended process, and requesting their endorsement to explore its feasibility further.

soft-system methodology, self-organization, higher education, e-learning, information system, Indonesia

AI Processed Human Approved

What drives digital gullibility, and how can systems be designed to help users critically evaluate online information?

Margeret Hall, Christian Haas

A Research Agenda to Understand Drivers of Digital Gullibility

International Conference on Information Systems (ICIS) · 2022

Key findings

Frames digital gullibility as a key antecedent to maladaptive online behaviors rather than focusing solely on misinformation output.
Integrates socio-technical system principles with concepts like context collapse and network externalities to explain persistent online gullibility.
Formulates ten research propositions across three gaps: information context, individual seeking behavior, and realized behavior.
Provides actionable guidance for designing user-centric systems that enhance epistemic vigilance and reduce information verification costs.

What it means for you

CIO / IT Executive: On Monday morning, initiate a cross-departmental working group with representatives from Security, User Experience, and Training to review the research agenda's propositions, specifically focusing on how to integrate 'epistemic vigilance' enhancement into the next IT security awareness training module, prioritizing the 'information context' gap.
IT Manager: On Monday morning, schedule a 30-minute review session with your team to discuss the concept of 'context collapse' as presented in the research and brainstorm one specific system design tweak (e.g., adding a 'source credibility' indicator to internal dashboards) that could reduce information verification costs for your users, to be piloted within the week.
Business Strategist: On Monday morning, draft a one-page executive summary for your leadership team that translates the research's core finding on 'digital gullibility as an antecedent to maladaptive online behaviors' into a business risk, proposing a brief for exploring how to quantify the cost of digital gullibility within your organization's operations.
Researcher: On Monday morning, begin a literature review focused on the 'individual seeking behavior' gap outlined in the research, specifically searching for empirical studies that measure user-reported 'epistemic vigilance' in different online environments to refine your next research question.
Policymaker: On Monday morning, draft an inquiry to relevant government agencies or industry bodies requesting current data on the prevalence and economic impact of 'digital scams' and 'phishing' within your jurisdiction, framing the request around understanding 'digital gullibility' as a root cause.

Digital gullibility, maladaptive outcomes, context collapse, network externalities, epistemic vigilance, socio-technical systems

AI Processed Human Approved

How do communities use social media to support mental health recovery during natural disasters?

Mohammad Ariful Islam, Julian Marx, Libo Liu, Ofir Turel

Digital Mental Health Resilience During Natural Disasters: A Scoping Review and Research Agenda

2025

Key findings

Identified seven key socio-technical themes describing how social media fosters mental health resilience across natural disaster phases.
Revealed that communities actively curate content and self-regulate online spaces to protect users from distress, misinformation, and trauma.
Demonstrated the importance of vulnerability-sensitive channels and cross-sector coordination among community groups, government agencies, and mental health providers.

What it means for you

CIO / IT Executive: On Monday morning, schedule a brief exploratory meeting with your innovation team to assess existing social media monitoring tools and identify potential enhancements for detecting and analyzing distress signals and misinformation during simulated disaster scenarios.
IT Manager: On Monday morning, review the current moderation policies for any public-facing community forums or social media channels managed by your organization, and identify specific criteria related to 'vulnerability-sensitive content' that could be integrated into moderation guidelines.
Business Strategist: On Monday morning, initiate a conversation with your communications and marketing teams to explore how your organization's social media presence could be adapted to proactively offer mental health resources or foster supportive community interactions, especially in light of potential regional disaster risks.
Researcher: On Monday morning, begin drafting a concise summary of the 'vulnerability-sensitive channels' and 'cross-sector coordination' findings from this review, identifying specific examples or hypothetical scenarios where these could be applied to digital mental health resilience research.
Policymaker: On Monday morning, draft a short memo to your relevant department heads, flagging the research finding on 'communities actively curate content and self-regulate online spaces' and suggesting a brief discussion on how public digital platforms could be better designed or supported to facilitate this self-regulation for mental health resilience during crises.

Social media, natural disasters, digital mental health resilience, community resilience, scoping review

AI Processed Human Approved

How can educators successfully use community partner projects to teach practical technology and project management skills?

Margeret Hall

Service Learning in Information Systems Education: Pedagogical Approaches to Support Experiential Learning and Higher-level Thinking

International Conference on Information Systems (ICIS) · 2018

Key findings

Integrating service learning into Information Systems classrooms increases student motivation and engagement by connecting technical tasks to pro-social community impacts.
Employing constructivist classroom management alongside Agile methodologies effectively supports higher-level thinking skills, including critical evaluation and self-reflection.
High team cohesion can be a double-edged sword, occasionally masking individual inefficiencies and inhibiting necessary exchanges with external stakeholders.
Assessing students on their execution of Agile project management processes rather than purely technical deliverables protects individual grading fairness and encourages risk-taking.

What it means for you

CIO / IT Executive: On Monday morning, initiate a pilot program to identify two to three non-profit organizations in our community that could benefit from IT support, and assign a dedicated internal IT liaison for each to manage the student engagement.
IT Manager: On Monday morning, schedule a 30-minute meeting with your team to brainstorm potential IT projects that could be beneficial for local non-profits, focusing on areas where student teams could realistically make an impact within a semester.
Business Strategist: On Monday morning, reach out to a local community foundation or non-profit umbrella organization to understand their most pressing IT needs and explore potential service learning partnerships for future student projects.
Researcher: On Monday morning, begin drafting a literature review section of your paper, specifically focusing on case studies of service learning in technical fields and the reported challenges and benefits of integrating Agile methodologies in such contexts.
Policymaker: On Monday morning, begin drafting a proposal for a new grant or funding initiative that encourages universities to partner with local non-profits for experiential learning opportunities in Information Systems, emphasizing the development of practical, community-focused IT skills.

Service Learning, IS Education, Constructivism, Bloom's Taxonomy, Experiential Learning

AI Processed Human Approved

How can visual tools help organizations understand and manage complex smart city mobility platforms?

Sven-Volker Rehm, Anne Faber, Lakshmi Goel

Visualizing Platform Hubs of Smart City Mobility Business Ecosystems

International Conference on Information Systems (ICIS) · 2017

Key findings

Implemented five distinct visualization techniques (Force-directed, Tree Map, Matrix, Radial Network, and Modified Ego-Network layouts) within a software prototype to match diverse stakeholder decision-making needs.
Demonstrated how macro-level visual analytics can uncover key industry players, platform overlaps, missing service niches, and dominant inter-firm relationships.
Conceptualized a framework showing how tailored ecosystem visualizations can stimulate vital knowledge flows across policy governance boards, commercial firms, and smart city citizen communities.

What it means for you

CIO / IT Executive: On Monday morning, review the 'business ecosystem explorer' software prototype documentation and identify which of the five visualization techniques (Force-directed, Tree Map, Matrix, Radial Network, Modified Ego-Network) are most technically feasible for immediate integration into existing smart city data infrastructure.
IT Manager: On Monday morning, schedule a brief technical assessment meeting with your team to evaluate the infrastructure requirements and potential integration points for a pilot implementation of one or two of the presented visualization techniques from the 'business ecosystem explorer' prototype.
Business Strategist: On Monday morning, initiate a discussion with your cross-functional team to brainstorm specific strategic questions about our smart city mobility ecosystem that could be answered by analyzing platform overlaps, missing service niches, and dominant inter-firm relationships using the described macro-level visual analytics.
Researcher: On Monday morning, analyze the research paper's findings on how tailored ecosystem visualizations stimulate knowledge flows and identify which specific visualization technique and corresponding stakeholder group (policy governance, commercial firms, citizen communities) offers the most promising avenue for a follow-up research experiment.
Policymaker: On Monday morning, identify a key policy governance challenge related to smart city mobility coordination and consider how a macro-level visualization of platform hubs, as described in the research, could provide actionable insights to address this challenge.

Digital platform, business ecosystem, smart city, visualization, knowledge flow

AI Processed Human Approved

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

Sven-Volker Rehm, Thomas Reschenhofer, Klym Shumaiev

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

International Conference on Information Systems (ICIS) · 2014

Key findings

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

What it means for you

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

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

AI Processed Human Approved

How do entrepreneurs understand and manage bias when developing artificial intelligence solutions?

Marco Smacchia, Michele Cipriano, and Stefano Za

Entrepreneurial Perspective of AI Bias: A Preliminary Investigation

2025

Key findings

Revealed two primary interpretations of AI bias held by entrepreneurs: computational (technical) bias and systemic (societal) bias.
Proposed a 2x2 framework mapping potential negative outcomes and mitigation strategies according to decision levels (operational vs. strategic) and bias types (technical vs. social).
Showed that computational bias requires iterative algorithmic training and data validation, whereas systemic bias demands targeted stakeholder communication and organizational alignment.
Emphasized the necessity of adopting a socio-technical approach to foster fair, equitable, and effective AI adoption across different organizational contexts.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute call with your AI development leads to review the current AI project portfolio and identify any projects that are close to deployment or already in production, specifically asking them to flag any known or suspected computational bias issues that might require iterative algorithmic retraining or enhanced data validation.
IT Manager: On Monday morning, initiate a review of your team's standard data validation protocols for AI projects and draft a concise checklist to specifically include steps for identifying and documenting potential computational bias (e.g., data skewness, outlier analysis) that your team can start implementing immediately.
Business Strategist: On Monday morning, draft a one-page briefing document for your executive team that outlines the distinction between computational and systemic AI bias and proposes one concrete example of how systemic bias could negatively impact a current business strategy, suggesting the need for proactive stakeholder communication as a first mitigation step.
Researcher: On Monday morning, begin a literature search for academic papers and industry reports specifically focusing on 'stakeholder engagement frameworks for AI ethics' and 'organizational alignment strategies for AI fairness' to gather preliminary data for a proposed socio-technical approach to AI bias management.
Policymaker: On Monday morning, send an internal email to your policy advisory team with a link to this research summary and request they begin researching existing regulatory frameworks or best practices in other jurisdictions related to mandating 'socio-technical impact assessments' for AI systems before broad deployment.

AI bias, Artificial intelligence, Case study, AI artifacts, AI fairness, Socio-technical approach

AI Processed Human Approved

What combinations of contract terms make IT outsourcing projects successful as technology changes over time?

Onkar S. Malgonde, Moez Farokhnia Hamedani, Sunil Mithas, Manish Agrawal, Kaushal Chari

How Do Technology Paradigms Influence Configurations of Contract Characteristics for Success of Inter-Organizational Outsourcing Projects, 1991–2009?

2026

Key findings

Configurations of contract characteristics associated with outsourcing success differ significantly across technology paradigms.
Three overarching configurational themes lead to success: Economic Imperative, Conservative Relational, and Conservative Imperative.
Over time, firms have placed an increasing emphasis on the relational component to manage contracting risk alongside formal contractual terms.

What it means for you

CIO / IT Executive: On Monday morning, convene your IT leadership team to review the five core contract features analyzed in this research and discuss how they might be re-weighted or re-configured for your current technology paradigm and outsourcing portfolio. Specifically, identify which features (e.g., scope definition, performance metrics, governance mechanisms) are most critical for upcoming or existing projects and how they align with 'Economic Imperative', 'Conservative Relational', or 'Conservative Imperative' themes.
IT Manager: On Monday morning, pull the contract documents for your most critical ongoing IT outsourcing project. Identify and list the specific clauses related to scope, performance metrics, payment terms, and dispute resolution. Then, for each clause, assess its 'formality' (highly specific and legalistic vs. more flexible and outcome-oriented) and 'relationality' (terms fostering collaboration and trust vs. purely transactional terms), noting potential areas for adjustment to better fit your project's technology paradigm.
Business Strategist: On Monday morning, identify the top 2-3 inter-organizational IT outsourcing projects that are currently critical to achieving your business objectives. For each of these projects, analyze whether the current contract characteristics lean more towards an 'Economic Imperative' (cost reduction focus), 'Conservative Relational' (balancing cost with strong partnership), or 'Conservative Imperative' (risk aversion through strict control). Then, evaluate if this alignment is still optimal given the project's technology and your evolving business strategy.
Researcher: On Monday morning, identify the primary technology paradigm (e.g., cloud computing, AI/ML, legacy systems) that is dominant in your current research focus on IT outsourcing. Then, cross-reference this with the three overarching configurational themes identified in the research ('Economic Imperative', 'Conservative Relational', and 'Conservative Imperative'). Begin outlining a framework for how you will specifically analyze the contract characteristics within that chosen paradigm to identify context-sensitive recipes for success.
Policymaker: On Monday morning, review existing government procurement guidelines for IT outsourcing contracts. Specifically, identify how flexible or rigid these guidelines are regarding the interplay of formal contractual terms and the emphasis on relational components. Consider proposing an amendment to encourage a more nuanced approach that allows for adaptable contract configurations based on the technology paradigm and the increasing importance of building trust and collaboration in outsourcing relationships.

contracting risk, equifinality, interorganizational projects, qualitative comparative analysis, technology outsourcing, set-theoretical approach

AI Processed Human Approved

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

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

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

Business & Information Systems Engineering · 2026

Key findings

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

What it means for you

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

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

AI Processed Human Approved

How do laws banning businesses from suppressing negative reviews affect online customer ratings?

Aida Sanatizadeh, Gordon Burtch, Yili Hong, Yuheng Hu

Can Regulation Restore Authenticity? Evidence from Policies Against Online Review Suppression

Information Systems Research · 2025

Key findings

Following the CRFA enactment, U.S. hotel reviews became systematically more negative in both star ratings and textual sentiment.
Reviews became significantly longer on average, indicating that consumers shared more detailed feedback once legal risks were mitigated.
Rating declines were most concentrated among less reputable hotels and those in highly competitive local markets, which previously suppressed negative reviews more aggressively.
The policy's effects were strongest among American reviewers and users with longer platform tenure.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of our internal policies and legal contracts to ensure no clauses inadvertently threaten or discourage customers from providing honest feedback on public review platforms.
IT Manager: On Monday morning, task your team with monitoring online review platforms for any patterns of review suppression tactics being used by our competitors, specifically looking for increases in negative reviews that are quickly removed or challenged.
Business Strategist: On Monday morning, analyze recent customer feedback data, particularly any negative reviews received in the last quarter, and assess if there are opportunities to engage with these customers to improve our offerings and address concerns openly.
Researcher: On Monday morning, identify and gather publicly available datasets on consumer review sentiment and volume for companies in sectors known for aggressive review suppression prior to recent regulatory changes, to replicate or extend the findings on the CRFA.
Policymaker: On Monday morning, schedule a meeting with your legislative staff to discuss potential avenues for expanding regulatory protections to cover other forms of consumer feedback, such as social media comments or product testimonials, building upon the success of the CRFA.

Consumer Review Fairness Act, Online Reviews, Review Suppression, Chilling Effect, Digital Platforms, Consumer Protection

AI Processed Human Approved

How can organizations successfully implement digital identity wallets and overcome common adoption challenges?

Andre Kudra, Alexander Rieger, Johannes Sedlmeir, Tamara Roth, Gilbert Fridgen, Amber Young

Digital Identity Wallets: A Guide to the EU’s New Identity Model

Information Systems Journal (ISJ) · 2026

Key findings

Synthesizes empirical lessons from 12 real-world digital wallet implementation projects across various industries.
Categorizes adoption obstacles into technical maturity, organizational integration, and ecosystem value co-creation challenges.
Establishes a four-step implementation roadmap guiding organizations from strategic readiness to active ecosystem engagement.

What it means for you

CIO / IT Executive: Schedule a 30-minute internal briefing for your core IT leadership team to present the attached research summary and assign a point person to investigate the 'technical maturity' obstacles identified in the key findings within the context of our existing identity and access management infrastructure.
IT Manager: On Monday morning, compile a list of all current third-party single sign-on providers used by the organization and identify which of these might be candidates for integration or replacement with a digital identity wallet solution in the future, flagging any immediate integration challenges or risks.
Business Strategist: Dedicate one hour on Monday morning to reviewing the 'ecosystem value co-creation' challenges mentioned in the research and brainstorm at least three potential business use cases within our organization where a digital identity wallet could unlock new revenue streams or significantly improve customer experience.
Researcher: Begin analyzing the 12 practical experience case studies mentioned in the research by focusing on the 'organizational integration' obstacles. Identify and document recurring themes and successful mitigation strategies from at least three diverse industry examples to inform our own adoption framework.
Policymaker: Initiate a dialogue with legal and compliance teams on Monday morning to assess the implications of the 2026 EU mandate for digital wallet support, specifically identifying any current regulatory gaps or areas where policy clarification will be needed to facilitate the adoption of wallet-based identity models.

digital identity wallets, identity and access management, eIDAS, attribute-based access control, self-sovereign identity

AI Processed Human Approved

How does integrating generative AI into coursework affect student learning and practical skills?

Sarah Hönigsberg, Laura Watkowski, Andreas Drechsler

Generative Artificial Intelligence in Higher Education: Mediating Learning for Literacy Development

Communications of the Association for Information Systems · 2025

Key findings

GAI tools act as active learning mediators that help bridge knowledge disparities and deliver adaptive, scalable support to diverse student groups.
Experiential engagement with GAI develops crucial literacy skills including prompt engineering, task allocation, and critical output evaluation.
Human educators remain essential to supply emotional, ethical, and contextual guidance that GAI cannot replicate.

What it means for you

CIO / IT Executive: On Monday morning, direct your team to initiate a pilot program with ChatGPT-4 for a select group of faculty in the business school to explore its integration into curriculum design and assess infrastructure readiness for wider deployment.
IT Manager: On Monday morning, schedule a meeting with the faculty involved in the pilot program to gather initial feedback on their experience using GAI tools and document any technical challenges encountered during their setup or usage.
Business Strategist: On Monday morning, begin researching and analyzing how competitor business schools are currently leveraging generative AI in their curricula and draft a preliminary report on potential competitive advantages or disadvantages for our institution.
Researcher: On Monday morning, begin drafting a research proposal to investigate the specific prompt engineering strategies employed by students in the pilot program and how these strategies correlate with improved literacy development outcomes.
Policymaker: On Monday morning, initiate a review of existing academic integrity policies to identify areas that need to be updated or expanded to address the ethical use of generative AI by students and faculty.

Generative AI, AI Literacy, Technology-Mediated Learning, Higher Education, Course Design

AI Processed Human Approved

How can conversational AI be designed and integrated into learning platforms to provide personalized, accessible education?

Ahmed Elragal, Ali Ismail Awad, Ingemar Andersson, Jörgen Nilsson

A Conversational AI Bot for Efficient Learning: A Prototypical Design

2024

Key findings

Successfully designed and implemented COFFEE, an AI conversational bot integrated with Canvas LMS, ChatGPT, and machine learning prediction models to enable adaptive learning pathways.
Incorporated accessibility tools such as the Infusion framework and OpenAI Whisper to support students with visual and hearing impairments.
Formulated comprehensive design science patterns and evaluation frameworks to assist researchers and practitioners in deploying AI-driven educational bots.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your technology innovation team to review the COFFEE research paper and discuss potential pilot program feasibility for integrating similar AI conversational bot capabilities into our existing LMS infrastructure.
IT Manager: On Monday morning, initiate a technical deep-dive into the integration points of Google Dialogflow, Canvas LMS, and OpenAI Whisper as described in the COFFEE research, and identify the specific APIs or plugins required for potential implementation.
Business Strategist: On Monday morning, begin drafting a brief (1-page) executive summary outlining the potential competitive advantages and cost-benefit analysis of adopting an AI-powered conversational learning platform similar to COFFEE, focusing on improved student retention and accessibility.
Researcher: On Monday morning, identify three key design science patterns presented in the COFFEE research that are most applicable to your current research project and outline a plan to adapt and integrate them into your next experimental design.
Policymaker: On Monday morning, research existing educational technology grant opportunities that support the development and implementation of AI-driven adaptive learning solutions, and identify specific funding bodies aligned with the goals of the COFFEE research.

Conversational AI, Adaptive Learning, Design Science Research, Educational Technology, Accessibility, Google Dialogflow

AI Processed Human Approved

How does generative AI transform the way digital platforms create value, operate, and manage their users?

Michael Wessel, Martin Adam, Alexander Benlian, Ann Majchrzak, Ferdinand Thies

Generative AI and its Transformative Value for Digital Platforms

Journal of Management Information Systems · 2025

Key findings

Proposed a conceptual framework detailing four key GenAI mechanisms: intelligent automation, democratization, hyper-personalization, and collaborative innovation.\n- Explained how GenAI converts passive boundary resources (e.g., APIs, SDKs) into active, intelligent mediators that autonomously interpret, process, and generate context-aware outputs.\n- Highlighted that GenAI systematically lowers technical and cognitive entry barriers for non-experts, amplifying network effects while requiring novel quality and authenticity governance mechanisms.\n- Illustrated how human-AI collaborative innovation extends platform generativity by positioning GenAI as an active co-creator rather than a mere efficiency tool.\n- Outlined a multi-perspective research agenda focusing on the engineering, economic, and organizational implications of GenAI for platform owners, complementors, users, and society.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute exploratory meeting with your core architecture and data science teams to identify 1-2 pilot use cases for intelligent automation within our existing platform, focusing on areas where GenAI can autonomously process and interpret existing boundary resources like APIs or SDKs to generate context-aware outputs.
IT Manager: On Monday morning, initiate a review of the platform's current developer documentation and onboarding process. Identify specific sections or tasks that could be simplified or made more accessible to non-experts by integrating GenAI-powered tools for code generation, API explanation, or prompt engineering guidance.
Business Strategist: On Monday morning, begin drafting a one-page concept document outlining how GenAI could be leveraged for hyper-personalization on our platform. Focus on identifying 2-3 specific user segments and detailing how GenAI could generate unique, context-aware content or recommendations for each, differentiating from current rule-based personalization.
Researcher: On Monday morning, select one of the four key GenAI mechanisms (intelligent automation, democratization, hyper-personalization, or collaborative innovation) and spend two hours outlining a specific research question and potential methodology to investigate its impact on a particular aspect of platform stakeholder dynamics, such as complementor participation or user engagement.
Policymaker: On Monday morning, convene a brief working session with legal and compliance advisors to discuss potential novel governance mechanisms needed to ensure the quality and authenticity of GenAI-generated outputs on digital platforms, specifically addressing the challenges highlighted by amplified network effects from lowered entry barriers.

Generative AI, Digital Platforms, Intelligent Automation, Platform Democratization, Hyper-Personalization, Collaborative Innovation, Platform Governance

AI Processed Human Approved

Can software that reads facial expressions reliably track student engagement and help-seeking across different cultures?

Guan-Yun Wang, Yasuhiro Hatori, Yoshiyuki Sato, Chia-Huei Tseng, Hsiu-Ping Yueh, Satoshi Shioiri

Cultural differences in facial features based on SHAP analysis from machine learning model estimating learners' mental states

2026

Key findings

LightGBM models effectively classified engagement and help-seeking states for Taiwanese learners, though accuracy scores were significantly higher for Japanese participants.
Estimating engagement relied more heavily on facial features around the eyes for Japanese participants, whereas features around the mouth were more important for Taiwanese participants.
Help-seeking estimations demonstrated significant cultural differences in SHAP values, particularly with mouth-related features like lip stretcher (AU20) and lips part (AU25).
Cross-cultural correlation analysis revealed that facial expressions of engagement are relatively consistent across cultures, whereas help-seeking expressions are more culturally specific.

What it means for you

CIO / IT Executive: On Monday morning, direct your R&D team to prioritize exploring and piloting machine learning models that incorporate culturally-aware feature weighting for facial expression analysis in our intelligent tutoring systems, specifically investigating the differential importance of eye vs. mouth features for engagement and the impact of mouth-related Action Units (e.g., AU20, AU25) on help-seeking predictions across different cultural user bases.
IT Manager: On Monday morning, task your data science team to begin a comprehensive review of our current intelligent tutoring system's facial recognition module, focusing on identifying and documenting the specific facial features and Action Units (AUs) it currently utilizes, and initiate a plan to evaluate the potential impact of cultural variations in these features on model performance, particularly for engagement and help-seeking, based on the provided research findings.
Business Strategist: On Monday morning, initiate a market assessment to identify key international target markets for our intelligent tutoring systems where cultural variations in facial expressions might significantly impact user engagement and help-seeking detection, and begin preliminary discussions with the product development team about incorporating adaptive facial analysis capabilities to improve cross-cultural user experience and market penetration.
Researcher: On Monday morning, design a controlled experiment to quantitatively measure the accuracy difference in engagement and help-seeking prediction models when using feature sets weighted for Japanese participants versus Taiwanese participants (or vice versa) on a mixed-cultural test dataset, specifically focusing on the SHAP values for mouth-related Action Units (AU20, AU25) in help-seeking scenarios.
Policymaker: On Monday morning, draft a mandate for internal technology development guidelines that requires the evaluation and documentation of potential cultural biases in any AI models used for user interaction analysis, specifically requesting that future intelligent tutoring system deployments undergo a cross-cultural validation phase, with a focus on facial feature interpretation, before broad international rollout.

Cultural differences, Machine learning, Facial expression, Hint processing, Action units, SHAP analysis

AI Processed Human Approved

Can facial expressions and head movements accurately identify when online learners are engaged or need help?

Guan-Yun Wang, Yasuhiro Hatori, Yoshiyuki Sato, Chia-Huei Tseng, Satoshi Shioiri

Predicting learners’ engagement and help-seeking behaviors in an e-learning environment by using facial and head pose features

Computers and Education: Artificial Intelligence · 2025

Key findings

LightGBM classifiers outperformed SVM models, achieving classification accuracy between 0.69 and 0.93 for both engagement and help-seeking states.
Facial Action Units around the brows and lips (such as outer brow raiser, brow lowerer, lip tightener, and dimpler) proved to be key predictors for learner mental states.
Head position and orientation features were highly effective indicators, with head pitch being particularly relevant for engagement and head yaw for help-seeking behavior.

What it means for you

CIO / IT Executive: On Monday morning, initiate a vendor evaluation process to identify potential solutions or platforms that can integrate real-time facial and head pose analysis for e-learning engagement and help-seeking detection, prioritizing those that leverage machine learning models similar to LightGBM.
IT Manager: On Monday morning, schedule a technical deep-dive meeting with your IT team to explore the feasibility of deploying existing or evaluating new webcam-based computer vision tools and libraries on your institution's e-learning platform, focusing on capturing facial action units and head pose data.
Business Strategist: On Monday morning, begin market research to identify competitors or innovative e-learning platforms that are already incorporating or piloting AI-driven engagement and help-seeking prediction features, and analyze their go-to-market strategies.
Researcher: On Monday morning, refine your current LightGBM model by isolating and specifically analyzing the predictive power of the identified facial action units (outer brow raiser, brow lowerer, lip tightener, dimpler) and head pose features (pitch for engagement, yaw for help-seeking) in a controlled simulated e-learning environment.
Policymaker: On Monday morning, draft a proposal for a pilot program to explore the ethical implementation of AI-driven learner engagement and help-seeking detection tools within a controlled e-learning environment, including provisions for data privacy and transparent communication with learners.

e-learning, intelligent tutoring system, machine learning, facial expression, head pose, engagement, help-seeking

AI Processed Human Approved

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

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

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

2026

Key findings

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

What it means for you

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

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

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