AIS Research Insights

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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

Why do similar small businesses adopting the same digital platform experience vastly different business model outcomes?

Sarah Hönigsberg, Mahei Manhai Li, Ekaterina Korneeva, Hendrik Wache, Barbara Dinter

The impact of digital platform introduction on business models in SMEs—The interplay of efficiency and innovation orientation

2026

Key findings

All SMEs initially adopt an efficiency-focused platform orientation as a low-risk, resource-conserving entry logic to streamline operations.
Deep operational embedding of the platform during the efficiency phase strengthens SMEs' digital, agile, and network capabilities.
Accumulated capabilities allow firms to dynamically shift toward an innovation-focused orientation, enabling transformative business model changes such as co-branding and forming virtual companies.
SMEs with shallow platform engagement remain locked in an efficiency orientation, achieving only incremental business model improvements.

What it means for you

CIO / IT Executive: On Monday morning, review the platform's user adoption metrics from the past month, specifically looking at the depth of operational embedding (e.g., number of workflows integrated, data points shared) across different teams, and identify any teams demonstrating shallow engagement.
IT Manager: On Monday morning, schedule a brief check-in with the team responsible for platform integration, asking them to identify one specific, recurring operational task that could be further streamlined through deeper platform usage, and outline the steps to achieve this.
Business Strategist: On Monday morning, analyze the platform's current capabilities and identify one potential co-branding opportunity with another SME on the platform that leverages existing operational efficiencies but could lead to a novel offering.
Researcher: On Monday morning, begin compiling a list of the specific operational metrics used by the four case study firms to measure 'efficiency-focused platform orientation' and note down any qualitative data collected on 'deep operational embedding'.
Policymaker: On Monday morning, initiate a review of existing SME digital platform adoption support programs, focusing on how they encourage 'deep operational embedding' beyond initial efficiency gains and measure long-term transformative outcomes.

Digital platform, Capability, Business model, Small- and medium-sized enterprises, Ambidexterity, Digital transformation

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

How can immersive virtual spaces help remote teams collaborate creatively and generate innovative ideas?

Rosemary Francisco, Sharon Geeling, Grant Oosterwyk, Carolyn Tauro, Gerard De Leoz

A Metaverse-Based Proof of Concept for Innovation in Distributed Teams

MIS Quarterly Executive · 2025

Key findings

Immersive avatar-based interactions and informal spatial movement lowered social pressure, significantly enhancing psychological safety and participant engagement.
Combining spatial metaverse platforms with digital whiteboard tools allowed teams to transition fluidly between open exploration and structured prototyping.
Skilled, continuous facilitation proved essential for guiding virtual transitions, maintaining momentum, and reducing technical adoption barriers.
Assembling cross-functional teams across different geographic locations successfully broke organizational silos and fostered more diverse, novel solution concepts.

What it means for you

CIO / IT Executive: On Monday morning, initiate a pilot program to explore and test a specific metaverse platform (e.g., Spatial, ENGAGE) by scheduling a 30-minute exploratory session with your direct reports to assess its potential for distributed team collaboration and innovation, focusing on avatar-based interaction and spatial navigation.
IT Manager: On Monday morning, research and identify two to three user-friendly metaverse platforms and one digital whiteboard tool that integrate well, and then schedule a brief (15-minute) demo for your team to evaluate their potential for facilitating dynamic collaboration and creative ideation in distributed teams.
Business Strategist: On Monday morning, identify one specific cross-functional innovation challenge your distributed teams are currently facing and outline how an immersive metaverse environment could potentially foster more spontaneous interaction and diverse solution ideation for that specific challenge.
Researcher: On Monday morning, begin by reviewing the 'Key Findings' of this study and formulate three specific hypotheses about how the integration of metaverse environments and digital whiteboards can measurably improve psychological safety and idea generation in your next distributed team project.
Policymaker: On Monday morning, draft a preliminary proposal for a 'Virtual Innovation Hub' policy, outlining the potential benefits of adopting immersive metaverse technologies for distributed teams, referencing the research findings on enhanced engagement and reduced social pressure.

Metaverse, Distributed Teams, Virtual Workspace, Design Thinking, Action Research, Psychological Safety

AI Processed Human Approved

Does adopting cloud computing improve energy efficiency enough to offset the energy data centers consume?

Jiyong Park, Kunsoo Han, Byungtae Lee

Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency

2022

Key findings

Cloud-based IT services significantly improve client energy efficiency, resulting in estimated user-side cost savings of $2.8–$12.6 billion in 2017 alone.
Software-as-a-Service (SaaS) enhances both electric and nonelectric energy efficiency across all industries by driving operational efficiency and reducing resource waste.
Infrastructure-as-a-Service (IaaS) primarily improves electric energy efficiency in hardware-intensive industries by replacing inefficient internal IT equipment and infrastructure.
Net energy savings on the user side far outweigh the energy consumed by cloud vendor industries and are comparable to total electricity consumption by U.S. data centers.

What it means for you

CIO / IT Executive: On Monday morning, review your organization's current SaaS and IaaS adoption levels and identify the top 2-3 cloud services that could be further optimized or expanded to maximize user-side energy efficiency gains, based on the findings that SaaS enhances overall energy efficiency and IaaS improves electric efficiency in hardware-intensive areas.
IT Manager: On Monday morning, schedule a brief technical review with your team to identify specific internal IT equipment or infrastructure in hardware-intensive departments that could be replaced by IaaS solutions, focusing on those contributing most to electric energy consumption.
Business Strategist: On Monday morning, quantify the estimated user-side cost savings from your organization's current cloud computing usage by applying the study's estimated range of $2.8–$12.6 billion in savings per year (scaled appropriately for your organization's IT spend) and present this as a tangible benefit to senior leadership.
Researcher: On Monday morning, begin drafting a proposal to investigate the energy efficiency impact of specific SaaS applications within your organization, focusing on how they drive operational efficiency and reduce resource waste, mirroring the study's distinction between SaaS and IaaS.
Policymaker: On Monday morning, initiate a discussion with relevant stakeholders about exploring incentives or guidelines that encourage businesses to adopt cloud-based IT services, particularly SaaS, by highlighting the significant user-side energy efficiency gains and cost savings demonstrated in this research.

Cloud Computing, Software-as-a-Service, Infrastructure-as-a-Service, Energy Efficiency, Green IT, Stochastic Frontier Analysis

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 does subscribing to a video streaming service affect people's spending at movie theaters?

Sihan Fang, Hyeokkoo Eric Kwon, Anandasivam Gopal, Yongjin Park

Return of the Movie Night? Analyzing the Impact of Netflix Subscriptions on Offline Movie Spending

Information Systems Research · 2025

Key findings

Subscribing to Netflix leads to a statistically significant increase in subsequent offline movie ticket spending and viewing frequency.\n- Post-subscription offline moviegoing is frequently accompanied by increased spending on complementary social and hedonic activities such as dining out and entertainment.\n- The positive treatment effect is more pronounced among younger and lower-income individuals who exhibit higher ex-ante social and hedonic needs.\n- Subscribers to premium Netflix plans show a higher post-subscription increase in offline cinema consumption, supporting expectation disconfirmation theory.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of our current digital content delivery infrastructure to identify potential synergies and opportunities to bundle or cross-promote offline entertainment experiences with our streaming service offerings, especially targeting younger and lower-income demographics.
IT Manager: On Monday morning, analyze user engagement data for our streaming platform, specifically looking for patterns of usage among younger and lower-income users, and investigate if there are any integrations or notification systems we can implement to suggest local cinema showtimes or associated dining deals as complementary activities.
Business Strategist: On Monday morning, schedule a meeting with the marketing and partnerships teams to explore co-marketing opportunities with local cinemas and restaurants, focusing on developing bundled packages or loyalty programs that incentivize a 'Netflix and then Out' experience, particularly targeting younger demographics and those with perceived higher hedonic needs.
Researcher: On Monday morning, begin designing a follow-up study that specifically quantifies the causal impact of premium streaming plan subscriptions on offline movie spending and complementary hedonic spending, utilizing a controlled experiment or a refined difference-in-differences approach to test expectation disconfirmation theory more rigorously.
Policymaker: On Monday morning, begin drafting a policy brief that examines the potential for digital entertainment subscriptions to indirectly stimulate local economies through increased spending on complementary offline social and hedonic activities, and explore incentives for businesses to create integrated entertainment packages.

subscription-based services, video streaming, social and hedonic activity, imperfect disruptive technology, difference in differences, offline movie consumption

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

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

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