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A Multi-Method Analysis of the Evolution of Social Media Discourse in Prolonged Crises: through the Lens of Crisis and Emergency Risk Communication (CERC)
AI Processed Human Approved

How do public discussions on social media shift across different stages of a prolonged health crisis?

Wenting (Kayla) Jiang, Ashish Gupta, Han Li
A Multi-Method Analysis of the Evolution of Social Media Discourse in Prolonged Crises: through the Lens of Crisis and Emergency Risk Communication (CERC)
2025
Key findings
  • Public discussions shifted over time from initial preventative measures and viral impacts to broader concerns surrounding emerging variants.
  • Upward variant phases featured more focused and interconnected social media discussions, whereas downward phases exhibited more varied conversations.
  • Results highlight the critical need for phase-tailored messaging strategies by government agencies to effectively mitigate anxiety, foster trust, and counter misinformation during prolonged crises.
What it means for you
  • CIO / IT Executive: On Monday morning, direct your data science team to explore leveraging sentiment analysis and topic modeling tools to categorize incoming social media data related to ongoing public health issues, enabling proactive identification of shifting public concerns.
  • IT Manager: On Monday morning, schedule a brief meeting with your social media monitoring team to review the existing dashboards and identify if they can be reconfigured to visually highlight shifts in discussion topics and the interconnectedness of conversations, aligning with the research's emphasis on phase-tailored messaging.
  • Business Strategist: On Monday morning, initiate a brainstorming session with your communications and marketing teams to consider how your organization's messaging can adapt to different phases of a prolonged public health crisis, drawing parallels from the COVID-19 discourse shifts identified in the research.
  • Researcher: On Monday morning, begin outlining a research proposal that analyzes social media discourse in another prolonged crisis (e.g., climate change, economic instability) using a similar multi-method approach to the one described, focusing on mapping public concerns onto relevant communication frameworks.
  • Policymaker: On Monday morning, task your policy analysis unit with reviewing existing crisis communication guidelines and recommend updates to incorporate phase-specific strategies informed by the research on evolving social media discourse, particularly concerning anxiety mitigation and misinformation countering.
social media discourse, crisis communication, prolonged crises, CERC model, text network analytics, guided LDA, COVID-19
Managing Performance in Collaborative Networks: A Method for Defining Key Performance Indicators for Collaborative Business Models
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
How do organizational capabilities mature? maturity level characteristics for organizational capabilities
AI Processed Human Approved

What stages do organizational capabilities go through as they develop and mature over time?

Ginger Korsten, Baris Ozkan, Banu Aysolmaz, Daan Mul, Oktay Turetken
How do organizational capabilities mature? maturity level characteristics for organizational capabilities
2025
Key findings
  • Developed the Maturity Level Characterization Model (MLCM) defining six distinct capability maturity stages from unaware to expert.
  • Adapted the Dreyfus model of skill acquisition and institutional theory to establish a solid theoretical foundation for capability maturity.
  • Provided an instantiation template that allows researchers and practitioners to systematically generate domain-specific capability maturity levels.
  • Validated the model through practitioner evaluations in advanced data analytics, confirming its high validity, relevance, completeness, clarity, and usefulness.
What it means for you
  • CIO / IT Executive: On Monday morning, identify ONE core IT capability (e.g., cybersecurity incident response, cloud adoption) that is critical to your organization's strategy and initiate a discussion with your direct reports to informally benchmark its current maturity using the six MLCM stages (unaware to expert).
  • IT Manager: On Monday morning, choose ONE specific IT process or technology area under your direct management (e.g., database administration, help desk ticketing system) and map its current operational state to the relevant MLCM stage, focusing on observable behaviors and knowledge rather than just process documentation.
  • Business Strategist: On Monday morning, select ONE strategic business objective that relies heavily on organizational capabilities (e.g., faster product innovation, improved customer segmentation) and begin brainstorming how the MLCM's distinct maturity stages could be used to assess progress towards achieving that objective.
  • Researcher: On Monday morning, take the provided MLCM instantiation template and begin populating it for a specific organizational capability relevant to your current research focus, defining clear, observable characteristics for each of the six maturity stages.
  • Policymaker: On Monday morning, identify ONE area of public service or government function where improved organizational capability is a known challenge (e.g., disaster response coordination, digital service delivery) and start considering how a capability maturity framework like MLCM could inform policy development and measurement.
Capability maturity models, Maturity levels, Organizational capability, Dreyfus model, Design science research, Data analytics
The Process of Process Science – Generating Actionable Insights from Digital Trace Data
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
TOWARD CULTURAL–ETHICAL FIT IN AI GOVERNANCE: A GLOBAL TYPOLOGY OF GOVERNANCE PROFILES
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
A Typology For Responsible Delegation Of Data Quality Management To Generative AI
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
THE VOICE OF COMMERCE: HOW SMART SPEAKERS RESHAPE DIGITAL CONTENT CONSUMPTION AND PREFERENCES
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
Boundary Spanning and Practical Impact in IS Research: A Bourdieusian Analysis
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
Process science: the interdisciplinary study of socio-technical change
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
Affordance-Based Pathway Model of Social Inclusion: A Case Study of Virtual Worlds and People With Lifelong Disability
AI Processed Human Approved

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

Karen Stendal, Maung K. Sein, Devinder Thapa
Affordance-Based Pathway Model of Social Inclusion: A Case Study of Virtual Worlds and People With Lifelong Disability
Journal of the Association for Information Systems · 2026
Key findings
  • Identified three functional affordances (communicability, mobility, and personalizability) and two social affordances (engageability and self-actualizability) crucial for social inclusion.
  • Established that the actualization of functional affordances enables and dynamically interacts with social affordances to foster interpersonal relationships and community participation.
  • Discovered that outcomes achieved by actualizing specific affordances serve as facilitating conditions that support the perception and realization of additional affordances.
  • Extended affordance theory by demonstrating a feedback loop where social engagement refines and enhances the perception and use of basic functional capabilities.
What it means for you
  • CIO / IT Executive: On Monday morning, task your accessibility team to conduct an initial assessment of our current platform's 'communicability' features (e.g., real-time chat, group messaging, avatar-based interaction quality) against the research's identified functional affordances to understand potential barriers and opportunities for individuals with disabilities.
  • IT Manager: On Monday morning, schedule a 30-minute working session with your frontline support staff to review existing user feedback logs for mentions of difficulty with navigation, communication, or customization within our virtual environment, specifically looking for patterns that might align with limitations in perceived 'mobility' or 'personalizability' affordances.
  • Business Strategist: On Monday morning, initiate a competitive analysis focusing on how emerging virtual platforms or digital communities are leveraging 'engageability' and 'self-actualizability' affordances to foster user connection and content creation, particularly noting any strategies that might appeal to or benefit users with disabilities.
  • Researcher: On Monday morning, begin drafting a protocol for a pilot study within our organization's virtual world that focuses on measuring the impact of enhanced 'communicability' features on user-reported feelings of 'engageability' and 'self-actualizability' among a diverse user group, potentially including participants with disabilities.
  • Policymaker: On Monday morning, convene a brief meeting with your advisory committee to discuss how current digital inclusion policies might be strengthened by explicitly incorporating the research's identified 'functional affordances' (communicability, mobility, personalizability) and 'social affordances' (engageability, self-actualizability) as measurable criteria for assessing technological accessibility and support for social participation.
Social Inclusion, Virtual Worlds, People With Lifelong Disability, Affordances, Second Life, ICT
Yoga Sutra-Based Memory Analysis of Information Systems Implementation
AI Processed Human Approved

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

Ranjan Vaidya
Yoga Sutra-Based Memory Analysis of Information Systems Implementation
Communications of the Association for Information Systems · 2026
Key findings
  • The Yoga Sutra framework demonstrates that subliminal impressions (Samskara) formed by past unfair trade practices deeply influence stakeholder behaviors and technology adoption.
  • Contextual factors and research interviews function as triggers (Alambana) that evoke unpleasant memories, reinforcing distrust between system users, government boards, and private vendors.
  • Information system failures in developing contexts often result from a lack of adherence to core ethical and behavioral values rather than culture acting as a barrier.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute discovery call with your Head of User Experience and your Chief Ethics Officer to brainstorm how to proactively identify and mitigate potential 'Samskara' (subliminal impressions) in upcoming system implementations by focusing on past negative stakeholder experiences.
  • IT Manager: On Monday morning, review the last three system user feedback reports for any recurring themes related to distrust or negative past experiences, and flag the top two recurring issues to investigate further for potential 'Alambana' (external triggers) in your current system.
  • Business Strategist: On Monday morning, draft a brief (one-page) internal memo to your team outlining the concept of 'Samskara' and its potential impact on technology adoption, and request them to identify one instance in recent projects where past negative impressions might have influenced user behavior.
  • Researcher: On Monday morning, begin drafting the introduction to your next research paper by clearly articulating the gap in current information systems research regarding the lack of focus on individual cognitive variations and Eastern philosophical perspectives on memory, using the Yoga Sutra framework as your proposed solution.
  • Policymaker: On Monday morning, initiate a discussion with your advisory board regarding the importance of embedding ethical and behavioral values into the design and implementation guidelines for all new government information systems, referencing the finding that failures often stem from a lack of adherence to these values, not culture.
Yoga, Information Systems, Memory, Behavioural Analysis, Samskara, ICT4D
Gamification for Community Building: Click to Shake Hands
AI Processed Human Approved

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

Faisal Al Thani, Chadi Aoun, Savanid Vatanasakdakul
Gamification for Community Building: Click to Shake Hands
Pacific Asia Conference on Information Systems (PACIS) · 2025
Key findings
  • Formulates a novel theoretical framework connecting technology adoption, gamification mechanics, external social influences, and intrinsic motivations to app stickiness and community engagement.
  • Identifies key drivers such as location incentives, perceived ease of use, reward value, achievement, network influence, herd behavior, social motive, and sense of belonging.
  • Establishes an empirical research design targeting over 1,000 active Pokémon GO players using Partial Least Squares Structural Equation Modeling (PLS-SEM).
  • Provides preliminary theoretical grounding for designing mobile gamified systems that promote social sustainability and persistent physical-world social interactions.
What it means for you
  • CIO / IT Executive: On Monday morning, allocate budget to pilot a location-based gamified application in one of our company's facilities, specifically focusing on fostering cross-departmental collaboration by integrating 'location incentives' for shared project spaces.
  • IT Manager: On Monday morning, schedule a meeting with your development team to discuss how to integrate elements like 'reward value' and 'achievement' into our existing internal communication platform to encourage more active participation and knowledge sharing.
  • Business Strategist: On Monday morning, initiate a brainstorming session with your marketing and product teams to identify opportunities to leverage 'network influence' and 'herd behavior' in upcoming product launches or community outreach initiatives, perhaps by creating exclusive early-access groups.
  • Researcher: On Monday morning, refine the survey instrument to include questions specifically measuring 'sense of belonging' and 'social motive' as distinct constructs, and prepare to recruit participants from diverse community groups for your next study.
  • Policymaker: On Monday morning, direct your staff to research successful public policy initiatives that have used location-based incentives or gamification to encourage citizen engagement with local services or community events, and identify potential applications for our jurisdiction.
Community Engagement, Gamification, Location-based games, Stickiness, Technology Adoption, Social Interaction
Social mobile analytics and cloud computing: a big data analytical approach
AI Processed Human Approved

What are the main benefits, challenges, and technical solutions when combining social, mobile, analytics, and cloud technologies?

Farhad Ali, Habib Ullah Khan, Shah Nazir
Social mobile analytics and cloud computing: a big data analytical approach
2026
Key findings
  • Identified main collaborative entities within SMAC ecosystems, including smartphones, cloud platforms, crowd participants, and environmental sensors.
  • Summarized key organizational and societal benefits such as enhanced decision-making, operational cost reduction, improved productivity, and expanded social engagement.
  • Classified primary adoption challenges, focusing on data privacy, security vulnerabilities, scalability limits, energy consumption, and system interoperability.
  • Compiled existing technical solutions from literature, highlighting techniques such as attribute-based encryption, anonymization methods, and hyper-heuristic frameworks.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a cross-departmental working group focused on assessing our current SMAC infrastructure against the identified adoption challenges (privacy, security, scalability, energy, interoperability) using the research's findings as a framework, and schedule an initial meeting for next week.
  • IT Manager: On Monday morning, begin a review of our existing data anonymization protocols and encryption techniques, cross-referencing them with the 'existing technical solutions' highlighted in the research to identify immediate gaps or opportunities for enhancement within our current cloud and mobile analytics platforms.
  • Business Strategist: On Monday morning, schedule a 30-minute brainstorming session with your core team to identify one specific business process that could significantly benefit from SMAC integration, and during that session, identify the most critical adoption challenge from the research that needs to be addressed first for this specific process.
  • Researcher: On Monday morning, create a detailed spreadsheet to categorize the 75 papers reviewed in this study by the specific SMAC component (Social, Mobile, Analytics, Cloud), the primary adoption challenge addressed, and the technical solution proposed, to facilitate further meta-analysis.
  • Policymaker: On Monday morning, draft a request for a preliminary impact assessment on user privacy and cybersecurity risks associated with the widespread adoption of SMAC technologies within our jurisdiction, drawing upon the challenges identified in the research as key areas to investigate.
SMAC Framework, Big Data, Cloud Computing, Social Media Analytics, Mobile Computing, Data Security
Location Analytics in Information Systems: Opportunities for Research and Teaching
AI Processed Human Approved

How can location analytics be integrated into academic research and teaching to meet modern business needs?

Michael A. Erskine, James B. Pick, Asish Satpathy, Andrés Díaz López, Avijit Sarkar, Namchul Shin
Location Analytics in Information Systems: Opportunities for Research and Teaching
Communications of the Association for Information Systems · 2024
Key findings
  • Demonstrates how incorporating spatial methodologies (such as spatial autocorrelation and geographically weighted regression) can address complex societal 'wicked problems' like climate change, network disruption, and privacy.
  • Provides a strategic roadmap for integrating location analytics into undergraduate and graduate IS curricula to prepare students for high-demand business roles.
  • Outlines actionable steps to bridge academia and industry, including collaborative case study development, special journal issues, and enhanced faculty training in geospatial tools.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your Head of Data Science and Head of Business Intelligence to discuss a pilot project integrating a basic spatial analysis layer (e.g., visualizing customer locations on a map) into one of your existing dashboards for a specific business unit.
  • IT Manager: On Monday morning, identify one internal IT project or operational challenge (e.g., network outage analysis, data center resource allocation) that could potentially benefit from a spatial perspective, and research one readily available open-source geospatial tool (like QGIS) for initial exploration.
  • Business Strategist: On Monday morning, review your current customer segmentation data and identify 2-3 key business questions that might be better answered by understanding the geographical distribution of your customers (e.g., optimal new store locations, targeted marketing campaigns by region).
  • Researcher: On Monday morning, identify one of your current research projects and brainstorm 2-3 ways that incorporating spatial data or analysis (e.g., geographic clustering of events, spatial dependencies) could strengthen your theoretical contributions or add methodological rigor.
  • Policymaker: On Monday morning, contact a representative from a local urban planning or environmental agency to inquire about any existing spatial datasets related to community infrastructure, resource distribution, or environmental impact that might be publicly accessible.
Location Analytics, Spatial Analysis, IS Curriculum, Research Methodologies, Geographic Information Systems, Decision Support Systems
Location Analytics in Information Systems: Opportunities for Research and Teaching
AI Processed Human Approved

How can business schools integrate location analytics into their curriculum and research to meet industry demand?

Michael A. Erskine, James B. Pick, Asish Satpathy, Andrés Díaz López, Avijit Sarkar, and Namchul Shin
Location Analytics in Information Systems: Opportunities for Research and Teaching
Communications of the Association for Information Systems · 2024
Key findings
  • Highlights key research opportunities where location analytics can address complex, multidisciplinary problems such as climate change, network disruptions, and location privacy.
  • Proposes a stepwise framework for business schools to integrate location analytics into IS courses, concentrations, and interdisciplinary programs.
  • Recommends action steps to strengthen industry-academic partnerships, train faculty, and promote spatial methodologies in leading IS journals.
What it means for you
  • CIO / IT Executive: On Monday morning, task your IT strategy team to identify one current business challenge (e.g., supply chain optimization, customer segmentation) that could be significantly improved by incorporating location data, and schedule a brief exploratory meeting to discuss potential pilot projects using spatial analysis tools.
  • IT Manager: On Monday morning, identify one existing data set within your organization that has inherent spatial components (e.g., customer addresses, store locations, sensor readings) and initiate a conversation with a data analyst about how to visualize and query this data using basic geospatial techniques.
  • Business Strategist: On Monday morning, review your current strategic plan and identify two key business objectives where understanding 'where' things happen or are located could provide a competitive advantage or unlock new opportunities, then brainstorm potential data sources for that 'where'.
  • Researcher: On Monday morning, identify one of your ongoing or future research projects that currently lacks a spatial dimension and draft a brief proposal outline for how integrating location analytics could address a key limitation or open new avenues of inquiry, referencing specific spatial methods mentioned in the literature.
  • Policymaker: On Monday morning, reach out to a representative from a local university's Information Systems or Geography department to inquire about potential collaborations or pilot programs for integrating location analytics into business curricula, focusing on areas relevant to local economic development or public services.
Location Analytics, Spatial Analysis, IS Curriculum, Research Methodologies, Geographic Information Systems, Spatial Business
ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS
AI Processed Human Approved

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

Jori Taipale, Olli Tyrväinen, Tuure Tuunanen, Joel Mero, Teiko Heinosaari
ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS
ECIS 2026 · 2026
Key findings
  • Organizing tensions were identified as the most frequent paradox, specifically focusing on the trade-offs between collaboration vs. resource competition and guidance vs. flexibility.
  • Performance tensions emerged regarding resource allocation between software vs. hardware development and building a shared vision vs. individual firm survival.
  • Belonging tensions highlighted a 'godfather' paradox, where the ecosystem must balance the state's role as a major supportive funder and customer with the ultimate goal of corporate independence.
  • Learning tensions were characterized by the challenge of managing multi-talent knowledge development versus narrow technology specialization.
  • The study is limited by its single qualitative case context in Finland with a small sample of 15 informants, requiring caution when applying these findings to other national contexts.
What it means for you
  • CIO / IT Executive: Schedule a 9:00 AM meeting with your lead enterprise architect to audit your current quantum-readiness roadmap, establishing a formal policy that divides software development and hardware access investments 50/50 to hedge against the performance tension of committing prematurely to a single unproven hardware stack.
  • IT Manager: Create a bi-weekly 1-hour 'Quantum Cross-Training' slot in your team's calendar starting this Friday, pairing your specialized quantum physics PhDs with your generalist cloud engineers to address the learning tension between narrow technical specialization and broad-based systems integration.
  • Business Strategist: Perform a revenue dependency audit to calculate the percentage of your quantum business unit's funding derived from state grants versus commercial clients, and draft a pitch deck for two private-sector pilots to actively transition away from 'godfather' state-funding reliance.
  • Researcher: Draft a pre-competitive collaboration agreement template to send to a peer researcher at a competing institution, outlining a shared framework for baseline algorithm testing while clearly defining IP boundaries to manage the tension of collaboration versus resource competition.
  • Policymaker: Draft a revision for the upcoming national quantum funding round guidelines that introduces a 'co-investment requirement,' mandating that state-funded consortia secure at least 25% of their budget from private corporate buyers to counter the 'godfather' dependency paradox.
Ecosystem orchestration, Paradox theory, Quantum computing innovation ecosystem, Explorative case study, Interpretive qualitative research
TOWARDS DESIGN PRINCIPLES FOR TEACHING INDUSTRY 4.0 COMPETENCIES TO CS/IS STUDENTS
AI Processed

How can educators effectively teach modern industrial software skills without expensive physical equipment?

Felix Salcher
TOWARDS DESIGN PRINCIPLES FOR TEACHING INDUSTRY 4.0 COMPETENCIES TO CS/IS STUDENTS
ECIS 2026 · 2026
Key findings
  • Eight design principles were established, emphasizing virtualization, containerization, and problem-based learning cases as alternatives to expensive learning factories.\n- Preliminary course evaluation yielded positive descriptive feedback, with a mean rating of 4.43 out of 5 across satisfaction categories.\n- No formal statistical significance tests were performed due to the pilot nature of the study and the small, specific evaluation sample.\n- The pilot is limited by a very small sample size (9 respondents from a single cohort) and relies on subjective student perceptions rather than objective competency-based assessments.\n- Students noted difficulty adapting to the course's fast pace and programming requirements, highlighting a need for aligned prerequisites.
What it means for you
  • CIO / IT Executive: Direct your enterprise architecture team to draft a transition plan to replace physical IT hardware training labs with virtualized, containerized sandbox environments (e.g., Docker/Kubernetes) to reduce infrastructure costs and align onboarding with modern Industry 4.0 software architectures.
  • IT Manager: Review the programming prerequisites of your junior developers and assign them a structured, hands-on troubleshooting task using a containerized microservice mock-up on Monday morning to assess their readiness for complex Industry 4.0 integration projects.
  • Business Strategist: Reallocate a portion of next quarter's talent development budget away from high-cost physical equipment training and toward scalable, software-based virtual simulators to cost-effectively build digital twin and data architecture competencies.
  • Researcher: Draft a research proposal to conduct a multi-institution comparative study on these eight design principles, incorporating objective pre- and post-intervention technical testing rather than relying on subjective student feedback to measure competency gains.
  • Policymaker: Create a state-level educational grant initiative that incentivizes higher education institutions to update computer science curricula with virtualization-based Industry 4.0 frameworks, shifting funding priority away from physical learning factories.
Design Principles, Design Science Research, Industry 4.0, Smart Manufacturing
THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION
AI Processed Human Approved

How should teams structure initial challenges to successfully launch AI projects?

Franziska Anna Röckel, Benjamin van Giffen, Andreas Janson, Jennifer Hehn
THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION
ECIS 2026 · 2026
Key findings
  • The study empirically validated a structural anatomy for the Design Challenge consisting of four robust elements (verb, object of innovation, target group, context) and three plastic elements (internal data, technology, potential), which was supported across all 42 cases analyzed.
  • The robust elements were validated to successfully create a stable, shared identity and common direction among diverse team members, acting as a center of authority.
  • The plastic elements were shown to successfully absorb technological uncertainty, providing a flexible framework for teams to negotiate and adapt to AI's emergent capabilities over time.
  • The qualitative analysis identified three distinct nuances in shaping the 'object of innovation' (modular, abstract, and space) that guide teams toward either incremental or more radical innovation pathways.
What it means for you
  • CIO / IT Executive: Mandate that all upcoming AI project proposals use a standardized 'Design Challenge' charter template structured around the 4 robust elements (verb, object of innovation, target group, context) and 3 plastic elements (internal data, technology, potential), explicitly forbidding teams from locking in a rigid technical architecture in their initial pitch.
  • IT Manager: Gather your interdisciplinary project team for a 60-minute workshop on Monday morning to co-create a 'Design Challenge' statement, explicitly defining the stable 'robust' elements to align the business and tech members, while leaving the 'plastic' elements (like specific data sources and AI models) open as flexible targets to be negotiated as the project progresses.
  • Business Strategist: Review your company's AI project portfolio on Monday morning and intentionally rewrite the 'object of innovation' for each project: frame them as 'modular' if you need predictable, incremental improvements, or rewrite them as 'space-based' (e.g., focused on broad capabilities) if you want to push the team toward radical, disruptive business model innovation.
  • Researcher: Update your qualitative coding scheme for ongoing field studies of AI adoption to track how frequently and at what project stages teams modify the 'plastic' elements (internal data, technology, potential) versus the 'robust' elements of their project charters to measure the empirical threshold where technological uncertainty triggers project misalignment.
  • Policymaker: Revise the application guidelines for the upcoming public AI innovation grant cycle to replace rigid 'technical specifications' requirements with a mandatory 'Design Challenge' framework, scoring applicants on the clarity of their robust elements (target group, context) and the adaptive flexibility of their plastic elements (data and technology integration plans).
Design Challenge, AI Innovation, Boundary Object, Anatomy, Interdisciplinary Collaboration, Problem Formulation
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