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New

AI Processed Human Approved

How can digital platforms help city leaders coordinate local experiments and adapt policies for long-term sustainability?

S.-V. Rehm, S. McLoughlin, G. Maccani

COPING WITH THE DYNAMICS OF URBAN SUSTAINABILITY: POSITIONING URBAN EXPERIMENTATION PLATFORMS AS TOOLS FOR POLICY MAKING

2021

Key findings

Urban experimentation platforms act as vital socio-technical bridges that align municipal policy-making, business ecosystems, and grassroots innovation.
A policy structure diagram illustrates key dynamic feedback loops where data and digital services directly inform and adapt local policy interventions.
Digital capabilities such as urban data observatories, open APIs, and challenge management portals empower collective intelligence and sustained civic engagement.

What it means for you

CIO / IT Executive: Allocate Q3 budget to architect an open API gateway and a centralized urban data observatory that connects municipal databases with external innovation platforms.
IT Manager: Draft technical requirements for a digital challenge management portal to ingest grassroots sensor data and track civic pilot projects.
Business Strategist: Schedule a meeting with local startup incubators and civic tech leaders to co-design a framework for integrating commercial solutions into municipal sustainability pilots.
Researcher: Map out the specific feedback loops between grassroots civic innovations and municipal policy outcomes using the study's System Dynamics approach as a baseline.
Policymaker: Draft a mandatory review checkpoint in the upcoming sustainability policy cycle that requires incorporating real-time data feeds from local urban experimentation platforms.

Digital Platform, Urban Experimentation, Urban Governance, System Dynamics, Urban Sustainability, Collective Intelligence

New

AI Processed Human Approved

How do combinations of different tools and routines help partner companies coordinate when developing new products?

Sven-V. Rehm, Lakshmi Goel

Composite Boundary Objects In Inter-Organizational Innovation Activities

2013

Key findings

Demonstrates that coordination across organizational boundaries is rarely driven by single, isolated tools, but rather through combinations of artifacts and routines termed composite boundary objects (CBOs).
Shows that effective collaboration emerges from the combined interplay of material objects (such as prototypes and central IT platforms), abstract concepts (such as shared product visions), and informal work routines.
Finds that cross-organizational coordination can emerge organically through everyday interactions, even when such coordination was not deliberately planned in advance.
Reveals that composite boundary objects are temporary and evolve across project milestones, requiring managers to support flexible environments of communication rather than focusing on standalone software applications.

What it means for you

CIO / IT Executive: Stop investing in standalone software silos and schedule a meeting with cross-company project leads on Monday morning to audit how your central IT platforms, shared physical prototypes, and communication routines are currently integrated to support external partners.
IT Manager: On Monday morning, map out the digital tools and informal communication channels your team uses with external partners, and set up a shared digital workspace that links these tools directly to ongoing physical prototyping milestones.
Business Strategist: On Monday morning, draft a flexible collaboration framework for your new product development project that combines shared product visions with routine check-ins, rather than relying on rigid, pre-planned coordination structures.
Researcher: On Monday morning, design a qualitative study protocol to trace how digital platforms, physical prototypes, and informal routines co-evolve across specific milestones in inter-organizational innovation projects.
Policymaker: On Monday morning, revise your innovation grant evaluation criteria to reward collaborative consortia that demonstrate flexible, multi-layered coordination mechanisms—such as combined digital and material artifact routines—rather than funding isolated software tools.

boundary objects, composite boundary objects, inter-organizational innovation, new product development, knowledge management, collaboration

New

AI Processed Human Approved

How can networked businesses quickly and cost-effectively integrate IT systems to support collaborative planning?

Sven-Volker Rehm

Architectural Design for Service-Supported Collaborative Work in Extended Enterprises

Americas Conference on Information Systems (AMCIS) · 2009

Key findings

A model-driven integration platform enables collaborative business applications to be built from reusable service components without modifying core internal systems.
Representing services as database models rather than custom software code drastically simplifies workflow modification and inter-firm data mapping.
A pilot implementation in a manufacturing network demonstrated that onboarding and integration time per partner could be reduced from four weeks to just a few days.
Shared service repositories allow networks of small and medium-sized enterprises to achieve significant economies of scale and extend enterprise planning capabilities to partners without advanced software.

What it means for you

CIO / IT Executive: Initiate an audit of all current custom point-to-point supply chain integrations and direct your architecture team to evaluate adopting a model-driven service platform to replace them.
IT Manager: Catalog your team's existing workflow and data integration tasks with external partners to identify which processes can be standardized into reusable database-backed service components.
Business Strategist: Draft a pilot project proposal to test a shared service repository with two key external manufacturing partners to evaluate reductions in onboarding time.
Researcher: Design a study to measure the exact time-to-onboard and transaction cost differentials between database-modeled services and traditional custom-code integrations in multi-firm networks.
Policymaker: Draft a grant funding framework that subsidizes shared service repositories and model-driven integration platforms specifically for small and medium-sized enterprise manufacturing networks.

Service-Oriented Architecture, Extended Enterprise, Enterprise Architecture, Enterprise Resource Planning, Business Process Modeling, Collaborative Networks

New

AI Processed Human Approved

How do remote teams collaborate and make complex decisions inside 3D virtual environments?

Lakshmi Goel, Sven-Volker Rehm

How teams complete tasks in virtual worlds

Americas Conference on Information Systems (AMCIS) · 2017

Key findings

Teams spend an extensive initial phase engaging in spatial and social sense-making rather than adopting an immediate task stance.
Progress occurs through multiple internal disruptions driven by hands-on tinkering rather than a single midpoint turnaround.
Virtual 3D objects serve as critical anchors that help members point, align perspectives, and reconcile individual viewpoints into shared team models.
Teams do not enter a second period of inertia, but rather cycle continuously through adjustments until reaching alignment.

What it means for you

CIO / IT Executive: Procure and provision enterprise-grade 3D virtual workspace software featuring interactive digital objects to serve as collaborative anchors for distributed teams.
IT Manager: Allocate the first 15 minutes of Monday's virtual team meeting to unguided spatial exploration and hands-on tinkering with the digital project board rather than jumping straight into the agenda.
Business Strategist: Restructure project timelines to eliminate expectations of a single midpoint review, replacing them with a framework that budgets time for continuous, iterative adjustments.
Researcher: Design a longitudinal study to observe how distributed teams utilize specific 3D visual anchors to reconcile divergent mental models over time.
Policymaker: Draft procurement standards for digital collaboration tools that mandate support for interactive 3D modeling and shared spatial environments to enhance remote worker accessibility and alignment.

Punctuated equilibrium, Boundary objects, Mental models, virtual worlds, team work, decision-making

New

AI Processed Human Approved

How can teams across an organization collaboratively map and visualize their business ecosystem to make better decisions?

Anne Faber, Adrian Hernandez-Mendez, Sven-Volker Rehm, Florian Matthes

Visualizing Business Ecosystems: Applying a Collaborative Modelling Process in Two Case Studies

2018

Key findings

Validated a six-step collaborative modeling process consisting of scoping, model instantiation, data collection, visualization, feedback adaptation, and storytelling.
Identified four critical collaboration roles that drive modeling success: the gatekeeper, facilitator, modeler, and recorder.
Discovered that ecosystem models evolve primarily at the attribute level rather than the entity level once visualization views are established.
Demonstrated that providing early, interactive visual layouts is vital to keeping stakeholders motivated during tedious data collection phases.
Showed that participants' prior modeling familiarity significantly affects progress, underscoring the need for hands-on preparatory training.

What it means for you

CIO / IT Executive: Procure and provision an agile, collaborative visualization software framework to support multi-stakeholder ecosystem mapping sessions this quarter.
IT Manager: Assign team members to specific collaboration roles—specifically gatekeeper, facilitator, modeler, and recorder—ahead of Monday's stakeholder workshop.
Business Strategist: Design an interactive, early visual layout of your target market ecosystem to present on Monday to maintain stakeholder engagement during data collection.
Researcher: Investigate how ecosystem models evolve specifically at the attribute level rather than the entity level during longitudinal case studies.
Policymaker: Develop hands-on preparatory training modules focused on modeling familiarity to better equip regional industry participants for collaborative workshops.

Business Ecosystem, Collaborative Modelling, Information Visualization, Action Research, Case Studies, Knowledge Management

New

AI Processed Human Approved

What different types of business ecosystems exist and how do they connect with one another?

Anne Faber, Maximilian Riemhofer, Sven-Volker Rehm, Gloria Bondel

A Systematic Mapping Study on Business Ecosystem Types

Americas Conference on Information Systems (AMCIS) · 2019

Key findings

Identifies 12 distinct business ecosystem types across academic literature, including platform, innovation, knowledge, software, IoT, and entrepreneurial ecosystems.
Maps ecosystems across two primary viewpoints: an organizational perspective centered on the enterprise and a product or service perspective centered on core value creation.
Highlights innovation ecosystems and platform business ecosystems as the most prominently discussed forms in literature.
Reveals that specialized types, such as IoT and customer-centric ecosystems, operate at the intersections of platform, knowledge, and innovation domains.
Demonstrates that software ecosystems uniquely bridge both organizational and product perspectives depending on their focus.

What it means for you

CIO / IT Executive: Review your current technology architecture on Monday morning to identify whether your software initiatives operate purely from an organizational or product perspective, and explicitly map them to one of the 12 recognized ecosystem types to align your IT investments with your enterprise's core value creation.
IT Manager: Audit your active platform and IoT projects on Monday morning by checking if they effectively bridge knowledge, innovation, and platform domains as outlined in the literature taxonomy, and flag any isolated technical silos for integration.
Business Strategist: Pull your strategic partnership portfolio on Monday morning and categorize each external alliance using the study's distinction between organization-centric and product-centric ecosystem types to identify gaps in your collaborative networks.
Researcher: Cross-reference your current literature review matrix on Monday morning against the 12 identified business ecosystem types to ensure your scope accounts for intersections between specialized domains like IoT and customer-centric ecosystems.
Policymaker: Examine your regional economic development grants on Monday morning to verify whether funding criteria adequately distinguish between broad innovation ecosystems and narrower platform or software ecosystems, ensuring targeted support for local tech clusters.

business ecosystem, innovation ecosystem, platform ecosystem, digital business ecosystem, systematic mapping study, software ecosystem

New

AI Processed Human Approved

What stops carmakers and software developers from creating shared data platforms, and what pushes them forward?

Gloria Bondel, Sven-Volker Rehm, Florian Matthes

JUST LINKING THE DOTS? BARRIERS AND DRIVERS TO CREATING VALUE FROM APPLICATION PROGRAMMING INTERFACES

2020

Key findings

External software developers drive early data access through workarounds like diagnostic hardware dongles and sustained pressure on carmakers.
Vehicle manufacturers implement APIs largely due to regulatory and standardization pressures rather than voluntary ecosystem growth, while maintaining tight gatekeeping.
The absence of proven commercial value propositions and fears of legal liability significantly delay manufacturer participation in open data sharing.
Intense competition over platform ownership and vehicle operating system control prevents manufacturers from standardizing on a common platform.
Emerging mobility trends, particularly autonomous driving, create future expectations that incentivize gradual steps toward open API ecosystems.

What it means for you

CIO / IT Executive: Initiate a legal risk-assessment workshop with compliance and legal counsel to draft clear liability frameworks for third-party API data sharing, removing internal roadblocks to manufacturer participation.
IT Manager: Audit all current vehicle diagnostic APIs and technical boundary resources to ensure they meet existing regulatory standards while safely accommodating third-party developer workarounds.
Business Strategist: Schedule a stakeholder alignment meeting to model a low-risk, shared-value commercial pilot focusing on autonomous driving data use cases, bypassing platform ownership deadlocks.
Researcher: Design a comparative interview study tracking how external software developers leverage diagnostic hardware dongles to pressure incumbent automakers into data access.
Policymaker: Draft standardized data-sharing guidelines that reduce legal liability fears for automotive manufacturers while mandating fair API access for third-party mobility start-ups.

Application Programming Interfaces, Boundary Resources, Digital Platforms, Automotive Industry, Connected Vehicles, Business Ecosystems

New

AI Processed Human Approved

How can digital experimentation platforms help city leaders make better, more adaptive urban sustainability policies?

Sven Volker Rehm, Shane McLoughlin, Brian Donnellan

HOW TO COPE WITH THE DYNAMICS OF URBAN SUSTAINABILITY: URBAN EXPERIMENTATION PLATFORMS AS TOOLS FOR ADAPTIVE POLICY-MAKING

2022

Key findings

Urban experimentation platforms function both as vehicles for implementing policy and as essential instruments for adaptive, feedback-driven policymaking.
Municipal decision-makers currently demonstrate low awareness of platform data, largely using platforms to broadcast priorities rather than using trial results to update policy.
Establishing structured feedback loops between urban business ecosystems, city authorities, and digital platforms substantially improves collaborative problem-solving and long-term sustainability.

What it means for you

CIO / IT Executive: Audit current urban data platforms to ensure they include incoming feedback channels from local experimentation trials, rather than functioning solely as one-way broadcast tools.
IT Manager: Configure dashboard alerts to aggregate and flag real-time stakeholder feedback and usage metrics from ongoing urban sustainability pilots.
Business Strategist: Initiate a meeting with municipal authorities and local business ecosystem partners to design a recurring framework for collaborative problem-solving using platform data.
Researcher: Apply system dynamics modeling to map the existing stakeholder feedback loops within your city's active urban experimentation platforms.
Policymaker: Review the latest data reports from active local sustainability trials and draft a plan to update an existing municipal policy based on the trial results.

Urban Sustainability, Urban Experimentation Platforms, Digital Platforms, Urban Governance, System Dynamics, Case Study

New

AI Processed Human Approved

What causes organizational inertia during digital transformation, and how does it block successful change?

Thomas Haskamp, Christian Dremel, Carolin Marx, Falk Uebernickel

Understanding Inertia in Digital Transformation: A Literature Review and Multilevel Research Framework

International Conference on Information Systems (ICIS) · 2021

Key findings

Conceptualizes inertia through five key attributes: paradoxical duality (vital for efficiency yet resistant to change), rigidity of social/material entities, relational character, time-dependency, and misalignment with the environment.
Identifies nine distinct dimensions of inertia spanning individual (behavioral, cognitive, affective), organizational (structural, cultural, socio-technical, political, economic), and industry levels.
Maps 12 antecedents—ranging from individual cognitive flexibility to organizational routines, legacy IT, culture, and external uncertainty—that give rise to inertia.
Outlines 12 critical consequences of inertia, including active employee resistance, biased management decision-making, project failures, compromised agility, and failure to renew business models.
Pinpoints 15 moderating factors, such as dynamic capabilities, agility, absorptive capacity, and managerial cognition, that influence how inertia translates into operational impacts.

What it means for you

CIO / IT Executive: Audit your portfolio for socio-technical legacy IT misalignments by scheduling a joint review with department heads to identify legacy systems anchoring obsolete business routines.
IT Manager: Map your team's current project workflows against the five inertia attributes to pinpoint where cognitive and behavioral rigidity is slowing down daily software deployment.
Business Strategist: Review the 12 documented inertia antecedents against your current transformation roadmap to identify which cultural and structural barriers threaten your upcoming business model renewal.
Researcher: Formulate a new empirical study focusing on the interactions between individual-level cognitive inertia and organizational-level structural inertia during enterprise cloud migrations.
Policymaker: Draft guidelines for state-backed digital grants that require organizations to explicitly account for socio-technical and cultural inertia mitigation strategies before receiving funding.

digital transformation, organizational inertia, socio-technical inertia, change resistance, dynamic capabilities, literature review

AI Processed Human Approved

How do sending and receiving emails shape employee stress and productivity?

Jean-François Stich

Email stress: Towards a new understanding of its causes and impacts

2025

Key findings

The paper proposes the Transactional Model of Email Stress (TMES), establishing that email stress is a dynamic process shaped by sender and receiver roles.
Sending emails generally triggers eustress, offering senders a sense of control, task delegation, and productivity.
Receiving emails primarily creates distress through incoming volume, constant interruptions, and emotional ambiguity in messages.
Targeted email training and explicit organizational communication norms can mitigate distress while preserving productivity benefits.

What it means for you

CIO / IT Executive: On Monday morning, initiate a discussion with your direct reports and heads of departments to explore piloting a specific email management training program for all employees, focusing on techniques that address both sender eustress and receiver distress, as outlined by the TMES model.
IT Manager: On Monday morning, review the current email client settings and identify opportunities to implement or enhance features that automatically categorize or filter incoming emails based on sender or keywords, thereby reducing receiver distress for your team.
Business Strategist: On Monday morning, draft a proposal for a new organizational communication norm that encourages concise and action-oriented email content from senders, aiming to maximize sender eustress by clearly delegating tasks and minimizing receiver distress caused by ambiguity.
Researcher: On Monday morning, begin drafting a research proposal to empirically test the TMES model by designing a study that measures sender eustress and receiver distress across different organizational contexts and email usage patterns.
Policymaker: On Monday morning, schedule a meeting with key stakeholders to discuss the development of an organizational policy that mandates specific email etiquette training, emphasizing balanced sender and receiver perspectives as informed by the TMES framework.

Email stress, Technostress, Overload, Hyperconnection, Eustress, Transactional model

AI Processed Human Approved

How do algorithms delegate work to groups of humans on digital platforms?

Marta Stelmaszak, Mareike Möhlmann, Carsten Sørensen

WHEN ALGORITHMS DELEGATE TO HUMANS: EXPLORING HUMAN-ALGORITHM INTERACTION AT UBER

MIS Quarterly · 2024

Key findings

Conceptualizes distributed delegation as a collective, hybrid, and relational process where multiple algorithms work together while drawing on human data inputs.
Identifies three key mechanisms of algorithmic delegation in multi-agent settings: collective hybrid appraisal, collective hybrid distribution, and collective hybrid coordination.
Reveals that delegation functions along a continuum rather than as a complete, binary transfer of rights and responsibilities between algorithms and humans.
Demonstrates that human involvement—through trace data, choice data, and direct interventions during breakdowns—is necessary for algorithmic agency to operate.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of our current platform architecture to identify all instances of multi-agent algorithmic systems and assess their delegation models, specifically looking for 'distributed delegation' patterns to ensure human input is integrated effectively.
IT Manager: On Monday morning, schedule a technical deep-dive with your team focused on one specific algorithmic system that delegates to humans; analyze its current delegation logic and identify specific points where human trace data, choice data, or direct intervention are critical inputs, and document these dependencies.
Business Strategist: On Monday morning, begin a strategic assessment of our core business processes that rely on algorithmic delegation; map out the human touchpoints and decision points that are currently integrated, or could be integrated, within a 'distributed delegation' framework to enhance system resilience and adaptability.
Researcher: On Monday morning, draft a research proposal outlining a study to quantitatively measure the impact of 'distributed delegation' on system performance and user satisfaction in our specific domain, focusing on how varying levels of human input influence outcomes.
Policymaker: On Monday morning, convene a working group to examine existing regulations and internal policies related to algorithmic decision-making and human oversight; identify any gaps that need addressing to better accommodate 'distributed delegation' scenarios, ensuring clarity on accountability and human agency.

IS Delegation, Human-AI Interaction, Artificial Intelligence, Algorithms, Patents

AI Processed Human Approved

How can organizations use blockchain to verify hiring credentials while complying with data privacy laws?

Janne Parkkila, AKM Bahalul Haque, Jaakko Vuolasto, Anastasiia Gurzhii, Sami Hyrynsalmi, Najmul Islam

DESIGNING GDPR-COMPLIANT CREDENTIAL VERIFICATION USING BLOCKCHAIN: A DESIGN SCIENCE RESEARCH APPROACH

2024

Key findings

Identified four major categories of design requirements: trust, automation, usability, and regulation.
Formulated three core design principles for building GDPR-compliant, self-sovereign identity verification systems.
Developed and evaluated a prototype system ('SafeRecords') leveraging Polygon ID and zero-knowledge proofs to ensure user data ownership and privacy.

Self-sovereign identity, Blockchain, Zero-Knowledge, GDPR Compliance, Digital Identity Management, Credentials Verification, Design Science Research

AI Processed Human Approved

How do explanations with AI feedback affect people's learning and task performance based on their prior knowledge?

Maximilian Förster, Hanna R. Broder, Marie C. Fahr, Mathias Klier, Lior Fink

Tell me more, tell me more: the impact of explanations on learning from feedback provided by Artificial Intelligence

European Journal of Information Systems · 2025

Key findings

Explanations positively impact learning outcomes for users with less prior knowledge, an effect fully mediated by perceived informativeness.
Explanations improve task performance directly for users with high prior knowledge.
For users with low prior knowledge, the positive impact of explanations on task performance is fully mediated by informativeness.

What it means for you

CIO / IT Executive: On Monday morning, initiate a cross-functional working group involving IT, product development, and user experience teams to prioritize the integration of AI-driven feedback systems that include clear, context-specific explanations, focusing initially on applications with less experienced user bases.
IT Manager: On Monday morning, review the current AI feedback mechanisms within your team's systems and identify 1-2 pilot opportunities to implement or enhance explanations, specifically targeting areas where user error or confusion is frequently observed, and gather baseline performance data.
Business Strategist: On Monday morning, task your team with identifying key business processes or customer-facing interactions where AI feedback is being used or considered, and assess the potential impact of explanation-enhanced AI on user adoption, efficiency, and overall customer satisfaction, particularly for novice users.
Researcher: On Monday morning, design a pilot study protocol to specifically investigate how the *type* and *level of detail* of explanations provided by AI feedback influence perceived informativeness and learning outcomes for users with varying levels of domain expertise in a controlled environment.
Policymaker: On Monday morning, begin drafting guidelines or a framework for the ethical deployment of AI systems that mandate, where feasible, the inclusion of explanations for AI-generated feedback, prioritizing sectors with high user impact and potential for knowledge gaps, such as education and public services.

Explainable Artificial Intelligence, XAI, AI feedback, learning outcome, informativeness, feedback theory

AI Processed Human Approved

How do on-screen interruptions affect task performance on mobile devices compared to desktop computers?

Lior Fink, Esti Baranes, Naama Ilany-Tzur

Mobile use in an age of interruption: Implications of capacity and structural interference for mobile users

2025

Key findings

Mobile users suffer higher resumption costs, taking significantly longer to resume a primary task after an interruption compared to PC users.
Mobile users experience lower task-switching costs because they store less primary-task information in working memory, making them less vulnerable to cognitive conflict during interrupting tasks.
PC users face greater structural interference and longer completion times on interrupting tasks that share cognitive mechanisms with the primary task.
Application developers should adapt mobile interfaces by modularizing complex workflows into smaller, independent subtasks to accommodate mobile attention limitations.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of the current mobile application development roadmap, prioritizing projects that involve complex workflows to identify opportunities for modularization into smaller, independent subtasks in the next development cycle.
IT Manager: On Monday morning, schedule a brief meeting with your mobile development team to discuss the research findings on higher resumption costs for mobile users and explore practical strategies for simplifying user interfaces and reducing cognitive load on mobile applications they manage.
Business Strategist: On Monday morning, analyze customer feedback and usage data for core mobile applications to identify specific complex workflows that are likely causing high resumption costs and explore opportunities to redesign these for improved user efficiency.
Researcher: On Monday morning, begin drafting a follow-up experiment proposal that specifically quantifies the impact of pre-defined 'modularized subtask' interfaces on mobile resumption costs compared to current complex workflows, using the findings from this study as a baseline.
Policymaker: On Monday morning, draft a memo to relevant committees and agencies proposing the inclusion of 'mobile interface design standards' that encourage modularization of complex tasks to improve digital accessibility and productivity for mobile users.

Mobile Device, Interruption, Attention, Capacity Interference, Structural Interference, Task Performance

AI Processed Human Approved

How do service complaints and call patterns relate to whether mobile customers cancel their service?

Hussein Ibrahim and Vladimir Dimitrov

Customer Behavior Analysis and Service Enhancement in Telecom Company Using Machine Learning Methods

Engineering Proceedings · 2026

Key findings

A significant portion of customers encountering service errors (26.7%) choose not to file direct complaints, creating a hidden risk of churn.
Strong correlation was identified between customer complaints and churn events, proving that customers voicing dissatisfaction are substantially more likely to leave the service.
Non-churning customers exhibited significantly higher daily call engagement and durations, while churned users showed consistently low call activity across all times.
Almost 15% of customers filed complaints but did not churn, proving that proactive and timely issue resolution can successfully retain at-risk customers.

What it means for you

CIO / IT Executive: Initiate a project to integrate the complaint logging system with the CRM and call center data, prioritizing the development of an early warning system that flags customers exhibiting patterns of low call activity and previous service errors, even if no formal complaint was filed.
IT Manager: Schedule a meeting with the data engineering team to review the current data pipelines for call logs and service tickets, and identify the technical requirements and feasibility for linking users who experienced service errors but did not file a complaint with their recent call activity data.
Business Strategist: Develop a pilot program targeting the 26.7% of customers identified as silent non-complainers. This program should involve proactive outreach (e.g., personalized SMS, email, or targeted call campaigns) to this segment, offering service check-ins and incentives for feedback following any recorded service error.
Researcher: Begin refining the machine learning model by incorporating features that specifically capture 'silent churn risk' indicators derived from call pattern analysis of users who experienced service errors but did not complain, and further investigate the characteristics of the 15% of complaining customers who were retained to identify key resolution success factors.
Policymaker: Propose an amendment to the customer service policy that mandates a mandatory follow-up protocol for any customer who has experienced a logged service error, regardless of whether a formal complaint was lodged, with the goal of understanding their satisfaction and identifying potential churn risks proactively.

customer churn, customer complaints behavior, machine learning, data analysis, telecommunications, predictive modeling, customer satisfaction

AI Processed Human Approved

How is artificial intelligence reshaping academic work, and what opportunities and risks does it present?

Aizhan Tursunbayeva, Maarten Renkema, Andy Charlwood, Christian-Andreas Schumann, Emelie Schwill

A Panel Report on the Implications of Artificial Intelligence for Academic Knowledge Work

Communications of the Association for Information Systems · 2026

Key findings

Academics actively utilize AI tools like ChatGPT and Copilot primarily for research, followed by teaching and administrative service tasks.
Key opportunities include automating routine teaching preparation, supporting scholarly writing for non-native English speakers, and streamlining grant application workflows.
Participants raised major concerns regarding ethical risks, AI hallucinations, the degradation of peer-review quality, and the potential erosion of critical learning and research skills.
AI impacts are mediated by individual AI literacy, institutional policies, and disciplinary norms, necessitating proactive university governance.
A research agenda is outlined for Information Systems scholars emphasizing human-AI collaboration, responsible AI design, and the career impacts on early-career academics.

What it means for you

CIO / IT Executive: On Monday morning, initiate a cross-departmental working group meeting to inventory existing AI tool usage across teaching, research, and administrative functions, identifying commonalities and immediate risks based on the 'Key Findings' regarding AI hallucinations and ethical concerns.
IT Manager: On Monday morning, schedule a technical assessment to evaluate the security protocols and data privacy implications of the most commonly used AI tools (e.g., ChatGPT, Copilot) currently being utilized by academic staff, referencing the 'Key Findings' on ethical risks.
Business Strategist: On Monday morning, begin drafting a preliminary AI integration roadmap for academic workflows by identifying 2-3 specific routine teaching preparation or scholarly writing tasks (as per 'Key Findings') that could be piloted for AI automation within the next quarter.
Researcher: On Monday morning, experiment with using an AI tool like ChatGPT or Copilot to draft an initial outline for a grant application section (referencing 'Key Findings' on streamlining grant workflows) or to refine a paragraph for clarity if English is not your primary language, noting both the efficiencies gained and any potential 'hallucinations' or quality degradation.
Policymaker: On Monday morning, schedule a review of current institutional policies related to academic integrity and technology use, specifically flagging areas that need to be updated to address the 'Key Findings' on AI hallucinations, the degradation of peer-review quality, and the erosion of critical learning skills.

Academics, Knowledge Work, Artificial Intelligence, Implication, Future of Work

AI Processed Human Approved

What types of harassment do women in STEM face on TikTok, and how common are they?

Gordon Amidu

Cyberbullying and the Mental Health Burden on Young Women in STEM Fields in the U.S.

2025

Key findings

Non-cyberbullying comments occurred significantly more frequently than cyberbullying comments on videos created by women in STEM.
Gendered slurs (34.10%), sexualization (29.64%), and mansplaining (29.04%) were the most predominant cyberbullying tactics, occurring at rates significantly higher than expected.
Overt harassment tactics like belittlement (2.79%), invalidation (2.06%), discrediting (1.88%), and gatekeeping (0.49%) occurred significantly less frequently than expected.
Sentiment analysis indicated that neutral responses dominated user interactions, followed by positive expressions, with negative sentiment being the least frequent.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of your organization's current internal communication policies and harassment reporting mechanisms, specifically checking if they adequately address online platforms like TikTok and include provisions for gendered slurs, sexualization, and mansplaining experienced by employees.
IT Manager: On Monday morning, schedule a brief team meeting to discuss the importance of fostering inclusive online professional spaces, and task a team member with researching readily available, simple tools or platform settings (e.g., comment filtering on social media) that could be shared with employees to manage online harassment.
Business Strategist: On Monday morning, begin drafting talking points for leadership on the company's commitment to supporting women in STEM, specifically acknowledging the unique online challenges they may face and outlining how the company can offer resources or support in managing professional online presence.
Researcher: On Monday morning, begin outlining a follow-up research proposal focusing on the impact of cyberbullying tactics like gendered slurs, sexualization, and mansplaining on the career progression and retention of young women in STEM within corporate environments, beyond just social media platforms.
Policymaker: On Monday morning, draft a memo to relevant legislative committees highlighting the findings on gendered cyberbullying in STEM on platforms like TikTok and proposing the exploration of policy initiatives that could encourage tech companies to implement more robust content moderation tools specifically targeting sexualization and gendered slurs.

cyberbullying, women in STEM, TikTok, gender-based harassment, social media, text mining

AI Processed Human Approved

How do online reactions and bullying differ for young males and females posting about school shootings?

Gordon Amidu

Cyberbullying in Times of School Shootings: Effects on Youth Mental Health in the U.S.

2025

Key findings

Non-cyberbullying comments occurred significantly more frequently than cyberbullying comments across posts from both male and female youth creators.
Emotional trauma responses were the predominant cyberbullying behavior, comprising 52.69% of harassment on male posts and 63.97% on female posts, while aggression, sarcasm, and blame occurred less than expected.
Topic modeling revealed gendered discussion dynamics, where male posts prompted political discourse and threat-focused debates, while female posts elicited supportive, emotionally processing, and healing-oriented conversations.
Sentiment analysis indicated that posts by young females received a higher proportion of neutral comments and fewer negative reactions compared to posts by young males.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of existing content moderation policies and technologies to ensure they are sufficiently equipped to detect and flag 'emotional trauma responses' as a primary form of cyberbullying, particularly in the context of sensitive national crises, and prioritize implementing enhanced AI-driven sentiment analysis tools capable of nuanced interpretation.
IT Manager: On Monday morning, schedule a team meeting to brief the IT support staff on the research findings regarding gendered cyberbullying dynamics on platforms like TikTok, and instruct them to proactively monitor user reports for comments exhibiting 'emotional trauma responses' on posts related to sensitive topics, escalating any patterns or severe instances to the security team.
Business Strategist: On Monday morning, task a cross-functional team (including marketing, communications, and product development) to analyze how the company's online platforms and content strategies can better support 'emotionally processing and healing-oriented conversations,' particularly in times of national crisis, drawing inspiration from the supportive dynamics observed on female youth posts.
Researcher: On Monday morning, design a follow-up study proposal that specifically investigates the efficacy of different intervention strategies for 'emotional trauma responses' in cyberbullying, perhaps comparing the impact of direct moderation versus community-driven support mechanisms on platforms like TikTok, with a focus on gendered responses.
Policymaker: On Monday morning, draft a policy brief to relevant stakeholders outlining the research findings on gendered cyberbullying and the prevalence of 'emotional trauma responses' during national crises, recommending the development of targeted digital literacy programs for youth that address empathetic online communication and healthy coping mechanisms for dealing with sensitive content.

cyberbullying, school shootings, TikTok, gender differences, sentiment analysis, youth mental health

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