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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

New

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

AI Processed Human Approved

What forms of cyberbullying do adolescents experience in gamified TikTok trends, and how do they differ by gender?

Gordon Amidu

The Impact of Gamified Cyberbullying Trends on Adolescent Mental Health on TikTok in the U.S.

2025

Key findings

Non-cyberbullying comments comprised over 96% of all interactions, demonstrating the impact of automated platform moderation and bystander intervention.
Sexualization emerged as the predominant cyberbullying tactic, accounting for 92.46% of male-directed cyberbullying and 89.19% of female-directed cyberbullying.
Gendered patterns were prominent in topic modeling: male content elicited performance-based roasts, whereas female content attracted appearance-focused rating commentary.
Sentiment analysis showed that female adolescents received proportionally more neutral audience responses and fewer negative reactions than male peers.

What it means for you

CIO / IT Executive: Initiate a review of the current content moderation algorithms and their effectiveness in identifying and flagging subtle forms of gamified cyberbullying, particularly sexualization and appearance-focused commentary, as outlined in the research. Schedule a follow-up meeting with the AI/ML team by Wednesday to discuss potential enhancements based on these findings.
IT Manager: Begin analyzing the platform's reporting mechanisms. Identify any user-facing features that might be inadvertently hindering adolescents from reporting gamified cyberbullying by making it appear 'playful'. Prepare a brief report by end of day Monday on potential UI/UX improvements for reporting tools, focusing on clarity and ease of use.
Business Strategist: Develop a preliminary risk assessment framework for new gamified campaign launches on TikTok. This framework should explicitly incorporate potential negative externalities like gamified cyberbullying and its gendered manifestations, as identified in the research, and assess how these risks might impact brand reputation and user engagement.
Researcher: Begin designing a follow-up study protocol. Focus on how to specifically capture and analyze 'playful' or gamified cyberbullying that might evade current detection methods, using the research findings on sexualization and appearance-based commentary as key indicators. Prioritize developing an analytical framework that distinguishes between genuine 'roasts' and harmful cyberbullying.
Policymaker: Draft a policy brief for internal review that outlines the need for updated platform guidelines. This brief should specifically address the challenge of gamified cyberbullying on platforms like TikTok, referencing the research's findings on sexualization and gendered patterns, and proposing that platforms implement clearer definitions and more robust enforcement mechanisms for these evolving forms of online harassment.

cyberbullying, gamification-trolling, TikTok, gender, adolescent online behavior, social media

AI Processed Human Approved

How do TikTok users respond when young adults share their struggles with depression?

Gordon Amidu

The Impact of Cyberbullying on Help-Seeking Among Depressed Young adults in the United States

2025

Key findings

Non-cyberbullying comments significantly outnumbered cyberbullying comments, accounting for 92.2% of overall engagement compared to 7.8% cyberbullying interactions.
Supportive responses were the most common interaction style (53.51%), followed by harmful (17.68%), dismissive (15.91%), sarcastic (8.83%), peer invalidation (3.42%), and blaming (0.65%) comments.
Sentiment analysis revealed that responses to depression-related content were predominantly neutral, reflecting cautious and non-judgmental user engagement.
Topic modeling identified five primary thematic areas in user discussions: Emotional Vulnerability, Identity Struggles, Social Connection, Negative Emotions, and Emotional Expression.

What it means for you

CIO / IT Executive: On Monday morning, direct your cybersecurity team to review existing platform moderation tools and policies for social media platforms commonly used by young adults, with a specific focus on identifying potential enhancements to detect and flag harmful comment patterns identified in the research (e.g., dismissive, sarcastic, peer invalidation).
IT Manager: On Monday morning, initiate a review of our internal IT support resources and documentation related to online safety and responsible social media use, ensuring it includes guidance on identifying and reporting cyberbullying, and consider adding specific examples of the types of harmful comments found in the research.
Business Strategist: On Monday morning, task your market research team to analyze the brand's current social media engagement strategy on platforms like TikTok, specifically looking for opportunities to amplify supportive and positive content in response to mental health disclosures, and to identify potential risks associated with negative comment trends.
Researcher: On Monday morning, begin planning your next research project by drafting a proposal to replicate this study's methodology on other emerging social media platforms or with different demographic groups to broaden the understanding of cyberbullying's impact on help-seeking behavior.
Policymaker: On Monday morning, schedule a meeting with your legislative aides to begin drafting policy recommendations that encourage social media platforms to implement more robust content moderation systems specifically designed to identify and mitigate cyberbullying that hinders vulnerable individuals from seeking help.

cyberbullying, mental health, TikTok, depression, social media harassment, help-seeking

AI Processed Human Approved

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

Gordon Amidu

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

2025

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

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

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

International Conference on Information Systems (ICIS) · 2024

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

Margeret Hall, Christian Haas

A Research Agenda to Understand Drivers of Digital Gullibility

International Conference on Information Systems (ICIS) · 2022

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

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

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

2025

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

Margeret Hall

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

International Conference on Information Systems (ICIS) · 2018

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

Sven-Volker Rehm, Anne Faber, Lakshmi Goel

Visualizing Platform Hubs of Smart City Mobility Business Ecosystems

International Conference on Information Systems (ICIS) · 2017

Key findings

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

What it means for you

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

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

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