AIS Research Insights

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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

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

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

Sven-Volker Rehm, Thomas Reschenhofer, Klym Shumaiev

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

International Conference on Information Systems (ICIS) · 2014

Key findings

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

What it means for you

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

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

AI Processed Human Approved

How do 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

AI Processed Human Approved

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

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

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

Business & Information Systems Engineering · 2026

Key findings

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

What it means for you

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

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

AI Processed Human Approved

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

AI Processed Human Approved

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

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

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

2026

Key findings

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

What it means for you

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

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

AI Processed Human Approved

How can AI governance be adapted to fit local cultural and ethical values around the world?

Dimitrios Symeonidis, Johannes Schneider, Anastasija Nikiforova

TOWARD CULTURAL–ETHICAL FIT IN AI GOVERNANCE: A GLOBAL TYPOLOGY OF GOVERNANCE PROFILES

ECIS 2026 · 2026

Key findings

Identified eight distinct ideal-type AI governance profiles reflecting specific cultural and ethical alignments: group fairness, individual fairness, market-led growth, value-led growth, human oversight fortress, autonomous growth, individual control, and collective security.
Demonstrated that global AI governance is structured by two key meta-tensions: social justice versus economic competitiveness, and autonomy versus security and control.
Formulated three theoretical propositions outlining how cultural alignment determines governance legitimacy, shapes patterns of societal resistance, and guides regulatory evolution over time.
Offered a diagnostic decision framework for policymakers to adapt international AI guidelines to local socio-cultural contexts without sacrificing core safety requirements.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute deep dive with your legal and ethics teams to identify which of the eight AI governance profiles identified in the research most closely aligns with your organization's current AI development and deployment practices, and flag any immediate misalignments.
IT Manager: On Monday morning, initiate a review of your team's current AI project documentation and development methodologies, specifically looking for instances where decisions about fairness, transparency, or accountability might have been implicitly influenced by a single cultural perspective (e.g., U.S.-centric) and note them for further discussion.
Business Strategist: On Monday morning, begin analyzing your organization's key international markets and identify the primary cultural value orientations that characterize them, then cross-reference this with the meta-tensions (social justice vs. economic competitiveness, autonomy vs. security) to anticipate potential governance friction for upcoming AI initiatives.
Researcher: On Monday morning, begin drafting a short literature review outline focusing on how cultural value orientations (e.g., individualism vs. collectivism) have historically shaped the adoption and regulation of other transformative technologies (e.g., the internet, biotechnology) to draw parallels with AI governance.
Policymaker: On Monday morning, review the 'diagnostic decision framework' mentioned in the research, and identify one specific AI guideline (e.g., data privacy regulations) that your jurisdiction currently applies universally, then brainstorm how to adapt its interpretation to better fit the dominant cultural value orientations of your primary constituent groups.

AI governance, cultural values, governance typology, ethical AI, regulatory design

AI Processed Human Approved

What roles can generative AI play in data quality management, and how should leaders govern them?

Karen Roht, Anastasija Nikiforova, Hippolyte Lefebvre

A Typology For Responsible Delegation Of Data Quality Management To Generative AI

ECIS 2026 · 2026

Key findings

Formulated a four-role typology for GenAI in DQM: Translator (natural language to rule logic), Explainer (narrative summaries of anomalies and metadata), Resolver (data correction suggestions), and Integrator (cross-system schema alignment).
Revealed that current GenAI adoption in commercial DQM tools remains nascent (~17%), functioning largely as an interpretive co-pilot rather than an autonomous decision-maker.
Proposed five theoretical governance guidelines highlighting task decomposability appraisal, mandatory human validation for high-impact actions, reverse delegation protocols, explainability standards, and retained human accountability.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of your organization's current DQM tool landscape to identify which, if any, are actively experimenting with or have integrated GenAI capabilities, and schedule a follow-up meeting with the IT Manager to discuss initial findings and potential pilot opportunities based on the 'Translator' role.
IT Manager: On Monday morning, dedicate an hour to review the existing data quality rulebooks and documentation within your team's purview, noting down specific areas where natural language descriptions of data quality issues or desired rules are prevalent, to prepare for a discussion with the CIO/IT Executive about the potential application of the 'Translator' GenAI role.
Business Strategist: On Monday morning, identify one critical business process that is currently hampered by data quality issues and then, using the 'Explainer' GenAI role as a conceptual framework, draft three potential narrative-style questions you would ask an AI about the root causes and impact of these data quality problems to present to stakeholders.
Researcher: On Monday morning, begin by outlining a research proposal to investigate the effectiveness and potential biases of GenAI 'Resolver' roles in correcting common data anomalies within a specific industry domain, drawing upon the governance guidelines related to mandatory human validation for high-impact actions.
Policymaker: On Monday morning, begin drafting a preliminary policy document that outlines the principle of 'retained human accountability' for any data quality decisions made or influenced by GenAI within the organization, focusing on the need for clear audit trails and oversight for 'Resolver' and 'Integrator' roles.

Data Quality Management, Generative AI, Human-AI Collaboration, AI Governance, Task Delegation, Agentic IS

AI Processed Human Approved

How does adopting smart speakers change how consumers search for, buy, and consume digital content?

Yoonseock Son, Wonseok Oh, Il Im

THE VOICE OF COMMERCE: HOW SMART SPEAKERS RESHAPE DIGITAL CONTENT CONSUMPTION AND PREFERENCES

MIS Quarterly · 2023

Key findings

Smart speaker adoption led to a 21.5% increase in monthly VOD content expenditures, alongside a 3.0% decrease in average content completion rates.
The increase in purchases is driven by lowered search friction, resulting in more search incidences, higher conversion rates, and less labor-intensive evaluation of content.
Using smart speakers for conversational purposes beyond simple informational queries amplifies the positive impact of device adoption on sales.

What it means for you

CIO / IT Executive: On Monday morning, task your team with investigating integration points between existing VOD platforms and major smart speaker voice assistant APIs (e.g., Alexa, Google Assistant) to enable seamless content discovery and playback via voice commands.
IT Manager: On Monday morning, analyze the current VOD platform's data infrastructure to identify how to track and log voice-initiated search queries and content playback events, distinguishing them from traditional remote control interactions.
Business Strategist: On Monday morning, initiate a brainstorm session to explore new content promotion strategies that leverage voice-activated search, considering how to make titles more discoverable through natural language queries and conversational recommendations.
Researcher: On Monday morning, begin outlining a follow-up study to quantify the correlation between specific conversational voice interactions (beyond simple search) and increased VOD content consumption, perhaps through A/B testing of voice-guided discovery features.
Policymaker: On Monday morning, convene a working group to assess the potential implications of increased voice-activated content consumption on digital accessibility standards and to explore how to ensure equitable access to content across all user interfaces.

Voice channel, search, smart speaker, digital content, econometrics

AI Processed Human Approved

How can practitioner researchers overcome the divide between academia and industry to create real-world impact?

Stephen McCarthy, Hendrik Scholta, Geir Inge Hausvik, Peter André Busch

Boundary Spanning and Practical Impact in IS Research: A Bourdieusian Analysis

Information Systems Journal (ISJ) · 2025

Key findings

IS practitioner doctorates facilitate knowledge exchange through boundary-spanning activities including research design adaptation, multi-channel dissemination, and creating symbolic discourse.
Practical impact progresses through proof-of-concept, proof-of-value, and proof-of-use stages, each requiring different combinations of social, cultural, economic, and symbolic capital.
Environmental forces such as academic vs. practice doxa (institutional norms) and habitus (professional dispositions) create positional conflicts that require active negotiation and tailored incentives to sustain engagement.

What it means for you

CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your direct reports to initiate a 'practice impact review' focused on one ongoing IS project. Ask each person to identify one specific academic insight or research finding from a recent external study or internal research initiative that could be directly applied to improve that project's current proof-of-concept or proof-of-value stage, and assign them to explore this application by end of week.
IT Manager: On Monday morning, identify one specific, recent academic paper or conference proceeding relevant to a current team challenge. Forward this paper to your team with a concise note asking for a brief, 1-2 sentence summary of its most practical takeaway and how it *might* apply to your current proof-of-value stage objective by Tuesday afternoon.
Business Strategist: On Monday morning, review the key performance indicators (KPIs) of one strategic business initiative currently underway. Identify which KPIs are not meeting expectations and, using the 'proof-of-value' framework, draft a one-page hypothesis on how a novel IS application or process, potentially informed by academic research, could demonstrably improve these specific KPIs within the next quarter.
Researcher: On Monday morning, select one recent academic publication where you are the lead author and identify its 'proof-of-use' stage potential. Draft a single-slide executive summary that articulates the core problem addressed, the practical solution proposed, and one tangible benefit for a specific industry practitioner role (e.g., IT manager, business analyst), and share it with three relevant practitioner contacts for feedback by Wednesday.
Policymaker: On Monday morning, review the current grant application criteria for technology innovation funding. Identify if and how 'demonstrable practical impact' is explicitly assessed, and draft a brief proposal to incorporate a specific requirement for applicants to detail their strategy for achieving 'proof-of-value' or 'proof-of-use' within a defined timeframe, alongside theoretical contributions.

academia-practice gap, boundary spanning, design science research, engaged IS scholarship, practical impact, theory of practice

AI Processed Human Approved

How can digital data be used to understand and guide ongoing changes involving people and technology?

Jan vom Brocke, Wil M. P. van der Aalst, Nicholas Berente, Boudewijn van Dongen, Thomas Grisold, Waldemar Kremser, Jan Mendling, Brian T. Pentland, Maximilian Roeglinger, Michael Rosemann, Barbara Weber

Process science: the interdisciplinary study of socio-technical change

Process Science · 2024

Key findings

Defines process science as an interdisciplinary domain aimed at capturing, understanding, and intervening in socio-technical change across multiple levels of abstraction.
Categorizes key activities of process science into discovery (detecting patterns via event data), explanation (identifying cause-effect relations), and intervention (shaping process trajectories).
Advocates for shifting from an 'entity-first' to a 'process-first' perspective to identify analogies across diverse domains and support computationally intensive theory building.
Introduces a research framework that integrates digital trace data from practice with theoretical foundations to solve real-world problems and drive societal impact.

What it means for you

CIO / IT Executive: On Monday morning, initiate a review of your organization's current digital infrastructure and data collection capabilities to identify which systems generate 'digital trace data' relevant to socio-technical processes. Prioritize understanding the flow of this data and its potential for analysis.
IT Manager: On Monday morning, investigate your team's current methods for logging and tracking user interactions with key IT systems. Identify specific 'events' that could be captured and analyzed to reveal patterns of socio-technical change within your department.
Business Strategist: On Monday morning, schedule a brainstorming session with your core team to identify one specific business process currently undergoing significant change (e.g., customer onboarding, product development). Brainstorm what digital 'trace data' this process generates and how understanding its dynamics could inform strategy.
Researcher: On Monday morning, start cataloging existing datasets within your research domain that represent 'digital traces' of human-technology interaction. Begin exploring computational tools that can analyze event sequences rather than just static entities.
Policymaker: On Monday morning, identify a specific public policy area (e.g., digital inclusion, citizen engagement) that is experiencing rapid socio-technical evolution. Begin researching existing digital platforms and services that generate trace data related to this policy area.

Process Science, Socio-Technical Processes, Digital Trace Data, Process Mining, Organizational Change, Interdisciplinary Research

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