AIS Research Insights

Browse the library 70 papers

All Artificial Intelligence & Automation Information Systems & Management Human-Computer Interaction Data Analytics & Big Data Digital Transformation & Strategy Cybersecurity & Privacy Cloud & Infrastructure E-Commerce & Digital Platforms Operations & Supply Chain Sustainability & Green IS

HOW GENERATIVE AI DRIVES FIELD-LEVEL CHANGE IN MANAGEMENT CONSULTING

AI Processed

How does using generative AI in everyday work change how management consulting firms operate and deliver value?

Robin Killewald, Thomas Haskamp

HOW GENERATIVE AI DRIVES FIELD-LEVEL CHANGE IN MANAGEMENT CONSULTING

ECIS 2026 · 2026

Key findings

The study identified four core mechanisms of change—negotiated legitimacy, provisionality of reliance, skill polarity, and contestation of value—which were qualitatively supported by empirical interview data.
Under the 'provisionality of reliance' mechanism, temporal pressures cause consultants to bypass validation routines, creating fragile outputs that risk client trust (supported qualitatively).
Under the 'skill polarity' mechanism, generative AI simultaneously expands short-term capabilities and erodes long-term analytical thinking and mentoring routines (supported qualitatively).
Under the 'contestation of value' mechanism, the commoditization of standardized tasks shifts the focus of consulting value toward human-centered contextual judgment and experience (supported qualitatively).
Quantitative hypothesis testing was not performed due to the study's qualitative, inductive methodology, which limits statistical generalizability to other contexts or regions.

What it means for you

CIO / IT Executive: Integrate an automated 'Validation Gateway' into the firm’s proprietary GenAI workspace that blocks users from exporting drafts into client-ready formats (like PPTX or PDF) until they complete a digital checklist verifying the accuracy and sources of the underlying data.
IT Manager: Set up a weekly automated audit report in your GenAI usage analytics platform to identify teams with high-volume AI prompt generations but low average document-editing times, and flag these teams for manual quality-assurance reviews.
Business Strategist: Redesign the firm's standard client engagement proposal template to phase out hourly billing for commoditized deliverables (like market research summaries) and replace it with value-based pricing focused on human-led strategic judgment workshops.
Researcher: Draft a quantitative survey instrument targeting 500 management consultants across North America and Asia to test if the qualitative mechanisms of 'skill polarity' and 'provisionality of reliance' negatively correlate with long-term client retention metrics.
Policymaker: Introduce an industry-standard certification framework for professional service firms that mandates documented 'human-in-the-loop' validation protocols and minimum structured mentoring hours to protect junior workforce development against generative AI skill erosion.

Generative AI, Professional Service Firms, Causal Mechanisms, Routines, Management Consulting

RETHINKING KNOWLEDGE WORK: DESIGNING LLM-BASED SYSTEMS FOR COMPLEXITY MANAGEMENT

AI Processed Human Approved

How effectively can artificial intelligence tools screen and extract information from complex procurement documents?

Moritz Diener, Simon Kaps, Philipp Spitzer, Robin Hirt, Michael Vössing, Gerhard Satzger

RETHINKING KNOWLEDGE WORK: DESIGNING LLM-BASED SYSTEMS FOR COMPLEXITY MANAGEMENT

ECIS 2026 · 2026

Key findings

The LLM-based system reduced initial document screening time by 59% (from an average of 3 minutes 42 seconds manually to 1 minute 30 seconds via the system), which was statistically supported.
The system achieved an overall content-match accuracy of 88.82% for text extraction, with highest accuracy for identifying the procuring entity and place of execution (94.10% each) and lowest for the type of service (79.40%).
For binary classification tasks, the system achieved an average accuracy of 83.85%, performing significantly better on detecting required certifications (91.20%) than on required references (76.50%).
Qualitative feedback supported the system's ability to lower perceived document, compliance, and process complexity, shifting human workflows from manual reading and extraction to output verification.
Limitations include testing within a single German SME in the data center sector, a short-term study duration, and a small evaluation dataset (34 documents), restricting direct generalizability to other industries.

What it means for you

CIO / IT Executive: On Monday morning, issue an IT directive mandating that any generative AI tools deployed for document screening must include a 'human-in-the-loop' verification interface, explicitly prohibiting automated final submissions due to the system's average 11.18% error rate in text extraction.
IT Manager: On Monday morning, configure your document-processing pipeline's UI to visually highlight and force manual validation of 'type of service' and 'required references' fields, as the research shows these have lower extraction accuracies (79.4% and 76.5%) compared to highly reliable fields like 'procuring entity' (94.1%).
Business Strategist: On Monday morning, calculate the potential ROI of deploying an LLM-based screening tool for your proposal-writing team, using the study's proven 59% reduction in initial document screening time (down to 1 minute 30 seconds per document) to project resource hours saved.
Researcher: On Monday morning, write a research proposal to extend this design science framework by testing it on a dataset larger than 34 documents across multi-industry and non-German settings, focusing specifically on optimizing prompts to improve the low 76.5% accuracy baseline for identifying required references.
Policymaker: On Monday morning, initiate a draft proposal for public procurement formatting standards that mandates standardized, machine-readable sections for 'required certifications' and 'references' to minimize AI extraction errors (currently at 8.8% and 23.5% respectively) and ensure fair bidding processes for SMEs.

large language models, generative AI, complexity management, knowledge work, design science research

DESIGNING FOR IMPERFECT PROMPTS: A SIMULATION-BASED EVALUATION OF AN LLM-BASED ENTERPRISE DATA ASSISTANT USING KNOWLEDGE GRAPHS

AI Processed Human Approved

Which approach best helps AI assistants translate imperfect user prompts into accurate database queries?

Niklas Wagner, Alexander Mdche

DESIGNING FOR IMPERFECT PROMPTS: A SIMULATION-BASED EVALUATION OF AN LLM-BASED ENTERPRISE DATA ASSISTANT USING KNOWLEDGE GRAPHS

ECIS 2026 · 2026

Key findings

The KG-based mediation statistically outperformed the schema-based mediation across Precision, Recall, and F1-Score metrics.\n- For GPT-4.1, the KG-based approach achieved a Precision of 90.67% (compared to 69.05% for schema-based) and an F1-Score of 89.47% (compared to 71.02% for schema-based).\n- For Claude Sonnet 4, the KG-based approach achieved a Precision of 87.23% (compared to 75.17% for schema-based) and an F1-Score of 90.29% (compared to 82.17% for schema-based), while Recall remained statistically comparable (93.52% vs 92.56%).\n- The study is limited by its focus on a single-enterprise setting within a CFO unit, a finite dataset of 158 interactions from 29 users, and reliance on a simulation-based evaluation instead of live field deployments.

What it means for you

CIO / IT Executive: Issue a directive to transition the enterprise's LLM-based data assistant architecture away from simple schema-based semantic similarity matching and onto a Knowledge Graph-based mediation layer constructed from database schemas to capture the demonstrated 12% to 21% precision improvements.
IT Manager: Instruct your database and engineering teams to begin constructing a Knowledge Graph from your current database schemas to serve as a semantic mediation layer for your LLM assistant, replacing manual prompt engineering.
Business Strategist: Launch a pilot program within the CFO business unit to deploy the Knowledge Graph-based data assistant, leveraging its verified ability to resolve incomplete, domain-specific prompts into accurate database queries for business users.
Researcher: Draft a research proposal to test the Knowledge Graph-based mediation layer in a live, real-world field deployment to gather empirical performance data beyond the current study's simulation of 158 interactions across 29 users.
Policymaker: Draft an internal data governance policy requiring that all natural-language LLM interfaces querying corporate databases must use a schema-derived Knowledge Graph mediation layer to ensure structural query alignment and prevent inaccurate data retrieval.

Large Language Models, Enterprise Data Assistants, Text-to-SQL, Knowledge Graphs, Design Science Research

FROM BROWN TO GREEN: HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION

AI Processed Human Approved

How can businesses use artificial intelligence to transition their business models toward sustainability?

Elaine Mosconi, Marie-Claude Boudreau

FROM BROWN TO GREEN: HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION

ECIS 2026 · 2026

Key findings

The AI-enabled platform qualitatively supported a business model shift from transactional, time-based billing to recurring, outcome-based services focused on asset durability and lifecycle performance.
A strategic alignment gap was identified: executives framed the AI and sustainability transformation as radical, whereas operational managers experienced it as incremental.
Successful scaling of the AI initiative qualitatively depended on adaptive governance (looser decision-making, rapid experimentation), pre-requisite capability building in data engineering, and strong leadership support.
Key organizational trade-offs and barriers emerged, specifically related to skills gaps, legacy system integration, high energy consumption of AI models, and delayed financial return on investment.
Limitation: The findings of this study are based on a single qualitative case study of a Canadian engineering firm, which may limit the direct generalizability of the results to other industries or regions.

What it means for you

CIO / IT Executive: Initiate an audit of legacy data pipelines to identify bottlenecks for AI integration, and establish a 'green sandbox' environment with relaxed governance rules to allow developers to experiment with asset-durability algorithms while tracking model energy consumption.
IT Manager: Hold a 30-minute team sync to map out existing data engineering skill gaps and assign one developer to clean and structure a single historical dataset of asset lifecycle performance to prepare it for future AI model training.
Business Strategist: Draft a pilot proposal to transition one high-trust client from hourly billing to a recurring, outcome-based pricing model focused on extending their asset lifecycle, and schedule a meeting with operational managers to align on realistic, incremental milestones.
Researcher: Design a survey instrument targeting business and IT leaders across multiple industries to quantitatively test whether the perceptual gap between executive (radical) and operational (incremental) views on AI-driven green transitions exists outside of the engineering sector.
Policymaker: Draft a policy brief proposing a targeted grant program that subsidizes data-engineering upskilling and offsets energy-efficiency auditing costs for mid-sized firms adopting AI-enabled, sustainable business models.

Twin Transition, Artificial Intelligence, Sustainability, Business Model, Dynamic Capabilities

THE AI-ENABLED CIRCULAR ECONOMY: EXTRACTING AND SYNTHESIZING DESIGN KNOWLEDGE

AI Processed

How can organizations design and use artificial intelligence to support circular economy practices?

Linda Sagnier Eckert, Christoph Hoppe-Ludwig, Timo Strohmann, Anne Ixmeier, Daniel Heinz

THE AI-ENABLED CIRCULAR ECONOMY: EXTRACTING AND SYNTHESIZING DESIGN KNOWLEDGE

ECIS 2026 · 2026

Key findings

Synthesized design knowledge from 99 empirically grounded studies supported four distinct meta-principles for AI-enabled circular economies.
Meta-Principle 1 (AI for slowing resource loops) is supported by 15 empirical studies showing predictive AI models successfully forecast asset degradation to trigger life-extending actions.
Meta-Principle 2 (AI for closing resource loops) is supported by 29 empirical studies showing integrated computer vision and robotic sorting systems reliably classify and separate materials.
Meta-Principle 3 (AI for narrowing resource loops) is supported by 18 empirical studies showing hybrid optimization models successfully reduce resource and energy consumption.
Meta-Principle 4 (AI for socio-technical orchestration) is supported by 37 empirical studies demonstrating that AI can drive the organizational dynamic capabilities needed to coordinate circular workflows.
A recognized limitation is that the corpus is restricted to English-language, peer-reviewed publications from the last five years, and the synthesized principles have not been longitudinally tested in new field environments.

What it means for you

CIO / IT Executive: Schedule a 9:00 AM workshop with your Enterprise Architecture and Sustainability teams to map your organization's existing data infrastructure against the study's two-layered framework, explicitly linking your micro-level AI model choices with macro-level organizational capabilities to support circular workflows.
IT Manager: Initiate a pilot project on your team's backlog to deploy a predictive maintenance model on a single class of high-wear manufacturing assets, using telemetry data to forecast asset degradation and automatically trigger life-extending maintenance tickets.
Business Strategist: Conduct a value-chain audit to identify where materials are lost, and draft a business case for integrating computer vision and robotic sorting systems at your primary waste-handling facility to automate material classification and close resource loops.
Researcher: Draft a research proposal for a 12-month longitudinal field study in partnership with a local electronics manufacturer to empirically validate the paper's four synthesized meta-principles in a real-world operational environment, addressing the literature's lack of longitudinal testing.
Policymaker: Draft a policy memo proposing a targeted grant program that provides R&D tax credits to industrial recycling facilities that implement integrated computer vision and automated sorting systems to improve regional material recycling rates.

Circular economy, Artificial intelligence, Design knowledge, Meta-Synthesis, Design science research, Dynamic capabilities

ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS

AI Processed Human Approved

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

Jori Taipale, Olli Tyrväinen, Tuure Tuunanen, Joel Mero, Teiko Heinosaari

ORCHESTRATION PARADOXES IN NATIONAL QUANTUM COMPUTING INNOVATION ECOSYSTEMS

ECIS 2026 · 2026

Key findings

Organizing tensions were identified as the most frequent paradox, specifically focusing on the trade-offs between collaboration vs. resource competition and guidance vs. flexibility.
Performance tensions emerged regarding resource allocation between software vs. hardware development and building a shared vision vs. individual firm survival.
Belonging tensions highlighted a 'godfather' paradox, where the ecosystem must balance the state's role as a major supportive funder and customer with the ultimate goal of corporate independence.
Learning tensions were characterized by the challenge of managing multi-talent knowledge development versus narrow technology specialization.
The study is limited by its single qualitative case context in Finland with a small sample of 15 informants, requiring caution when applying these findings to other national contexts.

What it means for you

CIO / IT Executive: Schedule a 9:00 AM meeting with your lead enterprise architect to audit your current quantum-readiness roadmap, establishing a formal policy that divides software development and hardware access investments 50/50 to hedge against the performance tension of committing prematurely to a single unproven hardware stack.
IT Manager: Create a bi-weekly 1-hour 'Quantum Cross-Training' slot in your team's calendar starting this Friday, pairing your specialized quantum physics PhDs with your generalist cloud engineers to address the learning tension between narrow technical specialization and broad-based systems integration.
Business Strategist: Perform a revenue dependency audit to calculate the percentage of your quantum business unit's funding derived from state grants versus commercial clients, and draft a pitch deck for two private-sector pilots to actively transition away from 'godfather' state-funding reliance.
Researcher: Draft a pre-competitive collaboration agreement template to send to a peer researcher at a competing institution, outlining a shared framework for baseline algorithm testing while clearly defining IP boundaries to manage the tension of collaboration versus resource competition.
Policymaker: Draft a revision for the upcoming national quantum funding round guidelines that introduces a 'co-investment requirement,' mandating that state-funded consortia secure at least 25% of their budget from private corporate buyers to counter the 'godfather' dependency paradox.

Ecosystem orchestration, Paradox theory, Quantum computing innovation ecosystem, Explorative case study, Interpretive qualitative research

TOWARDS DESIGN PRINCIPLES FOR TEACHING INDUSTRY 4.0 COMPETENCIES TO CS/IS STUDENTS

AI Processed

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

Felix Salcher

TOWARDS DESIGN PRINCIPLES FOR TEACHING INDUSTRY 4.0 COMPETENCIES TO CS/IS STUDENTS

ECIS 2026 · 2026

Key findings

Eight design principles were established, emphasizing virtualization, containerization, and problem-based learning cases as alternatives to expensive learning factories.\n- Preliminary course evaluation yielded positive descriptive feedback, with a mean rating of 4.43 out of 5 across satisfaction categories.\n- No formal statistical significance tests were performed due to the pilot nature of the study and the small, specific evaluation sample.\n- The pilot is limited by a very small sample size (9 respondents from a single cohort) and relies on subjective student perceptions rather than objective competency-based assessments.\n- Students noted difficulty adapting to the course's fast pace and programming requirements, highlighting a need for aligned prerequisites.

What it means for you

CIO / IT Executive: Direct your enterprise architecture team to draft a transition plan to replace physical IT hardware training labs with virtualized, containerized sandbox environments (e.g., Docker/Kubernetes) to reduce infrastructure costs and align onboarding with modern Industry 4.0 software architectures.
IT Manager: Review the programming prerequisites of your junior developers and assign them a structured, hands-on troubleshooting task using a containerized microservice mock-up on Monday morning to assess their readiness for complex Industry 4.0 integration projects.
Business Strategist: Reallocate a portion of next quarter's talent development budget away from high-cost physical equipment training and toward scalable, software-based virtual simulators to cost-effectively build digital twin and data architecture competencies.
Researcher: Draft a research proposal to conduct a multi-institution comparative study on these eight design principles, incorporating objective pre- and post-intervention technical testing rather than relying on subjective student feedback to measure competency gains.
Policymaker: Create a state-level educational grant initiative that incentivizes higher education institutions to update computer science curricula with virtualization-based Industry 4.0 frameworks, shifting funding priority away from physical learning factories.

Design Principles, Design Science Research, Industry 4.0, Smart Manufacturing

SUPPORT OR SUBSTITUTION: HOW CHATGPT AFFECTS COGNITIVE LOAD, OWNERSHIP, AND PERFORMANCE IN PROBLEM-SOLVING

AI Processed

How does using ChatGPT affect worker performance, workload, and their sense of ownership over problem-solving tasks?

Leonie Rebecca Freise, Josephine Mago Moritz

SUPPORT OR SUBSTITUTION: HOW CHATGPT AFFECTS COGNITIVE LOAD, OWNERSHIP, AND PERFORMANCE IN PROBLEM-SOLVING

ECIS 2026 · 2026

Key findings

Both ChatGPT-only access and ChatGPT with a prompting guide significantly improved task performance compared to the control group (Supported).\n- ChatGPT access significantly reduced temporal demand, but neither ChatGPT condition significantly reduced mental demand, and there was no significant effect of cognitive load on task performance (Partially Supported).\n- Both experimental conditions significantly reduced psychological ownership of the task outcome (Supported).\n- Reduced psychological ownership did not significantly impair task performance in this study (Not Supported).\n- Higher AI literacy significantly moderated and weakened the negative effects of AI access on temporal demand, and partially weakened the negative effect of the prompting guide on psychological ownership (Partially Supported).\n- The study's limitations include a modest sample size of 136 participants, a single-session experimental setting, and a reliance on self-reported measures.

What it means for you

CIO / IT Executive: Sign off on the procurement and scheduling of a mandatory 'AI Literacy and Collaborative Prompting' training program for all departments currently using generative AI, rather than just distributing licenses, to actively counter the erosion of psychological ownership and help staff manage cognitive load.
IT Manager: Schedule a 30-minute team meeting to establish a 'Human-in-the-Loop' workflow review protocol, requiring team members to explicitly document, edit, and present their personal contributions to any ChatGPT-generated deliverables to re-establish psychological ownership over their outcomes.
Business Strategist: Draft a proposal to revise departmental key performance indicators (KPIs), shifting the metrics of success from speed and volume (temporal savings) to qualitative, human-curated excellence to prevent employees from disengaging and treating their roles as outsourced, low-ownership tasks.
Researcher: Write a research proposal for a multi-week longitudinal field study with a cohort of at least 250 participants to track whether the observed short-term reduction in psychological ownership from AI usage leads to long-term employee disengagement or performance declines over time.
Policymaker: Publish a revised corporate governance policy that defines a 'Responsible Authorship' standard, mandating that the human employee remains 100% accountable for the accuracy and quality of AI-assisted outputs to prevent the dilution of organizational responsibility.

Generative Artificial Intelligence, Human-AI Collaboration, Cognitive Load, Psychological Ownership, AI Literacy, Task Performance

THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION

AI Processed Human Approved

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

Franziska Anna Röckel, Benjamin van Giffen, Andreas Janson, Jennifer Hehn

THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION

ECIS 2026 · 2026

Key findings

The study empirically validated a structural anatomy for the Design Challenge consisting of four robust elements (verb, object of innovation, target group, context) and three plastic elements (internal data, technology, potential), which was supported across all 42 cases analyzed.
The robust elements were validated to successfully create a stable, shared identity and common direction among diverse team members, acting as a center of authority.
The plastic elements were shown to successfully absorb technological uncertainty, providing a flexible framework for teams to negotiate and adapt to AI's emergent capabilities over time.
The qualitative analysis identified three distinct nuances in shaping the 'object of innovation' (modular, abstract, and space) that guide teams toward either incremental or more radical innovation pathways.

What it means for you

CIO / IT Executive: Mandate that all upcoming AI project proposals use a standardized 'Design Challenge' charter template structured around the 4 robust elements (verb, object of innovation, target group, context) and 3 plastic elements (internal data, technology, potential), explicitly forbidding teams from locking in a rigid technical architecture in their initial pitch.
IT Manager: Gather your interdisciplinary project team for a 60-minute workshop on Monday morning to co-create a 'Design Challenge' statement, explicitly defining the stable 'robust' elements to align the business and tech members, while leaving the 'plastic' elements (like specific data sources and AI models) open as flexible targets to be negotiated as the project progresses.
Business Strategist: Review your company's AI project portfolio on Monday morning and intentionally rewrite the 'object of innovation' for each project: frame them as 'modular' if you need predictable, incremental improvements, or rewrite them as 'space-based' (e.g., focused on broad capabilities) if you want to push the team toward radical, disruptive business model innovation.
Researcher: Update your qualitative coding scheme for ongoing field studies of AI adoption to track how frequently and at what project stages teams modify the 'plastic' elements (internal data, technology, potential) versus the 'robust' elements of their project charters to measure the empirical threshold where technological uncertainty triggers project misalignment.
Policymaker: Revise the application guidelines for the upcoming public AI innovation grant cycle to replace rigid 'technical specifications' requirements with a mandatory 'Design Challenge' framework, scoring applicants on the clarity of their robust elements (target group, context) and the adaptive flexibility of their plastic elements (data and technology integration plans).

Design Challenge, AI Innovation, Boundary Object, Anatomy, Interdisciplinary Collaboration, Problem Formulation

WHEN WE CANNOT STOP THE DATA FLOW: THE EMOTIONAL LIFE OF DATA IN A CHILDREN’S DAYCARE CENTER

AI Processed

How does continuous digital reporting affect emotional pressure, trust, and staff autonomy in childcare centers?

Lea Püchel, Tobias Brandt

WHEN WE CANNOT STOP THE DATA FLOW: THE EMOTIONAL LIFE OF DATA IN A CHILDREN’S DAYCARE CENTER

ECIS 2026 · 2026

Key findings

This is a qualitative, ethnographic case study, meaning it does not test quantitative hypotheses or report statistical significance values.
The qualitative data supports a sequential three-stage framework showing how digital opportunity transitions into perceived control and safety, data reassurance dependency, and ultimately internalized data-driven surveillance.
In the highly digitalized center, continuous digital updates successfully provided a feeling of safety but created an addictive loop where any absence of data was interpreted as a signal of risk.
Educators did not openly resist the system but instead practiced adaptive, situated compliance, adjusting their timing, tone, and messaging to meet platform expectations.
The comparative boundary case with lower-intensity digital reporting demonstrated that a slower communication rhythm can temper the emergence of the full data-dependency cycle.

What it means for you

CIO / IT Executive: On Monday morning, reconfigure your enterprise communication and tracking platforms to aggregate non-critical status updates into a single daily digest at 4:00 PM, rather than allowing real-time push notifications, to actively break the expectation of continuous monitoring.
IT Manager: On Monday morning, audit the daycare platform's dashboard interface and replace all default automatic 'No Data' warning flags or empty progress bars with a neutral status indicator to eliminate the false perception of operational risk during quiet hours.
Business Strategist: On Monday morning, draft and distribute a 'Communication Cadence Agreement' to clients that explicitly sets the expectation of only two digital updates per day, actively framing this slower rhythm as a premium service feature that ensures staff are focused on active care rather than screens.
Researcher: On Monday morning, draft a research proposal to conduct a comparative diary study of frontline workers in relationship-heavy sectors like eldercare, specifically measuring the psychological friction and time spent on 'situated compliance' tasks versus actual patient care.
Policymaker: On Monday morning, draft a policy amendment for child and eldercare labor standards that prohibits the use of real-time telemetry and continuous digital logging frequency as metrics for assessing staff performance and regulatory compliance.

Data work, future of work, reporting systems, relational data, data dependency

UNPACKING THE ROLE OF INDIVIDUALS IN PROCESS MINING: A LITERATURE REVIEW AND RESEARCH FRAMEWORK

AI Processed

How do individual user behaviors and attitudes impact the business value generated from process mining?

Julian Dyong, Sandro Franzoi, Jan vom Brocke

UNPACKING THE ROLE OF INDIVIDUALS IN PROCESS MINING: A LITERATURE REVIEW AND RESEARCH FRAMEWORK

ECIS 2026 · 2026

Key findings

The review identifies and establishes user behavior, usage patterns, and affect (such as uncertainty and trust) as the three core elements constituting the individual level of process mining, supported by the synthesized literature.
The analysis reveals that technical, individual, and organizational contingency factors systematically influence user behavior and affect; for example, data inaccuracies undermine user trust, while formal training and methodological guidance support correct execution.
Individual behaviors and affective states are shown to directly impact value outcomes, with structured behaviors like hypothesis testing decreasing analysis duration and increasing the quality of insights.
Key limitations of the study include its reliance on a limited sample of 23 reviewed papers, a static framework that does not capture sequential or temporal dynamics, and a lack of true inter-coder reliability during the qualitative coding process.

What it means for you

CIO / IT Executive: Initiate a data audit of the source pipelines feeding your enterprise process mining tools to eliminate data discrepancies, directly addressing the research finding that data inaccuracies severely undermine user trust and derail tool adoption.
IT Manager: Add a mandatory 'Hypothesis-First' template to your team's process mining workflow on Monday morning, requiring analysts to document their specific diagnostic hypothesis before running queries to enforce structured behavior and reduce analysis duration.
Business Strategist: Redesign the process mining ROI dashboard to measure actual business value outcomes (e.g., cost savings from process changes) rather than user log-in frequency, linking individual analytical insights directly to operational KPIs.
Researcher: Draft a research design for a longitudinal study tracking 20 process mining analysts over six months to capture the sequential and temporal dynamics of user affect and trust, directly addressing the static limitations of current literature.
Policymaker: Establish an internal organizational standard mandating that no department may deploy process mining software without integrating a formal training and methodological guidance program, ensuring compliant and high-quality data execution.

Process Mining, Individual Level, Literature Review, Research Framework, Value Creation, User Behavior

AI Processed Human Approved

How can companies and employees successfully negotiate a nomadic lifestyle alongside permanent corporate employment?

Julian Marx, Milad Mirbabaie, Stefan Stieglitz

Corporate Nomads: Working at the Boundary Between Corporate Work and Digital Nomadism

Journal of the Association for Information Systems · 2025

Key findings

The study develops a three-phase process model (splintering, calibrating, and harmonizing) that explains how corporate nomads and their organizations successfully negotiate this new work arrangement.
The integration of corporate nomads is not a one-sided decision but a mutual process of 'boundary work' requiring engagement, negotiation, and trade-offs from both the employee and the company.
Corporate nomads operate as individual outliers who change their personal work boundaries (e.g., location and time) without transforming the entire organization's structure.
Information Technology (IT) is crucial in managing the inherent tensions of this lifestyle, helping to balance organizational control with employee autonomy and enabling integration from a distance.

Corporate Nomads, Digital Nomads, Boundary Work, Digital Work, Information Systems

AI Processed

How do activity levels and connection counts shape the quality of employee interactions on corporate social networks?

Christian Meske, Iris Junglas, Matthias Trier, Johannes Schneider, Roope Jaakonmäki, Jan vom Brocke

Capturing the “Social” in Social Networks: The Conceptualization and Empirical Application of Relational Quality

Journal of the Association for Information Systems · 2025

Key findings

Relational quality is a distinct and relevant dimension that complements traditional structural social network analysis (SNA), which typically only focuses on network structure.
The study identifies and measures four key facets of relational quality: being personal, being curious, being polite, and sharing.
Different types of users exhibit distinct patterns of relational quality; for instance, 'connectors' (users with many connections but low activity) are the most personal, while 'broadcasters' (users with high activity but few connections) share the most resources.
As a user's activity (e.g., number of posts) increases, their interactions tend to become less personal, curious, and polite, while their sharing of resources increases.
In contrast, as a user's number of connections grows, their interactions become more personal and curious, but they tend to share fewer resources.

Enterprise Social Network, Social Capital, Relational Quality, Social Network Analysis, Linguistic Analysis, Computational Research

AI Processed

How can manufacturers successfully scale artificial intelligence solutions from pilot projects into full operational use?

André Sagodi, Benjamin van Giffen, Johannes Schniertshauer, Klemens Niehues, Jan vom Brocke

How Audi Scales Artificial Intelligence in Manufacturing

MIS Quarterly Executive · 2024

Key findings

Audi successfully scaled an AI-based system to automate the detection of cracks in sheet metal parts, a crucial quality control step in its press shops.
The success was driven by a strategic four-stage approach: Exploring, Developing, Implementing, and Scaling, with a focus on designing for scalability from the outset.
Key success factors included creating a single, universal AI model for multiple deployments, leveraging data from various sources to improve the model, and integrating the solution into the broader Volkswagen Group's digital production platform to create synergies.
The study highlights the importance of decoupling value from cost, which Audi achieved by automating monitoring and deployment pipelines, thereby scaling operations without proportionally increasing expenses.
Recommendations for other businesses include making AI scaling a strategic priority, fostering collaboration between AI experts and domain specialists, and streamlining operations through automation and robust governance.

Artificial Intelligence, AI Scaling, Manufacturing, Automotive Industry, Case Study, Digital Transformation, Quality Inspection

AI Processed Human Approved

Why does the same technology succeed in one organization but fail in another?

Abdul Sesay, Elena Karahanna, and Marie-Claude Boudreau

EMERGENCE OF IT IMPLEMENTATION CONSEQUENCES IN ORGANIZATIONS: AN ASSEMBLAGE APPROACH

MIS Quarterly · 2025

Key findings

The process through which technology creates new behaviors and practices is complex and non-linear, occurring in three distinct phases (individuation, composition, and actualization).
Successful implementation is not guaranteed; it depends on the careful alignment of the technology itself (material components) with policies, training, and user adoption (expressive components) at each stage.
The study found that of the three police departments, only one successfully implemented body cameras because it carefully selected high-quality equipment, developed specific policies for its use, and ensured officers were trained and held accountable.
The other two departments experienced failure or delays due to poor quality equipment, generic policies, and inconsistent use, which prevented new, positive practices from taking hold.
The model shows that outcomes emerge over time and may require continuous adjustments, demonstrating that success is an ongoing process, not a one-time event.

IT implementation, Assemblage theory, body-worn camera, organizational change, police technology, process model

AI Processed

How can governments and industry leaders create effective rules for emerging technologies without stifling innovation?

Stefan Seidel, Christoph J. Frick, Jan vom Brocke

REGULATING EMERGING TECHNOLOGIES: PROSPECTIVE SENSEMAKING THROUGH ABSTRACTION AND ELABORATION

MIS Quarterly · 2025

Key findings

Creating effective regulation for new technologies is a process of 'collective prospective sensemaking,' where diverse stakeholders build a shared understanding over time.
This process relies on two interrelated activities: 'abstraction' and 'elaboration'. Abstraction involves generalizing the essential properties of a technology to create flexible, technology-neutral rules that encourage innovation.
Elaboration involves specifying details and requirements to provide legal certainty and protect users.
Through this process, the regulatory target can evolve significantly, as seen in the case study's shift from regulating 'blockchain/cryptocurrency' to a broader, more durable law for the 'token economy' and 'trustworthy technology'.

Technology regulation, prospective sensemaking, sensemaking, institutional construction, emerging technology, blockchain, token economy

Showing all 70 podcasts