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IS Design Principles for Empowering Domain Experts in Innovation: Findings From Three Case Studies
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
Managing Performance in Collaborative Networks: A Method for Defining Key Performance Indicators for Collaborative Business Models
AI Processed Human Approved

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

Montijn van de Ven, Paola Lara Machado, Banu Aysolmaz, Oktay Turetken
Managing Performance in Collaborative Networks: A Method for Defining Key Performance Indicators for Collaborative Business Models
Business & Information Systems Engineering · 2026
Key findings
  • Introduced a three-step method covering KPI specification, multi-criteria KPI selection, and attribute operationalization.
  • Differentiated KPI scopes into network, subnet, and actor levels to balance network alignment with individual flexibility.
  • Demonstrated practical utility through business scenarios including car navigation services, urban indoor farming, and sustainable households.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a pilot project to apply the three-step KPI definition method to one specific collaborative network your organization participates in, focusing on identifying and documenting initial network-level KPIs using your existing data infrastructure.
  • IT Manager: On Monday morning, schedule a working session with a key business stakeholder from a collaborative project to begin the 'KPI specification' step of the research method, focusing on articulating the desired outcomes for that specific collaboration.
  • Business Strategist: On Monday morning, review the research's findings on differentiating KPI scopes and draft a preliminary list of potential network, subnet, and actor-level KPIs for a newly forming collaborative initiative.
  • Researcher: On Monday morning, begin designing a case study to apply and further refine the proposed KPI definition method within a new collaborative business scenario, focusing on the 'multi-criteria KPI selection' and 'attribute operationalization' steps.
  • Policymaker: On Monday morning, identify a specific public-private collaborative initiative and begin exploring how the research's approach to KPI definition could be adapted to measure its network-wide alignment and individual actor contributions effectively.
Business models, Collaborative business models, Collaborative networks, Key performance indicators, Performance management, Design science research
The Process of Process Science – Generating Actionable Insights from Digital Trace Data
AI Processed Human Approved

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

Jan vom Brocke, Sandro Franzoi, Sophie Hartl, Thomas Grisold
The Process of Process Science – Generating Actionable Insights from Digital Trace Data
2026
Key findings
  • Defines three core methodological principles for process science: contextual grounding, iterative reasoning, and the integration of computational and human sense-making.
  • Proposes three interconnected, recursive cycles of inquiry: a descriptive cycle (from scope via data to description), an explanatory cycle (from analysis via pattern to explanation), and a prescriptive cycle (from intervention via evaluation to prescription).
  • Demonstrates the practical execution of these cycles through a real-world case study of a financial institution's digital customer onboarding process.
  • Provides concrete guidance for researchers and practitioners on combining qualitative insights with computational techniques to move from descriptive analytics to actionable organizational interventions.
What it means for you
  • CIO / IT Executive: Schedule a 30-minute meeting with your IT Manager for Monday morning to initiate the 'descriptive cycle' by identifying a specific digital process (e.g., customer support ticket resolution) and confirming the availability of its digital trace data for analysis.
  • IT Manager: On Monday morning, begin by downloading and organizing the raw digital trace data logs (e.g., timestamps, event names, user IDs) for the chosen IT process from your systems and prepare to import them into a process mining tool.
  • Business Strategist: On Monday morning, prepare a concise list of 3-5 key business questions you want to understand about a specific digital process (e.g., 'Why is our customer onboarding taking so long?') to inform the 'explanatory cycle'.
  • Researcher: On Monday morning, review the provided case study from the research paper and begin to outline a potential scope for a process science study based on a digital process you have access to data for, focusing on defining the initial research questions.
  • Policymaker: On Monday morning, request a brief overview from your IT Executive and Business Strategist regarding the current state of understanding and identified inefficiencies within a key digital customer-facing process, preparing to guide future intervention strategies.
Process Science, Methodology, Iteration, Process Mining, Digital Trace Data, Socio-Technical Processes
THE VOICE OF COMMERCE: HOW SMART SPEAKERS RESHAPE DIGITAL CONTENT CONSUMPTION AND PREFERENCES
AI Processed Human Approved

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

Yoonseock Son, Wonseok Oh, Il Im
THE VOICE OF COMMERCE: HOW SMART SPEAKERS RESHAPE DIGITAL CONTENT CONSUMPTION AND PREFERENCES
MIS Quarterly · 2023
Key findings
  • Smart speaker adoption led to a 21.5% increase in monthly VOD content expenditures, alongside a 3.0% decrease in average content completion rates.
  • The increase in purchases is driven by lowered search friction, resulting in more search incidences, higher conversion rates, and less labor-intensive evaluation of content.
  • Using smart speakers for conversational purposes beyond simple informational queries amplifies the positive impact of device adoption on sales.
What it means for you
  • CIO / IT Executive: On Monday morning, task your team with investigating integration points between existing VOD platforms and major smart speaker voice assistant APIs (e.g., Alexa, Google Assistant) to enable seamless content discovery and playback via voice commands.
  • IT Manager: On Monday morning, analyze the current VOD platform's data infrastructure to identify how to track and log voice-initiated search queries and content playback events, distinguishing them from traditional remote control interactions.
  • Business Strategist: On Monday morning, initiate a brainstorm session to explore new content promotion strategies that leverage voice-activated search, considering how to make titles more discoverable through natural language queries and conversational recommendations.
  • Researcher: On Monday morning, begin outlining a follow-up study to quantify the correlation between specific conversational voice interactions (beyond simple search) and increased VOD content consumption, perhaps through A/B testing of voice-guided discovery features.
  • Policymaker: On Monday morning, convene a working group to assess the potential implications of increased voice-activated content consumption on digital accessibility standards and to explore how to ensure equitable access to content across all user interfaces.
Voice channel, search, smart speaker, digital content, econometrics
Boundary Spanning and Practical Impact in IS Research: A Bourdieusian Analysis
AI Processed Human Approved

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

Stephen McCarthy, Hendrik Scholta, Geir Inge Hausvik, Peter André Busch
Boundary Spanning and Practical Impact in IS Research: A Bourdieusian Analysis
Information Systems Journal (ISJ) · 2025
Key findings
  • IS practitioner doctorates facilitate knowledge exchange through boundary-spanning activities including research design adaptation, multi-channel dissemination, and creating symbolic discourse.
  • Practical impact progresses through proof-of-concept, proof-of-value, and proof-of-use stages, each requiring different combinations of social, cultural, economic, and symbolic capital.
  • Environmental forces such as academic vs. practice doxa (institutional norms) and habitus (professional dispositions) create positional conflicts that require active negotiation and tailored incentives to sustain engagement.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute meeting with your direct reports to initiate a 'practice impact review' focused on one ongoing IS project. Ask each person to identify one specific academic insight or research finding from a recent external study or internal research initiative that could be directly applied to improve that project's current proof-of-concept or proof-of-value stage, and assign them to explore this application by end of week.
  • IT Manager: On Monday morning, identify one specific, recent academic paper or conference proceeding relevant to a current team challenge. Forward this paper to your team with a concise note asking for a brief, 1-2 sentence summary of its most practical takeaway and how it *might* apply to your current proof-of-value stage objective by Tuesday afternoon.
  • Business Strategist: On Monday morning, review the key performance indicators (KPIs) of one strategic business initiative currently underway. Identify which KPIs are not meeting expectations and, using the 'proof-of-value' framework, draft a one-page hypothesis on how a novel IS application or process, potentially informed by academic research, could demonstrably improve these specific KPIs within the next quarter.
  • Researcher: On Monday morning, select one recent academic publication where you are the lead author and identify its 'proof-of-use' stage potential. Draft a single-slide executive summary that articulates the core problem addressed, the practical solution proposed, and one tangible benefit for a specific industry practitioner role (e.g., IT manager, business analyst), and share it with three relevant practitioner contacts for feedback by Wednesday.
  • Policymaker: On Monday morning, review the current grant application criteria for technology innovation funding. Identify if and how 'demonstrable practical impact' is explicitly assessed, and draft a brief proposal to incorporate a specific requirement for applicants to detail their strategy for achieving 'proof-of-value' or 'proof-of-use' within a defined timeframe, alongside theoretical contributions.
academia-practice gap, boundary spanning, design science research, engaged IS scholarship, practical impact, theory of practice
Process science: the interdisciplinary study of socio-technical change
AI Processed Human Approved

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

Jan vom Brocke, Wil M. P. van der Aalst, Nicholas Berente, Boudewijn van Dongen, Thomas Grisold, Waldemar Kremser, Jan Mendling, Brian T. Pentland, Maximilian Roeglinger, Michael Rosemann, Barbara Weber
Process science: the interdisciplinary study of socio-technical change
Process Science · 2024
Key findings
  • Defines process science as an interdisciplinary domain aimed at capturing, understanding, and intervening in socio-technical change across multiple levels of abstraction.
  • Categorizes key activities of process science into discovery (detecting patterns via event data), explanation (identifying cause-effect relations), and intervention (shaping process trajectories).
  • Advocates for shifting from an 'entity-first' to a 'process-first' perspective to identify analogies across diverse domains and support computationally intensive theory building.
  • Introduces a research framework that integrates digital trace data from practice with theoretical foundations to solve real-world problems and drive societal impact.
What it means for you
  • CIO / IT Executive: On Monday morning, initiate a review of your organization's current digital infrastructure and data collection capabilities to identify which systems generate 'digital trace data' relevant to socio-technical processes. Prioritize understanding the flow of this data and its potential for analysis.
  • IT Manager: On Monday morning, investigate your team's current methods for logging and tracking user interactions with key IT systems. Identify specific 'events' that could be captured and analyzed to reveal patterns of socio-technical change within your department.
  • Business Strategist: On Monday morning, schedule a brainstorming session with your core team to identify one specific business process currently undergoing significant change (e.g., customer onboarding, product development). Brainstorm what digital 'trace data' this process generates and how understanding its dynamics could inform strategy.
  • Researcher: On Monday morning, start cataloging existing datasets within your research domain that represent 'digital traces' of human-technology interaction. Begin exploring computational tools that can analyze event sequences rather than just static entities.
  • Policymaker: On Monday morning, identify a specific public policy area (e.g., digital inclusion, citizen engagement) that is experiencing rapid socio-technical evolution. Begin researching existing digital platforms and services that generate trace data related to this policy area.
Process Science, Socio-Technical Processes, Digital Trace Data, Process Mining, Organizational Change, Interdisciplinary Research
TIME TO TURN OFF THE LIGHTS: A SUCCESS FACTOR FRAMEWORK FOR ADVANCING HYPERAUTOMATION IN ORGANIZATIONS
AI Processed Human Approved

What are the key factors needed to successfully scale automation across an organization?

Lisa Straub, Christian Zeiß, Ulvi Ibrahimli, Christoph Tomitza, Benedikt Wirsing, Axel Winkelmann
TIME TO TURN OFF THE LIGHTS: A SUCCESS FACTOR FRAMEWORK FOR ADVANCING HYPERAUTOMATION IN ORGANIZATIONS
ECIS 2026 · 2026
Key findings
  • The survey results statistically supported all 15 success factors within the framework, achieving a high average agreement score of 6.05 out of 7, with 93.2% of respondents selecting positive agreement.
  • 'Leadership Commitment' (SF3) and 'Communication and Trust Building' (SF4) received the highest levels of overall agreement, indicating strong statistical support.
  • 'Strategic Partner Collaboration' (SF15) and 'Customer Integration' (SF13) received statistically supported relevance, although they had comparatively lower overall agreement scores.
  • The study's generalizability is limited by the German context of the initial qualitative interviews, a modest quantitative survey sample size (n = 103), and its reliance on subjective assessments rather than objective performance metrics.
What it means for you
  • CIO / IT Executive: On Monday morning, schedule a 30-minute alignment meeting with business unit heads to establish a joint 'Hyperautomation Governance Board' to audit all active shadow-IT automation tools (like RPA and low-code apps) and map them against your long-term IT integration roadmap to prevent fragmented technical debt.
  • IT Manager: On Monday morning, block out two hours to audit your current automation deployment backlog and draft a standardized 'API-First Integration Checklist' that your development team must use to ensure new automations do not bypass APIs and create system incompatibilities.
  • Business Strategist: On Monday morning, select one high-volume customer-facing workflow and schedule a scoping call with a key strategic partner to co-design an integrated, automated data-sharing process, directly applying the 'Customer Integration' (SF13) and 'Strategic Partner Collaboration' (SF15) success factors.
  • Researcher: On Monday morning, draft a research proposal and outreach email to target three non-German, US-based enterprises to secure pilot sites for a comparative case study that measures objective operational KPIs (such as transaction error rates and processing speed) to validate the subjective findings of the 15-factor framework.
  • Policymaker: On Monday morning, draft an amendment to your organization's IT Procurement Policy mandating that any new business unit purchasing low-code or RPA tools must submit a 'Systemic Compatibility and Trust Impact Assessment' addressing how the tool aligns with the enterprise IT architecture and employee trust-building guidelines.
Hyperautomation, Transformation Management, Organizational Change, Success Factor Framework, Affordance Theory
ARE THE REGULATORS WINNING THE FIGHT WITH ANONYMITY-ENHANCING COINS? STUDYING BINANCE’S EUROPEAN DELISTING OF MONERO AS A NATURAL EXPERIMENT
AI Processed Human Approved

How does delisting a privacy coin from a major exchange impact its price over time?

Robin Renwick, Rob Gleasure
ARE THE REGULATORS WINNING THE FIGHT WITH ANONYMITY-ENHANCING COINS? STUDYING BINANCE’S EUROPEAN DELISTING OF MONERO AS A NATURAL EXPERIMENT
ECIS 2026 · 2026
Key findings
  • Hypothesis 1 (delisting will cause an immediate negative price impact) was STATISTICALLY SUPPORTED: Monero's price was significantly lower (estimated at 63% to 72% lower) post-delisting compared to its synthetic counterfactual.\n- Hypothesis 2 (the negative price impact will diminish over time) was NOT SUPPORTED: Rather than recovering, the negative price impact intensified when longer time lags of up to 200 days were examined.\n- Limitations: The findings are restricted to a single delisting event of one specific privacy coin (Monero) and use trading price as an imperfect proxy for perceived value.
What it means for you
  • CIO / IT Executive: Audit our organization's digital asset holdings and treasury to identify any exposure to privacy coins like Monero, and immediately initiate a migration plan to convert these assets to compliant, liquid cryptocurrencies to avoid permanent valuation loss from regulatory delistings.
  • IT Manager: Update network monitoring tools and firewall rules to detect and block any Monero nodes, mining scripts, or wallet connections on company-owned infrastructure to prevent compliance violations arising from tightening centralized exchange regulations.
  • Business Strategist: Review our payment gateway integrations and product roadmap to remove Monero or other privacy coins as accepted payment methods, and pivot our privacy-focused initiatives toward compliant layer-2 scaling solutions (like Zero-Knowledge rollups) that satisfy regulators.
  • Researcher: Draft a new research proposal to apply the synthetic control method to other privacy-centric assets (like Zcash or Dash) across different geographic regions, incorporating decentralized exchange (DEX) trading volumes to test if the value shifted off centralized order books.
  • Policymaker: Draft an amendment to the national crypto-asset regulatory framework to mandate that all registered centralized exchanges delist anonymity-enhancing coins, citing this empirical evidence that exchange-level bans are highly effective at permanently reducing these assets' market value and liquidity.
Cryptocurrencies, Monero, Privacy, Regulation, Natural Experiment, Synthetic Control Method
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
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
EMERGENCE OF IT IMPLEMENTATION CONSEQUENCES IN ORGANIZATIONS: AN ASSEMBLAGE APPROACH
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
REGULATING EMERGING TECHNOLOGIES: PROSPECTIVE SENSEMAKING THROUGH ABSTRACTION AND ELABORATION
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
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