This study investigates how digital ventures create and adapt their internal business processes to support rapid firm growth. Utilizing a qualitative multiple case study design across seven digital ventures, the authors analyze process changes in key operational areas such as sales, recruitment, and product management. Problem Although business process management literature recognizes that digital technology can facilitate business process changes, the specific technology characteristics that enable these adaptations remain underexplored. Moreover, fast-growing digital ventures face severe operational scaling challenges that require continuous process flexibility and structure. Outcome - Identified four distinct patterns of business process change in digital ventures: creating minimum viable processes, creating encapsulated processes, creating centralized control flow integrations, and creating centralized data flow integrations.
- Revealed that digital ventures typically start with flexible, human-orchestrated processes supported by low-specificity tools before migrating to specialized enterprise systems to boost efficiency.
- Showed how proxy communication tools and centralized analytics systems are used to reduce system complexity and eliminate data silos in maturing ventures.

This study evaluates the depth and quality of domain-specific knowledge in ChatGPT (GPT-3.5) within the field of Information Systems (IS). Using an established IS competency framework, the researchers analyzed ChatGPT's performance on over 3,000 questions gathered from university IS courses and professional certification exams. Problem While Generative AI tools demonstrate impressive general academic abilities, their specific knowledge accuracy across specialized fields like Information Systems remains uncertain. Educators and industry professionals lack clear benchmarks regarding whether ChatGPT can reliably produce accurate IS content, pass professional certifications, or answer complex technical queries without hallucinating. Outcome - ChatGPT answered 65% to 85% of Information Systems queries correctly across all core framework areas, performing roughly at the level of an average student.
- The model performed significantly better on university course exams than on professional certification exams, failing to reach passing scores for the PMP and Microsoft Azure Administrator tests.
- ChatGPT achieved higher relative accuracy against class averages on closed-book exams compared to open-book or online exam environments where students had external resource access.
- While ChatGPT displayed broad coverage across diverse IS topics, its responses favored listing structured information over demonstrating expert-level critical thinking or complex problem-solving.

This study examines the economic impact of cryptocurrency regulation by analyzing Binance's February 2024 delisting of the privacy coin Monero. Utilizing the synthetic control method, the researchers construct a counterfactual scenario to evaluate short- and medium-term price effects. They complement this quantitative analysis with qualitative, semi-structured interviews with community stewards. Problem Regulators are placing strict compliance pressures on centralized exchanges to delist anonymity-enhancing coins, yet it remains highly debated whether these regulatory actions effectively reduce the value of these privacy-centric assets. Additionally, there has been limited empirical evidence on how such delisting events alter consumer choices and asset valuations over time. Outcome - 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.

This qualitative case study utilizes Actor-Network Theory to investigate how corporate actors shape and negotiate digital sustainability initiatives. By examining a specialized working group within a German insurance company, the researchers analyze the interactions and power relations between sustainability and IT/IS departments across four translation moments. The study provides practical insights on governance mechanisms to stabilize collaborative actor networks. Problem Organizations struggle to manage digital sustainability initiatives because they require aligning the distinct strategies, competencies, and interests of IT and sustainability departments. This tension often leads to misalignment, inefficiencies, and competing demands that hinder successful project implementation. Additionally, there is a lack of empirical understanding regarding how these diverse actors negotiate and shape digital sustainability governance in practice. Outcome - The case study supported that sustainability management acts as a stabilizer by establishing structural frameworks, securing senior executive backing, and fostering interdepartmental knowledge sharing.
- The analysis supported that IT/IS management often acts as a destabilizing force by imposing resource limitations, prioritizing short-term operational goals, and withholding full active engagement.
- The research demonstrated that the corporate digital sustainability actor-network remains unstable due to unresolved conflicts between sustainability goals and IT efficiency metrics.
- A limitation of these findings is their reliance on a single-case study of a German insurance company with an interview sample dominated by IT-department stakeholders.

This qualitative study investigates how organizations evaluate and establish readiness for artificial intelligence (AI) initiatives before formally launching them. The authors analyze the usage of a collaboratively authored document, the Design Challenge Brief (DCB), across five AI innovation projects to observe how it structures pre-project planning. Problem Many AI initiatives fail because standard project frameworks cannot adequately address the unique uncertainties of AI, such as fluid data availability and non-deterministic system outcomes. Currently, there is little research explaining how diverse team members successfully navigate these challenges to determine when a project is mature enough to begin. Outcome - The study outlines a preliminary three-phase qualitative model for establishing AI project readiness: divergent exploration, negotiation/translation, and convergent alignment, though these findings are descriptive and lack statistical testing.
- The Design Challenge Brief is shown to act as a gatekeeping artifact that structures readiness reviews and allows teams to selectively revisit earlier project assumptions without resetting the entire process.
- The study is limited by its small qualitative sample of five projects and its setting within a structured, university-led innovation lab, which may restrict generalizability to less-structured corporate settings.

This study reports on a nine-month action design research project that developed a collaborative human-AI forecasting system for corporate cafeteria operations. The researchers combined machine learning models with large language models to categorize dishes semantically, while integrating human override features to maintain chef autonomy. Problem Cafeterias generate substantial food waste due to inaccurate demand planning, which is traditionally treated as either a purely automated AI problem or an isolated human task. Traditional forecasting models struggle with the cold-start problem when new dishes are introduced, and automated models fail to adjust for local contextual exceptions known only to managers. Outcome - The algorithmic machine learning model achieved a 30% reduction in forecasting errors compared to a naive 7-day lag baseline.
- The hypothesis that the algorithmic model would statistically outperform experienced human planners across all menu items was not supported, as direct statistical comparisons showed no overall significant difference (p = 0.454).
- Experienced human planners statistically outperformed the algorithmic model for novel menu items with limited historical data (MAE of 10.46 for humans versus 17.00 for the algorithm), supporting the hypothesis that human judgment remains superior for high-uncertainty contextual forecasting.
- Acknowledgeable limitations of this research include its single-case design at a German firm, a small quantitative evaluation scope of 24 operating days, and qualitative insights derived from two think-aloud sessions.

This qualitative multiple-case study examines how the integration of generative AI into daily practices reconfigures routines in professional service firms. Analyzing 31 semi-structured interviews across two European management consultancies, the study develops a multi-level framework linking micro-level routine changes to broader industry-level shifts. Problem While generative AI is rapidly transforming knowledge-intensive work, current research remains fragmented across different levels of analysis, leaving its broader impacts poorly understood. Specifically, there is a lack of explanation regarding how local, day-to-day adaptations of routines aggregate to reshape industry-wide practices and value creation logics. Outcome - 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.

This study designs and evaluates a semantic mediation layer to automatically resolve imperfect natural-language queries from business users. The authors compare two architectural variants: schema-based mediation utilizing semantic similarity and knowledge graph (KG)-based mediation constructed from database schemas. The evaluation is performed through a simulation using real-world interaction logs and validated using human annotators and LLM-as-a-Judge frameworks. Problem Business users frequently submit brief, incomplete, and structurally deficient prompts containing domain-specific terminology rather than executable database instructions. Standalone large language models fail to align these imperfect inputs with underlying database schemas, leading to inaccurate queries and incorrect data retrieval. Prior work relying on manual prompt engineering and few-shot examples is highly labor-intensive and fails to scale effectively. Outcome - 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.

This paper conducts an exploratory, embedded case study of 'BankIT,' a major European IT service provider, to examine concurrent digital and sustainability transformations. Drawing on paradox theory, the researchers investigate how organizations experience and navigate the competing demands that arise during this 'twin transition.' Problem Although digital and sustainability goals are frequently portrayed as highly complementary, empirical research on how these dual agendas conflict in everyday practice is scarce. Organizations struggle to move beyond high-level alignment rhetoric because they lack concrete understanding of the persistent, non-negotiable tensions that actors face on the ground. Outcome - The study qualitatively identifies four persistent tensions in the twin transition: cost efficiency vs. sustainability impact, IT as enabler vs. IT as concern, individual values vs. organizational inertia, and fast IT iteration vs. slow sustainability institutionalization, though these are exploratory and not statistically tested.\n- The findings reveal that actors manage these paradoxes through a portfolio of splitting, suppressing, adjusting, and opposing responses, which often remain fragmented rather than coordinated.\n- Successful navigation was shown to occur gradually through sequencing and formalizing responses (such as in UI/UX accessibility standards), indicating that twin transition synergies are an outcome of continuous paradox management rather than a starting condition.\n- The study's findings are limited by its exploratory nature and single-case, cross-sectional design at one European IT service provider, meaning they are not statistically generalizable.

This study conducts a meta-synthesis of design knowledge across 99 empirically grounded studies on artificial intelligence applications in the circular economy. Using a structured coding protocol and an AI-assisted extraction pipeline, the authors generalize and synthesize individual design principles into four overarching meta-principles. The research provides a two-layered architecture that links micro-level AI model choices with macro-level organizational capabilities. Problem While numerous engineering studies demonstrate artificial intelligence applications in circular economy practices, the embedded design knowledge remains fragmented and highly context-specific. Practical implementers lack generalizable principles to guide design choices, weigh trade-offs, and understand the socio-technical factors that influence successful adoption. This gap limits the systematic accumulation of design knowledge and hinders organizations from translating technical recipes into actionable strategies. Outcome - 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.

This study is a systematic literature review of 70 papers examining how individuals construct their virtual identities through avatar customization in the metaverse. It integrates psychological theories, specifically the Big Five personality traits, with technological affordances to explain digital representation across multiple disciplines. Problem Prior research on avatar design and digital identity construction remains highly fragmented across fields like computer science, psychology, and business. This disciplinary division creates conceptual ambiguity and prevents a comprehensive understanding of how users' real-world personality traits interact with metaverse-specific platform features. Outcome - Conscientious individuals systematically tend to design avatars that closely resemble their physical, real-world selves, reflecting their responsible and organized personality traits (statistically supported across synthesized literature).
- Users with high levels of openness are statistically more likely to select radical and experimental avatar designs (statistically supported across synthesized literature).
- Extraversion systematically predicts the choice of socially oriented avatars with high visibility and outgoing styles, whereas agreeableness exhibits only a weak, non-significant relationship with avatar design choices (statistically supported across synthesized literature).
- Neuroticism demonstrates mixed effects on avatar choices, sometimes resulting in greater self-avatar discrepancy and other times in idealized digital representations, especially among women (mixed results explicitly noted in literature).
- High-fidelity technological immersion (e.g., through VR headsets and motion tracking) systematically increases presence and embodiment, which in turn drives identity salience and stronger user-avatar behavioral alignment (statistically supported across synthesized literature).
- The findings are limited by a reliance on English-only publications and a dataset largely containing studies on older virtual environments (e.g., Second Life) rather than modern, integrated metaverses.

This study explores how ecosystem orchestrators navigate paradoxical tensions within national quantum computing innovation (QCI) ecosystems. Using an explorative, interpretive case study of the Finnish QCI ecosystem, the authors analyze qualitative data gathered from 15 semi-structured interviews with diverse ecosystem stakeholders. Problem While ecosystem orchestration is vital for technological innovation, existing research has primarily focused on industry-led, single-firm B2B ecosystems. Consequently, there is a lack of understanding regarding how paradoxical tensions manifest and challenge orchestration in cross-sector, government-led ecosystems like those in the emerging quantum computing sector. Outcome - 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.

This study applies a Design Science Research approach to develop and evaluate the 'Heat Companion,' a context-aware participatory sensing system that integrates wearable environmental sensors, a mobile application, and on-device adaptive feedback. Grounded in Prosumer Theory and the Just-in-Time Adaptive Interventions framework, the system aims to enable citizens to collect micro-climate data while receiving real-time, context-specific recommendations. The artifact's feasibility and usability were evaluated through a two-week field study with eight participants in Karlsruhe, Germany. Problem Traditional meteorological infrastructures fail to capture local micro-climate variations and individual heat exposure. While participatory sensing can address this data gap, maintaining long-term citizen engagement is difficult because contributors often experience participation fatigue or perceive a lack of direct personal benefit. Consequently, there is a lack of prescriptive design knowledge on how to sustain reciprocal value exchange and interaction in volunteer-driven environmental sensing. Outcome - The Heat Companion system demonstrated technical reliability, capturing micro-climate variations with an average temperature deviation of +1.9 °C and relative-humidity deviation of -6.9% compared to the nearest official weather stations.
- High system engagement was observed during the field study, with participants opening 93% of the 690 generated recommendations and responding with a median time of 14.2 seconds.
- Engagement and compliance varied across recommendation domains, with short, actionable prompts like 'Hydration & Cooling' achieving a 33.6% follow rate, while broader domains like 'Social Care' had a 5% follow rate.
- Perceived relevance of feedback was higher during warmer afternoon hours (median rating of 4.0 out of 5) than in the morning (median rating of 2.0 out of 5), suggesting relevance aligns with actual heat exposure.
- Due to the small sample size (8 participants) and exploratory feasibility design, no inferential statistical tests were conducted to confirm the statistical significance of the behavioral changes, which remains a key limitation.

This study details the development and preliminary pilot of a design-science-based pedagogical framework to teach Industry 4.0 competencies to undergraduate Computer Science and Information Systems students. The authors proposed four design requirements and eight design principles, which were implemented in a 3 ECTS elective course. Problem Industry 4.0 requires specialized competencies in digital and software infrastructures that are currently missing from standard computer science and information systems curricula. Existing training environments, like physical learning factories, are cost-prohibitive and overly focused on physical manufacturing equipment rather than the software and data architectures relevant to CS/IS graduates. Outcome - 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.

This online between-subjects experiment (n = 136) examines how access to ChatGPT and additional prompting guidance affect task performance, cognitive load (mental and temporal demand), and psychological ownership in complex problem-solving tasks. The research analyzes whether integrating AI enhances productivity while introducing cognitive and motivational trade-offs. Problem While generative AI tools improve short-term productivity, their integration into knowledge work may undermine human agency and responsibility. It is critical to understand how performance gains are achieved at a cognitive and psychological level, particularly whether outsourcing tasks erodes workers' sense of authorship and motivation. Outcome - 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.

This qualitative study conceptualizes the 'Design Challenge' as a boundary object designed to initiate and guide AI innovation in interdisciplinary teams. The researchers performed a qualitative cross-case analysis of 42 corporate AI innovation projects using a theory-guided comparative artifact analysis method. Problem Organizations frequently struggle to launch AI projects due to the technological complexity, friction between interdisciplinary teams, and the emergent nature of AI capabilities. Without a structured starting framework, teams often experience misalignment, set unrealistic expectations, or prematurely commit to rigid technical architectures that fail. Outcome - 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.

This study presents a 16-month qualitative ethnographic case study of two childcare centers in Germany to analyze how digital management platforms reshape emotional and organizational workflows. By observing interactions among educators, parents, and administrators, the researchers track the evolution of administrative tools into systems of emotional dependency. Problem Although digital management systems are introduced to enhance efficiency and transparency, they can create unintended emotional pressures and constant surveillance. There is a lack of understanding regarding the specific socio-technical mechanisms through which digital reporting reconfigures interpersonal trust and professional autonomy in relational work environments. Outcome - 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.

This study investigates how digital transformation (DT) alters organizational identity within the nonprofit sector through an interpretive, longitudinal case study of Greenpeace International. The researchers analyze over 400 pages of observation notes, 10 semi-structured interviews, focus groups, and 200 pages of strategic documents to map out the digital transformation process. The study develops a five-period recursive framework that outlines the cyclical relationship between technological shifts and values-driven identity. Problem Prior Information Systems literature theorizes digital transformation as a force that redefines organizational identity, but this research has been conducted almost exclusively on for-profit firms. Nonprofits operate under different, non-commercial logics with highly stable, values-based identities, leaving a research gap regarding how they navigate the tension between digital adoption and core mission preservation. This study addresses how digital transformation changes a nonprofit's organizational identity and how identity conflicts are resolved in this unique context. Outcome - Digital transformation in nonprofits is a cyclical, five-period recursive process (comprising internal alignment, strategy production, democratization, implementation, and resistance/re-anchoring) rather than a linear transition (Supported by qualitative case analysis).
- Unlike for-profit firms where digital transformation fundamentally redefines identity, a nonprofit's core values-based identity remains stable, whereas only the methods of how they work are challenged and reconfigured (Supported by qualitative case analysis).
- Transitioning from foundation-setting to a proactive digital role triggers organizational resistance and identity dissonance, as new digital structures and departments challenge existing activist culture and localized power dynamics (Supported by qualitative case analysis).
- Momentum in a nonprofit's digital transformation is re-established not by resolving technical deficits, but by re-anchoring the digital strategy within a moral narrative against a common value-based antagonist (Supported by qualitative case analysis).
- The study's generalizability may be limited due to its focus on a single, large-scale, globally federated nonprofit, and the patterns of resistance might vary in smaller or less decentralized organizations (Acknowledged study limitation).