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 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 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 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 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).

This study conducts a systematic literature review of 23 academic articles to consolidate and analyze research on the individual level of process mining. The authors synthesize the fragmented literature to map out how individual behaviors, usage patterns, and emotional affect interact with various contingency factors to drive business value. Problem Prior process mining research has predominantly focused on technical algorithms or organizational-level adoption, leaving the critical role of individual users largely overlooked. This gap results in fragmented terminology and lack of a coherent understanding of how individual actions and decision-making actually translate into realized business value. Outcome - 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.

This study introduces and validates a concept called "relational quality" to better understand the social dynamics within online networks beyond just connection counts. By analyzing over 440,000 messages from two large corporate social networks, the researchers developed four measurable markers—being personal, curious, respectful, and sharing—to capture the richness of online relationships. Problem Traditional analysis of social networks focuses heavily on structural aspects, such as who is connected to whom, but often overlooks the actual quality and nature of the interactions. This creates a research gap where the 'social' element of social networks is not fully understood, limiting our ability to see how online relationships create value. This study addresses this by developing a framework to conceptualize and measure the quality of these digital social interactions. Outcome - 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.

This study presents a case study of Watkins Steel, an Australian small and medium-sized enterprise (SME), detailing its successful digital transformation from a traditional steel fabricator to a digital services provider. It introduces and analyzes two key strategic concepts, 'augmentation' and 'adjacency', as a framework for how SMEs can innovate and add new revenue streams without abandoning their core business. Problem While digital transformation success stories for large corporations are common, there is a significant lack of practical guidance and documented examples for small and medium-sized enterprises (SMEs). This gap leaves many SMEs unaware of the potential of digital technologies and constrained by organizational inertia, hindering their ability to innovate and remain competitive. Outcome - Watkins Steel successfully transitioned by augmenting its core steel fabrication business with new, high-value digital services like 3D scanning, modeling, and data reporting.
- The study proposes a transformation framework for SMEs based on two concepts: 'digital augmentation' (adding new services) and 'digital adjacency' (leveraging existing assets like customers, data, and skills for these new services).
- Key success factors included contagious leadership from the CEO, embracing business constraints as innovation opportunities, and a customer-centric approach to solving their clients' problems.
- Instead of hiring new talent, Watkins Steel successfully cultivated its own digital experts by empowering existing employees with domain knowledge to learn new skills, fostering a culture of experimentation.
- The transformation allowed the company to move up the value chain, from being a materials provider to coordinating and managing construction processes, creating a more defensible market position.

This paper presents a case study on 'freeyou,' the digital innovation spinoff of a major German insurance company. It examines how the company successfully transitioned its online-only car insurance product from an initial 'exploring' phase to a profitable 'scaling' phase. The study highlights the necessary shifts in IT approaches, organizational structure, and data analytics required to manage this transition. Problem Many digital innovations fail when they move from the idea validation stage to the scaling stage, where they need to become profitable and handle large volumes of users. This study addresses the common IT-related challenges that cause these failures and provides practical guidance for managers on how to navigate this critical transition successfully. Outcome - Prepare for a significant cultural shift: Management must explicitly communicate the change in focus from creative exploration and prototyping to efficient and profitable operations to align the team and manage expectations.
- Rearchitect IT systems for scalability: Systems built for speed and flexibility in the exploration phase must be redesigned or replaced with robust, efficient, and reliable platforms capable of handling a large user base.
- Adjust team composition and skills: The transition to scaling requires different expertise, shifting from IT generalists who explore new technologies to specialists focused on process automation, data analytics, and stable operations. Companies must be prepared to bring in new talent and restructure teams accordingly.

This paper presents a case study on how the automotive manufacturer Audi successfully scaled an artificial intelligence (AI) solution for quality inspection in its manufacturing press shops. It analyzes Audi's four-year journey, from initial exploration to multi-site deployment, to identify key strategies and challenges. The study provides actionable recommendations for senior leaders aiming to capture business value by scaling AI innovations. Problem Many organizations struggle to move their AI initiatives from the pilot phase to full-scale operational use, failing to realize the technology's full economic potential. This is a particular challenge in manufacturing, where integrating AI with legacy systems and processes presents significant barriers. This study addresses how a company can overcome these challenges to successfully scale an AI solution and unlock long-term business value. Outcome - 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.

This study examines how various actors, including legal experts, government officials, and industry leaders, collaborated to create laws for new technologies like blockchain. Through a case study in Liechtenstein, it analyzes the process of developing a law on "trustworthy technology," focusing on how the participants collectively made sense of a complex and evolving subject to construct a new regulatory framework. Problem Governments face a significant challenge in regulating emerging digital technologies. They must create rules that prevent harmful effects and protect users without stifling innovation. This is particularly difficult when the full potential and risks of a new technology are not yet clear, creating regulatory gaps and uncertainty for businesses. Outcome - 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'.