In an era of rapid technological disruption, organizations must increasingly balance the exploitation of existing capabilities with the exploration of innovative business models and technologies, a duality known as ambidexterity. While prior research has largely focused on ambidexterity at the organizational and individual levels, its operationalization at the team level, particularly within project contexts, remains underexplored. 

Our recently published study addresses this gap by designing and evaluating a decision-support tool that enables structured, ambidexterity-informed staffing of project teams. The tool supports the scoping of business transformation projects, assesses the required balance between exploration and exploitation across different phases, and recommends team compositions aligned with these needs. Following a Design Science Research approach, the artifact was iteratively developed and refined through expert interviews, practitioner workshops, and a scholarly focus group. Additionally, we derive three design principles to guide the effective staffing of ambidextrous project teams and to support the development of information systems in ambidextrous contexts. 

Our study advances ambidexterity research by identifying micro-mechanisms and phase-contingent requirements, linking these to concrete skill profiles through validated measurement, and demonstrating how organizations can embed evidence-based ambidextrous staffing into existing processes.

I am happy that our paper "Balancing Exploration and Exploitation—Designing a Staffing Tool to Leverage Ambidextrous Team Compositionshas" has been accepted for publication in the Schmalenbach Journal of Business Research and is now availabe online (open access).

The inter-organizational processes in procurement remain burdened by media discontinuity, inefficiencies, and a lack of trust among trading partners. Blockchain-based information systems are frequently proposed as a remedy because they enable shared, tamper-evident records. However, existing instantiations rarely scale beyond small consortia because they fail to address the extended blockchain trilemma, which requires simultaneously achieving decentralization, scalability, security, and strict privacy requirements for sensitive commercial information.

In contrast to prior blockchain procurement prototypes that manage the extended blockchain trilemma primarily through permissioned architectures, our recent study investigates how a blockchain-based information system can be designed to reconcile the trade-offs inherent in the extended trilemma, achieving a viable balance through architectural allocation and cryptographic enforcement. Following the design-science research paradigm, an empirically validated problem statement is synthesized from a structured literature review and expert interviews. Five design objectives are derived, evaluated, and used to guide a prototype design, which is then iteratively refined and evaluated through quantitative and formative assessments and sixteen semi-structured expert interviews.

Reflection on the build–evaluate cycles yields two design principles: (1) Balancing decentralization, scalability, and security by using a public chain as a trust anchor, a Layer 2 for scaling, and decentralized communication between layers. (2) Maintaining that balance when privacy is required by integrating efficient, resilient cryptography and minimizing control points. These principles extend existing procurement research, linking business requirements to infrastructural choices, providing a transferable foundation for scholars and practitioners aiming to deploy secure, scalable, and privacy-preserving blockchain solutions in inter-organizational contexts.

I am happy that our paper "Designing a blockchain-based information system for procurement processes—Balancing decentralization, scalability, and security while maintaining privacy" has been accepted for publication in Information Systems and is now availabe online (open access).

AI coding is not just a matter of tools – it is a strategic leadership task
Those who establish a structured, company-specific approach early on will gain sustainable competitive advantages – while those who experiment in a piecemeal fashion or simply copy others risk wasting time, causing organizational friction, and creating inefficiency.

At this week's event “AI and the Future of Work – Job Security, Meaning, Participation” at the Evangelische Akademie in Frankfurt, I had the privilege of giving the opening lecture entitled “Artificial Intelligence and the Future of Work.”

In my keynote speech, I began by putting the current dynamics of AI development into context. The systems are rapidly becoming more powerful and are already surpassing human capabilities in certain areas of application. At the same time, their use in companies is increasing significantly: more and more organizations are integrating AI into their processes, and its use in the workplace is growing noticeably.

This reveals a nuanced picture of the effects. At the individual level, AI is increasing employee productivity, in some cases significantly. At the company level, however, these efficiency gains have not yet been reflected across the board in measurable productivity increases. In addition, there are increasing indications of ambivalent effects, such as quality risks or technostress.

At the same time, the increasing use of AI is having a substantial impact on the labor market. Existing job profiles will change fundamentally in some cases, tasks will be redesigned, and requirements will be redefined. It will therefore be crucial to systematically build new skills – from a sound understanding of AI systems and data-based decision-making abilities to critical reflection and control skills in dealing with algorithmic results.

The public sector faces increasing pressure to drive digital innovation to meet modern societies’ demands. Existing literature calls for systematic approaches to digital innovation in the public sector and a better understanding thereof. However, the public sector still struggles to foster digital innovation and often fails to promote explorative initiatives as a basis for innovation.

In a recently published research study, using a case study of a German consortium project, we investigated how public-private partnerships can promote digital innovation in the public sector. We did so by adopting the resourcing perspective and building on recent conceptual work on network resourcing.

Our findings revealed that the unique characteristics of digital innovation, as opposed to traditional forms of innovation, influence how public-private partnerships can effectively drive digital innovation. We identified decentralized, cross-sector digital innovation clusters as a critical factor for the emergence of digital innovation in public-private partnerships and theorized dissemination practices as an extension to network resourcing in cases of distributed innovation agency within networks of public-private partnerships.

I am happy that our paper “Digital Innovation in the Public Sector: A Resourcing Perspective on How the Public Sector Collaborates with the Private Sector” has been accepted for publication in Information and Organization and is now available online.

Artificial intelligence (AI) has made its way into the everyday work of many organizations. Hardly any other technological development in recent decades has been adopted so quickly and at the same time been the subject of such controversial debate. While proponents expect enormous productivity gains and new forms of value creation, critics warn of job losses, de-skilling, and increasing stress for employees. As is so often the case, the reality lies somewhere in between.

In this article, I classify current empirical findings on the performance and use of AI, analyze its effects on individuals, organizations, and the labor market, and derive implications for companies and employees.

In the project hessian.AI.literacy, our Research Lab for Digital Innovation & Transformation (ditlab), together with the Research Lab for Law and applied Technologies (ReLLaTe), the Frankfurt University of Applied Sciences, Technische Universität Darmstadt, and hessian.AI, examined what Article 4 of the EU AI Act truly demands in terms of AI literacy – and how companies can operationalize these requirements in practice.

Our interdisciplinary team combined legal analysis with organizational and technological perspectives to clarify what AI literacy means and how firms can build the necessary structures, skills, and governance mechanisms. The result is a concrete, actionable interpretation of AI literacy obligations that can directly support organizations on their compliance and capability-building journey. All core insights, frameworks, and recommendations have been brought together in a comprehensive brochure.

Our paper „One Solution to fix them all: Does Decentralization fix the Problems of Social Media?” has been nominated for the Best Paper Award at ICIS 2025. Huge thanks and congratulations to my fantastic co-authors Diana Fischer-Preßler and Sara Alida Volkmer – this recognition is well deserved. I’m grateful to have been able to contribute and to be part of such a great collaboration.