Research Area Distributed Ledger Technology

Presentation of the current research project: the development of a framework for Distributed Ledger Technology (DLT) implementations in cooperative relationships

The Competence Center Ecosystems of the Business Engineering Institute St. Gallen pursues within the framework of the consortium research approach according to Österle and Otto (2010) the development of solutions for relevant problems from practice. In addition, the Design Science Research (DSR) approach according to Hevner (2007) serves as a basis for the development of relevant and contemporary artifacts in the field of information systems.

In this blog post, I present my current research goal[1], which is to develop a process model that demonstrates and supports the methodological approach of DLT implementations. This model is intended to enable organizations to adequately address relevant requirements of the implementation process and aims to be used in project management to support the conceptualization, planning, and implementation phases of a DLT implementation (see Figure 1).

Figure 1: Addressed phases of the project management lifecycle in the context of the research

Who knows it from their everyday project work? The conceptualization, planning, and implementation (or execution) phases are particularly critical. If the project is not sufficiently elaborated, the further phases will probably fail. For the conceptualization, you need appropriate support “from the very top” and sufficient financial means – especially time and resources! With regard to the DLT, there is also the fact that the DLT ideally involves several companies, which can certainly make the project more complex if it is developed jointly.

First, a short recap on DLT[2] and process models follows. Finally, I briefly present the research approach and current results.

A brief insight into DLT

DLT describes a decentrally organized architecture that consists of independent but interconnected nodes. Each node can represent, for example, an individual or a company (Kannengießer et al., 2020), which can perform and view transactions in the network. Each node has the same status of the “cash book,” i.e., the transactions performed in the network, at all times. This leads to a high level of transparency and security, since on the one hand, everyone can view the transactions and data is stored in multiple locations. In addition, efficiency gains can be generated (for example, faster transactions) or transparency can be increased (for example, traceability of goods) (Malhotra et al., 2022). Decisions, for example about the true state of transactions, are ideally made collaboratively by consensus. Governance in such a network can be covered entirely electronically by smart contracts, which include coded “if-then” statements.

Due to the decentralized character, the network ideally consists of several participants so that the properties and advantages of DLT can also be used. Various concepts and designs are subordinate to the term DLT. The best-known concept is probably the blockchain, while the Bitcoin design had the initial goal of abolishing intermediary functions in payment transactions (Nakamoto, 2008). Since the publication of the first white paper by Satoshi Nakamoto, other concepts and designs have emerged that can enable companies to optimize business models or allow new business models to emerge (Heines, 2023). For example, business model optimization can be promoted by improving processes through the use of technology. New business models are created by entering previously non-existent markets, such as micro-payments, crypto platforms, or DAOs. In addition, 1) new collaborative structures, such as ecosystems[3], can emerge through the application of DLT (Malhotra et al., 2022) or 2) promote the application intensity of the technology through collaborative partnerships.

What is the challenge in practice?

The implementation of DLT, as with other technologies, is associated with requirements and challenges that have special features compared to other technologies, particularly due to its decentralized character and the involvement of additional players. Although many investments are being made in DLT (Statista, 2022), the actual implementation rate is low (cf. Trujillo et al., 2017). This may be due to various reasons. Possibly, it is due to lack of use cases or use cases suitable for DLT or also due to ignorance about necessary requirements in the implementation process. In practice, many DLT projects do not get beyond the concept or a pilot project. On the one hand, there are requirements of the process for DLT (“Is DLT even suitable for this?”) and requirements of the technology for the company(ies) (“We need skills for DLT!”). The process model to be developed starts precisely at this (last) point and is intended to support companies in the phases “conception, planning and implementation” with reference to the evaluated implementation requirements (requirements of the technology for the company(ies)).

What is a process model good for and how does it help companies?

Sequential arrangement of a methodology can be used to illustrate steps of work (March & Smith, 1995) and be used in the context of the project activity as supporting aids. Process models, as a subset of a methodology, serve in business informatics to represent certain sequences of specified activities in a sequential order (Winter, 2003). In addition, a process model shows the measures and activities required to accompany the transformation from an actual to a target state (Heinrich & Leist, 2000). A method is completed by detailed descriptions of activities, results, roles, and techniques to be used. The development of such models or methods is done by mapping real or imagined circumstances (Winter, 2003). In the context of this research project, the analysis of DLT-based use cases serves as the basis for the construction of such a process model. Thus, real use cases are comprehensively analyzed and used for model development. Figure 2 illustrates the components of a process model. A simple example of a process model is the waterfall model in the context of project management, which presents the activities (similar to Figure 1) sequentially.

Figure 2: Classification scheme of process models (adapted from Fischer et al., 1998)

For example in Business Process Management (BPM), process models help to capture complex business processes in a simplified and understandable way with different levels of abstraction for core (or sub-) and sub-processes. Process models contain various components: Roles, activities, sub-activities, associated processes, systems and software used, and outcomes. Capturing complex processes alone presents some challenges, especially when the process is standardized but executed by employees in a variety of ways. By capturing the processes, standardization can occur on the one hand and inefficiencies can be uncovered on the other. In addition, a factually documented process serves to ensure that new employees or employees from other areas of the company understand the process, even if the area of expertise is different.

A corresponding process model for DLT implementations has the same purpose: It should present the complex facts of a DLT implementation in detail but in a clear and understandable way. The process model must be understood equally by both technical and strategic experts and support the implementation in all required areas of the company, since transformation projects always include all areas of the company. Therefore, the model includes strategic, organizational, architectural, cultural, and external perspectives.

A brief insight into the research approach and first results

For the development of the process model, requirements for the implementation of DLT are first collected (“What do companies need for the implementation?”) and examined in interviews and case studies. So far, only a fragmented literature exists (e.g., with respect to specific use cases or industries), which is why the aggregation of requirements represents a novelty. However, it must be positively emphasized that the purely technical focus of the past is continuously expanded by other perspectives (e.g., sustainability, regulation) in the context of DLT.

The primary consolidation and analysis of requirements for DLT implementation has the advantage that roles, methods and procedures (see Figure 2) as well as the required areas of activity in the company can be specified from these. By considering different levels (strategic, organizational, architectural, cultural, external), the model development is comprehensive for the holistic enterprise view. The research conducted so far revealed that requirements exist at various levels (strategic, organizational, architectural, cultural, external) that need to be considered by companies. Thus, a total of 39 concrete requirements were collected, which are now being investigated in interviews and case studies. This enables a detailed exposition and differentiation depending on various factors (e.g., business model, concept or design). From the in-depth analysis, it is clear that requirements such as data protection, data security and interoperability enjoy high attention. Other requirements, such as evaluating the suitability of use cases for DLT or assessing the impact of DLT on customer perception, are mentioned but not examined in depth.

A brief look at the next steps

In the further course of the research, design principles for the implementation of DLT are derived from the requirements and the overarching framework is developed from them. Some meta-requirements derived from the requirements model as examples:

  • Assess the strategic impact and risks of DLT on established initiatives, conduct a strategic feasibility and economic analysis for DLT integration
  • Promotion of internal and external knowledge development and transfer
  • Evaluation of end-to-end data integrity in accordance with the selected conceptual design

The research project has a total duration of 3.5 years, ending in January 2025 with the submission of my dissertation to the University of St. Gallen and the subsequent disputation. The partial results of the research project will be published at renowned conferences and in further publications.

Due to the high practical relevance, I am continuously searching for interview partners who accompany or have accompanied DLT projects in practice. A broad coverage of use cases also allows to elaborate the research results in detail. If you are interested in contributing to the current research project through interviews, you can reach me at


[1] Detailed insights into the research project are available upon request.

[2] For further reading, you can find numerous articles on the topic of DLT in our blog.

[3] The current status of Dennis Vetterling’s Business Ecosystems research area is highlighted in this blog post: Research Area Business Ecosystems: Presentation of the current research project: Conceptualizing the accumulation of value (value capture) through a framework |

Fischer, T., Biskup, H., & Müller-Luschnat, G. (1998). Conceptual foundations for process models (pp. 13-31).

Heines, R. (2023). A Framework for Enabling Asset Tokenization Business Models in the Financial Services Sector.

Heinrich, B., & Leist, S. (2000). Banking architectures in the information age-On the role of the business model. In H. Österle & R. Winter (Eds.), Business engineering: Towards the information age enterprise (pp. 141-165). Springer.

Hevner, A. R. (2007). A three cycle view of design science research. Scandinavian Journal of Information Systems, 19(2).

Kannengießer, N., Lins, S., Dehling, T., & Sunyaev, A. (2020). Trade-offs between distributed ledger technology characteristics. ACM Computing Surveys, 53(2), 1-37.

Malhotra, A., O’Neill, H., & Stowell, P. (2022). Thinking strategically about blockchain adoption and risk mitigation. Business Horizons, 65(2), 159-171.

March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251-266.

Österle, H., & Otto, B. (2010). Consortium Research. Business & Information Systems Engineering, 2(5), 283-293.

Statista. (2022). Global spending on blockchain solutions 2024. Statista.

Winter, R. (2003). Models, techniques, and tools in business engineering. In H. Österle & R. Winter (Eds.), Business engineering (pp. 87-118). Springer Berlin Heidelberg.

Benjamin Schaefer
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