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Distributed ledger technology (DLT) emerged as a disruptive force towards decentralization and has expanded beyond its origins in cryptocurrencies like Bitcoin. At the heart of DLT is an infrastructure that replicates data across multiple network nodes, enabling new opportunities for data integrity, transparency, and trust in distributed business environments. In recent years, technological advances have improved the performance, energy efficiency, and functionality of DLT, expanding its application to various sectors such as finance, healthcare, trade and media, logistics, and the public sector. Despite these advances, adoption remained limited, with notable successes primarily in areas such as decentralized finance and non-fungible tokens. By placing DLT within the historical development of ledgers and distributed databases, this Fundamental provides a business-oriented foundation for structuring and assessing DLT-based solutions. It presents, a unified definition covering blockchain technologies, describes the key characteristics of DLT, and offers a structured analysis of its potential and challenges using a multi-dimensional interaction framework. Ultimately, it serves to carve out where and under which conditions DLT infrastructures add value for interorganizational relationships.
Distributed economic and technological systems
The rise of blockchain
Since 2008, blockchain technology is regarded as one of the major disruptive general-purpose technologies in the business world (Frizzo-Barker et al., 2020; Patel et al., 2022). Clearly, this evolution is driven by cryptocurrencies like Bitcoin, which still is the most well-known manifestation of blockchain technology with a current market capitalization of around $2.17 trillion (CoinMarketCap, 2025). However, Bitcoin’s applications in the business world remain rather limited and despite being termed cryptocurrency, Bitcoin falls short of being a currency in economic terms. In view of its high volatility, Bitcoin has primarily served as a high-risk investment object but fails to serve as a widespread means of payment and as a reliable means for assessing the value of goods or for storing value. While this assessment may change in the future, research and practice alike indicate that the underlying technology already offers significant application potential. This potential is rooted in the fundamental differences in how data is processed and stored. Contrary to centralized databases used in the field of enterprise systems, blockchain technology employs a unique replication process that safely disseminates data across multiple (network) nodes.
Over the past years, blockchain-based systems have seen technological improvements and a broadened spectrum of application areas beyond Bitcoin and other cryptocurrencies. The technology has become more performant as well as energy efficient and is now able to embed more complex application logic and to provide more leeway in governing the distributed system. The application areas comprise a broader variety of use cases from managing property rights and identities, to tracking the status of goods and to trading tokens as well as certificates in a wide variety of industries such as finance, healthcare, trade and media, logistics or the public sector (Casino et al., 2019). Given these developments, many systems have deviated from the initial blockchain design, and the wider term of distributed ledger technologies (DLT) emerged. Despite these developments, projects based on DLT have repeatedly been discontinued and have lived up to their expectations only in a small number of domains, such as decentralized finance (DeFi) (Fridgen et al., 2024) and non-fungible tokens (NFT) (Fan et al., 2024). This appears surprising since the technology promises to address the challenge of sharing information among multiple business partners, which is a key characteristic for today's value chains. If trust between the participants in these distributed scenarios is missing, a trusted third party is absent or where the need for an immutable record is high, research has indicated that distributed infrastructures like DLT might be suitable for similarly distributed business settings (Chowdhury et al., 2018; INATBA, 2024; Wüst & Gervais, 2018).
Compared to the broad body of literature on blockchain technologies highlighting the technology’s potential and business implications (e.g., Beck et al., 2017; Bendig & Charlet, 2025; Bons et al., 2020; Nofer et al., 2017; Ostern, 2020; Risius & Spohrer, 2017; Rogalski & Schiereck, 2024), literature on DLT is rather sparse and often focused on the technical aspects of DLT (Kannengießer et al., 2020; Sunyaev, 2024). In view of this gap, this Fundamental aims to provide a business-oriented foundation for DLT in information systems research by contributing a unified definition with the key characteristics of DLT solutions as well as a structured analysis of potentials and challenges, highlighting DLT’s abilities in distributed business settings.
Ubiquity of ledgers
From the business perspective, DLT is based on the concept of accounting ledgers. These ledgers are cornerstones in economic life and ubiquitous in everyday business processes. Ledgers keep records of transactions and calculate debits and credits, which allow to calculate, document and transfer balances for individual accounts (Barnett et al., 1999). Among the earliest ledgers are the tables used by the Romans, who kept accounts on wax tablets. For example, debts, purchases, and obligations of trade with business partners were recorded in day books. This mostly single-entry tabular method is seen as the predecessor of the more advanced double-entry bookkeeping, which dates back to Italy in the thirteenth century (Sangster, 2016). As included in the notion of book, most ledgers were paper-based – a situation that changed with the rise of (digital) information technology (IT) during the twentieth century. Application systems now stored the entries in a database and an application logic reflected the bookkeeping operations taking place on this database. These systems enabled transactions to be accessed, monitored, and modified almost in real-time for each ledger (e.g., financial accounting, warehousing). An alignment of multiple functional ledgers came with enterprise resource planning systems (ERP), which represented integrated business application (or enterprise) systems that relied on the concept of a single enterprise-wide database (Davenport, 1998). Today, most companies use some kind of ERP system, be it for their original purposes of bookkeeping, storage management or other. Table 1 shows some classical examples of where ledgers are present in business.
Table 1. Examples of ledgers in varous industries
Industry | Example | Industry | Example |
|---|---|---|---|
Banking | Credit accounts | Financial accounting | Bookkeeping, cash books |
Healthcare | Patient records | Logistics | Stock books |
Real estate | Land charge register | Travel | Reservations |
Legal rights | Patent records | Civic administration | Citizen records |
Arising from the division of labor is the need to align ledger positions among organizations. Since ERP systems integrate ledgers within a company, they are centralized for each organization. Integration within a value chain therefore requires the exchange of data between multiple ERP systems. It was one rationale of electronic business to substitute manual and paper-based interfaces with automated technologies, such as electronic data interchange (EDI) and portals. These solutions improved the linking of ledgers across company borders, but it also became clear that the effort involved in establishing EDI linkages constrained EDI to standardized high-volume transactions. Portals required less up-front investments but at the expense of lower levels of automation. This led to the situation that even after electronic business emerged, substantial inefficiencies remained regarding the transfer of data between ledgers of individual companies (Hammer, 2001). Developments such as cloud computing and APIs have further reduced these inefficiencies. Cloud computing allows organizations to access shared resources and services over the internet, facilitating real-time data sharing and collaboration between organizations (Battleson et al., 2016). APIs provide standardized interfaces that reduce the effort of linking resources from external partners and enable the creation of platform models when multiple actors share data (Benzell et al., 2024). While these platforms feature centralized topologies, DLT foresees a decentralized topology for sharing data in a ledger that is replicated among multiple parties in a consistent and trustful way. Figure 1 summarizes the three main scenarios: In a typical supply chain, each economic actor operates its ledger and data is transferred sequentially from one ledger to the other, resulting in a lack of data transparency and missing data transfers across the chain. This led to the emergence of platforms operated by central actors in the supply chain or by independent intermediaries (e.g., as electronic marketplaces with dotted lines in Figure 1 depicting that multiple actors are present on both sides of the market) who consolidate data in their ledgers (e.g., catalogs, order books). The obvious downside is that all actors in a value chain need to connect to the platform and that the centralized position of the platform provider also involves critical political (e.g., opportunism, competition) as well as operational (e.g., single point of failure) risks (Bencic & Zarko, 2018). These problems could be avoided in peer-to-peer (P2P) settings, which - although definitions might differ (see Vergne, 2020) - are referred to synonymously as decentralized or distributed and work without an intermediary. However, decentralized settings involve the highest need for coordination and standardization since actors must trust each other and the ledgers must be compatible (Kumar & van Dissel, 1996).
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Fig. 1
Scenarios for the synchronization of ledgers
Distributed databases and application systems
From the technological perspective, a ledger may be conceived as a database (Özsu & Valduriez, 2020, p. 437), which is a collection of data that is organized following a defined paradigm, such as the relational (e.g., data organized in linked tables) or the non-relational (e.g., data organized in documents or graphs) model. In the relational model, datasets comprising entities and attributes link to each other via relationships and a query language (e.g., SQL) is offered by a database management system (DBMS) to describe, query, define, and write data to the database. As shown in Figure 2, the typical configuration in the enterprise system setting is to have a database with a dedicated DBMS accessible by one or multiple application systems (e.g., an ERP or an electronic market system). Over the years, distributed databases emerged, which may be defined as “a collection of multiple, logically interrelated databases located at the nodes of a distributed system” (Özsu & Valduriez, 2020, p. 1). “[They] are distributed, if there is some distance between [their elements], and if some significant cost and/or some intermediary process is entailed in connecting them” (Bond & Gasser, 1988, p. 8). Distance in this case is conceived as “conceptual distance, with respect to some conceptual frame, such as time, space, semantics, etc.” (Bond & Gasser, 1988, p. 8). To overcome this distance, distributed database systems aim to synchronize (or replicate) the data from the various involved databases.
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Fig. 2
Forms of centralized and distributed databases
The distribution of data across these multiple database entities may vary, with the two extremes being that either all entities contain a complete copy of data or that each entity only has unique data. The distribution of data also adds another level of complexity to storing since the network must agree on how information is created, read, updated, and deleted (Sunyaev, 2024, p. 240). This is necessary as each node must update to the current “truthful” state. These synchronization tasks are typically covered by distributed database management systems (DDBMS), which manage the database network and make “the distribution transparent to the users” (Özsu & Valduriez, 2020, p. 1). In addition to synchronization functionalities, DDBMS comprise functionalities for the homogenization of data along a global data scheme. While most DDBMS foresee such centralized coordination components, a more decentralized version would require the DDBMS functionality to be included in each participating database node. DLTs may be considered as a form of distributed database as they store and manage data in multiple nodes and offer functionalities to read and write data similar to DDBMS.
Distributed ledger technology evolution
The principle of blockchain systems to organize data not in a relational way, but as a chain of cryptographically linked transactions, which enables a tamper-proof transaction record, is attributed to Haber and Stornetta (1991). However, the rise of such blockchain systems has become popular with the publication of the Bitcoin whitepaper by Nakamoto (2008), which was also inspired by prior ideas on electronic cash systems (e.g., ECash, Chaum et al. 1990). Since then, blockchain systems have seen an evolution along application domains and technical advancements, which is reflected in the differentiation of multiple generations. These generations have been offered with partly differing meanings by researchers (e.g., Cheng et al., 2021; Swan, 2015; Xu et al., 2019) and are typically referred to as “blockchain generations”. As blockchain is mostly conceived as a subset of DLT (see definitions below), the term “DLT generation” appears more comprehensive and appropriate (see also Sunyaev, 2024, p. 247). As summarized in Table 2, the first generation is generally associated with the Bitcoin system itself (Yli-Huumo et al., 2016) and primarily comprises the application area of digital currencies. These cryptocurrencies (e.g., Bitcoin, Litecoin) were conceived as electronic schemes for decentralized payments to facilitate economic transactions between anonymous parties without involving centralized actors (e.g., banks). As mentioned in the introduction of this Fundamental, they still face critical limitations when measured against other functions of economic currencies (i.e., means of payment, measure of value and store of value).
Table 2. DLT generations according to application domains and technological advancements
DLT generations | Application domains | Technological advancements |
|---|---|---|
1.0 | Cryptocurrencies, especially payments | Decentralization |
2.0 | Cryptocurrencies and other financial applications such as insurance | Automation |
3.0 | Financial applications and beyond (e.g., health, government, art) | Scalability, integration, interoperability |
4.0 | Cross-domain applications | Convergence with other technologies |
The second generation (Swan, 2015) led to the use of blockchain systems in a broader set of financial applications (Patel et al., 2022), which included finance (Nguyen et al., 2021) or insurance (Hans et al., 2017). An essential element in this shift to other application areas were smart contracts that embed programmable business logic into distributed ledgers (DLs). They allowed DLT applications (e.g., Ethereum, Solana) to trigger actions based on defined events (e.g., transfer of ownership when full payment is received) or to include automated policies for coordination in decentralized autonomous organizations (DAO) (Augustin et al., 2023). These developments led to the emergence of the term “Web3”, which refers to the evolution of decentralized internet applications compared to the more centralized Web 2.0. DLT is considered a core building block for Web3, as it enables applications such as DAO and NFT (Murray et al., 2023).
It was mainly with the third generation that the term DLT spread to recognize another broadening of technological configurations and use cases. Generation 3.0 extended the application “beyond currency, finance, and markets – particularly in the areas of government, health, science, literacy, culture, and art” (Swan, 2015, p. ix). For example, in the domain of supply chain management, generation 3.0 applications (e.g., Hyperledger Fabric, IOTA) could not only foster interorganizational collaboration among many actors by supporting business logic but also by addressing integration problems (Queiroz et al., 2020). Among the examples are the connection to internet-of-things devices, the integration of DLT and organizational application systems as well as cross-ledger interoperability (CLI) (Zhang et al., 2024). Technically, the extensions are enabled by new developments known as layer 1 (e.g., new consensus mechanisms, directed acyclic graph) and layer 2 protocols (e.g., side chains, channels). Both also aim to overcome scalability and energy issues, which were important downsides of prior blockchain generations (Gangwal et al., 2023).
A possible fourth generation could be described as a convergence of DLT with other general-purpose technologies like artificial intelligence (AI), mixed reality, and quantum computing. One example of this convergence is the service provider Certik, which offers AI-assisted smart contract audits to check for security vulnerabilities. Such advanced functionalities allow for more complex business scenarios, which may foster cross-domain applications that link health or nutrition data with leisure, insurance, or medical solutions. Given the constant broadening of the technological abilities of DLT systems (layer 1 & 2 protocols) and their potential application domains, the spectrum of configuring distributed systems has become possible.
Distributed ledger technology framework
Distributed ledger technology definition
Terminologically, blockchain and distributed ledger technologies are closely related. As illustrated by the definitions in Table 3, multiple researchers conceive blockchain as part of DLT. For example, Kannengießer et al. (2020) understand blockchain as a concept (subcategory) of DLT and Chowdhury et al., (2019, p. 167931) describe blockchain as “just an example of a particular type of ledger” but mention that the terms are often used interchangeably. Joo et al., (2023, p. 13) note that “DLT encompasses the original Blockchain, though the term “Blockchain” is also frequently used to refer to a broader class of DLT systems than the original Blockchain implementation.” Shahaab et al., (2019, p. 43624) state that the architecture of DLT can be “categorised into two broad categories – the linear Blockchain and Direct Acyclic Graphs.” This supports the view that blockchain is a subcategory of DLT, in the sense that it is primarily the data organization that distinguishes blockchain from other DLTs.
Table 3. Overview of DLT-definitions (aspects of the derived definition are highlighted in bold)
Author(s) | Definition |
|---|---|
Aviv (2020, p. 6) | “Every transaction is cryptographically signed, and blocks are hash-chained together after all parties have agreed on block content, producing a distributed ledger (DLT) and recording blocks in the ledger. Because no one can be trusted, each network participant keeps their copy of the ledger, making it nearly difficult for a single person to falsify recorded transactions or refuse the agreement. Any attempt to falsify or replace portions of the transactions will be discovered, assuring data integrity and finality. All transactions are recorded in the ledger that has taken place.” |
Ballandies et al. (2021, p. 1818) | “A distributed ledger (DL) is a distributed data structure, whose entries are written by the participants of a DLT system after reaching consensus on the validity of the entries. A consensus mechanism is usually an integral part of a distributed ledger system and guarantees system reliability: all written entries are validated without a trusted third party.” |
Chowdhury et al., (2019, p. 167931) | “Even though the terms blockchain and DLT are used interchangeably in the literature, there is a subtle difference between them which is worth highlighting. A blockchain is just an example of a particular type of ledger where data can be stored in a specific format. There are other types of ledgers with different data formats. When a ledger (including a blockchain) is distributed across a network, it can be regarded as a Distributed Ledger or simply a ledger.” |
Gorbunova et al., (2022, p. 66) | “DLT has received growing attention as an innovative method of storing and updating data within and between organizations in recent years. A distributed ledger is a digital ledger that is different from centralized networks and ledger systems in two significant aspects. First, information is stored in a network of machines, with changes to the ledger reflected simultaneously for all ledger holders. Second, the data is authenticated by a cryptographic signature. Together, it provides a transparent and verifiable record of transactions.” |
Joo et al., (2023, p. 13) | “DLT is an umbrella term without a consensus definition, but most people agree that most DLT systems share a particular set of properties. Fundamentally, a distributed ledger is a digital, decentralized transaction database that is managed by a set of nodes without a central server.” |
Kannengießer et al., (2020, p. 42:1) | “Distributed ledger technology (DLT) enables the operation of a highly available, append-only database (a distributed ledger) that is maintained by physically distributed storage and computing devices (referred to as nodes) in an untrustworthy environment. DLT promises to increase efficiency and transparency of collaborations between individuals and/or organizations […].” |
Liu et al., (2020, p. 394) | “Distributed ledger technology is a general term that is used to describe technologies for the storage, distribution, and exchange of data between users over private or public distributed computer networks. Essentially, a distributed ledger is a database that is spread and stored over multiple computers located at physically different locations. Each of such computers is frequently referred to as a node. A distributed ledger can also be considered as a common datasheet stored on multiple distributed nodes.” |
Shahaab et al., (2019, p. 43623) | “DLT is an approach for maintaining distributed copies of a single ledger across multiple data stores. It allows to record, share and sync data across the network in such a way that the whole network reaches consensus on the content of the ledger and secures the information, such that it cannot be altered in the future. This immutability property of the DLTs make them suitable for a variety of businesses applications where accurate and honest record of historical transactions is important and data sharing between multiple participants is required.” |
Sunyaev (2024, pp. 242-243) | "Consensus mechanisms are protocols that guide entities (e.g., nodes) to reach agreements, in the case of DLT, on the respective state of replications of data stored on individual nodes. [...] A distributed ledger is a type of distributed database that records transactions between entities. [...] DLT enables the realization and operation of distributed ledgers." |
While not exhaustive, the definitions compiled in Table 3 highlight recurring terminological elements, which are valuable for defining DLT. First, following the concept of distributed databases, DLT is a form of distributed database comprising DLs (Ballandies et al., 2021; Sunyaev, 2024, p. 243). Second, a DLT is a distributed network of nodes through which data is stored reliably (Joo et al., 2023; Kannengießer et al., 2020; Shahaab et al., 2019). Third, DLTs employ a mechanism to achieve consensus on changes in the distributed set of data (Ballandies et al., 2021; Shahaab et al., 2019). Lastly, the read, submit, and validation access to DLT transactions can be adjusted (e.g., private, public) (Liu et al., 2020). These aspects lead to the following definition: DLT is a type of distributed database shared over a distributed network, where transaction data is synchronized among the network’s nodes and reliably stored. Network decisions are governed by consensus algorithms, ensuring agreement among participants with differing access rights.
Distributed ledger technology architecture
Closely related to the definitions are the elements of DLT. Based on the literature, these are typically organized in a layered architecture (e.g., Aviv, 2020; ISO, 2022; Kan et al., 2018; Zhu et al., 2019). Although the number of layers may vary, four layers are recurring in the literature. Along these four layers (see right in architecture model Fig. 3) six characteristics of DLT solutions are derived from the definitions in Table 3. (1) The first characteristic links to the network of nodes, which is similar to the P2P structure in Fig. 1. Thus, based on the term DL and its classification as a distributed database system, a DLT is distributed. Distribution refers to the physical notion of distance and to the synchronous storage of replications of the identical ledgers on multiple distant nodes (Kannengießer et al., 2020; Shahaab et al., 2019). Each of these nodes comprises a full or partial set of the data contained in the data layer.
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Fig. 3
Interaction model (left) and DLT architecture model (right)
The data layer comprises the transaction record and data organization. (2) The transaction record guarantees the traceability and immutability of all transactions by following an append-only approach (Aviv, 2020; Isaja & Soldatos, 2018; Kannengießer et al., 2020; Shahaab et al., 2019). Each transaction logs the change from one network state to the next. By storing data as transactions in a DLT, any temporal state of the ledger is restorable. This is possible by applying all transactions up to the desired date to the root of the ledger. In most well-known public systems (e.g., Bitcoin, Ethereum), ledger data is stored permanently and immutable (e.g., Isaja & Soldatos, 2018; Magazzeni et al., 2017). Consequently, incorrect entries cannot be corrected by editing, but only through a new transaction which counterbalances the incorrect one. With DLT generations 2.0 and 3.0, proposals of “redactable” blockchains may be observed that eased this limitation. They allow “re-writing one or more blocks, compressing any number of blocks into a smaller number of blocks, and inserting one or more blocks” (Ateniese et al., 2017, pp. 2–3) and are seen as relevant for private permissioned DLTs (see participation structure below) (Zhang et al., 2021).
(3) DLTs feature a data organization that follows blocks or graphs. The former is present in blockchain systems since the transactions (i.e., actions to create or process data) are combined as sequences of linked data segments (blocks). The latter foresees the data organization in a directed acyclic graph (DAG), where either blocks can have multiple predecessors or successors (blockDAG) or where transactions are linked directly (TDAG) (Wang et al., 2023; Kannengießer et al., 2020). The chosen data organization may differ in terms of transaction volume, scalability, etc. (e.g., DAGs are often more scalable) (Pervez et al., 2018; Yang et al., 2020). Additionally, data organization may involve additional databases. Hyperledger Fabric, for example, uses state databases (e.g., LevelDB) to replicate DLT transactions externally in a database, which improves search efficiency.
The consensus layer encompasses the characteristics of participation structure and consensus mechanism. (4) The participation structure concerns the read, submit and validate access of DLT transactions and is often structured as private and public as well as permissioned and permissionless. This results in three main types of DLT participation: public permissionless, public permissioned, and private permissioned (Beck et al., 2018). Public permissionless DLTs enable everyone to join the network, read all the public data and participate in the consensus. Public permissioned DLTs on the other hand are restrictive in terms of consensus participation and only allow certain parties to validate transactions. In private permissioned DLTs (sometimes referred to as consortium DLTs) participation is subject to an authentication process and only specific parties may join the network and participate in the consensus process (Beck et al., 2018). Since data reading and participation can be limited to a subset of parties, private permissioned DLTs have become relevant for businesses and allow configurations depending on the networks’ intended purpose (Chowdhury et al., 2018). There is also the possibility to restrict participation to specific parties differently. In Hyperledger Fabric, this is achieved by using an access control layer that is separated from the DL (Dinh et al., 2018).
(5) The consensus mechanism is key for synchronizing DL data and for assuring the valid state of a DL (Ballandies et al., 2021; Bencic & Zarko, 2018; Dunphy et al., 2018; Shahaab et al., 2019; Waldo, 2019). Over time, a broad variety of consensus mechanisms has emerged (e.g., Lashkari & Musilek, 2021; Lohachab et al., 2021; Shahaab et al., 2019) with the most widely known being proof-of-work (PoW) and proof-of-stake (PoS). The former is used in the Bitcoin and the latter in the Ethereum blockchain. Another well-known mechanism is practical byzantine fault tolerance (PBFT), which is especially present in enterprise DLTs like Hyperledger (Wan et al., 2020). PoW, PoS and PBFT differ in their approach to reach consensus. PoW requires miners to solve complex calculations, where the first miner to solve the calculation can validate the transactions and receive a reward (usually in the form of cryptocurrency tokens) (Sriman et al., 2021). The race to solve the mathematical problem by every miner leads to higher energy consumption (Biais et al., 2019; Wan et al., 2020). PoS allows participants to validate blocks according to their stake (often the number and/or age of coins) in the network. Since not everyone needs to calculate a complex mathematical problem, energy consumption is lower and transaction rates are higher (Saleh, 2021; Wan et al., 2020). The consensus process of PBFT only requires votes among permissioned participants, who receive no rewards for their efforts (Sunyaev, 2024, p. 258). Since performance decreases with an increasing number of nodes due to the necessary amount of messaging (Wu et al., 2020), PBFT is more suited for private permissioned DLTs. For permissionless DLTs, PoS has advantages compared to PoW (Saleh, 2021).
The contract layer consists of (6) smart contracts, which are scripts that automatically execute predefined actions when being triggered by an event (Ante, 2021). For example, smart contracts can be used to automatically execute contractual agreements, which are transparent and immutable (Hewa et al., 2021). Smart contracts primarily use data from the DLT data system (on-chain data), but interfaces known as oracles exist for exchanging on-chain and off-chain data, for example, with corporate ERP systems (Mühlberger et al., 2020; Pasdar et al., 2023). This means that they provide the functionality to address interfaces of other application systems, which is a prerequisite to realize digital workflows across organizations with individual organizational (enterprise) systems.
Finally, the application layer provides applications like DAOs and decentralized apps (dApps). DAOs use smart contracts to automate organizational procedures, which allows centralized actors (e.g., intermediaries, platforms) to be replaced by decentralized programs (Ladd et al., 2024). DApps may be conceived as software components being hosted on a DLT that provide additional business logic (Cai et al., 2018). Among the examples of dApps are decentralized exchanges that execute trades based on smart contracts (Hägele, 2024). Although both developments appear promising, most use cases still focus on trading digital assets (e.g., DeFi). Other use cases, such as dApps for e-commerce, have encountered difficulty in usage. An example is the decentralized e-commerce initiative OpenBazaar, which ceased operation in 2021 due to shortcomings in performance, high complexity of smart contracts and the lack of a profitable business model (Hochstein, 2021).
Distributed ledger technology use cases
Although OpenBazaar has not been successful, numerous use cases for DLT have been reported. They are based on the need to share data between multiple parties. Since this is a key feature of business networks, DLT can serve diverse application purposes and enable the digitalization and automated processing of physical as well as non-physical objects within value chains (Witt & Schoop, 2023). As shown in Table 4, this leads to many examples in multiple domains and promises a broad scope and diversity of potential DLT applications. However, the use cases often remained experimental without being followed by a successful operation. Even though the systems might have worked technically, they pointed at important business challenges for the adoption of DLT (see Table 5). In the financial sector, for example, DLT-based payment transactions and smart contracts can streamline processes and reduce costs, but adoption requires mastering interoperability and scalability, as well as regulatory compliance. In the healthcare sector, managing patient records or pharmaceutical supply chains depends on overcoming legitimate data privacy concerns and ensuring compatibility with legal frameworks such as the GDPR.
Table 4. DLT application domains and examples for applications in business (see Alt, 2022, based on Casino et al., 2019; Dutta et al., 2020; ITU, 2019; Maesa & Mori, 2019; Nofer et al., 2017; Tönnissen & Teuteberg, 2020)
Application domains | Examples |
|---|---|
Financial sector | • Payment transactions: virtual currencies (e.g., central bank digital currencies (CBDC), stable coins), payment transactions (e.g., bank transfers, letters of credit, bank guarantees) • Smart transactions: process automation (e.g., integrated insurance services, automated market making on crypto exchanges), clearing and settlement • Financing: digital assets (e.g., initial coin offerings (ICO), tokenization) and credit transactions (e.g., digital mortgages, loans) • Accounting and auditing compliance and audit (e.g., concept of “triple entry accounting”) |
Healthcare sector | • Pharma supply chain management: real-time (back)tracking (tracking & tracing (T&T)) of pharma products • Medical data: decentralized treatment/patient records, management of drug distribution, blood and organ donations, qualifications of medical staff • Billing of services: billing of treatments and prevention of fraud (“double spending”) |
Trade and media sector | • Electronic commerce: decentralized catalogs and trading platforms (e.g., decentralized auctions for digital assets) • Media: transfer and charging of media usage or charging of royalties, decentralized streaming, and distribution platforms • Art sector: decentralized directories for artworks, management of individual digital works (e.g., non-fungible token (NFT)), certificates of authenticity • Games sector: generation of virtual characters (e.g., NFT), decentralized management of cross-game player profiles • Telecommunications sector: decentralized directories for number assignment and porting |
Industrial sector | • Supply chain management: T&T of logistics and food chains, management of events (supply chain event management (SCEM)), customs clearance • Customer relationship management: decentralized management of loyalty programs and trading of credits • Energy: decentralized trading platforms for certificates and P2P energy, decentralized energy billing, calculation of CO2 footprints • Production: exchange of planning and order data in the supply chain, decentralized networking of manufacturing resources (industry 4.0), retrieval actions |
Public sector | • Smart city: registry services for real estate and real estate cadastre, intermodal public transport systems in and between cities (mobility-as-a-service) • Tax management: distributed real-time compliance audits (with tax advisor/authority), transaction register for digital audit trails • Notary services: decentralized securitization, management of (intellectual) property rights and/or patents • Education sector: management of certificates and credentials (e.g., digital ECTS) |
Cross-sector applications (horizontal application areas) | • Data management: distributed storage and management of documents, management of personal data • Identity management: authentication by means of decentralized identities (self-sovereign identities (SSI)), identity-as-a-service, decentralized public key infrastructures • Access controls: granting access rights to buildings, vehicles, services on a transaction basis (e.g., for one-time use, delivery) • Elections/e-voting: political elections, online voting, elections for general meetings |
Potentials and challenges of DLT
Interaction model
For a differentiated analysis of potentials and challenges from a business perspective, the remainder of this Fundamental introduces an interaction model that distinguishes multiple dimensions. Interaction models are established frameworks for analyzing interorganizational relationships (e.g., Baptista & Nunes, 2025; Danese et al., 2020; Ekman et al., 2020) and recognize interactions among parties as the basic or micro-level of analysis (Mattsson, 1997). With interactions as the unit of analysis, this appears suitable for DLT systems, which are based on transactions among multiple nodes. Interaction models embed these exchanges in a setting that distinguishes four dimensions (see left model in Fig. 3) (Cunningham & Tynan, 1993; Håkansson, 1982): First, the collaboration between the interacting parties can be viewed from a technological, organizational, and individual perspective. DLT may serve as the technological backbone that facilitates these interactions by enabling data exchange between parties (i.e. nodes). Second, the interaction process ranges from short-term episodes (e.g., information or financial exchanges) to long-term relationships (institutionalization). DLT supports these processes between parties by enabling synchronization, traceability, and transaction automation across the network. Third, interactions occur in an atmosphere, which affects and is affected by interactions. This includes factors such as trust among known or unknown partners, the willingness to share information, and the ability to reach consensus. DLT influences the atmosphere of interacting parties by providing distinctive trust-generating characteristics. Lastly, interactions occur within an environment, which encompasses the broader market structure and the entire ecosystem with exogenous factors (e.g., technology providers, legal regulations) that shape the context for the operation of DLT solutions.
By combining the interaction model from a business perspective (left side in Fig. 3) with the architecture model from a technical perspective (right side in Fig. 3), the framework provides a structured approach to highlight the potentials and challenges of DLT for businesses. Each of the four interaction model dimensions represents an aspect of interorganizational relationships and offers a perspective for discussing where DLT solutions make sense and add value. Complementing this, the architecture model aids in understanding how the six characteristics and/or the four layers of DLT might affect the respective four dimensions in the interaction model. In the following, each interaction dimension is discussed, considering the characteristics and layers of DLT. Table 5 summarizes the potentials and challenges along the four dimensions.
Table 5. Potentials and challenges for businesses along the four dimensions
Dimension | Potentials | Challenges |
|---|---|---|
Interacting parties | • Support of distributed business settings with varying business needs • Ability to configure the distributed system according to business needs • Facilitation of new roles and business models (e.g., validators, exchanges) | • Need for a minimum degree of distribution and broad participation within target group (trade-off between access restriction and participation) • Need for additional coordination tasks (trade-off between increased communication and single point of failure) |
Interaction process | • Transaction efficiency in distributed settings • Feasibility of integrated transaction scenarios • Automated transaction and coordination procedures | • Costs of distributed computing should be lower than with as-is solutions • Benefits of integration need to compensate for costs of integration • Lack of standardization on business level |
Interaction atmosphere | • Ability to create trust and transparency on distributed data in distributed business settings • Establishment of new relationships, especially low trust networks | • Risks of transparency might hamper participation and active usage, thereby favoring negative externality effects • Distributed solutions require convincing incentives for participation |
Interaction environment | • Strategic opportunities through re- and disintermediation • Knowledge sourcing options through service providers | • Lack of workforce skills and dependency on third-party providers • Complying with regulations may be complex (e.g., other regulations like GDPR might apply) |
Interacting parties
The parties reflect the entities that interact with each other and appear in the form of organizations, with each entity having its own role in contributing within a larger setting (i.e., supply chain, business network). The parties are technologically represented by nodes (Seebacher & Maleshkova, 2018), which store the data in ledgers and ensure that data is synchronized entirely or partially between the nodes (e.g., Isaja & Soldatos, 2018; Waldo, 2019). Three main areas of potentials and challenges may be associated with DLT on the level of parties.
First, DLTs have convincing properties to support distributed business settings but also require a critical degree of distribution (➊).1 In a fully distributed setup, DLTs are P2P networks (e.g., Dunphy et al., 2018; Isaja & Soldatos, 2018) where all nodes have equivalent capabilities in storing and sharing data (Özsu & Valduriez, 2020). Bitcoin, for example, foresees that each node stores its own copy of the chain and that each node can participate as miners in the PoW process (Magazzeni et al., 2017). Business-to-business (B2B) applications often involve stable, long-term interactions among defined parties, following a sequential supply chain or a platform model (see Fig. 1). In these models, the distribution might be insufficient to justify DLT use, favoring traditional centralized database solutions unless the business network model features a minimal degree of distribution (which is difficult to determine). This is in line with the “co-evolution hypothesis” (Alt, 2018), which posits that due to the interdependency among business and technological topologies, decentralized technologies will also benefit decentralized business settings and vice versa.
Second, DLTs can be tailored to serve different business needs. Public DLTs (➍) are suitable for business-to-consumer (B2C) or consumer-to-consumer (C2C) scenarios, where many participants need access to shared information, such as financial transactions (e.g., a digital asset like a payment token). These DLTs offer low onboarding costs, typically involving digital wallets and registration processes delivered by service providers (e.g., a crypto exchange like Binance). In contrast, private permissioned DLTs are more relevant for B2B settings with interactions among a limited number of parties. They can provide real-time data sharing for the parties in the sequential or platform models (e.g., to avoid distortions like the bullwhip effect). Although most business needs will benefit from broad participation within these communities, there is a trade-off with the above-mentioned distributed business setting: If the value of a DLT increases with the degree of distribution, a larger group of parties will benefit from the DLT. If business needs favor a smaller number of parties, this tends to reduce the DLT's value and leads to the challenge of determining the DLT’s minimal degree of distribution.
Third, each DLT involves the coordination of distributed nodes. Similar to intermediaries in typical value chains, dedicated parties are required that assume coordination tasks, which depend on the DLT's participation structure (➍) and the chosen form of consensus mechanism (➎) (Pietrewicz, 2019). In private permissioned DLTs, the business network members will have to determine the form of consensus mechanism and which parties exert the role of verifying data and adding it to the ledger. Although participation is limited in these DLTs, the consensus mechanisms will likely involve more communication with an increasing number of parties engaged in attaining consensus (e.g., all parties in a large private permissioned DLT). This marks another trade-off with the mandate of a broadly distributed network, which would benefit DLTs: If a single party or only a small number of parties is responsible for coordination, centralized solutions will be more likely. If this group is larger and even anonymous, then coordination will be more complex. The complexity of fulfilling coordination tasks is visible in public DLTs where requirements of consensus mechanisms are high to authorize transactions and to safeguard against double-spending. Due to this higher complexity, dedicated third parties are participating in public DLT, which are referred to as miners (e.g., in PoW) or validators (e.g., in PoS). Additionally, service providers facilitating participation (e.g., decentralized exchanges) have become new business models in DLT networks.
Interaction process
The interaction process focuses on the exchange of objects (e.g., products, information) between two or more parties. In economic settings, these value exchanges take place within transactions, which are also the basic units in DLT systems. For the analysis of potentials and challenges, three areas shall be mentioned regarding the interaction process.
First, transaction efficiency is essential for the operational cost–benefit assessment of a DLT solution. It is determined by the performance of a DLT in terms of scalability and energy efficiency, which are related to the speed and cost of executing transactions. While many advances of DLTs in data organization (➌) and consensus mechanisms (➎) have improved the transaction efficiency of DLT solutions, scalability remains a challenge since complex transaction verifications can increase transaction confirmation times when demand grows. This primarily applies to permissionless DLTs (for Bitcoin see Chauhan et al. (2018)) and less to permissioned DLTs, which are generally regarded as being more scalable (Toufaily et al., 2021). Transaction speed often favors conventional databases, but DLTs can potentially offer cheaper and faster transactions among multiple parties in real-time. For example, the cross-border payment system Ripple yields faster and cheaper settlements compared to traditional systems like SWIFT. Estimates show transaction times in seconds instead of days and transaction costs of several cents instead of several dollars (Miller, 2025). However, it remains uncertain if DLTs can outperform established systems regarding their scalability (Qiu et al., 2019). In addition, interaction processes via DLT incur costs beyond mere transactions. This includes the operation of mechanisms for integrating internal application systems (e.g., via oracles) and for managing incidents (e.g., fraud) since a lack of fixed guidelines involves higher communication costs. In summary, the efficiency potential of DLT needs to compensate for the additional effort that is inherently present in interaction processes among multiple parties.
Second, integrated transactions are of importance for complex business scenarios. Smart contracts (➏) and dApps in the contract and application layer allow transactions to trigger other transactions, thereby creating integrated transaction scenarios. However, these scenarios require integration with internal systems such as existing EDI (Finke & Schumann, 2025) or legacy systems (Anthony Jnr, 2023), and other ledgers. Technologically, this integration may be achieved by oracles (Mühlberger et al., 2020; Pasdar et al., 2023) or CLI techniques but the configuration of these integration mechanisms involves coordination costs prior to operation. CLI, for example, enables multi-DLT transaction execution (Lohachab et al., 2021), as demonstrated in international trade settlement pilots where cryptocurrency and trade DLTs interact (e.g., for exchanging money and bill of lading simultaneously) to accelerate processes and to reduce default risks and hedging costs (Hyperledger, 2022). Although smart contracts and dApps contribute automated routines to these integrated transaction scenarios, they also incur configuration and implementation costs. The expected benefits of integrated scenarios should therefore outweigh the additional effort required up-front, which may be significant in heterogeneous interorganizational settings.
Third, an important advantage of electronic markets is their ability to harmonize the description of exchanged objects and to standardize coordination processes. For example, electronic stock exchanges that emerged in the 1980s (e.g., Domowitz, 1993) consolidate offers in a centralized order book and provide an infrastructure for bidding, matching, and settlement processes. The same applies to electronic product catalogs, which consolidate products from various suppliers (e.g., for electronic procurement) and support ordering with shopping baskets and fulfillment functionalities. By means of smart contracts (➏), DLTs could pave the way towards automated trade procedures, which were already proposed for electronic commerce (Bons et al., 1998). Pietrewicz (2019) states that “Coordinating transactions with algorithms, i.e., algorithmic coordination, automates the interactions between parties to a transaction by establishing “objective” set[s] of rules encoded on a blockchain protocol” (p. 109). In the same vein, Möhring et al., (2018, p. 5) conclude that “smart contracts can be used in every business case with […] a specific causal relationship.” They describe these specific causal relationships with the possibility to formulate “if–then-else” conditions, which is the case for many business transactions. Standardizing such smart contracts has been recognized within the roadmaps of ISO and ITU (Li & Tang, 2022) but requires that technological standards are complemented by agreed definitions at the business application level (e.g., for business rules, see Capocasale & Perboli, 2022). Since the existing DLT standardization has predominantly focused on the technological level, identification, classification and transaction standards like those proposed for electronic business (Rebstock et al., 2008) are only about to evolve (e.g., the international token identification number (ITIN) for standardizing tokens).
Interaction atmosphere
The atmosphere dimension captures the overall relationship in which interactions occur. It is affected by interactions and affects interactions. Three areas of potentials and challenges are mentioned in this dimension.
One of the foremost potentials of DLT systems is their ability to generate trust among anonymous parties. For this reason, blockchain systems have also been termed as distributed (➊) “trust machines” (Economist, 2015) or as the “internet of trust” (Zamani, 2018). Key elements for trust are the transparency of the transaction record (➋) and the integrity enabled by the consensus mechanisms (➎). The transaction record has the potential to create trust by being tamper-resistant and by offering the traceability of transactions for all parties. Consensus mechanisms have a trust-building effect on the network since they are usually based on credible commitments (see Williamson, 1983) in the form of stakes, computing power or similar, combined with mechanisms that reward honest behavior (e.g., loss of stake due to misconduct). This trust-building capability is particularly valuable for establishing new relationships in business networks and facilitating transactions in low-trust environments, such as consumer interactions with atomistic market relationships. While DLT’s trust-building capability may be less needed in established, long-term relationships between businesses, the shared transaction record still serves as a facilitator for coordination and cooperation among parties working towards a common goal (Lumineau et al., 2021). In addition, DLT traceability has been proposed as beneficial in providing an audit trail in industries such as financial services, healthcare and pharmaceutical industries (Deshpande et al., 2017). Overall, DLT offers the potential to foster trust and collaboration in various distributed business settings.
A specific issue among parties are transparency challenges, which are manifold in DLT and affect regulations (e.g., GDPR) as well as business interests (Sedlmeir et al., 2022). First, access to the transaction record (➋) should be restricted if the indiscriminate disclosure of sensitive data is to be prevented. Solutions in this respect are to store sensitive data off-chain and to use zero-knowledge proofs (which enable certification without revealing credentials) to maintain verifiability as well as auditability for all nodes (Sedlmeir et al., 2022). Second, while traceable transaction records may benefit applications like DeFi, they can pose challenges in other settings, as increased transparency might expose sensitive business information, such as cost structures or competitive strategies, which some parties prefer to keep private. For example, logistics companies have been hesitant to join platforms that improve transparency and instead favored bilateral transactions (Alt & Klein, 1998). The situation might be different in business networks where trust among the parties already exists due to long-term relationships. At the same time, such scenarios reduce the benefits of a distributed (➊) “trust machine” since additional trust-creating measures are not necessary. Therefore, using a secure transaction infrastructure might prove adequate unless benefits in other dimensions (e.g., a high degree of distribution in the dimension “parties” or/and a high potential for integrated transactions in the dimension “interaction process”) are present. Third, transparency can be a drawback in scenarios involving the phenomenon of maximum extractable value, where parties exploit knowledge of unconfirmed transactions by strategically including, excluding, or ordering transactions to maximize individual value at the expense of others (Gramlich et al., 2024). Overall, transparency risks might hamper participation and active usage, particularly if they outweigh the benefits of increased trust.
Finally, DLTs are infrastructures for sharing and processing data in distributed business settings. As network goods, they are subject to externalities, which are based on the expectation of potential or existing participants as to how the network evolves. Following the literature on networks (e.g., Economides, 1996), the expectations may be positive or negative and either lead to further network growth or the decline of the network. From the large number of initiated DLT projects, many have been discontinued due to a lack of critical mass of participating parties within their intended target community (e.g., all members of a supply chain or business network). Technologically convincing pilot projects, such as Tradelens initiated by the logistics company Maersk, discontinued their operation since the critical mass of active participants could not be reached (Maersk, 2022). Creating sufficient incentives for all parties to participate in DLT solutions, therefore, remains a key challenge. It should be added that participation alone, i.e., having parties signed up for participation, is insufficient. If other actors observe that the participating parties are not actively using the system, externalities will likely be negative. In systems with market functionality, actors will quickly identify such shortcomings due to a lack of liquidity (i.e., the number of offers to buy or sell) in the system.
Interaction environment
The environment dimension refers to the wider context of interactions and cannot be influenced directly by the interacting parties. Following the technology-organization-environment framework (Baker, 2012), the environment dimension is an important factor for the adoption of innovations. It may be viewed from the perspective of industry structure, technology support infrastructure, and regulation. In the following, these three perspectives are used to describe the potentials and challenges of the environment.
The impact of advanced IT on industry structure has a long tradition and is reflected in changes in market power among existing competitors as well as in new competitors from outside the industry. Among the approaches to analyze such structural changes is transaction cost theory (e.g., Malone et al., 1987). IT tends to reduce economic transaction costs, which may either favor more market-based or closer hierarchical forms of organization within an industry. DLT, compared to centralized solutions, has been found to further lower transaction costs, as demonstrated in tokenization business models in bond markets (Cisar et al., 2025). However, the Tradelens example shows that DLT systems may struggle to outperform competing existing centralized platforms such as Tradeshift. In addition, centralized platform providers in the DLT area, such as the cryptocurrency exchanges MtGox and FTX, have illustrated that centralized platforms represent single points of failure and that disintermediation of centralized (trusted) platform providers by DLT solutions is not a fast-selling item, rather pointing at the need for new business models (Beinke et al., 2024; Lage et al., 2022). In this regard, Strebinger and Treiblmaier (2024) observed that DLTs which only support pure transaction functionality are unlikely to replace existing competitors. More likely is a re-intermediation when business models also include added services, such as call-center support or access to customer reviews. These findings are confirmed by Feulner et al. (2022), who find extensive disintermediation broadly present in the literature but an unlikely outcome for institutional intermediaries in the real world. Intermediation by new actors or re-intermediation by existing institutional intermediaries is identified as a potential with possible offerings focusing on trade facilitation and more complex market functions (e.g., matching). Following the broad existence of ledgers in economic life, more use cases for DLT should be combined to unlock the application potential of DLT.
Second, the technology support infrastructure posits that the availability of skilled labor fosters innovation (Baker, 2012, p. 235). With the rise of new technologies, existing competencies are challenged and new competencies are required. This has also been observed with interorganizational information systems (IOS), where the implementation “means that power structures, action frameworks, and the distribution of competencies may be challenged” (Christiaanse & Huigen, 1997, p. 84). On the one hand, new technologies like DLT will require knowledge of how they are implemented as well as the resources to operate them (e.g., providing mining capacities, programming of dApps and DAOs). Companies may source these competencies from technology providers or consultants and many blockchain-as-a-service providers have already appeared on the market (see Song et al. (2022) for a comprehensive list). Existing DLT solutions indicate that non-IT companies, such as Maersk (2022) from the logistics industry or Walmart from the retailing sector (Kamath, 2018), have partnered with IT providers to establish their solutions. On the other hand, electronic markets have shown that the workforce in participating companies might neither possess the necessary skills nor mindset (e.g., for collaboration and further education) and might oppose the use of the system (Alt & Klein, 1998). This might be due to lacking knowledge of how new procedures work (e.g., electronic auctions, digital currencies), but may also be grounded in the fear that the transparency of data and the automation of interactions might render their existing expertise (e.g., the overview on market developments, the mastery of manual processes) obsolete. Again, the benefits of the DLT solutions should be clear to the parties and convince them to see an advantage rather than a threat in using them.
Third, regulation is known as an ambiguous environmental factor since it can either sustain or hinder innovation. While it can prevent misuse and establish legal certainty, it may also impede new developments and burden businesses (especially start-ups) with bureaucracy. In its early days, DLT, especially in the form of cryptocurrencies and other financial products, used to be unregulated (Jabotinsky, 2020; Scholl et al., 2020). The U.S. government then began regulating the crypto market, generally treating cryptocurrencies as securities. However, apart from the cryptocurrency and the non-fungible token market (see Regulation 2023/1114 of the EU on markets in crypto-assets (MiCA)), the broader use of DLTs as a technology remains less explicitly regulated (Sarel et al., 2023) and regulation on protecting personal data in addition to the GDPR (Finck, 2018; Hawig et al., 2019; Scholl et al., 2020 are only emerging (e.g., guidelines 02/2025 in the EU, EDPB (2025)). In this context, Rieger et al. (2019) provide recommendations on how to ensure that DLTs comply with the GDPR (e.g., no personal data stored on-chain). The applicable regulation is dependent on the participation structure (➍) and the object of transaction (money, identity, status, property right). This leads to a picture where businesses must comply with different regulations, which are specific to technologies (e.g., blockchain laws in Switzerland versus generic regulations like GDPR), geographical regions (e.g., laws differ between the US and EU) and application domains (e.g., financial or healthcare sectors face different regulation than other industries). Such high regulation density may exceed what businesses demand, which is “a minimum clear, concise and DLT friendly legal framework […] to secure consumers and investors from malicious actions” (Hashimy & Sandner, 2020, p. 7). Smaller jurisdictions from national legislation (see case studies by Scholl et al. (2020)) may serve as a role model for larger jurisdictions such as the EU. To ensure effective regulation, Sarel et al. (2023, p. 51) recommend global regulation by embedding rules on-chain, creating incentives to comply, and automated enforcement of rules. It shows that regulation is closely related to technology and calls for collaboration with institutions like ITU or ISO. To date, some technical DLT standards exist (see overview by Lima (2018)) and ISO has published standards in the Technical Committee 307 (ISO, 2022). In addition, there are initial solutions for technological hurdles such as CLI (Anthony Jnr, 2023; Lohachab et al., 2021), but many of these proposals still await adoption in practice.
Conclusions
This Fundamental provided a structured overview of the concept, architecture, and business potentials as well as challenges for DLTs. On the one hand, it reflects the hype that blockchain and DLT have undergone since 2008 and argues that the technology bears a broader application potential than cryptocurrencies. This is the case since most business settings feature an interplay of multiple companies and organizational boundaries still represent substantial inefficiencies. Similarly to the distributed nature of these settings, DLTs provide a distributed infrastructure that not only serves to exchange data between multiple parties in a transparent, real-time, and trust-building manner but also allows to automate critical activities, such as coordination tasks and interaction routines. While this suggests substantial potentials that have a profound impact on current value chains (Witt & Schoop, 2023), the Fundamental on the other hand emphasized that DLT’s potentials are neither low-hanging fruits nor that the technology is a “silver bullet” for all business settings. Existing business structures should feature a minimum degree of distribution and inefficiency in (real-time) data sharing and automation, which implies that many intraorganizational and bilateral supply chain settings will not “automatically” bear sufficient potential for a DLT solution.
As with information systems in general and IOS in particular, change is not affected by replacing one technology with another. These systems operate in a social and organizational environment, which exerts a strong impact on the technology’s adoption. If these non-technical factors are considered, the picture of comparing centralized and distributed solutions becomes more complex and emphasizes that incentives for using a DLT need to be created in multiple dimensions. To understand the potentials and challenges of DLT for businesses, this Fundamental applied an interaction model with the four dimensions of interacting parties, interaction process, interaction atmosphere, and interaction environment. Since it depends on the use case as well as on the the specific organizational and industry setting whether DLT is superior to traditional approaches such as centralized databases (Chowdhury et al., 2018; Wüst & Gervais, 2018), the potentials and challenges serve as a guide for researchers and decision-makers on what to consider when adopting DLT, or which characteristics contribute to the “sales proposition” of a DLT compared to other solutions. Although many existing applications have not been successful, DLT is advancing and should be observed closely.
In the future, generation 4.0 technologies could provide opportunities for the convergence of DLT and other technologies. For example, Fan et al. (2024) point out that DLT’s qualities in enhancing trust and accountability could complement AI-generated content. In addition, the dichotomous either-or decision between centralized and decentralized solutions may vanish. On the one hand, DLT could enable shared data layers or contribute specific functionality for payments or proofs, which are used by enterprise applications. On the other hand, DLTs could provide shared functionalities across multiple enterprise systems. In the financial sector, the notion of centralized-decentralized finance (CeDeFi) (e.g., Scharfman, 2022) illustrates that such combinations could bring together the best of both worlds. Ultimately, the application potential for DLT-based innovations is still emerging and illustrates that identifying compelling business solutions remains a challenging endeavour.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Numbers in brackets refer to the DLT characteristics (see Fig. 3).
Martin Smits
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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