A TISM modeling of critical success factors of blockchain based cloud services

Sanjay Prasad (IBM India Pvt. Ltd, Bangalore, India)
Ravi Shankar (Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India)
Rachita Gupta (Indian Institute of Technology Delhi, New Delhi, India)
Sreejit Roy (IBM India Pvt. Ltd, Bangalore, India)

Journal of Advances in Management Research

ISSN: 0972-7981

Publication date: 1 October 2018



Over last few years, a major innovation known as blockchain technology has emerged as potentially one of the most disruptive technology of recent times. The purpose of this paper is to identify and analyze various critical success factors (CSFs) that can facilitate success of blockchain-based cloud services. Further, this paper aims to analyze and understand mutual interactions among these CSFs.


In this paper, 19 CSFs have been identified through literature review and expert opinions. The hierarchical framework developed using total interpretive structural modeling has revealed the inter-dependencies among these CSFs. The methodology employed in this study provides a mechanism to conduct an exploratory study by identifying the factors and analyzing their interactions through the development of a hierarchical framework. This research further categorizes CSFs into multiple clusters based on their driving power and dependence power.


This paper has identified 19 CSFs, namely, user engagement, industry collaboration, rich ecosystem, blockchain technology standardization, regulatory clarity, cost efficiency, energy efficiency (wasted resources), handling blockchain bloat, miner incentives, business case alignment to blockchain capability, sidechains development, blockchain talent pool availability, leadership readiness for a decentralized consensus based technology, technology investment and maturity, trust on blockchain networks, integration with other cloud services, robust and mature smart contracts platform, blockchain security and user control on data (privacy). Further, driver and dependent variables have been identified.

Research limitations/implications

Future research can discover and detail the sub-factors behind the 19 CSFs identified in this paper. Additionally, more work can be done to extend the current structural model for blockchain-based services to a more functional form.

Practical implications

It provides a comprehensive list of CSFs that are relevant for development of blockchain-based cloud services. This will help industry leaders to strategically focus on the main drivers that will ensure that businesses get maximum benefit of this disruptive technology.


This study makes a significant contribution in the literature of blockchain-based cloud services, which captures the perspective of different stakeholders. This study is one of the first (if not the first) systematic research on adoption of blockchain-based services. It creates the foundation to carry out further research in this area.



Prasad, S., Shankar, R., Gupta, R. and Roy, S. (2018), "A TISM modeling of critical success factors of blockchain based cloud services", Journal of Advances in Management Research, Vol. 15 No. 4, pp. 434-456. https://doi.org/10.1108/JAMR-03-2018-0027

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

1. Introduction

This is an exciting time for businesses and technologies. Fascinating new technologies, such as cloud, big data, machine learning and cognitive computing are emerging, and they promise to completely change the way business is done. Another disruptive and transformational technology is blockchain, first coined in 2008. Blockchain is the underlying technology behind Bitcoin, but it is lot more than Bitcoin. A blockchain is made of blocks of transactions chained together, where each block is represented by a hash value composed of previous block’s hash value, current block’s transaction and a random number called nonce (used to create a hash value as per the blockchain’s specifications). Figure 1 shows a typical blockchain structure.

Blockchain is a decentralized transaction and data management technology with attributes to provide security, anonymity and data integrity without any third-party organization in control of the transactions (Yli-Huumo et al., 2016). Evry (2015) defines blockchain as “a global distributed ledger, which facilitates the movement of assets across the world in seconds, with only a minimal transaction fee.” More correctly speaking, blockchain deals with digital representation of asset and that’s what gets transferred in an immutable and decentralized manner. It is built around the concept of a distributed consensus ledger, which is kept and maintained on a distributed network of computers. This allows entire network to jointly create, evolve and keep track of transactions which are immutable and completely auditable (Evry, 2015). Bogart and Rice (2015) state that this ability to directly exchange value over the internet without a middleman, and the associated friction in terms of cost, delays and risks is what makes blockchain technology a radical innovation. Figure 2 illustrates how blockchain transactions work. Every time a transaction is requested, it is validated by the mining nodes. Once verified, a block is created and added to the existing blockchain.

IBM Blockchain (2017) defines blockchain as a shared and replicated ledger that provides consensus, immutability, finality and provenance. Further, it identifies following basic components of blockchain:

  1. Business networks: business networks create value from connectivity and generate wealth by flow of goods and services across the network.

  2. Participants: participants are customers, suppliers, third-party service providers, partners, etc. Further, while Bitcoin participants are anonymous, for most of blockchains for business, participants will be known.

  3. Assets: IBM Blockchain (2017) defines assets as “Anything that is capable of being owned or controlled to produce value.” Assets can be tangible (cars, houses, etc.) or intangible (patents, bonds, etc.). Assets are exchanged over business networks and this exchange creates value for the parties involved.

  4. Transactions: transactions are the exchange of asset, and are the primary business activity creating value for the network. In Bitcoin, transactions are known (while participants are unknown). In blockchains for business, transactions will be mostly confidential between the parties involved.

  5. Contracts: contracts underpin transactions, and are conditions for a transaction to occur. A smart contract will trigger a transaction, as soon as the conditions are met.

  6. Ledger: ledger is a log of transactions, and is the system of record for a business. In blockchain, ledgers are shared and replicated across blockchain nodes. Further, a business may have multiple ledgers for different business networks that it participates into.

Mougayar (2015a) states that blockchain is not just a programmable ledger, but also a networked infrastructure of computing machinery. This networked infrastructure, already used for transaction validation, can also be used to run computer programs, and offers structural advantages such as durability, transparency, immutability and a trustless environment. Blockchain technology will see greater enterprise adoption, if we go by the trends in the industry in form of early enterprise adoption, usage trends, R&D expenditure and venture capital activity (Schatsky and Muraskin, 2015; Bogart and Rice, 2015; IBM, 2017; Goertzel et al., 2017). Various blockchain use cases, besides cryptocurrency, are being identified and explored. Wilson (2015) presents a case for blockchain data processing by banks to ensure the integrity of its transaction processing network. Smart contract is another high-potential blockchain application. Smart contracts are blockchain transactions with extensive embedded instructions beyond simple buy/sell instructions, and enable people to form agreements that gets automatically enforced once the pre-specified conditions are met (Swan, 2015; Bogart and Rice, 2015). Other companies are planning to embed Bitcoin mining into internet-connected devices enabling a thin, cloud-like computing infrastructure (Evry, 2015).

Blockchain can be further classified as public or permissionless blockchain, and private or permissioned blockchain (Thompson, 2017). A public blockchain is open for everyone to participate in any of the roles (user or node). In private blockchains, the access to blockchain is restricted by a private party or a consortium, and network has control over who can join the network, and who can do what role. Further, blockchain has been also viewed as a special implementation of distributed databases (Narayanan, 2015), although others differ (Greenspan, 2016). As blockchains also store and manage data, Table I illustrates the comparison of attributes among the two types of blockchains and its competing technologies (centralized database and distributed database).

Enterprises will increasingly use cloud for its computing needs (Ali et al., 2015; Tseng and Lin, 2011). Wahner (2016) predicts that most enterprises will leverage cloud services for blockchain, rather than building their own blockchain infrastructure. IBM and Microsoft have taken a leading role in providing blockchain-based cloud services, and enable integration into their other cloud services. IBM Blockchain (2017) on Bluemix leverages the Hyperledger project enabling users to build and run a secured blockchain network (IBM Blockchain, 2017). Microsoft uses Ethereum platform to offer Blockchain as a service on Azure (Microsoft Azure, 2017). However, there is a need for a systemic approach to evaluate and study key critical success factors (CSFs) that can ensure success of blockchain-based cloud services, and ensure that blockchain technology can be leveraged to its full potential. In view of the importance of this need, the paper attempts to develop an interrelationship framework of a blockchain-based cloud services ecosystem. In this paper, 19 CSFs have been identified through literature and brainstorming with experts from industry and academia. For analyzing inter-relationships among these enablers, total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis have been used.

Research objectives of this paper are as follows:

  • to identify the CSFs of the blockchain-based cloud services;

  • to establish mutual relationships, relative importance and interdependence of each CSF; and

  • to analyze the driving power and dependence of the factors affecting the blockchain-based cloud services.

The rest of the paper is organized as follows. Section 2 presents literature survey undertaken to identify relevant CSFs and gain a systematic understanding on the issues. Section 3 discusses research methods used in this paper and Section 4 analyzes the key variables and interprets the model. Finally, conclusions and future scope of research are discussed at the end of this paper.

2. Critical success factors

McCool (2017) describes cloud technologies as the forerunners to blockchain. Blockchain networks can be deployed on cloud, and there are active collaborations between Google, IBM, Microsoft and Amazon for blockchain on cloud (McCool, 2017). Blockchain-based cloud services can prove to be one of the most transformative technology of current times. However, this is still far from being a mature area, and there are number of factors that will determine if it realizes its transformation potential. Based on the literature survey, a total of 19 CSF have been identified that help in the success of the blockchain-based cloud services. These are discussed as follows.

2.1 User engagement

Blockchain, at the heart of it, is a value network, and a network’s success is defined by the engagement of its users. Mougayar (2015b) quantifies user engagement as at least 30 percent or more users returning to use the services weekly, if not daily. User engagement is of course a function of many parameters including user experience, value realized, etc. Therefore, user engagement is one of the primary success factors defining the success of blockchain technology.

2.2 Industry collaboration

Blockchain will be successful if a strong community and value-creating network can be formed. This will need shared solutions and shared solutions will require governance and consensus around technology choices. Industry leaders must collaborate to design the right solutions, and should form consortia and work with regulators early on (McKinsey, 2015). Further, if needed, resources and knowledge must be shared with competitors and technology providers to facilitate industry utilities and faster development cycles for blockchain projects. Therefore, strong and win–win collaboration is a very important driver for blockchain success. There have been a plethora of consortia or other collaborations operating either globally or focused on a region (Meijer, 2017). Most famous of these are R3CEV, the Hyperledger Project and the Post Trade Distributed Ledger Group. There are also regional consortium efforts like Project Jasper in Canada, Russian Banking Blockchain Consortium, China Ledger Alliance and Ripple Japanese Bank Consortium. Outside the financial industry, Blockchain Insurance Industry Initiative (B3i) is focused on insurance industry, Healthcare Blockchain Consortium (led by Hash Health) on healthcare industry and Commodity Blockchain Consortium (led by Kynetix) on commodities trading. However, there also have been some setbacks as seen in pullout of some prominent companies like Goldman Sachs and Santander from R3CEV (Hackett, 2016).

2.3 Rich ecosystem

Ruutu et al. (2017) identify gaining a critical mass of end users, developersand service providers, in other terms, a rich ecosystem, as a key issue for the success of technology platforms. Blockchains include several different types of participants as a business network, and its real value is achieved when these business networks grow (IBM, 2017). A rich ecosystem significantly reduces the barrier to innovation by enabling businesses and developers to build new business models, reduce time to market and initial investment for new products and services using an array of off-the-shelf plug-and-play technologies (Bogart and Rice, 2015). Development and maturity of this ecosystem will facilitate better mainstream adoption of blockchain technology, as it allows us to fully leverage the security, durability, autonomy and cost efficiencies of blockchain technology.

2.4 Blockchain technology standardization

Industry Standards will need to emerge for better enterprise adoption of blockchain technologies (Everest Group, 2016). Fallout of lack of a universal standard will be severe impact in network effect (Accenture, 2016). The lack of standards will drive a lot of bilateral agreements and altered processes between players, which would diminish the network effect and prevent companies from enjoying the full benefits of the ecosystem. However, international bodies are working in this direction. International Organization for Standardization (ISO) has established a new technical committee, ISO/TC 307, to develop international blockchain standards (Meguerditchian, 2017).

2.5 Regulatory clarity

Hekkert et al. (2007) note that a sustainable technology change cannot be just brought by technical change, but need changes in the social dimensions. One of such required social change is in the regulatory domain. Regulatory clarity is a significant driver for the industry, as it allows businesses and investors to confidently embrace and fund the technology (Bogart and Rice, 2015). Regulatory clarity (or lack thereof) will decide whether the blockchain industry will develop into a full-fledged industry (or not). Multiple countries have banned or continued to deliberate cryptocurrency related issues (Swan, 2015). The concept of digital currencies does not fit easily in the existing regulatory structures and will need new legislation. Again, there is no consensus on the legal status of cryptocurrencies – currency or property, taxable or non –taxable, licensed or open, etc. Recently, Union Budget of India 2018 declared cryptocurrencies to be illegal tender, but supported exploration of blockchain technology(Mathur, 2018). Regulatory clarity will not only reduce the ambiguity, but may also bring the much-needed user protection. This will hasten the adoption of blockchain greatly.

2.6 Cost efficiency

Any technology will need to show good returns on investment and a faster payback period to succeed. Blockchain-based applications are no exception. Therefore, blockchain-based cloud services must be highly cost efficient, even while scaling up and wasting lot of computational resources (mining) by design (Swan, 2015).

2.7 Energy efficiency (wasted resources)

Earlier, solo miners evenly provided the computation power in Bitcoin network. However, the share of pool miners increased with the evolution in Bitcoin network. Wang and Liu (2015) note that as each miner increases their computation power, total computation power in the network increases; leading to increase in the difficulty value and reducing the Bitcoin mining rate of individual miners. This leads to deteriorating energy efficiency over time.

Blockchain mining draws an enormous amount of energy, estimated to be $15m per day or even more, to compute and verify transactions securely and with trustworthiness (Swan, 2015). This is by design and multiple distributed nodes do the same work (wasted resources) to make a blockchain trustable. At the same time, for mainstream adoption, it is important to improve the energy efficiency either through technology improvement or through business model (private blockchains). There have been some work and proposals to improve energy efficiency through economic models (Wang and Liu, 2015), more efficient block design (Paul et al., 2014) and faster Bitcoin mining through simultaneous usage of CPUs and GPUs (Anish, 2014). However, there is a need for more work and more scalable design for large-scale enterprise adoption of blockchain-based cloud services.

2.8 Handling blockchain bloat

As the blockchain adoption increases, blockchains will grow exponentially, commonly referred to as blockchain bloat (Swan, 2015). Further, general trend in the industry is of big data (Chen and Zhang, 2016; Wang et al., 2016). Current state of art still assumes small blockchains. However, to become mainstream technology, blockchains of future must be scalable and fast (Schatsky and Muraskin, 2015). The size of the blockchain ledger is already more than the capability of some of smaller devices and may lead to making a vast part of the public unable to participate or delays in processing transactions (Innovalue, 2015). Therefore, enhancements in blockchain design are needed to improve scalability by reducing latency, increasing throughput and enhancing security. Progress in this domain will expand the technology’s adoption (Schatsky and Muraskin, 2015).

2.9 Miner incentives

Mining, in the context of blockchain, refers to the distributed review process performed on each “block” of data in a “block-chain” (Blockchain Technologies, 2017). This enables consensus in an environment where neither party knows or trusts each other (hence, also called as “trustless” system). However, solving the hash function for block validation is time and energy consuming for the nodes (Innovalue, 2015). Further, blockchain-based cloud services (especially public blockchains), to be scalable, will need more validation (mining) nodes in future. Therefore, attractive and sustainable miner incentive structure for new applications/chains will help attract more mining nodes and drive blockchain adoption.

2.10 Business case alignment to blockchain capability

Traditional business models might not seem applicable to blockchain, since the whole point of decentralized peer-to-peer models is that there are no facilitating intermediaries, whereas most of the businesses are formed around concept of intermediation (to reduce total transaction cost for customer while earning a fee for themselves). However, there are many worthwhile applications of blockchains even in a traditional business (Swan, 2015). The ability to pick up right business cases for exploring and blockchain implementation will be a CSF. Further, blockchain is neither panacea nor suitable for every business problem. It makes sense for applications needing databases that are modified, without a trusted intermediary, by multiple untrusting writers (Greenspan, 2015). To reduce disappointments down the road, it is essential to use blockchain for only those use cases, where it makes sense.

2.11 Sidechains development

Development of sidechains, alternative blockchains with additional features but still linked to Bitcoin, could be a CSF for the broader adoption of blockchain technology (Bogart and Rice, 2015). Private sidechain has already been launched by Blockstream and adopted by multiple startups to allow exchanges to move funds between order books without the need to transfer funds on the bitcoin blockchain (Rizzo, 2015). If successful, sidechains would allow much-needed extensibility (of functionalities) of blockchain. It will drive innovation by helping to create new solutions while still leveraging the network effect of the Bitcoin blockchain.

2.12 Blockchain talent pool availability

Blockchain is a new computing paradigm and will need different skills sets to be successful. Enterprises will need a smart contracts talent pool that can connect legal text to business logic and convert that to a programmed smart contract on blockchain (Everest Group, 2016). However, an acute shortage of available talent for blockchain industry jobs has been reported as a major problem preventing wider growth and use of the technology (Castillo, 2017). Skilling up current resources and/or hiring blockchain skills will be a CSF for better returns on blockchain investments and, therefore, increased blockchain adoption.

2.13 Leadership Readiness for a decentralized consensus based technology

Marie Wieck from IBM (2017) identifies top-down executive support for innovative blockchain use cases as one of the key CSFs. Further, blockchain, being a new way of business, needs leadership to devolve control to multiple nodes mostly anonymous and outside their authority. This is completely different than what has been practiced and believed till recently that more centralization is better for standardization, efficiency and profitability. Further, executive leadership also need to understand this new technology, so that they can bring out relevant use cases from their organization.

2.14 Technology investment and maturity

There are a number of open technical issues being actively debated in the developer community ranging from debates around block size to latency to security and throughput (Bogart and Rice, 2015). Technical community is also divided on the solution – some of them favor creation of new blockchains, and others would like to keep improving the Bitcoin blockchain (and generally favor only one blockchain) for its network effect. While only time will tell, which side is right, what is needed is continued investment in technology and maturing it so that the current open issues are resolved. This is one of the strategic drivers for improved blockchain adoption.

2.15 Trust on blockchain networks

Hengstler et al. (2016) state that the belief, that the quality of technological innovations is sufficient to convince users, is mistaken, and other reasons, especially trust in the innovation influence the adoption decision greatly. For quite some time, public perception of Bitcoin blockchain has been a venue (and possible abettor) for the dark net’s money-laundering, drug-related and other illicit activities (Swan, 2015). While Bitcoin and the blockchain are themselves neutral like any other technology, public perception and trust on blockchain networks need to be shaped for improved adoption of blockchain-based cloud services. One of the primary use cases for blockchain is to establish an irrefutable provenance. Everledger is using blockchain to establish diamond’s origins and help avoid conflict diamonds (Volpicelli, 2017). Walmart is leveraging this technology to provide a “farm to fork” traceability of food products (Kharif, 2016). Blockchain industry models need to solidify and mature so that there are better safeguards in place to stabilize the industry and allow both insiders and outsiders to distinguish between good and bad players. This will help build trust on the blockchain.

2.16 Integration with other cloud services

A key success factor for blockchains in an enterprise is middleware – integration of blockchains with each other and with many other systems in real time, using different technologies and communication protocols (Wahner, 2016). Blockchain may take its place in enterprises as a trusted back-office workhorse, rather than a front-ending application for business users. A blockchain infrastructure, being an open independent peer-to-peer network without a central backbone, will not be trivial to integrate with. Further, to analyze events on blockchain, it may be integrated with other enterprise applications in real time. Finlync has launched a blockchain integrator to allow seamless integration to SAP for Ethereum and Hyperledger blockchains (Finlync, 2017). Integration with ERP systems allow to leverage blockchain technology for corporate banking services like trade financing, payments management. Therefore, development in middleware facilitating easier and faster integration will be a key CSF for blockchain adoption.

2.17 Robust and mature smart contracts platform

One of the basic premises of Blockchain is ability of individuals to form smart contracts in a disintermediated way. A smart contract is a solution that creates contracts between two or more participants in a decentralized environment, where contract terms are automatically executed by the Blockchain system (Yli-Huumo et al., 2016). R3, a banking industry consortium, ran a series of banking trials to execute smart contracts on multiple blockchains and simulated trading of commercial paper for a global group of 40 banks (Eyers, 2016).

Such a decentralized smart contract system is one of the most promising applications of blockchain technology (Bigi et al., 2015). However, current state of art enables basic smart contract functionalities only (Bogart and Rice, 2015). A robust and mature smart contracts platform would be invaluable and will provide the opportunity to easily code business logic into regular operations. This would accelerate blockchain adoption.

2.18 Blockchain security

Hughes et al. (2017) state that companies are becoming more aware of cyber-threats and are going to increase spending on cyber-security. Further, they believe that this increase in spending will be primarily driven by exponential increase in costs of preventative spending in the pursuit of higher security levels. Blockchain, especially public ones, has some potential security issues (Swan, 2015). The most discussed one is the possibility of a 51-percent attack, in which 51 percent of nodes collude to take control of the blockchain and change previous transactions. Another security challenge is how to keep the cryptography standards ahead of the hackers (Swan, 2015). Cryptography and blockchain experts are working on this problem. An improved security perception will be a CSF of blockchain.

2.19 User control on data (privacy)

Blockchain is a distributed consensus network without a trusted party (Yli-Huumo et al., 2016). All blockchain transactions are transparent and privacy is maintained through anonymity. Public can see all transactions, but cannot link them to identities (Nakamoto, 2008). However, there are studies to show experimental evidence on the lack of anonymity in the Bitcoin network and possibility to do transaction linking to IP addresses (Moser et al., 2013; Koshy et al., 2014; Feld et al., 2014).

A number of works has been done to improve the anonymity of blockchain transaction (Meiklejohn and Orlandi, 2015; Valenta and Rowan, 2015; Ziegeldorf et al., 2015). However, potential privacy nightmare of secret key getting stolen or exposed is still out there and very much possible. Therefore, user control on data will be an important driver for success of blockchain-based cloud services, as that will help assuage user’s privacy concerns with a decentralized system with multiple (and potentially unverified) nodes (Bogart and Rice, 2015; Swan, 2015).

3. Research methodology

This study has been conducted in three steps.

3.1 Identification of CSFs

A preliminary set of CSFs for blockchain-based cloud services adoption has been identified from the literature. Section 2 has already presented and discussed these factors.

3.2 Prioritization and modeling of CSFs using TISM

Jena et al. (2016) identified development of a conceptual framework as the fundamental phase of an organizational research. This paper has conducted an exploratory study to develop a hierarchical framework for the blockchain-based cloud services. In the previous step, CSFs were identified and in this step, hierarchical relationships among the identified CSFs have been delineated and interpreted based on data collected through surveys and discussion with experts in this area.

Structural models are used to model and analyze complex systems with lot of interacting factors (Watson, 1978). Interpretive structural modeling (ISM), first proposed by Warfield (1974), facilitates an interactive learning process. As part of the ISM methodology, a set of different variables affecting the system under consideration is structured into a comprehensive systematic model. It allows us to converts a weakly articulated mental model into a visible and a well-defined model (Sushil, 2005). The methodology of ISM acts as a tool for imposing order and direction on the complexity of relationships among elements of a system (Pramod and Banwet, 2010). It allows us to portray the structure of a complex issue of the problem in an intuitive and visual pattern employing graphics as well as words (Talib et al., 2011).

There are multiple limitations of ISM as pointed in literature (Jena et al., 2016, 2017; Biswas, 2017; Lee et al., 2010; Rajesh, 2017):

  • ISM does not interpret the links, i.e. it does not explain in what way factor A will help in achieving factor B (Yadav and Sushil, 2013);

  • ISM identifies the direct relationship among factors and does not focus on transitive or indirect relationships; and

  • ISM digraph needs to be further interpreted for decision making and, therefore, it can be only used by domain experts.

TISM (Sushil, 2005; Dubey et al., 2016) is an extension of traditional ISM with additional modeling constructs to address above weaknesses. Key additional tool here is “interpretive matrix” which provides interpretation for relationships in structural models (Sushil, 2005). Interpretive matrix depicts interpretation of relationships in a matrix form for each pair-wise element.

The methodology adopted in the research is TISM as there is a need to establish the relationships logically among blockchain CSFs using a standard qualitative modeling technique. The TISM methodology enables to establish relationships among CSFs and help answer “Why” the relationship exists between two elements (Biswas, 2017). Various steps involved in the TISM methodology (Jena et al., 2016) have been depicted in Figure 3 and discussed below.

3.2.1 Step I: identify the factors to be linked

CSFs for blockchain-based cloud services have been identified and discussed in the previous sections.

3.2.2 Step II: identify the relationships between the factors

Relationships among identified factors have been established by understanding whether a particular CSF is influencing or enhancing all other CSFs.

3.2.3 Step III: interpretation of relationship

Each CSF has been compared against the other CSFs. From 19 identified CSFs, a total of 342 comparisons were made. For each comparison, an entry of “Y” on “N” has been made to indicate where a relationship exists between factors or does not exist. For each pair-wise comparison with an entry of “Y,” interpretive question “in what way the CSF-A is influencing or enhancing CSF-B” has been made and answered.

3.2.4 Step IV: interpretive logic-knowledge base

A pair-wise comparison is done in which each element is individually compared with all other elements, and an “Interpretive Logic-Knowledge base” is developed. For each pair-wise comparison, the existence of relationship (or lack of) is represented by entry “Y” (or “N”). If the entry is “Y,” it is further interpreted to understand the reasoning behind the relationship between two factors.

3.2.5 Step V: development of reachability matrix

Identified relationships established in Step III have been used as a basis to develop the initial reachability matrix by entering 1 wherever the row variable drives the column variable and 0 everywhere else. Then the matrix is tested for transitivity in an iterative fashion. Transitivity exists if an indirect relationship exists among factors, which means if factor A drives factor B and factor B drives factor C, then factor A will also drive factor C. By inserting transitivity in the initial reachability matrix, a final reachability matrix has been obtained (as shown in Table II). For each transitivity link, initial reachability matrix has been updated by altering the “N” entry to “Y” entry and updating the interpretation column.

3.2.6 Step VI: level partitions of reachability matrix

Level partitioning is done in the same way as for ISM. The reachability set and the antecedent set is identified for each CSF from the initial reachability matrix. When both the intersection set and the antecedent set are similar (meaning CSF does not drive any factor without being driven by it), CSF is assigned a level and excluded from analysis. This process is iterated, until all CSFs are assigned a level as shown in Tables III and IV.

3.2.7 Step VII: construction of digraph

A digraph consists of nodes and directed links. Each node represents CSFs and each link represents direction of relationships between two factors. Each factor is arranged in the digraph as per the level obtained during level partitioning. The direction of relationships is established as per the reachability matrix. Significant indirect relationships are also shown in the final digraph with dotted line.

3.2.8 Step VIII: interpretive matrix

A binary matrix is developed from the digraph; “1” indicating direct as well as transitive links between factors. Significant relationships among factors are then mentioned in the matrix by using the interpretation provided in the interpretive logic-knowledge base table. The interpretive matrix related to TISM for the blockchain-based cloud services has been exhibited in Table V.

3.2.9 Step IX: formation of TISM-based model

In the final step, TISM is developed using digraph and interpretive matrix. Here, information obtained from the interpretive matrix is illustrated by the side of respective links leading to total interpretation of structural model. Figure 4 shows the TISM for blockchain-based cloud services.

3.3 Graphical representation of driving power and dependence of each CSF

MICMAC analysis is done to classify variables in four groups – autonomous, dependent, linkage and driver/independent. Row totals in final reachability matrix indicate the driving power and Column total indicates dependence. The driving power and dependence of each CSF are then represented graphically as shown in Figure 5. The y-axis in the graph represents the driving power of the CSFs, whereas the x-axis in the graph represents the dependence of the CSFs. The graph is divided into four clusters as listed below:

  1. autonomous: low dependence – low driving power CSFs;

  2. dependent: high dependence – low driving power CSFs;

  3. linkage: high dependence – high driving power CSFs; and

  4. driver: low dependence – high driving power CSFs.

4. Analysis and discussions

Figure 4 presents the TISM-based conceptual framework for success of blockchain-based cloud services. The direction of relationship has been established based on driving power as well as dependence of factors.

Regulatory clarity (CSF 5), industry collaboration (CSF 2), leadership readiness (for a decentralized consensus based technology) (CSF 13) and technology investment & maturity (CSF 14) are at the bottom level in the TISM model and forms the core building block of blockchain-based cloud services. These CSFs have strong driving power and will play significant and strategic role in increasing the adoption of blockchain-based cloud services. Therefore, various technology and industry firms should come together, and form consortia to start collaborating on various aspects and drive technology investments. Further, these consortia should work closely with various government bodies to ensure that there is right level of regulations and laws to protect users and provide clarity to various service providers. Another key strategic task in front of these industry consortia and business leadership is to train business leaders on the new blockchain technology. This will drive an appreciation of the technology, its potential (and inevitability) and consequently more acceptance and sponsorship of blockchain-based projects. MICMAC analysis also reinforces the above findings, as all the above-mentioned CSFs are in the “Drivers/Independents” cluster. In addition, rich ecosystem (CSF 3) and blockchain technology standardization (CSF 4) are also in the “Drivers” cluster, indicating very high driving power (and low dependence). A strong ecosystem will bring more participants on the blockchain and, thus, triggering a network effect. Further, technology standardization will ensure that all technological developments in these areas are inter-operable and can be easily integrated together to build a new functionality. Therefore, there should be a strategic impetus to build a strong ecosystem and influence the industry bodies to converge on a single standard.

User engagement (CSF 1) and cost efficiency (CSF 6) are at the top level of the TISM model, and therefore defines the performance or success of the blockchain-based cloud services. Therefore, all activities, to promote blockchain-based cloud services, should be evaluated for their impact on these CSFs. Business/technology leaders in blockchain area should critically evaluate any new initiative on following criteria:

  1. What is the impact of this initiative/investment on user engagement?

    • Will this activity bring more users on the blockchain platform?

    • Will this activity help in better engaging existing users?

  2. Will this initiative/investment improve the cost efficiency of the blockchain technology, and subsequently provide better returns on investment (ROI)?

MICMAC analysis clusters CSFs 1 (user engagement) and 6 (cost efficiency) in “Dependents” cluster indicating high dependence (and low driving power). Additionally, it also categorizes two more CSFs in this cluster, namely, business case alignment to blockchain capability (CSF 10) and trust on blockchains (CSF 15). Therefore, care should be taken to introduce initiatives that help build business specific functionalities, and allows blockchain use cases specific to the business needs. Further, success of the blockchains will also be defined by the fact if users can trust it to host their data and digital assets in a safe and secure manner.

The rest of the CSFs has been clustered as “Autonomous” by MICMAC analysis, indicating the emerging nature of this area. This underlines the fact that there are lots of varied CSFs in the equation, which will impact the success of the blockchain-based cloud services. Therefore, for blockchain-based cloud services to succeed, concerted effort will be needed on multiple fronts. Further, most of these “Autonomous” CSFs have higher dependence (as compared to driving power). This underlines importance of the CSFs in the “Drivers” cluster for success of blockchain-based cloud services.

5. Managerial implications

To the best of the author’s knowledge, there has not been any systematic study to create a structural model for blockchain-based cloud services. This research will help the industry leaders to understand the criticality and interplay of various influencing CSFs and inter-relationships among the CSFs. The driver dependence diagram (MICMAC analysis) helps to categorize the factors into four clusters. Blockchain is still an emerging area, and industry focus should be on the “Drivers” cluster to ensure long-term success of blockchain-based cloud services. These “driver” CSFs are pre-requisite to achieve any kind of results, which are mainly reflected in the CSFs under “dependents” cluster (user engagement, cost efficiency, aligned business cases and trust on the system). One of the commonly committed mistakes is to put lot of effort into the CSFs, which have higher dependence and lesser driving power, as these denote the expected end-results. However, without a strong foundation of “driver” CSFs, results will be either elusive or unsustainable.

Therefore, to achieve sustained success of blockchain-based cloud services, industry and technology efforts should be heavily focused on improving regulatory clarity, driving industry collaboration, building a rich ecosystem, developing industry standards, investing in blockchain technology and, last but not the least, engaging and educating leaders on blockchains’ capability and applications.

6. Conclusion

This paper develops a framework for blockchain-based cloud services to present and interpret the hierarchical inter-relationships between identified CSF using TISM. Further, MICMAC (cross-impact matrix multiplication applied to classification) analysis has been employed to identify the driving power and dependence power of identified CSFs. This paper has attempted to provide insights for both academics and practitioners working in blockchain-based cloud services. Blockchain has been hailed as one of the most disruptive technologies of recent times, which will completely transform the way business is done. However, lot of effort is still needed to convert the great potential of blockchain into reality. The identification of CSFs and awareness of their driving power and dependence will help the industry and governments to focus on them and prioritize the strategic factors. TISM delineates those factors which are critical and need more focus for long-term benefits.

7. Scope for future research

This study is one of the first (if not the first) systematic studies on the adoption of blockchain-based cloud services. It creates the foundation to carry out further research in this area. Future research can discover and detail the sub-factors behind the 19 CSFs identified in this paper. Additionally, more work can be done to extend the current structural model for blockchain-based cloud services to a more functional form, which may help industry to quantity the impact of identified CSFs on the blockchain-based cloud services.


Blockchain structure

Figure 1

Blockchain structure

Blockchain transaction processing

Figure 2

Blockchain transaction processing

Basic steps involved in TISM

Figure 3

Basic steps involved in TISM

TISM of CSFs of blockchain-based cloud services

Figure 4

TISM of CSFs of blockchain-based cloud services

MICMAC analysis of the CSFs of blockchain-based cloud services

Figure 5

MICMAC analysis of the CSFs of blockchain-based cloud services

Comparison of blockchain and its competing technologies

Attributes Public blockchain Private blockchain Centralized databases Distributed databases
Data type Transactions Transactions Master and transaction data Master and transaction data
Storage Multiple locations Multiple locations Single location Multiple locations
Disintermediation Fully decentralized Some central control retained Fully centralized Controlled by a central database
Data consistency Probabilistically identical across the blockchain Probabilistically identical across the blockchain Fully consistent Some nodes may be inconsistent for some time
Immutability Completely immutable Completely immutable Can be changed by central authority Can be changed by central authority
Redundancy Part of design Part of design To be planned – expensive Part of design
Fault tolerance Extreme fault tolerance due to built-in redundancy and multiple nodes Extreme fault-tolerance due to built-in redundancy Building fault-tolerance need additional effort and cost Better fault tolerance than centralized database, but not as much as blockchains
Confidentiality Transactions are fully transparent by design Transactions are revealed to selected node High degree of confidentiality, everything controlled centrally Read and write access on transactions controlled, but lesser degree of confidentiality than centralized database due to distributed storage
Security Very high level of security, irreversibility, and censorship-resistance Very low risk, as it is cryptographically secured and uses “BFT Hardened” consensus protocols Moderate risk, as database can be hacked High risk. Security implementation at all nodes is needed and is considerably harder
Way to change information Through smart contracts Through smart contracts Through stored procedures Through stored procedures
Performance Slowest as it must do additional tasks such as signature verification, consensus mechanism and replication for every transaction Slow, but faster than public blockchain as consensus mechanism is comparatively relaxed Fast; once connection is established, no additional verification (unless needed by business) is done for individual transactions Faster than blockchains, but slower than centralized databases
Scalability Low scalability as multiple copies of full blockchain stored and replicated Better scalability than public blockchain, but not as scalable as databases High degree of vertical scalability by adding more resources (CPU, memory, storage) Very high scalability, as it scales horizontally by adding more nodes
Standards No industry standards available as of now No industry standards available as of now Standards such as ANSI for SQL available Standards such as ANSI for SQL available
Examples Bitcoin, Ethereum Eris, Multichain Oracle, IBM DB2 MongoDB, CouchDB

Final reachability matrix for CSFs of blockchain-based cloud services

CSF No. Description 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Driving power
 1 User engagement 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 2 Industry collaboration 1a 1 1 1 0 1a 1a 1a 0 1a 1a 1a 1 1 1a 1a 1a 1a 1a 17
 3 Rich ecosystem 1 1a 1 1 0 1a 1a 1a 0 1a 1 1a 1a 1a 1a 1 1a 1a 1a 17
 4 Blockchain technology standardization 1a 1a 1 1 0 1a 1a 1a 0 1a 1 1a 1a 1 1a 1 1a 1a 1a 17
 5 Regulatory clarity 1a 1a 1a 1 1 1a 1a 1a 0 1 1a 1a 1a 1a 1a 1a 1a 1a 1 18
 6 Cost efficiency 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 7 Energy efficiency 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 3
 8 Handling blockchain bloat 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 2
 9 Miner incentives 0 0 0 0 0 1a 0 1 1 0 0 0 0 0 0 0 0 0 0 3
10 Business case alignment to blockchain capability 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2
11 Sidechains development 1a 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 3
12 Blockchain talent pool 1a 0 0 0 0 1 0 0 0 1a 1 1 0 0 0 0 0 0 0 5
13 Leadership readiness for a decentralized consensus based technology 1a 1 1a 1a 0 1a 1a 1a 0 1a 1a 1 1 1a 1a 1a 1a 1a 1a 17
14 Technology investment and maturity 1a 1 1a 1 0 1a 1 1 0 1 1a 1a 1a 1 1a 1a 1 1 1 17
15 Trust on blockchain networks 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2
16 Integration with other cloud services 1a 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 3
17 Robust and mature Smart contracts platform 1a 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 3
18 Blockchain Security 1a 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 4
19 User control on data 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 3
Dependence Power 15 6 6 6 1 11 7 9 1 10 8 7 6 6 10 7 8 7 7

Note: aRepresents transitivity links between the factors

Iteration 1 of level partitioning

CSFs Reachability set Antecedent set Intersection set Level
 1 { } {3,10,15,19} { } I
 2 {3,4,13,14} {13,14} {13,14}
 3 {1,4,11,16} {2,4} {4}
 4 {3,11,14,16} {2,3,5,14} {3,14}
 5 {4,10,19} { } { }
 6 { } {7,8,12} { } I
 7 {6,8} {14} { }
 8 {6} {7,9,14} { }
 9 {8} { } { }
10 {1} {5,11,14,16} { }
11 {10} {3,4,12} { }
12 {6,11} {13} { }
13 {2,12} {2} {2}
14 {2,4,7,8,10,17,18,19} {2,4} {2,4}
15 {1} {17,18,19} { }
16 {10} {3,4} { }
17 {15} {14,18} { }
18 {15,17} {14} { }
19 {1,15} {5,14} { }

Iterations 2–5 of level partitioning

CSFs Reachability set Antecedent set Intersection set Level
 1 { } {3,10,15,19} { } I
 2 {3,4,13,14} {13,14} {13,14} V
 3 {1,4,11,16} {2,4} {4} IV
 4 {3,11,14,16} {2,3,5,14} {3,14} IV
 5 {4,10,19} { } { } V
 6 { } {7,8,12} { } I
 7 {6,8} {14} { } III
 8 {6} {7,9,14} { } II
 9 {8} { } { } III
10 {1} {5,11,14,16} { } II
11 {10} {3,4,12} { } III
12 {6,11} {13} { } IV
13 {2,12} {2} {2} V
14 {2,4,7,8,10,17,18,19} {2,4} {2,4} V
15 {1} {17,18,19} { } II
16 {10} {3,4} { } III
17 {15} {14,18} { } III
18 {15,17} {14} { } IV
19 {1,15} {5,14} { } III

Interpretive matrix for TISM of blockchain-based cloud services

1 2 3 4 5 6 7 8 9 10
 2 Network effect Converge fast
 3 Network effect Off-the-shelf solutions
 4 Drive convergence
 5 Converge fast Through technology improvement
 7 Reduced energy costs Ability to scale
 8 Economy of scale
 9 More nodes available
10 Business driven
11 Extensibility
12 Faster development cycles
13 Drives collaboration
14 Drives collaboration Reduces uncertainty Better technology Better technology Better technology
15 Improved confidence
16 Easier Implementation
19 Improved confidence
11 12 13 14 15 16 17 18 19
 2 Attracts new leaders Sharing to help technology
 3 Off-the-shelf solutions Off-the-shelf solutions
 4 Focused development Reduces uncertainty Off-the-shelf solutions
 5 Through regulation
12 More development
13 More investment
14 Better technology Better cryptography Better technology
17 Decentralized agreements
18 Lower risk of security breach Lower risk of security breach
19 Better privacy


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Corresponding author

Sanjay Prasad can be contacted at: skprasad@gmail.com