Determinants of blockchain adoption in the public sector: An empirical examination

Abstract

What are the factors that influence blockchain adoption in the public sector? This paper uses the diffusion of innovation theory to examine leading adopters of blockchain at the national government level. Six factors for blockchain adoption were tested using logistic regression: cybersecurity, control of corruption, e-government development, government effectiveness, political stability, and democratic participation. The analysis shows that cybersecurity, government effectiveness, and political stability are significant predictors. High levels of cybersecurity and government effectiveness increases the likelihood of countries to adopt blockchain. Paradoxically, higher degree of political stability decreases the likelihood of early blockchain adoption.

Keywords

Blockchain adoption public sector diffusion of innovation cyber-security

1. Introduction

In this paper, we examine the adoption of blockchain technologies at the national government level across the world. The principal question is: What are the factors that have influenced blockchain adoption in the public sector? This is an important question to understand since blockchain is a relatively new technology that was conceptualized in 2008 with Bitcoin (Nakomoto, 2008). To our knowledge, there is no empirical research that systematically examines factors that predict adoption of blockchain technology in the public sector across countries. Existing research on blockchain in the public sector has mostly examined use cases or provided literature reviews.

Out of the 213 countries worldwide (as per United Nations and World Bank), forty countries had launched over 200 blockchain-related initiatives by 2018 (Berryhill et al., 2018). With the proliferation of over 2000 cryptocurrencies in the last decade, blockchain technologies have predominantly been used in the financial services sector presently. Nearly 63% of central banks and 69% of other public sector institutions have been involved in proofs of concept or running trials (Hileman & Rauchs, 2017). However, blockchain technology has several other important uses for the public sector as well (Berryhill et al., 2018). The top five most common use cases are digital payments/currency, land registration, voting, identity management, and supply chain management.

We build on Rogers and Shoemaker (1971) diffusion of innovations (DOI) theory in order to examine the diffusion. They identified five categories of adopters: innovators, early adopters, early majority, late majority, and laggards. Since blockchain is still its early stages of diffusion, our analysis mainly pertains to innovators and early adoptors, which represent the leading adopters in the diffusion of innovation theory. While classical DOI focuses mainly on technological factors in the diffusion process, extant literature shows that internal organizational and external environmental factors are also crucial for the diffusion. We identified two factors in each of the categories that could predict blockchain adoption at the national level: technological (cybersecurity, e-government development), organizational (government effectiveness, control of corruption), and environmental (political stability, democratic participation). We use logistic regression to test the influence of these factors on blockchain adoption.

The rest of this article is organized in five sections. The second section examines blockchain adoption in the public sector. The third section outlines the DOI theory and the hypotheses to be tested. The fourth section highlights the data sources and analytic methods. The fifth section provides the results of the analysis. The last section concludes with implications of the analysis for blockchain policy, and identifies the study’s limitations and future research possibilities.

2. Blockchain adoption in the public sector

Blockchain is a distributed ledger technology (DLT) with an established protocol (e.g. proof of concept) as a way of obtaining consensus in the distributed network (Davidson et al., 2016). Blockchains are shared and distributed data structures that can securely store digital transactions without a central authority. Instead of managing a ledger by a single trusted entity, each node of the distributed network holds a copy of the ledger. DLT removes the need for an intermediary authority, which creates a new type of management; the intermediary is replaced by consensus protocol (Andoni et al., 2019). The protocol ensures data integrity across the network.

The blockchain introduces a new paradigm of management that can disrupt traditional governance. The disruption is the replacement of traditional consensus mechanisms such as centralized top-down hierarchical systems or third-party trust systems with a distributed consensus system that is open source, transparent, and community driven within the network (Andoni et al., 2019). Each participant can see all of the information instantaneously as any change made in one node is reflected immediately among other nodes in the network, thus creating greater efficiency (Killmeyer et al., 2017). One unique feature of blockchain technology is immutability, which implies that once data are written to the blockchain, no one – not even the system administrator – can change the data. Without blockchain, the traditional hierarchical or third-party systems would maintain separate records and require periodic reconciliations.

Blockchain could dramatically reduce the costs of recordkeeping and ensure transactions are taken in near real time (Iansiti & Lakhani, 2017). This is especially important for a public agencies that need to store vast amounts of records. Blockchain facilitates a distributed system which makes it easier to reconcile transactions across internal and external functions thereby saving resources. As transactions occur over records they are permanently updated in all ledgers. Since there is no need for a third-party intermediary to verify ownership, it reduces overall costs and increases data integrity. Since the data are maintained by the network, failure by any one node does not compromise the data.

Blockchain ledgers can be either open and closed (Ølnes & Jansen, 2017). Open blockchains (also referred to as public or permissionless blockchains) allow anyone to become a miner (agency maintaining a node in the network) in the blockchain process. Miners have an incentive to participate since they are rewarded with tokens like cryptocurrency. Miners provide their own resources such as hardware and electricity to identify a specific and unique hash value to secure the transaction (Ølnes & Jansen, 2017). In the public sector, closed (or private permissioned) blockchains are more common. They operate in an environment where participants are already known and vetted. This removes the gaming behavior that could otherwise occur in an open blockchain (Hileman & Rauchs, 2017). The mining process, however, consumes large amounts of computing power (and therefore electricity) to maintain up-to-date records.

Three characteristics of blockchain are important to note: 1) decentralized and distributed networks with the ledger distributed across nodes, each keeping a full record of transactions; 2) irreversible and immutable with the ledger being append-only with immediate reconciliation that allows trusted exchanges; and 3) near real-time updates of the ledger (Killmeyer et al., 2017). All of these three characteristics can be beneficial to government since they enable transactions to be done in a safe and secure manner; which is important for public agencies that deal with personal and confidential information collected from the public. Blockchain provides a simple and efficient method of recordkeeping in the public sector that preserves the integrity and confidentiality of records (Martinovic et al., 2017).

Blockchain technology is thus a disruptive innovation that has the potential to transform the traditional organizational structures. For example, it has been transforming the private fintech industry with new models of decentralized monetary transactions. It is also applied for non-monetary transactions. National government agencies across the world have undertaken various blockchain initiatives. In the United States, several federal agencies have launched blockchain programs (e.g. Food and Drug Administration’s Health Data Exchange and Real-Time Application for Portable Interactive Devices programs, the Health and Human Services Department’s GrantSolutions and Accelerate for grants and procurement). Estonia, which has become a leader in e-government, has piloted blockchain use for voting, identity management, and health care. Singapore, Switzerland and United Arab Emirates have also taken steps toward using blockchain for identity management. Brazil, Sweden, and United Kingdom have implemented blockchain for property records and transactions. With the increasing adoption of blockchain across countries globally for various public sector uses, an important question to ask is: what are the factors that have influenced the blockchain adoption? The examination is useful to identify the factors that enable technology adoption in the early stages.

3. Diffusion of innovation theory

We build on the Diffusion of innovation (DOI) theory to explain the factors influencing the blockchain adoption across countries. The DOI theory explains how an innovation such as a new idea, behavior, or product gains traction, spreads, and diffuses through a specific population or social system (Rogers & Shoemaker, 1971). The DOI theory had initially focused on explaining individual level characteristics, but has since been extended to explain the role of organizational characteristics in innovation diffusion (Slappendel, 1996). In regards to public sector’s adoption of technology, government characteristics arguably play a significant role (Damanpour & Schneider, 2008).

DOI postulates that organizations vary in their agility to adopt a technological innovation – some are faster adopters than others. There are five adoption categories: innovators, early adopters, early majority, late majority, and laggards (Fig. 1). The innovators and early adopters are venturesome and take risks in being among the leaders of adopting a technological innovation. The early majority are typically followers who need evidence about the innovation’s effectiveness. The late majority come onboard when they find that many others have adopted the innovation successfully. The laggards are conservative (bound to traditional methods) who are difficult to bring onboard. With respect to blockchain, since only 40 of the 213 countries worldwide (18%) have adopted the technology, the countries adopting the technology can be viewed still to be largely in the first two adoption categories (innovators and early adopters comprise about 16% in the DOI theory). As such, our examination mainly reveals the factors that contribute to blockchain adoption among the innovators and early adopters (who are henceforth referred to as “leading adopters”).

Figure 1.

Diffusion of innovations curve.

The DOI theory further posits that there are five attributes of an innovation or technology that influence its adoption. They are: relative advantage, complexity, compatibility, trialability and observability. Relative advantage refers to the extent to which the innovation is a better replacement for an extant technology or process. Compatibility is the innovation’s fit with the user’s values and experiences. Complexity refers to the difficulty of using the innovation. Trialability implies the extent to which the innovation can be tested or experimented before adoption. Observability is the evidence for tangible improvements that can be achieved with the technology. While these technological characteristics offer a good basis for analyzing the adoption of technological innovations across the spectrum of adopters from innovators to laggards, it is not adequate for explaining adoption of complex technologies like that of blockchain. The technological characteristics, by themselves, do not provide a guide for whether or not leading adopters will embrace blockchain. With new and emerging technologies, the technological factors may not be fully evident for leading adopters. These leading adopters are risk takers who experiment with the technologies.

Furthermore, the DOI theory misses the important organizational characteristics that motivate leading adopters. Lyytinen and Damsgaard (2001) argue that the analysis of technological adoption should include the role of institutional regimes, features of technological process, and key players who assist in the diffusion process. Indeed, the institutional forces cannot be ignored in the diffusion process especially in the context of national governments across the world. Studies on public sector innovation show how the governance features are critical to the diffusion process (Osborne et al., 2017; Torfing & Triantafillou, 2016).

Studies on innovation in the realm of e-government show that the diffusion is driven by internal organizational factors and external environmental pressures to change (Baker, 2012; Jun & Weare, 2010; Torfing & Triantafillou, 2016). Internally, public organizations emulate other government agencies to improve the effectiveness and efficiency of their operations (Karch, 2007). Technological leaders and professionals provide the internal capacity for the agencies to adopt e-government innovations (McNeal et al., 2003). Externally, public organizations innovate in response to environmental pressures of their stakeholders. Government reforms and modernization provide opportunities for public sector to adopt e-government innovations (Tolbert et al., 2008).

A comprehensive account of factors should thus encompass all of the above three factors, namely technological characteristics, internal organizational factors, and the external environmental factors. Indeed, in an extensive review of e-government diffusion globally, Zhang et al. (2014) show how these three factors influence e-government innovation adoption across countries. Accordingly, we hypothesize that technological, organizational, and environmental factors influence blockchain adoption. The blockchain specific technological factors are cyber-security (due to its relative advantage for security) and e-government development (due to its compatibility with other e-government services). The internal organizational factors include the motivations to increase government effectiveness and to control corruption (due to blockchain’s ability for transparency, and secure and efficient transactions). The external environmental pressures relate to political stability and public participation. The relevance of these six factors for blockchain adoption are further explored below.

3.1 Technological factors influencing blockchain adoption

Technologically, the fundamental relative advantage of blockchain is its potential to improve cyber-security in the public sector (Diallo et al., 2018). Blockchain has the most robust security because of public and private keys cryptography (Kshetri, 2017). Cryptography is the act of encrypting and decrypting information. The combination of public and private keys in the process of transmission ensures that the information is securely delivered to the intended user (Berryhill et al., 2018). Security experts argue that the cryptographic nature of the blockchain should be able to prevent hacking and security threats to the network (Hasanova et al., 2019).

The immutability with the distributed ledger provides a high degree of privacy and security of the data. There is no single point of failure or vulnerability in the distributed ledger system (Kshetri, 2017). Blockchains implement timestamps on each transaction, which allows traceability to verify and record transactions. Immutability also implies that blockchains are based on an append-only data structure that stores every transaction over the network. As a result of the rules embedded in the consensus protocol, it is almost impossible to alter historical records. The constant reconciliation guarantees the integrity of the data that is recorded on the blockchain. The transactions are processed more efficiently than centralized systems (Kim & Kang, 2017). Hence, technologically speaking, cybersecurity must arguably be a top relative advantage for countries to adopt blockchain technology. Consequently, the first hypothesis for testing is:

H1: Countries with higher degree of cyber-security will adopt blockchain.

Blockchain is a complex technology, which requires significant computing power for the distributed ledger systems. From a technological complexity and compatibility perspective, countries must already possess the required computing and internet infrastructure to facilitate the technology’s adoption. Trialability, and observability are not quite established as the countries are in their early phases of adoption. Countries which are leading adopters of blockchain are thus risk takers who should have well developed governmental technology infrastructure to support blockchain adoption (Martinovic et al., 2017 ). The leading adopters can arguably thus be advanced in their e-government development, which requires better technological infrastructure and capacity to provide online services. Countries that have greater e-government use are also more likely to be innovative and adopt other associated technologies such as blockchain. Blockchain makes sense for the advancing e-government because it enables secure record keeping. For example, Sweden’s Lantmäteriet has been testing blockchain technology for property sales and land records (Anand, 2018). In China, blockchain has been used to give an individual’s digital identity for secure public services (Hou, 2017). Blockchain is thus compatible with e-government operations and can even enhance these functions. It can be used to improve delivery of public services and increase the speed of transactions (Alexopoulos et al., 2018). Thus the second hypothesis is:

H2: Countries with higher degree of e-government development will adopt blockchain.

3.2 Internal organizational factors for adopting blockchain

From an internal organizational perspective, national governments could adopt blockchain to enhance their efficiency and effectiveness in delivering their services. Blockchain offers both prospects as well as challenges in this respect. From a prospective viewpoint, blockchain can enhance the efficiency of current processes of record keeping through the distributed ledger. Blockchains allow for faster reconciliation, stronger security, and less expensive granting of access (Davidson et al., 2016). Blockchain offers higher degree of data protection and data sharing among entities. It reduces the cost of operations by eliminating fraud, error in payments, and providing greater transparency of transactions between government agencies and citizens through smart contracts (Ojo & Adebayo, 2017). It also enables secure and trusted authentication of documents, which is a core necessity in the public sector (Ølnes, 2016). Without blockchain, trust is verified by a reliable third party, which can be expensive. Blockchain technology provides a less costly alternative by eliminating intermediaries and increasing overall government effectiveness (Macrinici et al., 2018; Sun et al., 2016).

Although it offers advantages, blockchain also has its challenges. It is a disruptive technology that could potentially transform the traditional role of bureaucracy (Jun, 2018). Blockchain forms the basis for smart contracts, which are computer codes to automate the payment process. Smart contracts could make some of the traditional bureaucratic functions of payment systems obsolete. The disruptive role of blockchain is especially evident with cryptocurrencies, which have transformed the fintech industry. Noted economists like Paul Krugman and Robert Shiller have been also been critical of cryptocurrency (Krugman, 2018; Shiller, 2017). Some countries (e.g. Bolivia, Ecuador, India, etc.) have banned or severely regulated cryptocurrency mining and/or transactions. Leading adopters in this context would take risk to adopt blockchain to increase government effectiveness, even if it transforms the traditional bureaucracy. Thus, the third hypothesis is:

H3: Countries with higher degree of government effectiveness will adopt blockchain.

Since blockchains enable secure recordkeeping and transactions, another internal motivation for adopting it in national governments is to control corruption (Kim & Kang, 2017; Ølnes et al., 2017). The immutable log of historical transactions on the blockchain facilitates secure book-keeping and auditing of the records (Hyvärinen et al., 2017). The “super audit trail” enables better record keeping for government and improved transparency (Kshetri, 2017). Transactions in the smart contracts are securely processed and recorded (Diallo et al., 2018). Smart contracts remove remove possible human errors in the process and reduce corruption since all the transactions are transparent.

Blockchain can also reduce corruption through greater transparency and traceability of public spending. There is a low risk of data manipulation since it is managed by a distributed network, not an individual entity. The constant reconciliation process guarantees integrity and reliability of data recorded on the blockchain. In developing countries, where corruption and red tape are common, blockchain provides a decentralized tamper-proof consensus mechanism (Kim & Kang, 2017). Blockchain could also eliminate the issue of double spending, which occurs when a user makes multiple payments with one spending form. This problem occurs when there is a time lapse between when transactions occur and when they are reconciled in the system (Hasanova, et al., 2019). Hence, the fourth hypothesis is:

H4: Countries with higher degree of corruption control will adopt blockchain.

3.3 External environmental factors affecting blockchain adoption

Blockchain is a disruptive technology that can transform traditional administrative functions of public agencies across nations. As hypothesized above (H3), from a government perspective, countries would be open to blockchain adoption to the extent they envisage enhancing government effectiveness, despite the potential impacts on bureaucracy. However, from a citizen perspective, blockchain adoption can depend on the extent to which stakeholders seek better services and influence the administrative transformation. Politicians need to respond to citizen pressures for the transformation to occur. There are two facets of external environmental factors for blockchain adoption in this context: political stability and public participation in the democratic processes.

Political stability is arguably required for countries to adopt disruptive innovations like blockchain early. The stability provides scope for citizens to demand better services through public participation in the decision-making processes. As blockchain is a distributed ledger, some scholars have envisioned the state itself can become irrelevant because of the distributed self-sustainable government run by algorithms and free of market rules (Atzori, 2017). Third-party trust systems (like banks) are redundant for blockchain transactions where the trust is inherent in the network. Blockchain prevents excessive concentration of power in the hands of a few and has a more transparent legal framework. Open government means that more information is made available to citizens and this higher level of transparency allows citizens to monitor public transactions (Hou, 2017). With the higher degree of transparency and security, malicious behaviors can be quickly detected and contained (Qi et al., 2017). The fifth hypothesis is thus:

H5: Countries will political stability will adopt blockchain early.

Public participation provides opportunity for citizens to voice their choices through democratic methods. Citizen participation in voting is an important aspect of the public decision-making process. Citizens should be able to vote securely and privately without the fear of their votes being tampered. Blockchain provides such a secure and efficient method of e-voting. With e-voting, each voter gets a single coin in the wallet. The coin can only be spent once, so that a person has only one opportunity to cast a vote. Consequently, blockchain would allay two major concerns of present-day voting: voter access and voter fraud. Blockchain provides a tamper-proof audit trail for voting (Kshetri & Voas, 2018). Alternative methods such as paper ballots and optical scanning voting machines can be compromised and are not secure. These problems can be avoided with blockchain (Qi et al., 2017). Estonia, for example, has implemented such blockchain based e-voting system. Hence, the sixth hypothesis is:

H6: Countries will higher democratic participation will adopt blockchain early.

4. Data and research methods

The dependent variable for this study is the adoption of blockchain by a country. At present, there is no multinational agency that collects data on blockchain adoption at the national level. The most reliable data comes from the Illinois Blockchain Initiative (IBI), which provides the Blockchain in Government Tracker (available at http://bit.ly/blockchain-govt-tracker). In the absence of alternative comprehensive data available through another reputable agency, we have used the IBI data in this paper. Berryhill et al. (2018) also used the IBI as the data source for their OECD Working Paper. The IBI data are used for our dependent variable to indicate if a country has adopted blockchain or not. The dependent variable is thus a binary value – 1 for adoption and 0 for non-adoption. There are 40 countries that have adopted blockchain initiatives as of 2018 (Berryhill et al., 2018). These countries are the leading adopters of blockchain, given that technology is still in the initial stages of adoption by countries. Our population comprises of 213 countries worldwide (for which data are available through United Nations and the World Bank). Since the dependent variable is binary, we use logistic regression to identify the factors that explain the blockchain adoption. The computations were done using STATA statistical package, version 15. There are six independent variables corresponding to the six hypotheses, as explained below. We have lagged the independent variables by a year.

The first independent variable is the Global Cybersecurity Index (CyberSec), which is a measure of a country’s cybersecurity. The data are obtained from the International Telecommunication Union (ITU), which is the United Nations’ specialized agency for information and communication technology. The index measures the commitment of ITU’s 193 Member States to cybersecurity. The index is measured along five pillars of the ITU Global Cybersecurity Agenda: legal, technical, organizational, capacity building, and international cooperation. This index ranges from 0 to 1, where higher values show greater commitment to cybersecurity policies. Countries with higher Cybersec score are hypothesized to adopt blockchain technology (H1).

The third independent variable is the U.N. E-Government Development Index (E-Gov), which measures e-government achievement across countries. This index presents the state of E-Government Development of the United Nations Member States. Along with an assessment of the website development in a country, the E-Government Development index incorporates access characteristics, such as the infrastructure and educational levels, to reflect how a country is using information technologies. The index is a composite measure of three important dimensions of e-government, namely: scope and quality of online services, telecommunication infrastructure, and internet human capital. This index ranges from 0 to 1 – countries where higher values indicate more e-government development. Countries that have a higher E-Gov values are hypothesized to adopt blockchain technology (H2).

The third independent variable, Government Effectiveness (GovEff), is taken from the World Bank governance indicators. This variable captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. The estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e., ranging from $-$ 2.5 to 2.5, with zero as the mean. We hypothesize that greater government effectiveness will lead to a country’s adoption of blockchain technology (H3).

The fourth independent variable is the Control of Corruption (Corruption) Index, which is a part of the World Bank’s governance indicators. This variable captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. The standard error indicates the precision of the estimate of governance. Larger values of the standard error indicate less precise estimates. The estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, ranging from $-$ 1.8 to 2.3, with zero as the mean. We hypothesize that greater control of corruption would entail blockchain adoption by a country (H4).

We used two World Bank governance indicators to capture the fifth and sixth variables of overall political stability of a country and democratic participation. The two indicators are: Political Stability and Absence of Violence/Terrorism(PolStab) and Voice and Accountability (Account). PolStab measures perceptions of political instability and/or politically-motivated violence, including terrorism. The estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e., ranging from approximately $-$ 3.0 to 2.0, with zero as the mean. Voice and Accountability captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government, including freedom of expression, freedom of association, and a free media. This estimate also uses a standard normal distribution, ranging from $-$ 2.0 to 1.7, with zero as the mean. We hypothesize that higher values of these measures of political stability (PolStab) and democratic participation (Account) imply blockchain adoption (H5 and H6 respectively).

Table 1
Descriptive statistics of dependent and independent variables

Variable label	Description	Obs	Mean	Std. Dev.	Min	Max
Blockchain (Dependent Variable)	Dummy variable “0” no blockchain cases; “1” presence of blockchain cases	213	0.1877934	0.3914671	0	1
CyberSec	Global Cybersecurity Index (2017)	193	0.3587617	0.2527914	0	0.925
E-Gov	UN Digital Government Index (2018)	193	0.5490637	0.2126119	0.0566	0.915
GovEff	Government Effectiveness – World Bank (2018)	208	$-$ 0.0060714	0.9985437	$-$ 2.478397	2.205368
Corruption	Control of Corruption – World Bank (2018)	208	$-$ 0.0056075	0.9991133	$-$ 1.825740	2.241001
PolStab	Political Stability and Absence of Violence/Terrorism – World Bank (2018)	210	$-$ 0.0060556	0.9985041	$-$ 2.961164	1.923889
Account	Voice and Accountability – World Bank (2018)	210	$-$ 0.0061628	0.9814478	$-$ 2.202031	1.692057
GDPpercap	GDP per capita – World Bank (2018)amounts in $1,000 USD	193	13.97822	18.91788	0.2920052	104.103

In addition to these six factors, we have included Gross Domestic Product per capita (GDPpercap) as a control variable to account for country’s economic size. We do so because empirical research on Bitcoin adoption shows that there is a positive correlation between GDP per capita and the user adoption of bitcoin (Parino et al., 2018). GDP is the sum of value added by all resident producers plus any product taxes not included in the valuation of output during a year. This indicator is not lagged in the model since it represents the average value added by resident during a year. GDP per capita is calculated for the midyear population (and is measured in $1,000 US).

Table 2

Correlation coefficients of dependent and independent variables

	Blockchain	CyberSec	E-Gov	GovEff	Corruption	PolStab	Account	GDPpercap
Blockchain	1
CyberSec	0.5242	1
E-Gov	0.4560	0.6882	1
GovEff	0.5159	0.6811	0.8471	1
Corruption	0.4635	0.6107	0.7383	0.9346	1
PolStab	0.2419	0.3603	0.6355	0.7623	0.7378	1
Account	0.3656	0.3364	0.55	0.6914	0.7224	0.6314	1
GDPpercap	0.4444	0.5248	0.7065	0.8277	0.8467	0.6183	0.5791	1

Figure 2.

Comparative of averages of main factors of public sector blockchain adoption between groups of countries.

5. Results

Table 1 shows the descriptive statistics of the variables that we tested in the logistic regression model. The mean score of the dependent variable, blockchain adoption, is 0.18, which means that 18% of the countries have undertaken blockchain initiatives in the public sector in 2018. The Global Cybersecurity Index average was 0.36 in 2017. The average country commitment to cybersecurity globally was low, with only 21 countries (out of 194 countries for which GCI was measured) in the “leading stage” of commitment. For the UN E-Government Development Index, the average score for the counties was 0.55 in 2017. There were 94 countries with high or very high EGDI values. The World Bank governance indicators data (Control of Corruption, Government Effectiveness, Political Stability and Absence of Violence/Terrorism and Voice and Accountability), were scored on a standard normal distribution, and the average scores were close to zero.

Table 2 shows the correlation coefficients. We can see that some variables are highly correlated. For example, government effectiveness and corruption show a high correlation of 0.9346. E-government development is also highly correlated with government effectiveness (0.8471).

Table 3
Logistic regression model

Blockchain (Dependent variable)	Coef.	Std. Err.	$z$	$P>z$	[95% Conf. Interval]
CyberSec	5.169908	1.709466	3.02	0.0020	1.819416	8.520401
E-Gov	0.2229594	3.071312	0.07	0.9420	$-$ 5.796702	6.242621
GovEff	2.348552	1.092958	2.15	0.0320	0.2063933	4.49071
Corruption	$-$ 1.100032	0.827787	$-$ 1.33	0.1840	$-$ 2.722465	0.5224003
PolStab	$-$ 1.02802	0.4873387	$-$ 2.11	0.0350	$-$ 1.983186	$-$ 0.0728538
Account	0.5851215	0.3866998	1.51	0.1300	$-$ 0.1727963	1.343039
GDPpercap	0.0092976	0.0215112	0.43	0.6660	$-$ 0.0328635	0.0514588
Constant	$-$ 4.761893	1.793097	$-$ 2.66	0.0080	$-$ 8.276297	$-$ 1.247488

Model diagnostics: $N=$ 184; Hosmer-Lemeshow chi2(8) $=$ 4.45; LR chi2(20) $=$ 88.15; Prob $>$ chi2 $=$ 0.8146; Prob $>$ chi ${}^{2}$ $=$ 0.0000; Linktest: Pseudo-R ${}^{2}$ $=$ 0.4575; Hat: 0.1853 ( $p>z$ 0.0000); Hat squared: 0.0742 ( $p>z$ 0.2050).

Figure 3.

Distribution of countries by groups between cybersecurity and control of corruption (group 1 in black, group 2 in blue).

Figure 2 shows a column chart of the independent variables, comparing the countries which have not adopted blockchain (Group 1 countries) and those that have adopted blockchain (Group 2 countries). Group 1 comprises of 173 countries, while Group 2 consists of 40 countries. In general, the average of each of the factors of blockchain adoption is higher for the countries in Group 2 than countries in Group 1. The average Global Cybersecurity Index for Group 1 countries is 0.28, while that of Group 2 countries is 0.65. The values for UN E-Government Development Index are 0.49 and 0.76 for Groups 1 and 2 countries respectively. The World Bank’s Governance indicators for corruption control, government effectiveness, political stability, and voice and accountability, also show similar patterns. In these, the averages of countries from Group 2 across these factors are positive and above zero, while the averages of countries from Group 1 are negative. This analysis shows that countries with blockchain adoption have higher scores on all of the independent variables. This is an indication that leading adopters of blockchain technologies present on average a better national context of cybersecurity, corruption, e-government development, government effectiveness and political stability.

Figure 4.

Distribution of countries by groups between cybersecurity and government effectiveness (group 1 in black, group 2 in blue).

Table 3 provides the results of logistic regression model. Our goodness of fit measure for the regression model (Pseudo-R ${}^{2}$ ) is 0.4575, which shows a moderately strong relationship. However, the results show that only cybersecurity, government effectiveness, and political stability (absence of violence/terrorism) were significant at the 95% confidence level or greater. Interestingly, e-government development and control of corruption does not appear to have an impact on the adoption of blockchain. The control variable of GDP per capita also was not statistically significant, which indicates that country’s economic size is not significant for blockchain adoption.

Figure 5.

Distribution of countries by groups between cybersecurity and political stability (group 1 in black, group 2 in blue).

Figure 6.

Distribution of countries by groups between cybersecurity and voice and accountability (group 1 in black, group 2 in blue).

Overall, the results of the logistic regression model do not confirm three hypotheses (H2, H4, and H5) as e-government development, corruption control, and democratic participation variables are not significant for blockchain adoption. The analysis confirms two hypotheses, H1 and H3, which respectively postulate that higher cybersecurity and government effectiveness are significant for blockchain adoption. For every additional point that a country advances in the Global Cybersecurity Index, the probability of adopting blockchain technologies increases 5.17 percentage points on average. Similarly, for every additional percentage point that a country improves in the Government Effectiveness indicator, the probability of adopting blockchain technologies increases 2.35 percentage points on average. Interestingly, the results indicate the opposite of H5 postulation, i.e. for every additional percentage point increase in Political Stability and Absence of Violence/Terrorism, the probability of adopting blockchain decreases by 1.03 percentage points on average. This is a counter-intuitive result and requires further discussion. Descriptive statistics indicate that the leading adopters of blockchain have high degree of political stability, much higher than the non-adopters. Although high political stability may facilitate blockchain adoption, the negative relationship indicates that the leading adopters also need to be risk-takers where the high degree of political stability is not taken as a given. Challenges to the political stability may spur blockchain adoption among the leading adopters.

A logistic regression model is a linear model that assumes that the logit of the outcome variable has a linear relationship with the independent variables. However, the outcome variable may have non-linear relationship, which could explain the decrease in probability of adopting blockchain with higher political stability values. In order to evaluate a possible nonlinear function, we conducted a linktest in STATA to detect a potential specification error. The results of this test are presented in model diagnostics below Table 3. In particular, the measurement for hat squared is not significant, which implies that we have chosen meaningful and relevant predictors. The other outputs from the logistic regression are (the log likelihood chi-square (88.15 with 7 degrees of freedom and pseudo R-square values) also show that our model fits the data properly. In addition, we conducted a Hosmer and Lemeshow’s goodness of fit test. The Hosmer-Lemeshow test value was 4.45 with 8 degrees of freedom and a $p$ -value of 0.8146, indicating that our model fits the data properly.

For a better visualization of these results, we depict four scatterplot graphs by distinguishing countries between Group 1 countries that have not adopted blockchain in black dots and Group 2 that have adopted blockchain in blue dots. The first is a scatterplot of these two groups of countries with the Global Cybersecurity Index and the Control of Corruption Index (Fig. 3) on the vertical and horizontal axes respectively. The second graph is a scatterplot of the two groups of countries with Global Cybersecurity Index and the Government Effectiveness on the vertical and horizontal axes respectively (Fig. 4). The third graph shows a scatterplot of these two groups of countries with Global Cybersecurity Index and the Political Stability (Fig. 5). The fourth graph is a scatterplot of these two groups with the Global Cybersecurity Index and Voice and Accountability (Fig. 6). Across these four graphs, it is possible to identify a common pattern: the majority of countries from group 2 are located in the higher quadrant implying their higher levels of cybersecurity, control of corruption, government effectiveness, political stability and voice and accountability.

5.1 Implications of the results

The results overall show the technological, organizational, and environmental factors that facilitate blockchain adoption among leading national government adopters. Technologically, the leading adopters are those who are concerned with cybersecurity. Blockchain offers several prospects for cybersecurity, particularly with the secure distributed ledger. E-government development did not turn out to be a significant predictor of the leading adopter of blockchain. This result can also be interesting in showing that technological adoption is not necessarily linear, i.e. countries do not need to have advanced technological infrastructure already to adopt innovation like blockchain. Countries may jumpstart with blockchain because of other perceived reasons (e.g. security, mining, cryptocurrency, or other government functions such as identity management, health, e-voting, etc.).

In the internal organizational side, government effectiveness is a significant predictor of leading blockchain adopter. Countries with higher degree of quality of public and civil services adopt blockchain early. Blockchain offers opportunities for increasing organizational efficiency and to offer the services in new ways. Smart contracts, for example, could increase efficiency of contractual systems with automatic payments and make the responses more secure, accurate, and efficient than the traditional bureaucracy (Jun, 2018). Despite blockchain’s promise of transparency and traceability, however, control of corruption does not appear as a significant predictor of the leading adopters. Corruption is a complicated political and social affair that may not necessarily be contained with technological solutions of the blockchain.

On the external environmental side, political stability is a significant predictor of blockchain adoption, but not in the same way as we hypothesized. Higher political stability decreases the probability of blockchain adoption among the leading adopters. Democratic participation is also not a significant predictor of blockchain adoption. Overall, the external environment has a paradoxical relationship with blockchain adoption. Our tests for non-linear relationship show that the model specifications are proper. We can only surmise that leading adopters could be risk takers, wherein challenges to the political stability up to a certain degree may spur blockchain adoption.

6. Conclusion

In this paper, we examined the adoption of innovation of public sector blockchain using Rogers and Shoemaker (1971) diffusion of innovation theory. In our analysis, we focused on the factors that predict the leading adopters of blockchain. The results of the study showed that of the six factors, only three variables were statistically significant for blockchain adoption. Our findings reiterate the strength of blockchain for cyber-security purposes. Cyber-security is a good predictor of national governments to adopt blockchain (Hasanova et al., 2019). Similarly, government effectiveness is another significant predictor for blockchain adoption (Jun, 2018). Blockchain enables governments to be more effective since it eschews the need for trusted third-party intermediaries. Political stability is also a significant predictor, but unlike the previous two variables, the direction is negative. Although some degree of stability is required for countries, they also need to face challenges to be leading blockchain adopters.

The factors that are not significant predictors of blockchain adoption also provide interesting insights into the leading blockchain adopters. Greater development in e-government is not a precursor for blockchain adoption. Leading adopters may not have a stage-wise adoption of technology; they may jumpstart on blockchain even if they are lacking in other allied technologies. Although control of corruption is an important benefit of blockchain implementation because of its transparency, it was not a statistically significant predictor. Democratic participation of citizens was also not a statistically significant predictor. Finally, the control variable of GDP per capita was not significant, which indicates that having more economic resources does not imply that a country would be a leading blockchain adopter.

There are several policy implications of this research. First, blockchain is significant for cybersecurity. Countries with advanced cybersecurity are on the forefront of blockchain adoption. Countries could do well to integrate blockchain into their national level cyber-security strategy. It would be hard to adopt blockchain without giving due consideration to cybersecurity. In this rapidly evolving digital era, cybersecurity is a fundamental requirement for governments across the world. Blockchain can help in providing secure and transparent digital operations.

Second, countries working toward greater government effectiveness have a promising opportunity in enhancing the effectiveness through blockchain solutions. Our evidence shows that governments should be encouraged to more actively adopt blockchain. Blockchain can be used for several purposes, including land registration and property management, e-voting, identity management, health management, and supply chain management. Blockchain can offer efficiencies in record keeping and allow for faster reconciliation, stronger security, and less expensive access management (Davidson et al., 2016). Smart contracts facilitate higher bureaucratic efficiency and quick response times through automatic payments.

Our study has a few limitations which need to be addressed in future research. First, we have a limited number of predictor variables and we could be missing some important factors of adoption. Our reasoning for selecting the six independent variables was to be parsimonious in identifying the most important factors that are germane to blockchain. Second, we are limited in just looking at adoption, we have not examined the level of sophistication of adoption within these countries. The countries have adopted various initiatives of blockchain. We have not differentiated between those that have had many initiatives and those that have adopted few. Third, the diffusion of innovation theory may also be inherently limiting in analyzing leading blockchain adopters. Alternative theories that incorporate institutional and governance mechanisms can also be applied for more insights.

Future research could address the above limitations by examining a broader set of independent variables that apply to new and emerging technologies in general. The research could also examine factors that determine the level of sophistication of blockchain adoption in these countries. There are several countries that could be considered outliers of leading blockchain adopters (e.g. Cambodia, Ghana, Senegal). The specific reasons for why these countries adopted blockchain could be illuminating for our paradoxical finding on political stability. Moreover, although we have identified predictors of leading blockchain adopters, we do not claim causality; there could indeed be reverse causation. In due course, as more longitudinal data becomes available, future research could focus on causal mechanisms. Last, but not the least, our data analysis is limited by the data sources on blockchain adoption. We used the Illinois Blockchain Initiative as the data source for blockchain adoption, but there is an urgent and important need for collecting data on blockchain initiatives systematically across the world. International donor organizations need to collect the data in a systematic way in the future, so that the models of blockchain adoption can be refined further.

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Determinants of blockchain adoption in the public sector: An empirical examination

Abstract

Keywords

1. Introduction

2. Blockchain adoption in the public sector

3. Diffusion of innovation theory

3.2 Internal organizational factors for adopting blockchain

3.3 External environmental factors affecting blockchain adoption

4. Data and research methods

Table 1 Descriptive statistics of dependent and independent variables

Table 3 Logistic regression model

6. Conclusion

References

Table 1
Descriptive statistics of dependent and independent variables

Table 3
Logistic regression model