Consumer perception of blockchain technology adoption by organizations : an analysis of consumer perception of sustainability, safety & security and privacy

CONSUMER PERCEPTION OF BLOCKCHAIN

TECHNOLOGY ADOPTION BY

ORGANIZATIONS

An analysis of consumer perception of sustainability, safety & security and

privacy

Author: Y.B.R.E Roosendaal Student ID: 10439242

22/06/2018

MSc. in Business Administration: Digital Business University of Amsterdam

Supervisor: M. Güvendik Second Supervisor: A. Alexiou

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S

TATEMENT OF ORIGINALITY

This document is written by student Yannick Roosendaal who declares to take full

responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is

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A

BSTRACT

Blockchain technology has the potential to create a new paradigm for transactional data within organizations. Whilst blockchain adoption is slowly starting to gain momentum, these new technical developments still suffer from teething troubles. Therefore, it is interesting to explore the consumer’s current point of view regarding this progress. Are consumers

receptive to these developments, or does this technology act dissuasive instead? To discover how the adoption of blockchain technology by organizations impacts consumers’ perception of these organizations, a 2x3 experimental design was employed. Adoption was examined in three sectors. Respectively, financial services, healthcare and the energy sector. Regression analyses did not find evidence that use of blockchain technology impacts consumer

perception of organizations’ in the areas sustainability, safety & security and privacy. Consumer resistance to innovation, industry type as well as familiarity and expertise with regard blockchain technology were treated as moderators. Analyses found evidence that consumers that show low resistance to innovation, perceive organizations that use blockchain technology more positively regarding privacy and safety & security. Furthermore, medium levels of blockchain expertise were shown to negatively impact perceived sustainability of organizations using blockchain technology. The findings contribute to research by providing a better understanding of the impact adoption of blockchain technology and new technologies have. For organizations, the findings contribute to a better decision-making process in

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T

ABLE OF

C

ONTENTS

Promises of blockchain technology ... 10

• Sustainability ... 10

• Safety & Security... 11

• Privacy ... 13

Consumer perception ... 14

• Sustainability ... 15

• Safety & Security... 17

• Privacy ... 18 Industries ... 19 • Energy sector ... 19 • Financial Services ... 20 • Healthcare ... 21 Resistance to innovation... 23

Familiarity & Expertise ... 26

Methodology ... 29 Research design ... 29 Procedure ... 30 Sample ... 32 Measurements... 33 • Sustainability ... 33

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• Privacy ... 34

• Familiarity ... 35

• Expertise ... 35

• Resistance to innovation ... 35

Analyses & predicitions ... 36

Results ... 37

Correlations ... 37

Main effect ... 38

• Sustainability ... 39

• Safety & security ... 40

• Privacy ... 41 Moderation ... 42 • Industry type ... 42 • Resistance to innovation ... 46 • Familiarity ... 50 • Expertise ... 55 Discussion... 60

Interpretation of results, contributions & practical implications... 60

Limitations and discussion points ... 64

Conclusion ... 65

Reference list ... 67

Appendix. ... 74

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I

NTRODUCTION

The amount of electricity used by the global network of ‘miners’ running the blockchain technology behind Bitcoin, hit a record high of 43 gigawatt per hour in December 2017, equivalent to 35 1.2-gigawatt nuclear reactors running at full capacity (Bloomberg, 2018). Managing director Christine Lagarde of the International Monetary Fund (IMF) said, in an interview at the World Economic Forum’s annual meeting, that mining for cryptocurrency blockchains is too energy-intensive. She stated that if this continues, the blockchain is expected to use as much electricity as Argentina in 2018 (Bloomberg, 2018). Khsetri (2017) argues that energy consumption related to blockchain technology is one of the biggest obstacles in blockchain adoption. Writing data in a blockchain is enormously energy-intensive due to the ‘proof-of-work’ validation method, which requires computationally expensive repeated calculations (Khsetri, 2017). Swiss bank Credit Suisse explicitly compares the blockchain used by bitcoin to data centres and even marijuana cultivation, industries infamous for their enormous power draws. Worryingly, the Guardian states that there is no easy technological solution coming for this problem (Hern, 2018).

Taking this information in regard, blockchain usability in the energy sector to increase sustainability is likely not the first thing that comes to mind. Interestingly however,

disrupting the energy sector and increasing sustainability is a goal which many people believe blockchain technology has a high potential to achieve. For example, Power Ledger, a

blockchain based energy-trading company aiming to develop a power system that is low-cost and zero-carbon, currently has a market capitalization of 385,839,454 USD

(Coinmarketcap.com, 2018). Whilst some might argue that this valuation is severely overpriced due to the current hype surrounding blockchain, it does show that there is a

significant interest in using blockchain technology to transform the energy sector and achieve higher environmental sustainability.

6 Not only the energy sector is attracted to the potential that blockchain technology offers. According to Iansiti & Lakhani (2017) financial services companies have already made big steps in blockchain adoption. Iansiti & Lakhani point to established organizations such as Bank of America, JP Morgan and Standard Chartered which are testing blockchain technology as a replacement for manual and paper-based transactions in foreign exchange, cross-border settlement and securities settlement. Furthermore, Deloitte and the US

Department of Health published a paper describing a range of opportunities for applying blockchain technology to healthcare. This paper is mainly focused on making health information exchanges (HIE) more secure, efficient and interoperable (Deloitte, 2016). Examples such as these endorse the current interest in blockchain technology in the financial services and the healthcare sector.

Whilst many industries show interest in this new technology, interest is only the first step in implementation. Blockchain technology is currently still in its infancy and an

important factor that organizations should consider when implementing blockchain

technology, is the perception that consumers have of blockchain technology. An article by Forbes (2017) states that average consumers still perceive blockchain technology to be new and risky. According to Forbes the technology has yet to gain wide consumer acceptance and must go beyond features like decentralization to gain wide consumer acceptance. Research conducted by Abramova & Böhme (2016) has shown that individuals have substantial

concerns about cryptocurrencies due to the way blockchain technology functions. They argue that mainly perceived risk influences consumer usage behaviour and blockchain adoption.

On this basis, this paper aims to explore literature on the current developments in

blockchain technology implementation to gain a better understanding of the various promises that blockchain technology has to offer. And, more specifically, how the adoption of this technology influences consumer perception of organizations. This paper will critically

7 examine the consumer perception of blockchain implementation in the energy sector, the financial services sector and the healthcare sector. In which blockchain technology is argued to have a promising fit, potentially increasing sustainability, safety & security and privacy. However, literature has not yet explored the effects of blockchain technology adoption by organizations on the consumer perception of these organizations. The relevant research question that remains is as follows:

How does the adoption of blockchain technology by organizations impact consumers’ perception of these organizations?

To find the answers to this question, the associated constructs need to be analysed in relation to each other. By means of an online experiment, consumer perception of a technology organization in the areas sustainability, safety & security and privacy will be assessed. Measurements will be done for various scenarios, considering different industry types and blockchain technology use. Consumer familiarity and expertise regarding blockchain technology will be taken into account as well as consumer resistance to innovation. Attempting to achieve a cross section of consumer perception of blockchain technology adoption.

Consumer perception of blockchain technology is important for various reasons. In general, blockchain technology is, like the internet, a foundational technology. This means that it has the potential to create new foundations for social and economic systems,

transforming business and government. Whilst the potential is immense, blockchain-induced transformation relies on gradual adoption of blockchain technology (Iansiti & Lakhani, 2017). In addition, the concept of consumer perception is arguably one of the most important factors which decides the success of a technology, a product, a brand or a business (Joseph, Sekhon, Stone & Tinson, 2005). Therefore, this study on consumer perception of blockchain

8 technology is interesting and important for understanding the impact of the ‘next internet revolution’ and the future of the digital world. For organizations it is important in particular to understand consumer perception of this promising technology, as it will provide insightful information on how to react to this transformative technology.

In this study, current literature will be reviewed first, to provide a detailed

background. Next, the data and research methodology will be assessed to ensure replicability of this study. Subsequently, the data will be analysed, and the hypotheses tested. Thereafter, the significance of the findings will be discussed, and the theory will be related to the data analyses. Finally, in the conclusion an overview of the outcomes of this study will be given, along with research contributions, limitations and suggestions for further research.

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ITERATURE

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EVIEW

In this section, an in-depth review of current literature regarding blockchain technology and its potential implications regarding sustainability, safety & security and privacy will be presented. Moreover, three different industries in which blockchain shows promise will be highlighted. Subsequently, resistance to innovation and the possible relation to blockchain technology will be discussed. Finally, literature on consumer familiarity and expertise with relation to new technology is reviewed. This literature review aims to provide a

representation of current relevant concepts. In addition, the relationships of these concepts will be explored. The literature reviewed will serve as a foundation on which a literature gap and a research question is defined and upon which subsequently, the rest of the thesis is established.

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Blockchain technology originates from the invention of a peer-to-peer version of digital cash: Bitcoin. Bitcoin allows online payments to be sent from one party to another directly, without the need of a financial institution as a third-party to handle the transaction. The original paper on Bitcoin was published in 2008, when trust and belief in the sustainability of the financial institutions was rapidly diminishing, due to the global financial crisis (Nakamoto, 2008).

The underlying technology, ‘blockchain’ is a distributed, decentralized ledger which records digital transactions in a secure way. Blockchain is a decentralized database, which means that there is no single owner or regulator. In addition, data uploaded on the blockchain is immutable, this implies that data cannot be falsified or tampered with once uploaded (Kumar & Iyengar, 2017).

Since the emergence of blockchain technology, people have come to understand the potential of this new technology. This technology is not only limited to peer-to-peer digital financial transactions. Freely programmable blockchains such as Ethereum allow all kinds of business logics to be put into code. Swan (2015) has identified three tiers of blockchain. The first tier has to do with currency. Blockchain 1.0 encompasses the deployment of

cryptocurrencies, enabling digital payments and transferring cash.

Second tier blockchains allow business logics, called ‘smart contracts’ to be put into code. These blockchains are identified as blockchain 2.0. Blockchain 2.0 enables transactions more extensive than simple currency transactions: blockchain 2.0 goes towards a

decentralization of markets and can include all forms of contracts. In addition, they can for example, enable transactions of property using decentralized apps (DApps) or decentralized autonomous organizations (DAO).

10 The last tier, blockchain 3.0 extends to applications beyond currency and markets. Blockchain 3.0 includes the use of the distributed ledger technology to organize things in government, health and science, organizing activities of people, organizations and institutions (Faber & Hadders, 2016).

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Blockchain technology facilitates transformation and innovative business models in various ways. In this section, three areas that are both important to organizations and to consumers will be discussed. Three areas, in which blockchain technology has the potential to be truly transformative. First, blockchain technology promises regarding sustainability will be discussed. Next, its potential regarding safety & security will be discussed. Finally, its potential regarding privacy will be highlighted.

Sustainability

Kewell, Adams & Parry (2017) state that to date, research on blockchain technology has mainly focused on the technical features, concerning profits and efficiency gains. However, attention is shifting from these initial focal points towards use cases that are beneficial to sustainability, such as social and environmental challenges.

These cases however are still in its infancy. The computational method called proof-of-work, on which the blockchain described by Nakamoto (2008) is based, is described to be highly energy-inefficient. Yli-Huumo, Ko, Choi, Park & Smolander (2016) estimated that mining on the Bitcoin blockchain uses 15-million-dollar worth of energy per day. Alternative methods are being researched and proposed but the amount of energy currently used indicates that blockchain and sustainability still have a long way to go. Apart from the still

energy-11 heavy validation method, the underlying technology shows potential to increase sustainability (Kewell et al., 2017). For example, the United Nations has already serviced more than 10,000 Syrian refugees using ‘IrisGuard’ iris recognition technology in combination with blockchain technology. Food purchases in store are settled on the blockchain. Blockchain technology and iris recognition ensure that receiving donor money from, for example UNICEF, is secure and that transactions can be executed without needing a bank card, or a mobile device. This way financial inclusion for unbanked refugees can be achieved efficiently, one step in the right direction for the UNs sustainable development goals (Azar & Raouf, 2017). Other areas where blockchain technology can aid sustainability are for example in supply chain, to track medicine and, as a result, reduce counterfeit medicine. Or by using blockchain technology in efficient energy generation and distribution by connecting micro-grids (Giungato, Rana, Tarabella & Tricase, 2017).

However, further research is required to understand which technology works best in which industries and circumstances, and to what extent these technologies can impact the sustainability agenda in different areas (Kewell et al., 2017). Most of the goals on the sustainability agenda have to do with providing basic human needs in a manner that reduces the negative ecological and social consequences of providing these human needs globally.

Safety & Security

Blockchain technology emerged out of the need for a way to conduct online transactions based on cryptographic proof rather than trust; allowing parties to process payments directly without needing a trusted third party. Hence, blockchain technology, a system in which transactions are computationally impractical to reverse, was developed. The irreversible nature of the technology protects sellers from fraudulence, whilst according to Nakamoto

12 (2008), routine escrow mechanisms could be implemented as a protection mechanism for buyers.

Crucial to the security and thus the success of blockchain technology is the ability to prevent double-spending. With the rise of the internet, double-spending became a frequently discussed topic. Osipkov, Vasserman & Hopper (2007) argued that all electronic cash

systems struggle with the same security issue: strings of bits are inherently copiable. Whereas the physical world has double-spending protected, transferable cash, the internet did not. Which consequently resulted in increased security risks for internet transactions. This, in turn, resulted in higher fees for internet transactions (Osipkov et al., 2007).

The blockchain system is a secure solution to this issue, as long as more than half of the network is controlled by honest ‘members’ also known as nodes. Nodes validate

transactions and verify the integrity of the network. This vulnerability is called the ‘51% attack’ (Yli-Humoo et al., 2017). A study by Beikvardi & Song (2015) shows that the

centralization of Bitcoin has been steadily increasing. As a result, multiple studies propose to implement modifications of protocols in the blockchain (Garay, Klaysias & Leonardos, 2015; Eyal, & Sirer 2014). Clearly, blockchain technology in the current form still faces

technological obstacles.

However, if these technological challenges are overcome, untrusted parties can, due to the security blockchain technology offers, establish trust in the truthful execution of the code. By using smart contracts, secure business collaborations can be established in general and inter-organizational business processes. This potential has for example already been tested in securities settlement and diamonds trading (Mendling, Weber, Van Der Aalst, Brocke, Cabanillas, Daniel & Gal, 2017). The concept is that each of the trading partners has access to the blockchain, via a ‘node’. All transactions are registered on this blockchain. It is

13 impossible to change information on the blockchain without approval from all the involved parties. Furthermore, the blockchain enables near real-time settlement since it is constantly updated. This saves days compared to common transfer systems, which in turn reduces risk. Smart contracts on the blockchain have been developed to enable the ability to charge back even tough the transaction is nearly instant. Because every transaction is recorded on multiple nodes in the blockchain, a clear audit trail is available. In addition, due to blockchain

operating on cryptographic proof rather than trust, parties can safely transact without needing a trusted third party. All parties must agree to the transaction beforehand, which prevents fraud, double-spending and manipulation of transactions. Finally, because the blockchain is of distributed nature, the mechanism will continue to work even when one party experiences system failure (Mori, 2016).

Privacy

In contemporary society, devices are increasingly connected and generate, process and exchange immense amounts of data. Aside from generating and processing security and safety-critical data, privacy sensitive data is being exchanged.

Data deluge refers to a situation where the vast amounts of new data is being

generated whilst outpacing the development of infrastructures and tools that are adequate in supporting the data-driven 21st century science and technology. The sheer volume is

overwhelming the capacity of institutions to manage and make use of this data (Marburger, Kvamme, Scalise & Reed, 2007). Data deluge that is caused by billions of entities creating data, is a big threat to privacy according to Roman, Zhou & Lopez (2013). In a paper on the features and challenges of security and privacy in the distributed internet of things, they argue that users must have tools which allow consumers to retain their anonymity in this

14 ‘superconnected’ society. Moreover, they argue that the internet of things should seriously consider the implementation of privacy by design principles, such as blockchain technology.

Many modern networkable devices (such as motion sensors, connected lighting, doorbell cameras and smart thermostats) are lightweight and low-energy use. These devices devote their sparse energy and processing power to executing their core tasks and thus make additional security and privacy protection challenging, using traditional security measures (Dorri, Kanhere, Jurdak & Gauravaram, 2017). By employing blockchain technology, this hurdle can potentially be overcome due to the cryptographic nature of the technology.

Another privacy issue that current privacy enhancing technologies face is the fact that existing methods either reveal incomplete or noisy data to protect user privacy. Noisy or incomplete data hinders applications from offering personalised services, an inconvenience that can be overcome using a blockchain that can be both secure whilst sharing complete and clear data. According to Dorri et al. (2017) blockchain technology has the potential to

overcome the aforementioned challenges due to its secure, distributed and private nature.

Yue, Wang, Jin, Li & Jiang (2016) argue for example that on their researched blockchain architecture, patients can share their healthcare data without compromising security or limiting the sharing of healthcare services. Customers do not have to trust any third parties and are always aware when and how their data will be used. Thus, making legal and regulatory decisions about collecting, storing and sharing customer data simpler. This provides a way to house and share data while keeping customers’ privacy intact.

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Consumer perception in this study refers to the process of organizing, selecting and interpreting information by consumers to create a meaningful picture of a product,

15 or organization. In the following paragraph, the relationship between areas in which

blockchain technology shows significant promise; sustainability, safety & security and privacy, and consumer perception of these concepts will be discussed.

Sustainability

In 1987 the United Nations defined the term sustainable development as: meeting present needs without compromising the ability of future generations to meet their own needs (World Commission on Environment and Development, 1987). The concept of sustainability is currently being considered on multiple dimensions; environmental, economic and social sustainability (Hammer & Pivo, 2017).

Environmental sustainability has become increasingly important to consumers since global issues such as increased drought, rising sea levels and habitat destruction became more apparent. Green marketing targets environmental concerns and businesses see this as part of the solution to sustainability. “Green” has become a product option (Hall, 2011).

The social dimension of sustainability focuses on the well-being of humans in the form of noneconomic wealth. In the social dimension of sustainability, it has become clear that public distrust towards business practices has been growing (Hall, 2011). A study conducted by Mohr and Webb (2005) found an increase in the public’s expectations of businesses to increase their efforts in social well-being. Firms found out that there can also be meeting of interests when they respond to sustainability demands.

The collapse of Wall Street in 2008 and the economic meltdown that followed, brought renewed attention to economic sustainability worldwide – this in turn was one of the

16 main catalysts for the development of blockchain technology. The enduring global recession raised concerns with economic sustainability such as fear for financial insecurity.

Research has shown a correlation between consumer confidence in the performance of ‘green’ products and pro-sustainability beliefs in general (Pickett-Baker & Ozaki, 2008). Findings show that most consumers find it difficult to identify greener products, even though they favour products by greener organizations. However, according to Pickett-Baker & Ozaki, studies on sustainable products demonstrated that perceived product performance forms a significant barrier to product selection. If consumers perceive a ‘green’ product to be inferior, this significantly and negatively influences their purchasing behaviour. Consumers rather pay slightly more for sustainable products than to sacrifice product performance (Pickett-Baker & Ozaki, 2008).

One category of products that might be particularly relevant to blockchain technology is known as green product innovations. In green product innovations, the green product performance is significantly better than conventional or competitive products. Because of this, such products are seen as a powerful solution to the dead-lock between economy and environment (Pickett-Baker & Ozaki, 2008). However, an issue related to sustainability is that consumers cannot evaluate it personally and thus must put trust in the source claiming the sustainability. This uncertainty leads to the use of social information; consumers will look at others to get an indication of the best outcome (Vermeir & Verbeke, 2006).

Taking the possibilities that blockchain technology has to offer regarding

sustainability into account, combined with the aforementioned information about consumers’ perception of sustainability, the following is hypothesized:

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Safety & Security

In almost all industries, business practices are increasingly shifting to the internet. However, building customer loyalty in an online environment is difficult as competition is only a click away. To create positive lock-in it is important to recognise which factors lead to customer satisfaction.

Chang & Chan (2009) argue that consumer attitudes and beliefs about security have significant effects on the intention to purchase online. Online, consumers must rely on electronic payment methods, which increases risk perceived by consumers. This lack of security as perceived by consumers, forms one of the main obstacles to the development of online commerce. Consumers that provide personal information during transactions take the risk of having this information compromised. Hence, the security of their transactions is of major concern to online customers. Common perceptions of risks involve transmitting sensitive information. For example, credit card numbers, across the internet.

Moreover, Chang & Cheng (2009) define perceived security as the extent to which a potential customer feels secure in transmitting sensitive information. They found that

perceived security significantly and positively influences customer satisfaction and customer loyalty.

Blockchain technology and its promises in the field of safety and security can arguably influence perception on the safety and security of organizations that apply this technology. Hence, the following is hypothesized:

H1b: Consumers perceive organizations using blockchain technology to be safer & more secure

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Privacy

A promising aspect of blockchain technology is the privacy options this technology brings. To comprehend the influence blockchain technology has on consumers it is important to understand consumers’ opinion on privacy. According to research, understanding the value that consumers assign to the protection of their personal data is of significant importance to various fields such as law, public policy and business (Acquisiti, John & Loewenstein, 2013).

Understanding this value is important to business because by gauging to what extent consumers value the protection of their data, executives can estimate which

privacy-enhancing efforts can become sources of competitive advantage. On the flipside managers can also deduce which intrusive initiatives could trigger adverse reactions from consumers (Acquisti, John & Loewenstein, 2013).

In recent years extensive research has been done attempting to quantify individual privacy valuations. Acquisti, John & Loewenstein (2013) found that in a physical store consumers’ willingness to accept money to reveal data was far higher (ratio 5.47) than consumers’ willingness to pay to protect that same data. This willingness to accept versus willingness to pay ratio is far higher for privacy than for ordinary private goods (2.92). This suggests that consumers’ willingness to give up privacy is relatively high.

However, research also found that privacy and trust assume a more crucial role in e-commerce, where sales force is absent. The willingness to provide personal information online is an important issue in e-commerce that can influence the success of e-businesses. Online privacy concern leads to lowered willingness to provide information online, a

rejection of e-commerce or even unwillingness to use the Internet, according to Wu, Huang, Yen & Popova (2012).

19 Since blockchain technology has the potential to solve many privacy issues it is not far-fetched to reason that consumers’ perception of organizations using this technology differs from their perception of organizations that do not use blockchain technology. The following is hypothesized:

H1c: Consumers perceive organizations using blockchain technology to have better privacy features

I

Energy sector

Contrary to how blockchain technology works, the energy sector remains a mainly

centralised system in which major power producers make up 94% of electricity production (Murkin, Chitchyan & Byrne, 2016). According to Münsing, Mather & Moura (2017) recently however, the energy production industry is shifting to more distributed energy resources (DERs) such as battery storage systems, solar panels and small wind power systems. They argue that if these systems are used in an intelligent way, distributed energy resources can improve reliability whilst reducing costs, by increasing efficiency and decreasing the need of third parties. Furthermore, blockchain technology can foster smart integration of renewable resources in the electric grid, which would be a step forward regarding sustainability.

Blockchain technology has caught the attention of the major power producers due to the potential it has as a secure, trustworthy system. On a blockchain, it is possible to develop an integrated decentralized system, where users can trade energy peer-to-peer (Basden & Cottrell, 2017). Murkin et al. (2016) argue that a peer-to-peer trading system could be implemented using blockchain technology, in which optimisation methods could be

20 developed. These methods can smooth load curves on the grid which in turn increase energy efficiency and reduce black-outs, improving sustainability in the long term. The further electricity travels, the more energy is lost (Murkin et al, 2016). If this cost could be calculated into the sale price, a better price could be offered to nearby customers. This could both

benefit the environment and the customers.

Consumers that store and generate energy are called “prosumers”. These so-called prosumers deliver energy back to the energy grid. However, the payments they receive for these distributed energy resources must be agreed upon with the companies in the electric industries. These are often monopolies that probably are invested in the preservation of conventional energy generation systems (Murkin et al, 2016). If prosumers can use the

blockchain to trade energy amongst each other, without the third parties, sustainable solutions could become even more attractive.

Financial Services

As mentioned previously the invention of blockchain technology is related to decreasing trust in the prevailing financial system. Dissatisfaction towards how financial services operated increased the call for alternative systems. Hence, it is no wonder that financial services are one of the industries where blockchain technology is expected to have a disruptive impact.

According to Fanning and Centers (2016) blockchain is believed to first change financial services’ back-office handling of transactions. For example, when a financial institution sells a syndicated loan (a loan offered by a group or ‘syndicate’ of lenders) the recording of the transaction is time- and labour-intensive. Said processes are based on contracts negotiated with numerous lawyers associated on intensive contact between the parties involved to complete the transaction. The average time to settle a syndicated loan

21 amounts to 20 days and the back-end activities involved are costly to the financial

institutions. Furthermore, increasing regulatory requirements for financial institutions regarding transparency, reporting and distribution of data form an increasingly big challenge in financial services. Blockchain technology shows a lot of promise to be the breakthrough that can streamline the process of financial transactions. Blockchain is estimated to save approximately 20 billion dollars in regulatory, settlement and cross border payment costs (Fanning & Centers, 2016).

Healthcare

Another sector where blockchain is causing hype and optimism is the healthcare sector. In the healthcare industry, many different parties often require access to the same information. A decentralized database that is consistently online and up to date presents many advantages to the healthcare industry and in the end the benefit of the patient.

Smart healthcare management in medical treatment processes for chronic diseases or elderly care, facilitated by blockchain technology can create added value (Mettler, 2016). According to Yue, Wang, Jin, Li & Jiang, the challenge for healthcare systems to become smarter is to gather, store, analyse and share personal health-data without raising privacy concerns. They propose a healthcare system based on blockchain technology that enables patients to control and share their own data without violating privacy.

In healthcare, a variety of involved parties such as hospitals, therapists, specialists and general practitioners work together in the treatment process. These parties must deal with media disruptions in the treatment process of the patient, including for example various medical health records, incompatible IT and changing communication channels. The

22 processes for all the medical stakeholders in the treatment process (Mettler, 2016) As such, streamlining this process using blockchain technology can also add value to the practitioners.

Besides streamlining existing healthcare processes, blockchain technology can also empower patients in the field of patient-generated health data. Health related wearables lead to an increasingly vast flood of data that can be processed in medical issues related research. Blockchain technology can be used when it comes to the handling of data transactions and the sharing of personal health data. Users can store and manage health information in a secure environment where data sovereignty completely lies in the hands of the user. Using

blockchain technology, personal patient-generated health data such as blood pressure, medicine taken, sleep patterns, eating habits and heart rate, can be retrieved from personal health applications, devices or physician visits (Mettler, 2016).

Finally, blockchain technology can be used to fight counterfeit drugs in the

pharmaceutical industry. Medicines subject to sensitive production processes and medicines that deal with extensive liability and reputational issues associated with the final product can benefit from blockchain technology as this technology can be used to monitor the production processes for drugs (Clauson, Breeden, Davidson & Mackey (2018).

Whilst research has shown many use-cases for blockchain technology in various industries, for each of these industries consumers perception of blockchain technology is of utmost importance for organizations to be able to fully utilize this technology. As different aspects and promises of blockchain technology might be perceived to be of varying

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H2a: Industry type moderates consumers’ perceived sustainability of organizations using blockchain technology

H2b: Industry type moderates consumers’ perceived safety & security of organizations using blockchain technology

H2c: Industry type moderates consumers’ perceived privacy concerns of organizations using blockchain technology

R

Even though blockchain technology seems to have the potential to bring consumer-oriented innovation and to deliver added value to the consumers, most companies in the field of high-tech products have experienced high rates of innovation failures in the past (Moore, 2002). This could also be a concern for in the future for blockchain technology.

While numerous studies focus on factors that contribute to consumer adoption of innovations, it is at least equally important to understand why consumers resist adoption (Kleijnen, Lee & Wetzels, 2009). Existing literature distinguishes three different ways in which resistance to adopt innovation happens; rejection, postponement and opposition.

Rejection implies an active evaluation of an innovation by the consumer, which

results in a strong disinclination to adopt this innovation (Rogers, 2010). This reluctance is often caused by suspicion towards new and unproven innovations. Moreover, rejection is intertwined with consumer hesitance to change the status quo.

Postponement occurs when consumers think of an innovation as acceptable, however

24 favourable. In the case of postponement, the decision is not final. For example, consumers took a long time to adopt Voice-Over-Internet-Protocol (a technique used by for example Skype to deliver voice communications over the internet) whilst it was in fact easy to use. However, it was not regarded as a standard and most consumers are suspicious of what they regard to be unproven technology (D’Errico, 2005).

When opposition to innovation occurs, consumers are convinced that an innovation is unsuitable and may decide to launch an attack, such as negative word-of-mouth. This

phenomenon can also be described as ‘innovation sabotage’ or ‘active rebellion’ where consumers actively engage in strategies to disturb the innovation’s success (Kleijnen, Lee & Wetzels, 2009).

To understand why consumers behave in such a manner, it is important to study the drivers of consumer resistance. Kleijnen, Wetzels & Lee (2009) argue that factors that drive consumer resistance can be split into two main types. First, an explicit change in consumers behaviours with regard to their norms, traditions and habits is likely to meet resistance. Second, resistance likely occurs when innovations cause a psychological conflict or a problem for consumers.

Literature suggests that this is related to the perceived product image. Product image and extrinsic product cues are important to consumers when actual product characteristics and functioning may be hard to observe, as is often the case with innovation. In such cases the image is typically derived from rumour, stereotypes or the innovation’s origin, such as product class or industry. In addition, negative media coverage can lead to negative image perceptions (Kleijnen Wetzels & Lee, 2009).

Product complexity is another important obstacle in adoption. Complexity relates to the extent to which an innovation is difficult to understand and use. Product complexity can

25 lead to information overload, which is recognized as a growing factor in consumer decision making (Oreg, 2006; Kleijnen, de Ruyter & Wetzels, 2007).

Relating this research to blockchain technology, it becomes apparent that resistance to innovation could be of significant influence on consumers’ perception and acceptance of this technology. As this technology is highly innovative, it could suffer from the high rates of innovation failure, as found to be true for other innovative technologies by Moore (2002).

Rejection can happen because of suspicion towards the new technology, as for example

blockchain ‘hacks’ and scams reach the news regularly. Postponement could occur due to blockchain technology not yet being the standard. Cryptocurrency is for example arguably easier to transfer than fiat money, yet not the standard. As blockchain technology is a

relatively new technology, these attitudes towards this technology are probably mainly based on the perceived product image because the actual potential product characteristics and functioning are still hard to observe and are thus typically derived from rumour as mentioned by Kleijnen, Wetzels & Lee (2009). Additionally, actual characteristics and functioning of blockchain technology are also factors that can increase consumer resistance (Kleijnen, Wetzels & Lee, 2009). For example, product complexity mentioned before, can further increase consumer resistance to innovations (Oreg, 2006). According, the following is hypothesized:

H3a: Resistance to innovation negatively moderates consumers’ perceived sustainability of organizations using blockchain technology

H3b: Resistance to innovation negatively moderates consumers’ perceived safety & security of organizations using blockchain technology

H3c: Resistance to innovation negatively moderates consumers’ privacy concerns of organizations using blockchain technology

26

F

&

E

Besides product complexity, consumer knowledge also influences consumer behaviour, such as willingness to pay. Consumer knowledge is a significant consumer construct that has been researched extensively. Consumer knowledge has influence on the way consumers process information, what products these consumers buy and how they use these products (Cordell 1997).

Knowledge level affects consumers price acceptance and quality acceptance

according to research conducted by Rao & Sieben (1992). Consumer knowledge can be split in two distinguished categories according to Cordell (1997). One of them being familiarity, and the other expertise.

Familiarity, also known as product-related experience, is the first constituent of product knowledge. It is defined as the number of product related experiences accumulated by the consumer. This manifests itself in search, ownership, usage and experience of a product. The other important component of product knowledge is expertise. This relates to the ability to perform product related tasks. Expertise exists in physical form, as

task-performance improvements that are gained through repetition. A different form of expertise is knowledge-based expertise. This form of expertise is gained primarily through exploration and learning (Cordell, 1997).

Rao & Monroe (1988) examined the relationship between prior knowledge and price effects and found low-knowledge consumers to perceive price to be a quality indicator more than moderate-knowledge consumers. Rao & Sieben (1992) found that what consumers perceived to be acceptable prices for products, approached actual market prices, when these consumers displayed higher consumer knowledge. This indicates that knowledgeable consumers should be better at estimating exact value. Cordell (1997) found that

lower-27 knowledge consumers are willing to pay more than higher-knowledge consumers. Following this reasoning, it could well be that consumers’ knowledge level influences the perception they have on certain products, such as blockchain technology.

In a paper by Hirunyawipada & Paswan (2006) research was conducted on

consumers’ perceived risk of high-technology product adoption and its implications. One of the factors researched was performance risk. Performance risk consists of concerns that products do not perform as anticipated. According to Mitchell & Harris (2005), cognitive abilities and consumer knowledge influence consumers’ evaluation of performance risk. High consumers’ expertise and interest in the high-tech product domain were found to mitigate perceived risk. This hints at the fact that consumer expertise of blockchain technology could influence things such as perceived security when organizations choose to use this technology (Hirunyawipada & Paswan, 2006).

Similar to resistance to innovation, which literature has shown to negatively influence consumer perception, familiarity and expertise seem to mitigate negative perceptions and positively influence consumer perception in certain situations. Accordingly, for blockchain technology the following is hypothesized:

H4a: Blockchain familiarity positively moderates consumers’ perceived sustainability of organizations using blockchain technology

H4b: Blockchain familiarity positively moderates consumers’ perceived safety & security of organizations using blockchain technology

H4c: Blockchain familiarity positively moderates consumers’ perceived privacy concerns of organizations using blockchain technology

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H5a: Blockchain expertise positively moderates consumers’ perceived sustainability of organizations using blockchain technology

H5b: Blockchain expertise positively moderates consumers’ perceived safety & security of organizations using blockchain technology

H5c: Blockchain expertise positively moderates consumers’ perceived privacy concerns of organizations using blockchain technology

According to the reviewed and analysed literature, the following model is conceptualized:

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ETHODOLOGY

In this paragraph the research methods used to conduct the analyses will be explained. An explanation of how the hypotheses are tested will be given along with the specific scale of the variables that are being used. An overview of the sample will be provided. Finally,

measurements, further procedures and predictions will be discussed to ensure reproducibility of this research.

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Blockchain technology potentially offers innovative developments in all kinds of industries concerning sustainability, safety & security and privacy. This technology however, needs consumer acceptance to flourish. To investigate consumer perception of blockchain

technology, research is to be conducted in multiple ways, starting with existing literature. In addition, an experimental research design will be used. To test the hypotheses, the outcomes of this experiment will be analysed using quantitative research methods. By using online survey platform Qualtrics, an experiment will be conducted, and a dataset will be formed. This dataset contains the perception of at least 250 consumers on an organization’s sustainability, privacy and safety & security competences. Data will be collected on respondent’s general demographic, their degree of resistance to innovation and their familiarity and expertise regarding blockchain technology.

More specifically, the survey is designed based on an 2x3 experimental research. The experimental factors consist of three levels; (three sectors) the energy sector, financial services sector and the healthcare sector, which are at the forefront of blockchain technology adaptation. Blockchain adaptation by organization consists of two levels; either yes or no. This amounts to six different conditions i.e. six different stimuli.

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P

An online survey will be conducted to gauge consumer perception. In this survey, a fictitious technology organization in the financial services sector, the healthcare sector and the energy sector will be described. The choice for using an online survey is deliberately made as it has a high potential to reach a wide variety of consumers which are either significantly educated or less educated on blockchain technology, which helps the validity of this research.

The experiment will be conducted using an online survey starting from the 18th of May 2018. An anonymous Qualtrics link is provided to the respondents. After opening this link, the respondents are briefed about general information regarding the research and about the anonymity provisions. Contact information is provided in case the participants have any remaining questions (see appendix for full text).

Subsequently, respondents will be randomly assigned to one of the six conditions:

1. blockchain, financial services sector; 2. blockchain, healthcare sector;

3. blockchain. energy sector;

4. non-blockchain, financial services sector; 5. non-blockchain, healthcare sector;

6. non-blockchain, energy sector;

These conditions contain different stimuli texts regarding the fictitious organization “Ensama Solutions”. Respondents are presented with a made-up logo and general information about the organization. The text also provides information on the sector in which “Ensama Solutions” operates and in one half of the conditions, it is explicitly mentioned that the organization makes use of blockchain technology. To ensure that respondents read the text

31 thoroughly, they must check off a box stating that they have read all the information carefully before they can continue.

In the following part, the participants are confronted with statements regarding the organization. Respondents are asked to indicate on a scale from 1 to 7 (1 being strongly disagree to 7 being strongly agree) how they feel about the organization based on the

description they have read and based on their intuition. The first part assesses the respondents perceived sustainability regarding the organization. Next, the perceived safety & security is assed and afterwards their perception on privacy features is assessed, as well as their degree of resistance to innovation. Afterwards, participants are asked to indicate their familiarity with blockchain technology and their expertise on blockchain technology is tested using a multiple-choice quiz with 5 answers, one of them being “I do not know the answer” to minimalize false positive possibilities.

In the final part questions regarding basic demographical information are presented. Finally, the respondents are debriefed about the research and once again thanked for their cooperation. Contact information is presented once again in case the respondents have questions or remarks regarding the experiment (see appendix for full text).

Before launching the survey, pre-tests were performed on the questionnaire. First a participating pre-test was used, followed by an undeclared pre-test to remove any remaining errors in the survey and to measure average time needed to fill in the survey. A progress bar was added, and manipulation checks. as discussed in the next part, were made based on the undeclared pre-test.

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Participants for the study were gathered by publishing the survey link on Amazon Mechanical Turk. The survey was only visible for participants from the United States, to ensure that none of the respondents were not fluent in English and thus possibly unable understand the stimuli and the questions properly.

In total the survey was filled in N = 468 times. However, two manipulation checks were used to determine whether the survey was properly filled in. Halfway through the survey the respondents were confronted with the following request: “Please press strongly disagree here so I know that you read this question”. A total of 139 respondents failed this manipulation check. Furthermore, after pre-testing the survey to achieve reliable answers, it was decided that the respondents should have invested at minimum 200 seconds filling in the survey, to control for manipulation. 151 participants failed this manipulation check.

Responses that did not comply to one or either of the manipulation checks were excluded from the final dataset.

As a result, a total of 257 participants remained in the final experiment data-set (n=257). This amounts to 121 participants assigned to the experimental group and 136 participants in the control group. Per condition the sample consisted of between 33 and 48 participants. This is in line with research stating that at least n=30 participants are required to achieve reliable results in such experiments (Gahsemi & Zahediasl, 2012).

The age of the respondents ranged from 18 years old to 85 years old with an average age of 39. 51% of the respondents were male and the other 49% consisted of females, almost approaching an equal gender ratio. Regarding education level, 55.6% achieved at least a bachelor’s degree.

33 Chi-squared Tests were conducted to check whether demographics within the groups were not significantly different for the six conditions. The results were as follows. Gender, χ2 (5, n = 257) = 4.025, p = .5456. Age, χ2 (280, n = 257) = 290.949, p = .314. Education level, χ2 (35, n = 257) = 35.282, p = .455. From these results it can be concluded that data for the various conditions has successfully been randomized.

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Consumer perception of organizations that use blockchain technology versus organizations that do not is examined. Accordingly, consumer perception is split out and measured in three different variables. These variables are sustainability, safety & security and privacy. The variables all play a significant role in blockchain technology. Whether or not an organization uses blockchain technology is the predictor variable. Thus, blockchain technology adaptation forms the independent variable.

Sustainability

To assess respondents’ perception of sustainability, the variable sustainability was measured with a scale consisting of 14 items, derived from a study on measures of perceived

sustainability by Kim, Taylor, Kim & Lee (2015). Three of the items assessed the economic sustainability dimension, six of the items assessed the social sustainability dimension and four assessed the environmental sustainability dimension and one assessed general sustainability. Minor changes were made with regard to the wording of the statements, to match the stimuli. For example; “Corporation utilizes green technology” was changed to “This organization most likely utilizes green technology”. Respondents were asked to indicate their perception on a scale from one to seven (1= Strongly disagree to 7= Strongly agree). The items have been combined into a general sustainability scale. Reliability analysis proved the general sustainability scale to be highly reliable (α = .944). The economic

34 subscale also proved to be reliable (α = .853), the same proved to be the case for social

sustainability (α = .896) and environmental sustainability (α = .943). The corrected item-total correlations are all above .30 indicating that all items have a good correlation with the total score of the scale. Besides, none of the items would significantly affect the reliability when deleted.

Safety & security

Subsequently, the variable perceived safety & security is measured using a scale derived from a study by Hartono, Holsapple, Kim & Na & Simpson (2014). The scale consists of seven items rated on a seven-point Likert scale. Minor changes were made to the wording to match the stimuli. “This internet store is capable of preventing illegal access” was changed to “This technology organization is most likely of preventing illegal access to its services”. The scale measuring safety & security proved to be highly reliable (7 items; α = .933).

Privacy

Finally, perceived privacy is measured using a scale that combined three items. These three items are derived from the previously mentioned study by Hartono et al. (2014) and a study by Salisbury, Pearson & Miller (2001). Respondents rated the statements on perceived privacy on a seven-point Likert scale. Reliability analysis confirmed the scale to be reliable (3 items; α = .858)

Research was also conducted on whether industry type has a moderating effect on the proposed hypotheses. Additionally, consumer knowledge, being split out in familiarity and expertise, was measured and analysed to see whether these have a moderating effect on the consumer’s perception of the organization. Finally, the same was done for the construct resistance to innovation.

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Familiarity

To measure whether familiarity with blockchain technology has a moderating effect on consumers’ perception of blockchain technology adoption, three items are derived from a study conducted by Barrutia & Gilsanz (2013) on consumer knowledge-related resources. Statements are re-written to measure familiarity with blockchain technology on a seven-point Likert scale. The familiarity with blockchain technology scale proved to be reliable (3 items; α = .941).

Expertise

To measure the second component of consumer knowledge, consumer expertise is measured using a blockchain pop-quiz inspired by online blockchain pop-quizzes. Consumer expertise is measured by seven items consisting of multiple-choice questions with five possibilities to answer. One of the five answer options is “I do not know the answer” to minimalize false positives due to guessing. The blockchain technology expertise scale also proved to be reliable (7 items; α = .739).

Resistance to innovation

Finally, the expected moderator resistance to innovation is measured based on a study by Laukkanen, Sinkkonen, Kivijärvi & Laukkanen (2007), who conducted a study on innovation resistance. The scale used for resistance to innovation consists of 12 items used in this study. Five of these items assess the usage barrier, one assesses the value barrier, four assess the risk barrier, one the tradition barrier and one assesses the image barrier. However, the statements are re-written slightly to measure innovation resistance to blockchain technology. For instance, “In my opinion, the use of mobile banking services is convenient” is rewritten to: “In my opinion, the use of blockchain technology services is convenient”. Again, the items are rated on a seven-point Likert scale. The resistance to innovation scale that was composed proved to be reliable (12 items; α = .885)

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A

&

The conceptual model that is tested in this study consists of one categorical independent predictor variable, blockchain technology adaptation. As mentioned before, this leads to more than one independent group. The model is tested to determine whether there are differences between the independent groups on more than one dependent variable; sustainability, privacy and safety & security. Hence, one-way ANOVAs are used to analyse the main effects

proposed in the conceptual model. These one-way ANOVAs are used to check whether the use of blockchain technology is a significant predictor for consumer perception in the areas sustainability, safety & security and privacy.

Additionally, in the conceptual model, four variables are proposed that could possibly act as moderators. These moderator variables are hypothesised to change the magnitude of the relationship between the independent and the dependent variables when they are introduced. Therefore, the next step is to test the suggested moderating effects. To do so, model 1 in the PROCESS macro written by Andrew F. Hayes is used to check the effects for each of the proposed moderator variables.

It is predicted that making use of blockchain technology has a significant positive effect on the consumer perception of the organization, as measured by the three distinct dependent variables. Moreover, it is predicted that the magnitude of this effect depends on the sector in which this organization operates. Significant differences between an organization in the financial services sector, the energy sector and the healthcare sector are expected.

Likewise, it is expected that when consumers are more familiar with blockchain technology, this increases the positive strength of the proposed main effect. The same positive effect is predicted to occur for higher levels of consumer blockchain technology expertise. However,

37 when consumers display resistance to innovation, this is expected to decrease the strength of the proposed main effect.

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ESULTS

In this paragraph the results of the statistical analyses are presented. An overview of the variables and their correlations is given. Finally, the results of the statistical analyses for the main effect and the moderating effects are presented.

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In the first table, an overview of the correlations between the variables in the

conceptual model is presented. These correlations are analysed to help assess and quantify the instensity and meaning of the relationship between the variables in the model (table 1).

Respondents that perceive the organization more positively regarding sustainability also perceive privacy (r = 0.58, p < .001) and safety & security (r = 0.56, p < .001) more positively. Participants that perceive safety & security positively also perceive privacy more positive (r = 0.76, p < .001). Interestingly, respondents who show resistance to innovation have a more negative perception of the organization’s sustainability (r = -0.34, p < .001) privacy (r = -0.42, p < .001) and safety & security (r = -0.42, p < .001). Furthermore,

respondents that are familiar with blockchain technology rate the organization’s sustainability (r = 0.13, p < .05) and privacy (r = 0.20, p < .001 more positively. Participants that indicate being familiar with blockchain technology scored lower on the resistance to innovation scale (r = -0.45, p < .001). Respondents that displayed higher expertise on regarding blockchain technology also scored lower on the resistance to innovation scale (r = -0.18, p < .05). In addition, these respondents also indicated to be more familiar with blockchain technology higher (r = 0.45, p < .001). Finally, a significant correlation between a change of sector in the stimuli and perceived sustainability was found (r = 0.14, p < .05).

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Table 1: Means, Standard deviations, Correlations

Variables M SD 1 2 3 4 5 6 7

1. Sustainability 4.96 0.95 -

2. Privacy 4.92 1.10 .578** _-

3. Safety & Security 5.37 0.98 .557** _.763** _-

4. Resistance to innovation 3.65 0.90 -.340** _-.420** _-.422** _-

5. Familiarity 3.65 1.71 .131* _.198** _{0.112 -.451}** _-

6. Expertise 3.27 1.97 0.001 0.054 0.012 -.183** _.466** _-

7. Sector 1.99 0.78 .137* _{-0.069 -0.069 -0.047 -0.083 0.026 -}

8. Blockchain 0.47 0.50 0.051 0.107 0.098 -0.055 -0.022 -0.065 -0.059

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

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The first hypothesis, 1a, suggests that an organization which uses blockchain technology is perceived to be more sustainable by consumers compared to organizations that do not use this technology. Hypothesis 1b states that an organization using blockchain technology is

perceived to be significantly safer & more secure. Finally, hypothesis 1c states that an organization using blockchain technology is perceived significantly more positive regarding privacy.

To assess the hypothesized main effects of the first hypothesis, one-way ANOVA analyses are conducted to assess whether predictor blockchain technology use in

organizations is statistically significantly related to the outcome variables sustainability, privacy and safety & security.

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Sustainability

First hypothesis 1a was tested. The descriptives table and the output of the ANOVA analysis between blockchain usage and perceived sustainability can be found below in table 2a and 2b.

Table 2a: Descriptive Statistics ANOVA between Blockchain Usage and Sustainability

N Mean Std. Deviation Std. Error

No Blockchain 136 4.9144 .98399 .08438

Blockchain 121 5.0118 .90920 .08265

Total 257 4.9603 .94893 .05919

Table 2a displays the means for an organization with and without adoption of blockchain technology for the scale sustainability. For the variable sustainability, it seems that blockchain technology adoption results in a more positive perception (M = 5.01, SD = 0.91) compared to an organization without blockchain technology (M = 4.91, SD = 0.98).

Table 2b: Analysis of variance (ANOVA) between Blockchain Usage and Sustainability

Sum of

Squares df Mean Square F Sig.

Between Groups .608 1 .608 .674 .412

Within Groups 229.910 255 .902

Total 230.518 256

As determined by one-way ANOVA F(1,255) = .674, p = .412) however, no statistically significant difference for perceived sustainability between groups was found. This indicates that there is no significant difference in a consumer’s perceived sustainability when an organization adopts blockchain technology. Thus, no evidence to support hypothesis 1a was found, therefore this hypothesis is rejected.

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Safety & security

Next, hypothesis 1b was tested. The descriptives table and the output of the ANOVA analysis between blockchain usage and perceived safety & security can be found below in tables 3a and 3b.

Table 3a: Descriptive Statistics ANOVA between Blockchain Usage and Safety & security

N Mean Std. Deviation Std. Error

No Blockchain 136 5.2784 .98151 .08416

Blockchain 121 5.4711 .98088 .08917

Total 257 5.3691 .98403 .06138

Table 3a displays the means for an organization with and without adoption of blockchain technology for the scale for safety & security. Safety & security with (M = 5.47, SD = 0.98) adoption of blockchain technology versus (M = 5.28 SD = 0.98) without adoption for blockchain technology.

Table 3b: Analysis of variance (ANOVA) between Blockchain Usage and Safety & security

Sum of Squares df Mean Square F Sig.

Between Groups 2.378 1 2.378 2.470 .117

Within Groups 245.509 255 .963

Total 247.887 256

Again, as determined by one-way ANOVA F(1,255) = 2.470, p = .117) no statistically significant difference for perceived safety & security between groups was found. This indicates that there is no significant difference in a consumer’s perceived safety & security when an organization uses blockchain technology. In conclusion, no evidence supporting hypothesis 1b was found and hypothesis 1b is therefore rejected.

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Privacy

Finally, hypothesis 1c was tested. The descriptives table and the output of the ANOVA analysis between blockchain usage and perceived privacy can be found in tables 4a and 4b below.

Table 4a: Descriptive Statistics ANOVA between Blockchain Usage and Privacy

N Mean Std. Deviation Std. Error

No Blockchain 136 4.8113 1.08085 .09268

Blockchain 121 5.0468 1.11081 .10098

Total 257 4.9222 1.09924 .06857

Table 4a displays the means for an organization with and without adoption of blockchain technology for the scale for privacy. Privacy (M = 5.05 SD = 1.11) with adoption of blockchain technology and (M = 4.81, SD = 1.08) without adoption of blockchain technology.

Table 4b: Analysis of variance (ANOVA) between Blockchain Usage and Privacy

Sum of Squares df Mean Square F Sig.

Between Groups 3.553 1 3.553 2.963 .086

Within Groups 305.780 255 1.199

Total 309.332 256

Finally, the one-way ANOVA F(1,255) = 2.963, p = .086) determines no statistically significant difference for perceived privacy when an organization uses blockchain technology. This indicates that there is no significant difference in consumer’s perceived privacy when an organization uses blockchain technology. Therefore, no evidence supporting hypothesis 1c was found and resulting in rejection of hypothesis 1c.

For all the variables, the dimension in which an organization did use blockchain technology, a higher mean score was shown, indicating a more positive consumer perception.

42 However, a non-significant result for the various scales indicates that there is no real

difference in consumer perception with respect to organizations adopting blockchain technology. Thus, again the conclusion is that hypothesis 1a, 1b and 1c can be wholly rejected.

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Industry type

To check whether industry type moderates the effect between blockchain technology use by an organization and consumer perception, as stated in hypothesis 2a, 2b and 2c, Hayes’ PROCESS macro model 1 is used.

Sustainability

The proposed moderating effect on blockchain technology use and perception of

sustainability is tested first. The following tables 6a and 6b give an overview of the results of the analyses.

Table 6a: Descriptive Statistics between Industry type and Sustainability for blockchain use

N Mean Std. Deviation Std. Error

Finance -No Blockchain -Blockchain 82 42 40 4.7317 4.5867 4.8839 .91642 .90358 .91626 .10120 .13943 .14487 Healthcare -No Blockchain -Blockchain 95 47 48 5.0759 5.0076 5.1429 .93816 .94070 .94071 .09625 .13722 .13578 Energy -No Blockchain -Blockchain 80 47 33 5.0571 5.1140 4.9762 .96425 1.0399 .85410 .10781 .15168 .14868 Total 257 4.9603 .94893 .05919