Digital Marketing Enhancement of Cryptocurrency Websites: History
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Contributor:
Nikolaos T. Giannakopoulos
,
Damianos Sakas
,
,
Christos Tryfonopoulos

Today, more than ever, the popularity of decentralized payment systems has risen, creating an outbreak of new cryptocurrencies hitting the market. Unique websites have been staged for each cryptocurrency, where information and means for mining cryptocurrencies are available daily. People visit those cryptocurrency websites either from desktop or mobile devices. Thus, the impulsion for appropriate promotion of cryptocurrency websites and customer factors affecting it rises. The above process increases cryptocurrency organizations’ website visibility, raising the need for customer relationships and satisfaction optimization concerning organizations’ supply chain strategy.

  • strategic digital marketing
  • innovation process
  • decentralized systems
  • data analysis
  • web analytics
  • customer electronics
  • decision support systems

1. Introduction

Nowadays, the hype about cryptocurrencies has led to the development of more than 1600 cryptocurrencies. Cryptocurrencies are digital coins that may be transferred online. Cryptographic encryption and digital certificates are used to validate transactions and prevent multiple spending of the very same coin. By forbidding users from replicating the data that form the coin, cryptocurrencies have brought the concept of shortage to the digital realm [1]. Cryptocurrencies might become lucrative because their shortage is maintained by the encryption built in their transparent code (normally auditable by anybody). In opposition to fiat currency, bitcoin is produced, exchanged, circulated, and preserved via a decentralized registration process and is referred to as a blockchain. The Bitcoin value is influenced by a wide range of factors, including global opinion, media, and buzz [2].
Despite Bitcoin being still the most popular cryptocurrency, many other digital currencies have already been created. Cryptocurrency commodities are divided into cryptocurrencies such as Ethereum, Ripple, and Dogecoin; stable coins such as Binance USD and Tether; and tokens [3]. Bitcoin contains characteristics that were not held by traditional financial transaction channels, such as the fact that the Bitcoin price fluctuates based on people’s perceptions and views as well as institutional practices. The increasing volatility of the crypto value leads to risky currency transactions [4].
Nonetheless, this ever-expanding financial sector is marked by substantial volatility and sharp price variations over time. Today, cryptocurrency prediction is widely regarded as one of the most difficult time-series forecastings, given the multitude of unpredictability variables involved as well as the considerable volatility of cryptocurrency prices, culminating in complex chronological correlations [5][6]. With the exception of fiat currencies, where the government may alter supply to counteract price bubbles, the distribution of a cryptocurrency is often built to meet a predefined route, making it more susceptible to price variations. The additional network factor of a cryptocurrency is a second crucial property. The advantage of acceptance for a user is determined by the quantity of all other users who can always trade [7].
While blockchain technologies were first used in the framework of bitcoin, they have now spread to regular enterprises, entrepreneurs, and everyday activities. It will be used, for instance, to provide an alternate payment solution to credit cards or PayPal in e-commerce and worldwide transactions [8]. Furthermore, it has been incorporated in financial institutions, as well as many other industries, with diverse applications from facilitating and standardizing financial intermediation to monitoring trader loyalty cards, and even constructing decentralized markets for commercial transactions [9]. It is already being used by Volkswagen and others for electricity exchange and grid management, as well as by mobile firms like Huawei and Apple that are creating blockchain-enabled devices to allow customers to pay in bitcoin through smartphones [10].
Big Data and web analytics are key factors of Researchers' research, enabling analysis of elements that affect the website’s performance, like user engagement metrics. Big data is defined as a vast amount of information with both a massive density and heterogeneity, whose velocity surpasses existing technology’s ability to manage correctly [11]. Web analytics entails collecting and analyzing massive volumes of data designed to check and enhance organizations’ site web usage [12]. Metrics are employed by web analytics systems to reduce web traffic data to plain values which are easy to understand. Customers utilize trade credit as a financial management tool to keep their organization’s current liquidity in check. From a company’s perspective, trade credit enables them to create an attractive payment schedule without jeopardizing their profitability [13].
The blockchain method is suitable for settings that demand high levels of verification and validation, and it can also respond to environmental modifications and regulatory measures, such as governing agencies [14]. Cryptocurrencies are a relatively new payment option that provides a competitive edge to company websites [15]. As a result, Key Performance Indicators (KPI) based on web analytic metrics [12] are a valuable measurement for evaluating site goal completion. Numerous indicators and KPIs are used in web analytics to collect internet usage information in intelligible and convenient ways. Advertisers and analysts may use several methods to increase customer engagement [16], brand recognition [16][17], and profitability [16][17], in addition to the digital payment alternatives that websites provide to users, increasing their value [18].

2. Digital Marketing of Cryptocurrency Websites

2.1. Importance of Customer Engagement and Device Preference in E-Commerce

Besides obtaining more attention, digital marketing advertises products and services through mobile applications and business websites [19]. As a consequence, corporate digital brand name and exposure will improve. Marketers use technological tools to provide a variety of direct and online advertising to attract customers’ engagement and enhance client loyalty [20][21]. Web analytics in digital advertising, for instance, enables you to customize the customer experience [22]. The overall value of the service supplied on corporate websites, as well as their compatibility with the targeted customers, influences website traffic, customer engagement, and visibility [23].
Within this framework, the parameters under which cryptocurrency traders and customers mutually reinforce one other in terms of sustaining risky profits and retention advantages, respectively. On the one side, customers profit from the network spillovers produced by speculative investors, promoting user engagement. Traders anticipating a steadier flow of users to acquire cryptocurrency, on the other side, might not even have to depend as largely on the faith of their other traders to secure a return. Wei & Dukes [6] suggest that if an event raises the core of prospective users, cryptocurrency inflation is more likely to arise (yet less likely to fall when created).
Cryptocurrencies highlight the necessity for greater practical research and exploration into virtual currency, with recent studies concentrating on individual intentions to embrace cryptocurrency [24] for various reasons such as payment, etc. Furthermore, the majority of the existing literature globally ignores end-user engagement parameters, i.e., individual customer level [25], as the innovation of cryptocurrencies could be dependent on explaining customer behavior and also predicting the drivers that can enhance the engagement process in each device (mobile and desktop).

2.2. Digital Promotion and Cryptocurrency Data Analysis

Digital promotion, big data, and web analytics together serve an essential function in the development and sustainability of a company’s digital brand name, as well as profitability [16][26]. Researchers' study concentrated on statistics relating to bitcoin webpage visibility, as measured by analytic metrics across their domains. Furthermore, one major issue that should be investigated properly in the coming years is the use of cryptocurrency relevant data such as estimated average price, opening and daily closing price, maximum and minimum everyday rates, the everyday amplitude of transactions, and perhaps even financial and technical trading measures [27].
Social networks may be utilized to discover what people are thinking about commodities, events, needs, and supplies. Twitter, a well-known social media platform, enables its users to express themselves and contribute information that influences the market situation [28]. As a result, expression analysis is critical for recognizing and comprehending adaptive and maladaptive user demands [29][30]. Cryptocurrency price fluctuations are strongly influenced by social networking attitudes, and analysis is based on online search techniques. Although individuals often Tweet favorably about cryptocurrencies as their values fall, Twitter attitude research believes forthcoming price levels will become favorable. As a result of their volatile nature in the current market, predicting cryptocurrency values is a difficult endeavor [31].
Researchers investigated the drivers of behavioral intention [32], among the most dominating dependent variable in engagement-related research [33]. Per the results of their study, performance expectation and price value have a favorable impact on cryptocurrency website customers’ behavioral intention to use bitcoin. This suggests that customers are more likely to accept cryptocurrencies if they perceive that using this innovation is beneficial and allows individuals to execute a task effectively. Furthermore, it is significantly confirmed the link involving performance expectancy with a market value in the context of bitcoin engagement. People see cryptocurrencies as a good technology that has a substantial influence on their daily lives and provides massive advantages (for example, simplicity, time savings, and effectiveness). They also consider engaging in cryptocurrency is more profitable than acquiring this technology [34].

3. Decentralized Systems of Payment and Customer Website Visits

3.1. Decentralization as an Innovative Strategical Payment Tool

Even the notion of innovation is not new. It has been estimated that it is as old as humans [35]. It is self-evident that all sorts of innovation do have a positive influence on the overall sustainability of enterprises and, by extension, economies. Implementing a new or significantly improved product (goods or services) or practice, a new advertising technique, or a new organizational strategy in company operations, workplace organization, or external relations is characterized as an innovation [36]. Companies employ innovation to gain a competitive edge, and it is a vital ingredient of commerce [37]. Blockchain technology includes many innovative elements in both its structure and operation.
A blockchain is an innovation that has moved to the forefront for having a safe, protected, and secret online identity. Distributed ledger technology (DLT), the most well-known example being blockchain, make sure that the information has never been maintained in a centralized file and instead is privately handled in decentralized networks. This would give individuals control of their identification by producing a universal ID that can be used for many reasons [38]. Furthermore, once the payments are stored in the blockchain, they cannot be altered. As more than just a result, it also can change many industries, including financial products [39], online payments [40], government services [41], Internet of Things (IoT) [42], brand image structures [43], and security agencies [44]. All participants on the blockchain network can track down the payments. So, each transaction stored on the blockchain may be verified as genuine. Nonetheless, the person involved in the trade is undetermined [45].
The potential of smart contracts to expedite paperwork and transaction procedures boosts productivity and consequently decreases the costs, which is thought to be simple to apply [46]. This is consistent with Kamble et al. [46] findings that observed engagement seems to be the primary driver underlying blockchain technology acceptance. Human ingenuity as a monitor has been effective. The moderate outcome implies that inventive customers would try to utilize and adapt cryptocurrencies even if they do not believe their worth is great (e.g., price value) [34].
Mobile transactions, which may not necessitate a desktop to make the transaction and are occasionally invoiced through the telecommunication provider, are another payment option [47]. Because of the launch of mobile trading software and its unique marketing approach, the aforementioned payment mechanism has seen exponential firm growth since its creation [48]. Blockchain enables payments to be made without the participation of a 3rd party, including a bank or PayPal, but it can be used for a broader range of financial products [49].

3.2. KPIs for Cryptocurrency User Engagement and Website Traffic Sources

Keeping their demands and expectations of currency purchasing in view, users’ incentive on evaluated performance expectancy and perceived price of cryptocurrencies offer an important study [50]. Consequently, the features of cryptocurrency websites need to include: faster page production, higher user satisfaction, continual updates, mobile applications or mobile-friendly websites, etc. The webpage system’s emphasis is on the frequency of updates at the rate of data collection from Web trading platforms and social networks. [51]. Therefore, the use of blockchain for better marketing and advertising has simultaneously boosted customer privacy [52].
Cryptocurrencies, for example, might offer long-term potential, especially if they encourage a speedier, more reliable, and more effective payment system [53]. However, while previous studies considered cryptocurrencies primarily as cash instead of technology, researchers hardly evaluated the link between innovation capability and bitcoin pricing. When considering cryptocurrency as an innovation, it presupposes that it will have a variety of use cases and implementations (e.g., payments, smart contracts, data gathering) that can generate a certain level of value [54].
Most customers arrive at websites via different channels such as direct, referral, search, paid, and social. The breakdown of website analytic customer engagement data, such as bounce rate, average time on site, and average pages per visit, may be used to assess digital marketing and promotion tactics [55]. To measure their digital promotion efficacy, cryptocurrency firms should study their monthly-tracked website performance indicators, which include worldwide rank and organic traffic, as well as analytic data such as average pages per visit and bounce rate. Key Performance Indicators (KPIs) are a quantitative indicator of performance over time for a given goal and must meet specified standards for web analytics and digital marketing [41]; consequently, authors describe, depict, and assess the impact of the chosen KPI rates each month. Table 1 depicts the investigated KPIs.
Table 1. Description of the examined web metrics.
Web Analytics/KPIs Description of the WA/KPIs
Global (Web) Rank/month The relatively less ranking a website has the more visibility it receives, since it depicts the ratings of all websites across the internet in terms of customer popularity. Web Rank measures a website’s attractiveness to other websites, providing it with a helpful KPI for comparative analysis [56].
Organic Traffic/month One of the most powerful markers of SEO effectiveness is the tracking of organic sessions over time. A month-over-month rise in organic search visits indicates that a website’s rankings are increasing. While other metrics may reveal trends, this statistic provides concrete evidence that the organization’s efforts bring in more visits, prospects, and, hopefully, conversions [57].
Traffic Sources/month Because the purpose of a website is the creation of traffic and leads, knowing how people arrive at a site is the most important tool in improving it. So, traffic sources refer to the way customers reach your website and expanded at direct, referral, search, paid, and social traffic sources. [58].
Returning Visitors/month Visitors who have recently visited a website are referred to as returning visitors. When a visitor returns to a webpage after two years, they are considered a returning visitor [59]
Unique Visitors/month A unique visitor is defined as someone who views a webpage once in a referred period. Unique visitor numbers reveal how many people a website truly attracts [59].
Bounce Rate/month The percentage of website visitors who leave after seeing a single page is called the bounce rate. The bounce rate shows how good the content is, so it has to be kept at a low rate [59].
Average Time on Site/month The time spent on site is calculated by dividing the duration of an average visit during a certain time period by the total number of visits over that period [59].
Average Pages per Visit/month Pageviews have been the most fundamental of all user engagement metrics, tracking the number of times a user visits a specific page on a website [59].

This entry is adapted from the peer-reviewed paper 10.3390/pr10050960

References

  1. Halaburda, H.; Sarvary, M. Beyond Bitcoin: The Economics of Digital Currencies, 1st ed.; Palgrave McMillan: London, UK, 2015; ISBN 978-1137506412.
  2. Islam, M.R.; Al-Shaikhli, I.F.; Nor, R.B.; Varadarajan, V. Technical approach in text mining for stock market prediction: A systematic review. Indones. J. Electr. Eng. Comput. Sci. 2018, 10, 770–777.
  3. Inamdar, A.; Bhagtani, A.; Bhatt, S.; Shetty, P.M. Predicting cryptocurrency value using sentiment analysis. In Proceedings of the International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 15–17 May 2019; pp. 932–934.
  4. Abraham, J.; Higdon, D.; Nelson, J.; Ibarra, J. Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci. Rev. 2018, 1, 1–22. Available online: https://scholar.smu.edu/datasciencereview/vol1/iss3/1 (accessed on 16 December 2021).
  5. Livieris, I.E.; Stavroyiannis, S.; Pintelas, E.; Pintelas, P. A novel validation framework to enhance deep learning models in time-series forecasting. Neural Comput. Appl. 2020, 32, 17149–17167.
  6. Wei, Y.; Dukes, A. Cryptocurrency adoption with speculative price bubbles. Mark. Sci. 2020, 40, 241–260.
  7. Chowdhury, R.; Rahman, M.A.; Rahman, M.S.; Mahdy, M. An approach to predicting and forecasting the price of constituents and index of cryptocurrency using machine learning. Phys. A Stat. Mech. Its Appl. 2019, 551, 124569.
  8. Antonopoulos, A. Mastering Bitcoin: Unlocking Digital Cryptocurrencies, 2nd ed.; O’Reilly: London, UK, 2017.
  9. Buterin, V. What Is Ethereum? 2016. Available online: https://coincenter.org/entry/whatis-ethereum (accessed on 1 December 2021).
  10. Forbes. Blockchain 50: Billion Dollar Babies. 2019. Available online: www.forbes.com/sites/michaeldelcastillo/2019/04/16/blockchain-50-billion-dollar-babies/#3e50701f57cc (accessed on 16 December 2021).
  11. Chaffey, D.; Ellis-Chadwick, F. Digital Marketing; Pearson: Harlow, UK, 2020.
  12. Clifton, B. Advanced Web Metrics with Google Analytics; John Wiley & Sons: Indianapolis, IN, USA, 2012.
  13. Hofmann, E. Supply Chain Finance—Some Conceptual Insights. In Logistik Management; Springer: Berlin/Heidelberg, Germany, 2005; pp. 203–214. Available online: https://link.springer.com/10.1007/978-3-322-82165-2_16 (accessed on 16 December 2021).
  14. Rijanto, A. Blockchain technology adoption in supply chain finance. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 3078–3098.
  15. ElBahrawy, A.; Alessandretti, L.; Kandler, A.; Pastor-Satorras, R.; Baronchelli, A. Evolutionary dynamics of the cryptocurrency market. R. Soc. Open Sci. 2017, 4, 170623.
  16. Sakas, D.; Giannakopoulos, N. Harvesting crowdsourcing platforms’ traffic in favour of air forwarders’ brand name and sustainability. Sustainability 2021, 13, 8222.
  17. Sakas, D.P.; Giannakopoulos, N.T. Big Data contribution in Desktop and Mobile devices comparison regarding Airlines’ digital brand name effect. Big Data Cogn. Comput. 2021, 5, 48.
  18. Liébana-Cabanillas, F.; Muñoz-Leiva, F.; Sánchez-Fernández, J.; Martínez-Fiestas, M. Electronic Payment Systems for Competitive Advantage in E-Commerce; IGI Global: Hershey, PA, USA, 2014.
  19. Rawool, S.; Boke, A.; Shejy, G. Gaining Advantages using Web Analytics: A case study on Ryanair. Int. J. Eng. Dev. Res. 2015, 3, 2321–9939.
  20. Molchanova, K.; Trushkina, N.; Katerna, O. Digital platforms and their application in the aviation industry. Electron. Sci. J. Intellect. Logist. Supply Chain Manag. 2020, 1, 83–98.
  21. ITIF Technology Explainer. What Are Digital Platforms? Information Technology & Innovation Foundation. 2020. Available online: https://itif.org/publications/2018/10/12/itif-technology-explainerwhat-are-digital-platforms (accessed on 10 November 2021).
  22. Abu-Dalbouh, M.A. Improving digital marketing strategy in jordanian air aviation sector for becoming a regional training center. Int. Bus. Res. 2020, 13, 139.
  23. Marrs, M. 5 Ways to Wield More Word of Mouth Marketing Power. 2015. Available online: https://www.wordstream.com/blog/ws/2014/06/26/word-ofmouth-marketing (accessed on 10 November 2021).
  24. Al-Amri, R.; Zakaria, N.H.; Habbal, A.; Hassan, S. Cryptocurrency adoption: Current stage, opportunities, and open challenges. Int. J. Adv. Comput. Res. 2019, 9, 293–307.
  25. Risius, M.; Spohrer, K. A blockchain research framework. Bus. Inf. Syst. Eng. 2017, 59, 385–409.
  26. Kasturi, E.; Devi, P.; Kiran, S.V.; Manivannan, S. Airline route profitability analysis and optimization using Big Data analytics on Aviation Data Sets under Heuristic Techniques. Procedia Comput. Sci. 2016, 87, 86–92.
  27. Ma, Y.; Yang, B.; Su, Y. Technical trading index, return predictability and idiosyncratic volatility. Int. Rev. Econ. Financ. 2020, 69, 879–900.
  28. Kraaijeveld, O.; De Smedt, J. The predictive power of public twitter sentiment for forecasting cryptocurrency prices. J. Int. Financ. Mark. Inst. Money 2020, 65, 101188.
  29. Pathak, S.; Kakkar, A. Cryptocurrency price prediction based on historical data and social media sentiment analysis. Innov. Comput. Sci. Eng. 2020, 103, 47–55.
  30. Nizzoli, L.; Tardelli, S.; Avvenuti, M.; Cresci, S.; Tesconi, M.; Ferrara, E. Charting the landscape of online cryptocurrency manipulation. IEEE Access 2020, 8, 113230–113245.
  31. Hasan, S.H.; Hasan, S.H.; Ahmed, M.S.; Hasan, S.H. A novel cryptocurrency prediction method using optimum CNN. CMC-Comput. Mater. Contin. 2022, 71, 1051–1063.
  32. Patil, P.; Tamilmani, K.; Rana, N.P.; Raghavan, V. Understanding customer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. Int. J. Inf. Manag. 2020, 54, 102144.
  33. Kapoor, K.K.; Dwivedi, Y.K.; Williams, M.D. Rogers’ Innovation adoption attributes: A review and synthesis of research findings. Eur. J. Innov. Manag. 2014, 31, 74–91.
  34. Abbasi, G.A.; Tiew, L.Y.; Tang, J.; Goh, Y.-N.; Thurasamy, R. The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis. PLoS ONE 2021, 16, 0247582.
  35. Fagerberg, J. Innovation: A Guide to the Literature, The Oxford Handbook of Innovation; Oxford University Press: Oxford, UK, 2006.
  36. OECD. The measurement of scientific and technological activities. In Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd ed.; OECD: Paris, France, 2005; Available online: https://ec.europa.eu/eurostat/documents/3859598/5889925/OSLO-EN.PDF (accessed on 24 November 2021).
  37. Kanellos, N.S. Exploring the characteristics of Knowledge-Based Entrepreneurship in Greek high-technology sectors. J. Enterprising Cult. 2013, 13, 69–88. Available online: https://conference.druid.dk/acc_papers/ji6bdjxuk1yi79hl5r2asugg4es0.pdf (accessed on 16 December 2021).
  38. Garg, R. Decentralized transaction mechanism based on smart contracts. In Proceedings of the 3rd International Conference on Blockchain and IoT, Sydney, Australia, 6–8 October 2021.
  39. Peters, G.W.; Panayi, E.; Chapelle, A. Trends in Crypto-Currencies and Blockchain Technologies: A Monetary Theory and Regulation Perspective. Innov. Cyberlaw Policy 2015, 3, 92–113.
  40. Tsilidou, A.L.; Foroglou, G. Further applications of the blockchain. In Proceedings of the 12th Student Conference on Managerial Science and Technology, Athens, Greece, 14 May 2015; Available online: https://www.researchgate.net/publication/276304843_Further_applications_of_the_blockchain (accessed on 27 November 2021).
  41. Akins, B.W.; Chapman, J.L.; Gordon, J.M. A whole new world: Income Tax considerations of the bitcoin economy. Pittsburgh Tax Rev. 2015, 12, 24–56.
  42. Zhang, Y.; Wen, J. An IoT electric business model based on the protocol of bitcoin. In Proceedings of the 18th International Conference on Intelligence in Next Generation Networks, Paris, France, 17–19 February 2015; pp. 184–191.
  43. Sharples, M.; Domingue, J. The blockchain and kudos: A distributed system for educational record, reputation and reward. In Proceedings of the European Conference on Technology Enhanced Learning, Lyon, France, 13–16 September 2016; Springer: Berlin/Heidelberg, Germany, 2016; pp. 490–496.
  44. Noyes, C. Bitav: Fast anti-malware by distributed blockchain consensus and feedforward scanning. arXiv 2016, arXiv:1601.01405.
  45. DIACC. Customer Digital Identity Leveraging Blockchain. 2016. Available online: https://diacc.ca/wp-content/uploads/2020/03/DIACC-White-Paper_Customer-Digital-Identity-Leveraging-Blockchain_Feb2020.pdf (accessed on 27 November 2021).
  46. Kamble, S.S.; Gunasekaran, A.; Sharma, R. Modeling the blockchain enabled traceability in agriculture supply chain. Int. J. Inf. Manag. 2020, 52, 101967.
  47. Zheng, Z.; Xie, S.; Dai, H.-N.; Chen, X.; Wang, H. Blockchain challenges and opportunities: A survey. Int. J. Web Grid Serv. 2018, 14, 352.
  48. Furnham, A.; Argyle, M. The Psychology of Money; Routledge: London, UK, 2013; pp. 4–50.
  49. Mallat, N. Exploring customer adoption of mobile payments: A qualitative study. J. Strat. Inf. Syst. 2007, 16, 413–432.
  50. Alalwan, A.A.; Dwivedi, Y.K.; Rana, N.P. Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. Int. J. Inf. Manag. 2017, 37, 99–110.
  51. Lytvyn, V.; Vysotska, V.; Kuchkovskiy, V.; Bobyk, I.; Malanchuk, O.; Ryshkovets, Y.; Pelekh, I.; Brodyak, O.; Bobrivetc, V.; Panasyuk, V. Development of the system to integrate and generate content considering the cryptocurrent needs of users. East.-Eur. J. Enterp. Technol. 2019, 1, 18–39.
  52. Hooper, A.; Holtbrügge, D. Blockchain technology in international business: Changing the agenda for global governance. Rev. Int. Bus. Strategy 2020, 30, 183–200.
  53. Wilhelm, A. Bitcoin $645? Yeah, That’s Totally Reasonable. TechCrunch. 2013. Available online: http://techcrunch.com/2013/11/18/bitcoin-645-yeah-thats-totally-reasonable/ (accessed on 1 December 2021).
  54. Ly, K.M. Coining bitcoin’s “legal-bits”: Examining the regulatory framework for bitcoin and virtual currencies. Harv. J. Law. Technol. 2014, 27, 587–608. Available online: http://www.woodllp.com/Media/Press/pdf/Coining.pdf (accessed on 16 December 2021).
  55. Hölbl, M.; Kompara, M.; Kamišalić, A.; Zlatolas, L.N. A systematic review of the use of blockchain in healthcare. Symmetry 2018, 10, 470.
  56. Slivka, M. What Is Alexa Rank and Its Value? 2020. Available online: https://attentioninsight.com/what-is-alexa-rank-and-itsvalue (accessed on 15 December 2021).
  57. Kindness, J. 15 Critical SEO Metrics You Need to Track. 2021. Available online: https://agencyanalytics.com/blog/seo-metrics (accessed on 15 December 2021).
  58. Laire, L. Website Traffic Sources Breakdown: What’s the Difference? 2020. Available online: https://www.lairedigital.com/blog/6-types-traffic-sources-for-websites (accessed on 15 December 2021).
  59. Osman, M. Top 10 User Engagement KPIs to Measure. 2019. Available online: https://www.searchenginejournal.com/content-marketing-kpis/user-engagement-metrics/#close (accessed on 15 December 2021).
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