Blockchain Technology

Blockchain technology received much attention when a whitepaper on Bitcoin was released by Satoshi Nakamoto in 2008 and the technology was put to use a year later. Bitcoin is a cryptocurrency’ driven by blockchain technology. However, blockchain’s origins go as far back as the 1990s when Stuart Haber and W. Scott Stornetta developed secure chain blocks through cryptography that prevented anyone tampering with document timestamps. Their findings appeared in their 1991 paper titled, “How to Time-Stamp a Digital Document.” Blockchain has continued to evolve into uses beyond cryptocurrency. It is, first, important to include a reminder as to what blockchain is.

Simply put, blockchain is a shared ledger, used to record transactions, track assets, improve visibility, and build trust in supply chain networks around the world. Immutable records mean no participant in a network can change information once it has been recorded, meaning errors must be reversed instead of covered up.

(Grimshaw 2020)

The key benefit of blockchain technology today is the existence of smart contracts, in which “rules are stored on the chain and automatically executed, meaning conditions for corporate bond transfers, terms for travel insurance to be paid and much more can be defined quickly, and with great ease” (Grimshaw 2020).

As Hughes et al. (2019, 278) explain, cryptocurrency and smart contracts are a part of the first and second phases of blockchain technology. The third phase is blockchain technology’s broader societal use. In the latter phase, blockchain technology can lead to greater efficiency and innovation in supply chains and industries such as the healthcare industry (Hughes et al. 2019). Although digital currency remained the leading use of blockchain technology in 2020 at 33%, other uses followed such as data access/sharing, 32%; data reconciliation, 31%; identity protection, 31%; and payments, 30% (Deloitte 2020).

Briefly on Artificial Intelligence

These are other technologies that are not discussed at length here, since the main focus of the chapter is on the digital technologies that allow for

Countries may use a different term when referring to cryptocurrency. For instance digital currency is used in Argentina, Thailand, and Australia; virtual commodity in Canada, China, and Taiwan; crypto-token in Germany; payment token in Switzerland; cyber currency in Italy and Lebanon; electronic currency in Colombia and Lebanon; and virtual asset in Honduras and Mexico (“Regulation of Cryptocurrency Around the World," 2018, p. 1).

increased access to other markets. However, in some discussions with an emphasis on technology, AI is mentioned. Artificial intelligence improves the processes and operations through automation, which allows for the enhancement of products and services.

There are varying definitions as to exactly what is meant by Artificial Intelligence. AI is the “broader concept of machines being able to carry out tasks in a way that we could consider ‘smart’” (Marr 2016). Although a broad concept, this type of AI has narrow applied functions, also referred to as weak AI, in which it can carry out specific tasks such as driving a car, completing translation services, and filling out a form (Marr 2016; Jain 2018; Meltzer 2018). The general application, or strong AI, which is still being researched and developed, refers to “self-learning systems that can learn from experience with humanlike breadth and surpass human performance on all tasks” (Meltzer 2018).

According to Davenport (2019), 25-30% of large companies are already going after AI in an aggressive form and have access to the data to do so. On the other hand, smaller firms, as well as B2B firms and basic manufacturing companies, use AI at a far lower rate, because they do not have substantial access to data and also do not possess the proper expertise and awareness (Davenport 2019). The United States, China, the United Kingdom, Canada, and Singapore are some of the countries that are pursuing AI at a fast pace. The way in which AI helps to improve operational processes so that they are more efficient is that tasks can now be automated (Brynjolfsson and McAfee 2019). (For a detailed overview of AI versus machine learning, as well as the categories within AI, see Marr 2016.)

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