The UN Development Goals are addressing the most pressing challenges for humanity. How can blockchain and DLT contribute to the achievement of those goals?

The use of established technology in achieving social good and the sustainable development goals can be generally accepted, but the question of whether blockchain, as a specific and emerging technology, can contribute to the goals is at very early stages of investigation. We should be aware of the risk of blockchain as ‘a solution in search of a problem’ and of being drawn into the hype surrounding its use and efficacy. In support of the use of blockchain, there are opinions that blockchain “has more near-term potential for social impact than originally thought” (Calvert, 2018), and that new technologies being deployed in developing countries means not having the legacy of existing technologies which can hinder adoption. Of the seventeen goals, some lend themselves more appropriately to, and could benefit more greatly from, using blockchain and distributed ledger technologies. 

The World Food Programme has delivered blockchain solutions that contribute to achieving Goal 2: Zero hunger. The programme distributes cash to 28 million people in 64 countries (WFP, 2021) but recognised that in some countries the existing financial solutions were insufficient, unreliable and incurred high transaction fees. The Building Blocks project implemented a private, permissioned blockchain to record transactions made by people purchasing groceries which reduced financial transaction fees and ensured greater security and privacy. 

The WFP’s project provides an example of how blockchain can be used in tackling a very specific problem; that of people in refugee camps purchasing food without concerns of centralised third-parties having access to their personal data or losing food vouchers. In this case, it could be argued that blockchain solves more problems for the organisation than it does for its beneficiaries, as it reduces the transaction fees the organisation pays and provides more accurate data about those using the system. Based on this example we can conclude that blockchain can have a role to play in achieving the goal of zero hunger but has a long way to go before that contribution can be considered significant.

Goal 8: Decent work and economic growth, is another goal that blockchain could contribute to. For the 2 billion ‘unbanked’ people in the world, not having access to financial services hinders the economic growth of individuals, families, towns, and entire countries. According to the central bank of Sierra Leone, over three-quarters of the country’s population does not access formal financial services (Ledger Insights, 2019). In an attempt to tackle this issue and so enable economic growth, the charity Kiva set up a blockchain solution to provide microloans to people who are unable to provide a credit history due to their unbanked status. Kiva reports on the impact of its service as an overall but it is difficult to measure the success of the blockchain technologies, either in comparison to a different solution or in achieving the sustainable development goal of “sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all” (UN, 2021). 

Blockchain technologies, among other emerging technologies, have much potential in contributing to achieving the sustainable development goals but must be approached cautiously and with consideration for the unintended consequences the introduction of new technologies can have. The use of blockchain technology can contribute to specific parts of solutions that contribute to achieving the Sustainable Development Goals but to suggest that a technology such as blockchain can achieve a goal alone would risk straying into more hype than reality.

In tackling some of these issues, the Blockchain Commission for Sustainable Development was set up in 2017 to “develop a multi-sectoral framework to support the UN system, along with Member States, intergovernmental organizations, the private sector and civil society in utilizing blockchain-based technologies to develop local, national and global solutions to urgent challenges.” (IISD, 2018). The IISD works to help other organisations better understand blockchain and it’s real world applications, including aspects such as how blockchain fits within a country’s legislation and telecoms infrastructure. These challenges are important to understand when considering how blockchain can contribute to achieving the sustainable development goals as the question becomes less about the application of blockchain technology and more about how to build the national and international infrastructure that would be necessary for blockchain to be implemented in order for it to contribute to the goals.


Calvert, D. (2018). Can Blockchain Be Used for Public Good? Stanford Business.

International Institute for Sustainable Development. (2018). Commission White Paper Explores Blockchain Use for SDGs.

Ledger Insights. (2019). Kiva sets up Sierra Leone blockchain ID system.

United Nations. (2021). The 17 Goals.

World Food Programme. (201). Building Blocks: Blockchain for Zero Hunger.

Can blockchain technology be used for delivering public services and for facilitating the engagement of citizens in public decision-making process?

Blockchain technology has a utility of application that means it can be utilised across any sector including the delivery of public services. In 2018, the Organisation for Economic Co-operation and Development studied over 200 public service blockchain projects (Table 1) in at least 46 countries around the world (Berryhill et al, 2018), concluding that whilst many of the projects were aimed at information sharing and exploring partnerships, some implemented practical applications of Blockchain technology. 

Table 1: Use of Blockchain in the public sector

Rank Types of projects (count)Industries (count)
Strategy/Research (42) Government Services (173)
Identity (Credentials/Licenses/Attestations) (25)Financial Services (73)
3Personal Records (Health, Financial, etc.) (25)Technology & IoT (26)
Economic Development (24)Healthcare (23)
Financial Services/Market Infrastructure (20) Real Estate (22) 
Land Title Registry (19) Supply Chain (19) 
Digital Currency (Central Bank Issued) (18) Energy (13) 
Benefits/Entitlements (13) Transportation (13) 
Compliance/Reporting (12) Education (8) 
10 Research/Standards (12) Telecom (4)
Source: Blockchains Unchained: Blockchain Technology and its Use in the Public Sector, OECD Working Papers on Public Governance

The study suggests that an area of public service delivery governments are most interested in is identity and personal records. The Government Office for Science (2016) defines ‘identity’ as a combination of authentication; that you are who you say you are, and authorisation; that you have the permission to do what you ask. A birth recorded on a blockchain becomes an immutable timestamp for the existence of a person and where perhaps ‘age’ becomes a characteristic that is used in authorising when that person can go to school, legally start work or retire. This use case allows governments to make identity management more secure and efficient. As Borrows et al describe (Figure 1), the aim is for governments to use blockchain to move identity management from low user control to high, where citizens will not only have access to the data stored about them, which isn’t the case with current identity management systems in use by governments, but will also be able to control which government departments have access to the data.

Figure 1: Framework of digital identity ownership

Diagram of low user control to high user control for identity sovereignty
Source: Reform, 2018 (Adapted from Christopher Allen, The Path to Self-Sovereign Identity, 2016.).

Figure 1 demonstrates that there are currently no examples of governments providing citizens with a self-sovereign model of identity management, and that the best example we have of movement in that direction is from Estonia. Estonian citizens each have an ID card that is managed using blockchain-like technology and that allows them to access public services, financial services, medical and emergency services, drive, pay taxes, vote and travel within the EU (Shen, 2016). Adoption of the e-ID was reportedly smooth for the government and people of Estonia, but given it was implemented around twenty years, at a time when data protection awareness was less than nowadays, and in a country that has a high level of trust in the government, it remains to be seen whether the same success can be achieved in other countries (Cater, 2021).

Voting presents another opportunity where governments could utilise blockchain technologies to reduce voter absenteeism and increase auditability and so trust in electoral processes (Foroglou & Tsilidou, 2015). Asking ‘why blockchain?’ leads us to ask why voting systems have never been digitised using an earlier technology, and the answer may be as simple as suggested by the Government Office for Science report which said that online voting was too costly and too centralised to be reliable, but that a blockchain solution could provide part of the answer (GOS, 2016).

Columbian expatriates faced these issues when voting on a peace treaty that resulted in only 10% registering a vote. The non-profit organisation Democracy Earth Foundation set-up a blockchain solution that allowed Colombians who lived abroad to cast symbolic votes and tested a new way of validating and authenticating electoral votes (OECD, 2017). Based on the results of demonstration, it was reported that the Colombian Ministry of Information and Communications Technologies recognised how traditional voting systems lack integrity and trustworthiness, and that blockchain solutions have the potential to radically alter voting systems towards using more secure technology (OECD, 2016a).

Counter to the use of innovative technologies by governments and organisations are the realities of the use of technology by people in countries like Colombia where nearly half of the total population is not yet online (OECD, 2016b). Whereas Estonia got the timing right for introducing blockchain to enable digital identity, Colombia could risk introducing emerging technologies too early before ensuring that a sufficient percentage of its population are connected to the internet and have the sufficient skills to participate as digital citizens.

Blockchain certainty has a place in delivering public services and facilitating engagement in public decision-making processes. Justification for using blockchain in the commercial sector oftens falls to increasing efficiency and reducing costs, both of which also apply to the public sector, but the public sector should also hold a greater vision about the use of blockchain to enable and empower citizens, to give them more control over their data and in how they interact with their government in an increasingly digital world.


Berryhill, J., Bourgery, T., & Hanson, A. (2018), Blockchains Unchained: Blockchain Technology and its Use in the Public Sector, OECD Working Papers on Public Governance, No. 28, OECD Publishing.

Borrows, M., Harwich, E., & Heselwood, L. (2017). The future of public service identity: blockchain. Reform & Accenture Consulting.

Cater, L. (2021). What Estonia’s digital ID scheme can teach Europe.

Foroglou, G. & Tsilidou, A. (2015) Further applications of the blockchain. 

Government Office for Science. (2016). Distributed Ledger Technology: beyond block chain. A report by the UK Government Chief Scientific Adviser.

OECD. (2016a). Interview with the project team of Democracy Earth. 1 December 2016.

OECD/IDB. (2016b). Broadband Policies for Latin America and the Caribbean: A Digital Economy Toolkit. OECD Publishing, Paris, 

OECD. (2017). Embracing Innovation in Government – Global Trends.

Shen, J. (2016). e-Estonia: The power and potential of digital identity.

Why are supply chain and logistics increasingly looking into the use of blockchain technology and DLT?

The logistics and supply chain sector is undergoing technology-driven digital transformation where emerging technologies result in a strategic response that adapts the way companies create value (Vial, 2019). This technology-push model of innovation (Rothwell, 1994) where emerging technologies are developed without a clear use case in mind and then adopted horizontally across sectors leads to the convergence of complementary technology vertically within a sector. In the logistics and supply chain sector the convergence between the Internet of Things providing the data production layer and Blockchain and DLT providing the data exchange layer enables new means of value creation for firms.

Blockchain and Distributed Ledger Technologies have particular applicability to global logistics and supply chain management as many different companies are involved in the processes of shipping and require accurate, timely and trusted data in order to conduct their operations efficiently. All of the firms accessing the data on the blockchain can trust that it is immutable, has been arrived at through consensus of the other nodes in the network, that is the other firms in the supply chain ecosystem, and has absolute auditability. Being able to trust in the data rather than rely on established business relationships offers the potential of all firms involved to establish different kinds of business relationships where firms provide business-to-business services without the need for existing business practices of price negotiation and written contracts.

Wang et al (2019) list four areas where blockchain technologies and the increased trust they provide has benefits in the logistics industry: “extended visibility and traceability, supply chain digitalisation and disintermediation, improved data security and smart contracts.” Extended visibility and traceability is a result of the trust enabled by the use of an immutable data ledger such as blockchain. With an average of 1,382 shipping containers lost at sea each year (BIFA, 2020), the ability of shipping firms to have accurate and up-to-date data about lost cargo enables them to work more effectively with manufacturers and insurance companies. EY is exploring blockchain-enabled insurance solutions, “because marine insurance is a complex international ecosystem, with multiple parties, multiple jurisdictions, high transaction volumes and significant levels of reconciliation.” (EY, 2017). The high degree of trust required in order to process insurance claims of the value and complexity that EY handles relies on multiple human processes that involve investigation and approval, processes that would become redundant if using a single, trusted, and immutable source of data in support of insurance claims and processing.

The use of smart contracts aims to reduce complexity in a supply chain by automating business transactions that rely on the verification of predetermined conditions and the execution of agreements written into the code of the smart contract. ShipChain is a logistics utility ecosystem using blockchain technology to provide smart contracts that enable “trustless contract execution, historical data immutability, and no single point of failure.” (Shipchain, 2021). The solution provides greater visibility of delivery hand-offs and so reduces loss and theft whilst enabling the transactions written into smart contracts to be executed as a delivery takes place.

IBM estimates that the cost of trade documentation is approximately one fifth the cost of shipping physical goods (IBM, 2018). This makes the cost of moving information between exporters, freight forwarders, ports and terminals, ocean carriers, authorities, transportation management and importers in the billions of dollars a year. And this does not include the ancillary costs of the data entry for good manufactures, insurance processes, etc. By providing a single trusted source of data, blockchain can reduce these transaction costs (Schmidt & Wagner, 2019) and increase profitability across the sector.

Although blockchain and distributed ledger technologies stand to offer considerable benefits to the entire logistics and supply chain sector, they do not come without risks and challenges. The introduction of new technologies into a business is a costly endeavour that risks pushing out smaller firms that are unable to invest in such infrastructure as quickly as larger firms. The use of smart contracts, for example, relies on the coding of the contract to be well-defined, deterministic, and visible to all parties, and although referred to as ‘contracts’, smart contracts are not legally enforceable (Kruse et al, 2020). 

In summary, logistics and supply chain companies are increasingly exploring Blockchain and DLT as part of the digital transformation of the sector, and to enable faster and more accurate data exchange between all companies within the supply chain to increase operational efficiency and reduce costs. Emerging technologies, including the Internet of Things and Distributed Ledger Technologies stand to introduce a great deal of change to the global logistics and supply chain industry.


British International Freight Association. (2020). Containers Lost At Sea – 2020 Update.

EY. (2017) Better-working insurance: moving blockchain from concept to reality.

IBM. (2018). Digitizing Global Trade with Maersk and IBM.

Kruse, C. J., Villa, J. C., Mileski, J. P. & Galvao, C. (2020). Analysis of Blockchain’s Impacts on and Applicability to the Maritime Industry – May 2019 to August 2020. Maritime Transportation Research and Education Center.

Rothwell, R. (1994). Towards the Fifth-generation Innovation Process. International Marketing Review, 11(1), 7-31.

Schmidt, C., G. & Wagner, S., M. (2019). Blockchain and supply chain relations: A transaction cost theory perspective. Journal of Purchasing and Supply Management. Volume 25, Issue 4, 2019.

Shipchain. (2012). The ShipChain Ecosystem.

Vial, G. (2019) Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, Volume 28, Issue 2.

Wang, Y., Han, J.H. and Beynon-Davies, P. (2019). Understanding blockchain technology for future supply chains: a systematic literature review and research agenda, Supply Chain Management, Vol. 24 No. 1, pp. 62-84.

The future is already here, it’s just distributed across a decentralised network: the role of DLT and blockchain in the future of work


It is generally accepted that emerging technologies act as enabling forces for economic, social, and business transformation (Cohen & Amorós, 2014; Paschen, Kietzmann, & Kietzmann, in press, in Morkunas et al, 2019). Morkunas et al (2019) predict that blockchain technologies will “challenge existing business models and offer opportunities for new value creation”, whilst the UK Government Chief Scientific Officer described distributed ledger technology as a “potential explosions of creative potential that catalyse exceptional levels of innovation“ that can “reform our financial markets, supply chains, consumer and business-to-business services, and publicly-held registers” (2016). If these predictions come to fruition then we can expect Distributed Ledger Technology and Blockchain to have considerable impact on business and the future of work.

This essay attempts to reach an answer to the question ‘What is the role of DLT and blockchain in the future of work?’ The question is explored through looking at recent literature for a definition of the future of work, how emerging technologies, specifically Distributed Ledger Technology and Blockchain, are expected to affect the nature of work, and which sectors are likely to be most impacted. The discussion considers examples of the use, issues and challenges for DLT and blockchain in the top three affected sectors. In drawing a conclusion about the role of DLT and Blockchain in the future of work I argue that emerging technologies have an amplified impact where more than just a single technology is applied, and that DLT and blockchain are likely to have a greater impact on some sectors than others.

Literature Review

What is the future of work?

Work in the 21st century is entering a Fourth Industrial Revolution, a revolution built on an increasing number of emerging and interacting technologies that is “more comprehensive and all-encompassing than anything we have ever seen” (Schwab & Samans, 2016). ‘The future of work’ is a current and ongoing debate about how every occupation in every sector is undergoing a fundamental transformation as a result of the impacts of emerging technologies and digital transformation. The debate is wide-ranging, with far-reaching consequences, spanning from the offer of benefits for employers and employees augmented by technology (Grabowski, 2018) to mass economic disruption from the loss of jobs (Ernst et al, 2019). While some jobs are threatened by redundancy and others grow rapidly, existing jobs are also going through a change in the skill sets required to do them (Schwab & Samans, 2016). Some sectors can expect greater change than others.

What factors will affect the future of work?

Along with the changes to work and working life brought about by technology there are wider trends such as globalisation of labour markets through the diffusion of outsourcing and offshoring, and job polarisation (Berg et al, 2018) that impact the future of work debate. For the purposes of this paper we cannot consider all of these factors and will instead focus on the potential impacts of emerging technologies in general, and Distributed Ledger Technologies and Blockchain specifically as we try to understand what role they may play in the future of work. However, one closely related trend that is worth discussing is the increase of flexible working arrangements as shown in Figure 1. The graph from the World Economic Forum shows 44% of respondents stated that the changing nature of work was the greatest driver for change across all industries. This matches with the findings from Berg et al (2018) where the two most important reasons for crowdworking were to “complement pay from other jobs” (32%) and because they “prefer to work from home” (22%). This suggests people are looking for more flexibility in their working life and turning to technology to enable it.

Figure 1: Demographic and socio-economic drivers of change, industries overall

Source: The future of jobs: employment, skills, and workforce strategies for the Fourth Industrial Revolution, World Economic Forum.

Which technologies will affect the future of work?

The World Economic Forum report lists the nine technologies it’s respondents considered drivers of change (Fig. 2). Noticeably, DLT & Blockchain are not specifically listed as technologies that are expected to drive change. It could be argued that DLT & Blockchain technology has yet to achieve the maturity and adoption necessary for a study of this breadth to recognise its potential impact. This is backed-up by the British Standards Institution report which states the challenges of Blockchain adoption as including: “lack of clarity on the terminology and perceived immaturity of the technology, perceived risks in early adoption and likely disruption to existing industry practices, and insufficient evidence on business gains and wider economic impact” (Deshpande, 2017). DLT and Blockchain should be expected to impact the future of work, but perhaps those expectations are not arising just yet.

Figure 2: Technology drivers of change, industries overall

Source: The future of jobs: employment, skills, and workforce strategies for the Fourth Industrial Revolution, World Economic Forum.

If Distributed Ledger Technologies and Blockchain are not yet impacting businesses and the future of work in a generic way like mobile and cloud are, then which specific industries are being affected by DLT and Blockchain?

Which sectors are likely to be most affected by Blockchain?

The Global Blockchain Benchmarking Study (Hileman & Rauchs, 2017) (Fig.3) shows how blockchain is at use by different industry sectors, with the banking & finance industry the highest user, followed by government & public goods, and then insurance. Those sectors which use Blockchain the most stand to be the most affected by its use, and so it is these sectors that we should expect DLT and blockchain to play more of a role in shaping the future of work.

Figure 3. Sectors currently using blockchain.

Source: Global Blockchain Benchmarking Study


The following discussion looks at the role DLT and blockchain may play in the future of work in the top three sectors identified in the Global Blockchain Benchmarking Study, including examples and considering some of the challenges and issues.

Distributed Ledger Technology and Blockchain in the Banking and finance sector

The finance sector is being disrupted by DLT and blockchain (Buitenhek, 2016., Natarajan et al, 2017, Treleaven et al, 2017, Hassani et al, 2018). This seems undeniable. Blockchain technology serves a finance use case very well, offering as it does an immutable record of transactions and means of solving the double-spend problem (Nakamoto, 2008). Services such as Corda which “enables businesses in Banking, Capital Markets, Trade Finance, Insurance and beyond to transact directly and in strict privacy using smart contracts, reducing transaction and record-keeping costs and streamlining business operations” (Corda, 2021) demonstrate how large corporations in highly regulated industries are beginning to adopt blockchain technologies.

However, blockchain is not without its weaknesses of security, scalability, and efficiency (Dinh & Thai, 2018) which present considerable challenges in a banking and finance setting. Adoption of new technologies is always dependent on multiple factors, but perhaps it is precisely its disruptiveness that presents a challenge to the adoption of DLT and blockchain in the finance industry. As Tapscott and Tapscott (2017) state, the finance industry suffers from being “centralized, which makes it resistant to change… but the solution to this innovation logjam has emerged: blockchain.”

Let’s consider a case study of how one start-up is using blockchain to provide a disruptive microfinance solution.

Blockchain in Microfinance

“As the fintech landscape evolves at an unprecedented speed, Mastercard provides the infrastructure and assets to help fintech innovators grow and ultimately bring more people into the digital economy,” said Amy Neale, Senior Vice President, Fintech & Enablers. (Mastercard, 2021). One of those innovators is Brazil-based Moeda Seeds, a digital banking, payment and micro credit services powered by blockchain. “Since its founding in 2017, the company has used blockchain technology and has focused on the unbanked and under-banking population in Brazil“ (Moeda, 2021). They use blockchain technology to decrease lending costs, allowing microfinance lenders to send money directly to the recipients without the need for various middlemen (Hofer, 2018). 

Microfinance Institutions, like Moeda, are organizations that provide small loans to borrowers who typically lack collateral, steady employment, or a verifiable credit history and therefore do not have access to traditional commercial banking (Coli et al, 2021). The use of Blockchain technology in microfinance introduces a number of benefits that are difficult to achieve through traditional financial institutions and technologies, including:

  • Transparency for investors to monitor repayments.
  • Reduced transaction fees by removing the need for intermediary organisations.
  • Builds an immutable and publicly available credit history for each lender (Adebaki, 2019).

In the case of Moeda, blockchain plays a role in the future of work for the Brazilian farmers, enabling them to fund their businesses without the need for traditional finance mechanisms such as capital or credit history.

Distributed Ledger Technology and Blockchain’s role in Government & Public Goods

Distributed Ledger Technologies and Blockchain have a role to play in government by performing a range of activities, including: 

  • verification of documents such as licenses, proofs of records, transactions, processes or events such as birth of a child, 
  • movement of assets such as transferring money from one entity to another after some work conditions are met, 
  • asset ownership registers such as land registries, property titles and other types of ownership of physical assets and 
  • management of identities like e-identities for citizens and city residents. (Ojo & Adebayo)

Blockchain can be used to address inefficiencies in government systems to increase the effectiveness of public service activities (Ojo & Adebayo). An example of this can be found in the Illinois Department of Innovation & Technology’s proof-of-concept for providing physicians with a means to obtain licenses to practice in multiple states. The Interstate Medical Licensure Compact, built on blockchain, not only offers advantages for the physicians but also strengthens public protection by enhancing the ability of states to share investigative and disciplinary information (Thomas, 2018).

The challenges around government departments adopting emerging technologies such as blockchain include justifying the use of public money and whether blockchain serves sufficient use cases. Estonia has been testing blockchain for limited use cases such as land registry to improve data integrity but has clearly stated that investment in other emerging technologies such as artificial intelligence is a greater priority (Govchain, 2019).

Distributed Ledger Technology and Blockchain in the insurance industry

DLT and blockchain continue to be explored in the insurance industry as a means of achieving the vision where “data is linked automatically to digital contracts which can trigger automated processes. Everyone trusts the accuracy of the data and can share it easily. World-class encryption provides the necessary security, and there’s a clear, immutable audit trail to underpin end-to-end underwriting and claims governance.”  (EY, 2017). EY’s Insurwave, a blockchain enabled marine insurance product, aims to “deliver major gains in transparency, efficiency and auditability in the insurance value chain.” (EY, 2017). Replacing traditional databases with a blockchain creates immutable records allowing for better risk assessments on the part of insurers and quicker claims payouts for shippers. The operational efficiency gained by such solutions reduces the cost of moving information within and between companies. However, an issue to be faced by the insurance industry as a whole is how to work with regulators to ensure legal requirements and regulations evolve in line with new use cases for the technology. Clearly this is a policy issue rather than a technology one but could stand in the way of blockchain solutions becoming the standard across the insurance industry.


This essay started with a definition of the future of work debate as a response to emerging technologies and digital transformation, and considered which sectors are likely to be the most affected by the use of DLT & blockchain. The discussion looked at the use of blockchain in the finance sector, government and insurance industry, considering some current uses, challenges and opportunities. From looking at this we can draw the following conclusions:

  • DLT & blockchain will have a far greater role in changing some industries than it does in others. The effects will be greater in sectors where multiple organisations need access to the same data and for that data to be trusted as a single source of truth.
  • DLT & blockchain is likely to change the nature of the relationship between businesses, transforming aspects where a trust-based relationship exists. It has the potential to enable businesses to move away from centralised authorities.
  • All sectors and industries suffer similar challenges with the adoption of DLT & blockchain, including regulation, understanding and clearly defined use cases.
  • The effects on the future of work are likely to be considerably more profound where DLT & blockchain intersect with other emerging technology such as the Internet of Things and Artificial Intelligence.

Distributed ledger technologies and blockchain, as two of many emerging technologies, can be expected to have a contributory role in changing the future of work, and it’s clear how these technologies can be used in specific use cases, but it would be less prudent to conclude a general direct impact in the way that automation technologies, for example, can be expected to affect employment and the types of jobs available.


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Does digital creativity differ from non-digital creativity?


In order to answer the question of whether digital creativity differs from non-digital creativity we will explore the definition of the creative act as that of bringing together previously unassociated ideas from within or across domains (Koestler, 1981) and whether creativity is domain-specific (Baer, 1998) in order to understand how creativity in a digital context differs from the traditional. 

In exploring how digital media utilises ‘the double logic of remediation’ (Bolter & Grusin, 2000) we see how new media oscillates between immediacy and hypermediacy as it hides and reveals itself, and how it is digital media’s interactivity and multiplicity that results in it surpassing traditional media to become experience for participants.

And to consider how digital technology affects the production and consumption of new media we briefly discuss the foundational technologies and ‘proto-affordances’ (McMullan, 2020) that make new digital media fundamentally different from other forms of media.

The combination of this understanding of creativity in the digital context, modes of production and consumption for new media, and the underpinning technologies lead us to conclude that digital creativity is indeed different from a popular perception of non-digital creativity; different in origin, in format, in production, and in experience.

Defining creativity

In attempting to define creativity, Koestler speaks of conscious and unconscious processes which enact through the three forms of creative activity which he defines and have a basic pattern in common (1981). Describing that all creative activity falls into one or another of three categories: artistic originality, scientific discovery, and comic inspiration, or, more frequently, into a combination of them, we can consider creativity as a context-specific activity, that is to say that the way in which one is creative whilst making a scientific discovery differs from making a work of art. And in describing creative activity as having common patterns Koestler says, “The creative act consists in combining previously unrelated structures in such a way that you get more out of the emergent whole than you have put in” (1981). These definitions help us understand creativity as having categorically-specific and common characteristics.

General or specific creativity

How general or specific is creativity? Is it the same process for artistic creativity as it is for scientific discovery? The domain-specific view of creativity says that content matters, that no one is creative across all domains, that an artist has specific skills in visual arts that allows them to be creative in producing artworks, but wouldn’t be able to be creative in all domains. These domains are broadly defined as cognitive domains, for example mathematical, musical, and visual (Baer,1998). This raises the question of whether traditional arts and digital arts are fundamentally cognitively visual and so within the same domain, and so sharing the same creative processes. Or do we consider digital art to be a different domain to the traditional arts, perhaps less visual and more technical in cognitive processing, in which case we may conclude that digital creativity is within a different domain to the visual creativity that produces the traditional arts.

The computational bisociation of ideas

Creative activity does not create something out of nothing. It is an activity that, “combines, reshuffles, and relates already existing but hitherto separate ideas, facts, frames of perception, associative contexts. This act of cross-fertilization – or self-fertilization within a single brain – seems to be the essence of creativity. I have proposed for it the term bisociation.” (Koestler, 1981). The more unfamiliar and unconnected the joined ideas are, the more creative and original they appear (Koestler, 1981), which poses questions about collective creativity in the digital age. In new media artwork is it only the artist that conceived of the idea for the artwork who is being creative, or in the case of artwork that requires input from multiple people, are they all being creative? And if creativity in the digital age is the process of bisociation, then can computers be creative?

In considering the complex challenge of computational creativity, Dubitzky and Kötter (2012) utilise Koestler’s concept of bisociation to present a framework. They highlight a number of issues that require resolution in order for computational creativity to become a reality. The interoperability of the knowledge bases to allow ideas from one domain to be intersected with another, recognising usefulness and applicability of the idea, and deciding whether a new idea meets the definition of being creative, that is being new, surprising and valuable (Boden, 1994).

This attempt to discover how computers might be creative provides some insight into the complexities of human creativity in the realm of digital technologies. It highlights how the bisociation of ideas to lead to creative insight requires far more than simply joining two previously unconnected ideas together, the resulting creativity must be useful and new. As such, we can say that digital creativity must not only meet the criteria of traditional creativity but has additional criteria based on the limits of the technology being used.

How new media differs from traditional

If the digital creative act can be said to be different to the traditional act of creating something new, then how different can the outputs of those creative acts be? How different is new media from traditional media?

From the development of spoken language, to written language and the formalisation of an alphabet, the printing press and electronic media, to the world wide web, all were built on top of the previous media (McLuhan, 1964). The new media often fails to acknowledge the previous media (Bolter & Gromala, 2005) but arguably could not exist without earlier those versions. McLuhan’s assertions that the “content of any medium is always another medium” and “the “message” of any medium or technology is the change of scale or pace or pattern that it introduces into human affairs” (1964) helps us conceive of the ways in which new digital media differs from traditional media. It is not only the contents of the message of traditional media that become the message of new media, in the way that a story in a book is made into a film, it is also the traditional media itself that becomes the message of new media, such as characteristic notions of narrative and sequential story-telling that are taken from the medium of printed books and become raw materials of new media to be explored, critiqued, disrupted and challenged as the new medium evolves.

Means of comparing media

It is degrees of definition that separates hot and cool media. Hot media are high definition, that is containing lots of sensory data, whilst cool media are low-data, low-definition (McLuhan, 1964). Although McLuhan’s idea of hot and cold media may lack empirical basis (Douglas,1970) it provides a means by which we can compare one media with another. Cool media require higher involvement from the viewer, expecting them to fill in the gaps in their understanding whilst hot media is usually linear, sequential and logical requiring less of the viewer in order to understand the message. (McLuhan, 1964). Writing in the sixties, McLuhan did not have any examples of modern digital media to consider in his definitions but it is useful to consider both of McLuhan’s concepts, that of ‘the medium is the message’ and that of ‘hot and cool media’, together in order understand how new media builds on existing media and may change its nature in comparison to the existing media.

We can consider, as an example, the remediation of the moving image in how online videos took from cinema. Online videos on platforms such as YouTube maintain and reference many of the conventions established by earlier cinema including the rectangular format of the image and the synchronised image and sound. Remediating moving images changed video from hot media to cool, taking the immersive and single-sense experience that cinema provides and replacing it with a low-definition experience that asks of the viewer far more participation in order to gain any value from the experience. YouTube, as a cool media, provides viewers with the means to pause, skip, replay, choose another video; all mechanisms for increasing engagement that do not exist, and are not required, as part of the hot experience cinema provides. YouTube did not invent watching videos but it has accelerated and enlarged the scale (McLuhan, 1964) of the production and consumption of video, making cinema the message of its medium.

New media converges in the minds of the viewer

The idea of convergence offers a contrast with older notions of media spectatorship (Jenkins, 2006) and passive consumption, transforming those who experience the media into participants each at the centre of their own network of multiple media platforms. The experiencers of new media have no choice in this. Convergence occurs within the brains of every individual consumer (Jenkins, 2006) as they interact with media generated by each other and every kind of organisation. Social media, as a pervasive, widely used, and culturally relevant (Appel et al, 2020) means of propagating content in various forms to billions of people, offers an example of the difference convergence suggests occurs from consuming television. Social media offers a multitude of points-of-view leading to what Appel et al refer to as how “digitally enabled social interactivity is shaping culture itself” (2020) by removing the trusted authority of a single source of media. New media, utilising network effects and not presenting itself with singular coherent narrative as in traditional media forces different patterns of consumption.

How digital changed consumption of new media

Having critiqued the position that creativity differs depending on the cognitive domain and that technology creativity may exist within a different domain to the visual, we can consider the medium by which those creative outputs are engaged with to understand how digital media may differ from traditional media.

In traditional media such as painting, artists have adopted the mechanisms of perspective, natural light and shade and the removal of brush strokes from the painting in an attempt to achieve immediacy and cause the viewer to forget the presence of the medium (Bolter & Grusin, 2000). In expressing this cultural need to reflect our reality through the media we consume, media producers have adopted these mechanisms to allow the viewer to regard the medium as transparent. This transparency, whilst not intending to fool the viewer into believing that the medium they engage with is reality (Bolter & Grusin, 2000) has sought to express that reality in ways that enable interaction. Perhaps an artist’s expression of the reality and their emotional experience of the reality can be felt more deeply by a viewer where the interface does not seem to be a barrier.

Digital media requires and creates a different relationship with its viewer. It also intends to make itself disappear and so enable a direct “confrontation with the original” (Bolter & Gromala, 2005), but recognises that this can never be possible. Digital media is interactive and multiple in nature, which requires that it reveals the interface to the viewer. Hypermediacy is a reusing and refashioning of traditional and contemporary media to offer a more authentic experience (Bolter & Gromala, 2005). McLuhan’s point that the, “The business of art is no longer the communication of thoughts or feelings which are to be conceptually ordered, but a direct participation in an experience. The whole tendency of modern communication… is towards participation in a process, rather than apprehension of concepts.” (1951, p.73) expresses a recognition of the difference in intention between traditional and new media. Experience rather than expression is the aim of digital media. In participating, or converging as Jenkins would describe it (2006), the viewer becomes part of a digital artwork, fundamentally challenging conceptions of originality and creative ownership held as essential aspects of traditional art.

The oscillation of media

This oscillation between immediacy and hypermediacy, hiding the medium and revealing it, is what Bolter and Grusin refer to as the double logic of remediation (2000). It expects of the viewer a shifting between interacting with the media through a transparent interface and knowing that the interface is mediating that experience. Bolter and Gromala argue that “remediation is a defining characteristic of the new digital media” (2005) and as such guides us to consider how remediation takes place within a medium and between mediums, in often contradictory ways.

Stories, the mobile-device full-screen vertical video format introduced by Snapchat in 2012 and adopted by many other products, including Twitter, Spotify and LinkedIn since (Moriarty, 2017), is an example of the remediation of the portrait format from painting and an attempt by digital media to achieve immediacy by enabling viewers to use video in ways that are more natural to their use of the mobile device. Despite the initial barriers to adoption (Glove and Boots, 2012) vertical video has become mainstream and can be expected to shift towards hypermediacy, making viewers aware that their act of viewing is not reality. “If the ultimate purpose of media is indeed to transfer sense experiences from one person to another” (Bolter & Grusin, 2000), then vertical video will experience the oscillation of remediation as the technology that replaces it emerges with increased immediacy. 

New media attempts to surpass old media, to replace it with something that better meets the promise of immediacy (Bolter & Grusin, 2000). It attempts to create the belief that digital technologies have passed beyond mediation, that they have achieved such immediacy of experience that the interface no longer exists and the viewer is interacting directly with reality. In many respects new media is on a continuum with old media. All expressions of creativity are remediations of other mediums and digital expressions are no different (Bolter, & Gromala, 2005). But where new digital media deverges and differs is in its interactivity and multiplicity leading us to adopt a position that creativity expressed through digital media differs from creativity expressed in traditional media.

The foundational technologies of new media

McMullan goes even further, suggesting that digital media is not merely a continuation and remediation of older media but that it is based on different foundational technology and so is fundamentally different. This difference is explained through the concept of proto-affordances (McMullan, 2020) which define a technology’s relationship with a culture. The predominant foundational technology in play prior to the digital age was ‘electronic’ from which we see media that is instantaneous in nature and associated with technologies such as television. The proto-affordance of the foundational technologies of digital media is computability, that is, McMullan says, that all digital media has in common that it was produced by computer and as such is determinable by mathematical means. It could be considered counter-intuitive to speak of mathematics when discussing creativity but this only serves to further reinforce our earlier conclusion about the cognitive nature of digital creativity.

Artificial Intelligence has been used to create music since the 1990’s (Deahl, 2018). If any creative endeavour lends itself to being mathematically determinable, then music with its formalised language and relationships must be it. A wide range of methods have been successfully used in music composition including heuristics in evolutionary algorithms, neural networks, stochastic methods, generative models, agents, decision trees, declarative programming and grammatical representation (Lopez-Rincon, 2018) with results indistinguishable from that of human composers (Barbican, 2019). This remediation of music by software into data where production can be automated (Manovich, 2003) is indicative of the effect digital technology has and will continue to have on media production.

The most fundamental of digital technologies; the internet, has and stands to continue to have a profound effect on the remediation of traditional media. The internet combined with other modern technologies such as 3D printing and artificial intelligence has the potential to remediate all other mediums (McMullan, 2020) and generate entirely new, new media (Manovich, 2003). No other technology in the history of our culture has had that power.


Digital creativity differs from non-digital creativity. It differs in the nature of the creative act, in its definition of creativity, in its outputs as digital media, in how new media is experienced by its participants, and in the technology that underpins new media. 

Taking Koestler’s definition of the creative act as “combining previously unrelated structures in such a way that you get more out of the emergent whole than you have put in” (1981) and his notion of the bisociation of ideas across domains, we developed an understanding of creativity having common patterns and categorical specificites. Baer’s work on the domains of creativity (1998) builds on Koestler and provides insight into the creative act leading us to conclude that when digital creativity stems from a different cognitive domain to more traditional visual creativity then we can consider this a fundamental difference in the source and nature of the creativity, especially as it pertains to the production of new and digital media. 

McLuhan’s oft quoted, “the medium is the message” (1964) began our understanding of the difference between old media and new media, and how new media references what has been before but generates a change of scale or pace for that message. The nature of this change is particularly important for understanding how different digital media in the 21st century is from traditional media in previous centuries as the internet has enabled a speed of change that has been impossible in earlier decades. The ways in which we understand new media as different from old media continued with McLuhan’s definition of hot media as high definition whilst cool media requires more viewer participation (1964). Using this perspective we considered cinema as old media and online videos such as those on YouTube as cool media, showing that for the medium of online video the message of the moving image had undergone a change in scale and pace from how viewers experience cinema. Also, in appreciating the difference between new and old media we looked at the concept of convergence (Jenkins, 2006) which described how old media is consumed in a passive spectator mode whilst new digital media is more of an experience participated in by individual consumers at the centre of a network of media content. New digital media differs in these many aspects from traditional media.

Finally, in considering the effects that foundational digital technology (McMullan, 2020), artificial intelligence and the internet has on new media production we conclude, as McMullan (2020) and Manovich (2003) do, that new media is fundamentally different from traditional media. 

We can also put forward the opinion that digital creativity will continue to diverge from traditional creativity as technology becomes more embedded in more creative endeavours.


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Barbican Centre. (2019). 12 songs created by AI – How musicians are already embracing new technologies.

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Bolter, D.J. & Gromala, D. (2005). Windows and Mirrors, London: The MIT Press.

Bolter, D.J. & Grisin, R. (2000). Remediation: Understanding new media. The MIT Press.

Deahl, D. (2018). How AI-Generated music is changing the way hits are made – The Future of Music, episode 2. The Verge.

Douglas, G. H. (1970). The hot and cold media principle: Theory or Rhetoric? A Review of General Semantics , September 1970, Vol. 27, No. 3, pp. 339-344. Institute of General Semantics

Dubitzky, W. and Kötter, T. (2012). Towards Creative Information Exploration Based on Koestler’s Concept of Bisociation. In book: Bisociative Knowledge Discovery (pp.11 – 32.) Eds: Michael R. Berthold. Springer Verlag.

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Jenkins, H. (2006). Convergence Culture: Where Old and New Media Collide. New York University Press.

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Lopez-Rincon, O., Starostenko, O.,  and Martín, G. A. (2018). Algoritmic music composition based on artificial intelligence: A survey, 2018 International Conference on Electronics, Communications and Computers. (CONIELECOMP), Cholula, 2018, pp. 187-193.

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McLuhan, M. (1964). Understanding Media. McGraw Hill Education.

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Walker, E. (2020). 100 art-world Instagram accounts to follow right now — Artists. Retrieved 14/01/2021.

During the coronavirus lockdown in the United Kingdom (2020), 45% of workers reported to be working from home (fully online). Using the theories discussed in the module Digital Business, explain the phenomenon from the enterprise digitalisation perspective. Critically discuss the potential outcomes of this experiment


When a large percentage of the workforce adopts an enforced new way of working, the organisations that have the technology in place, are quick to adapt their methods of communication, and understand the impact of such a drastic change on their workforce are better placed to weather the external disruption to their business by minimising the internal disruption. 

Technologies that enable working online

Working from home would not be possible without internet-connected digital tools and platforms that allow workers to connect, communicate and collaborate. These Enterprise 2.0 technologies are networked through internet connections and contain ‘social’ or collaborative layer functionality such as sharing documents with other workers, communicating in faster, less formal ways through instant messaging, and finding information across a wider pool of sources.

Types of Enterprise 2.0 technology

Enterprise 2.0 technologies are defined by their characteristic collaborative layer that increases workers productivity through fostering connected, collaborative ways of working.

The technologies include:

  • Shared documents
  • Wikis
  • Social networks
  • Blogs
  • Video sharing
  • Video conferencing
  • Instant messaging
  • Podcasts
  • RSS
  • Microblogging
  • Tagging
  • Rating
  • Mash-ups
  • Prediction markets

Video conferencing, social networking and collaborative document editing are the most adopted of the various types of technologies. These are all internal working tools, perhaps suggesting that companies haven’t yet fully realised the benefits of using these technologies to create more permeable boundaries between the organisation and its customers, suppliers, other organisations, etc., in order to increase openness and drive innovation.

Benefits of Enterprise 2.0 technology

During a time of global crisis organisations might consider the ability to continue to operate to be a sufficient benefit from having implemented Enterprise 2.0 technologies, but there are also additional longer term benefits. Andrew McAffe says that Enterprise 2.0 “offers significant improvements, not just incremental ones, in areas such as generating, capturing, and sharing knowledge” (McAfee, 2009). 

The top five measurable benefits from technology adoption are (McKinsey, 2013):

  • Increasing speed to access knowledge
  • Reducing communication costs
  • Reducing travel costs
  • Increasing speed to access internal experts
  • Reducing operational costs

Enterprise 2.0 technologies grew rapidly between 2006 and 2013 (McKinsey, 2013) with 61% of companies reporting using video conferencing in 2013. The growth of these collaborative tools had plateaued (McKinsey 2015) but it is not inconceivable to assume that during the lockdown far more companies are utilising the benefits of technology to undertake almost every business task. The lockdown may serve as an accelerator for better utilisation of collaborative working technologies and achieve greater and previously unrealised benefits than if organisations had not been forced to adopt them.

Communication methods that support distributed workforces

The emergence of Enterprise 2.0 as a new form of interaction (rather than purely a technological phenomenon) between workers has enabled those who had to work from home during the lockdown to continue to communicate effectively with colleagues. The new communication methods required acceptance of the reconceptualisation of how information flows in Enterprise 2.0.

Communication networks 

Traditional enterprise communication followed the lines of organisational hierarchy whereas Enterprise 2.0 communication follows the paths of a network and so flows more quickly and efficiently. 

Steven Johnson (2010) suggests that individuals perform better when they belong to more networks as they can benefit from information shared by other people. The more nurturing a network, the more information openly shared, the more innovative ideas that can emerge. 

Collaborative communities

Enterprise 2.0 enables the creation and growth of collaborative communities; groups of people that leverage technology and communication networks to organise themselves around different principles to the traditional hierarchical organisation, in order to have a collective means to participate and collaborate. This means of organising, foregoing the authority of traditional means, would have enabled employees to quickly mobilise to figure out new ways of responding to the challenges they faced during the lockdown.

A collaborative community could include the following characteristics (Savalle et al, 2010):

  • Organic
  • Decentralised
  • Self-organising
  • Autonomous
  • Asynchronous
  • Self-regulating
  • Varied in size

Collaborative communities emerge bottom up when people see the value of their contribution. In this there are network effects occurring as the more people contribute to the community, more people experience a benefit and so contribute more.

Companies benefit from providing the technologies and allowing this type of organisation to prosper as information sharing and crowd thinking can solve problems that traditional siloed team structures cannot, it supports new ideas to emerge, and strengthens social ties.

Ways of collaborating

Different types of organisational structure require different ways of collaborating, especially in a crisis situation such as lockdown. Allowing collaborative ways of working to emerge through communities takes more time than companies may have to enable effective working from home, and so considering the ways in which collaboration can be initiated and supported can speed up adoption among a distributed workforce.

Pisano and Verganti (2008) proposed a model of governance and participation that whilst describing how companies can approach innovation with partners could also be a valid model for describing how innovative ways of collaborative working could be understood. This model provides some understanding of how bottom up communities and top down hierarchies may interact in collaborative ways to develop innovative solutions to problems, such as the pressing problem facing companies at the start of the lockdown of how to begin working collaboratively.

Impacts on employees

Introducing Enterprise 2.0 technologies and ways of working to an organisation carries with it a considerable impact for its employees, especially if undertaken during a crisis such as lockdown. Understanding the social ties between individuals, how they develop social capital, and what motivates them to adopt the new technologies and ways of working can provide some insight into how the shift to Enterprise 2.0 can be more successful.

Social ties

McAfee (2009) described four types of ties people have with others. The ties can be weak, strong, potential or none. Strong ties exist between people who know each other and work together, but it is weak ties that are important for connecting people who don’t know each other very well in order to spread information (Gravonetter, 1973). Enterprise 2.0 enables more weak ties to form across an organisation and so encourage information to flow that might have otherwise if it was reliant on the hierarchical structure.

Social capital

Social capital exists in the relations between individuals in a group. Faraj and Wasko (2001) refer to it as a “collective orientation”, a social system that develops because of “closure, shared history, goal interdependence, and frequent interactions”. When those interactions happen online whilst using Enterprise 2.0 technologies the norms of acceptable behaviour become even more paramount, and the opportunities for sharing information and resources are increased. 


Achieving adoption of Enterprise 2.0 technologies and ways of working requires an appreciation of the intrinsic and extrinsic motivations of the employees, even more so at a time of crisis where they may have additional pressures outside of the workplace. If workers are intrinsically motivated to be successful in the roles, and they understand how new technologies can help with this, they would seem to be more likely to adopt and adapt to the change.


For some businesses the coronavirus lockdown will serve as an accelerator for the adoption of Enterprise 2.0 technologies, new ways of working, and new ways of unlocking value within the organisation. This enforced innovation that is making the organisational boundaries more permeable, spreading knowledge and new ways of collaborating, and enabling employees to make the most of the shift to Enterprise 2.0 has the potential to support businesses to be more innovative and successful.

Using the theories presented in the module Digital Business, critically discuss the differences between a DVD film and streaming services such as Netflix. You must use theories to frame the analysis and discussion.


We can understand the characteristics of a DVD renting service business model and a video streaming service business model using Rayna & Struikova’s business model framework to compare to analyse the business models, and then consider how the characteristic differences between information and digital goods caused a change in the business models.

Characteristics of a DVD renting service business model

Value creation

The value network includes:

  • Licensing with movie distributors.
  • Purchasing DVDs from production companies
  • Renting of shop space.

These complementary assets create a high barrier to entry into the market for competing businesses and ensure market dominance.

Value proposition

Offered the latest movies that are no longer at the cinema.

Implied urgency and limited availability through a fixed number of DVDs.

Reactive pricing with newly released movies are priced higher than older, less popular movies allows the revenue to be maximised.

Value delivery

Shops were the primary distribution channel, although delivery through mail existed for a short period of time.

The target market was people who owned DVD players and watched movies at home.

Value capture

Understanding customer behaviour through sales data which is used to plan the distribution of upcoming movie releases to optimise the number of DVDs available in the most popular shops.

The revenue model involved customers having membership so that the shop could record which DVDs were rented by which customers and a rental fee with late charges.

Value communication

The value communicated was in shops as customers browsed shelves full of DVDs, showing them that a large number of choices were available.

Characteristics of a video streaming service business model

Value creation

Video streaming services attempt to control their supply chain through licensing and then producing content.

The value networks include cloud infrastructure such as AWS for Netflix and Disney Plus, and payment services to collect subscription fees.

Value proposition

Customer’s watching whenever they want using high speed internet connections and mobile devices, shifted the customer’s relationship with watching movies away from something done at a particular time in their own home.

Discovery of new content is made as easy as possible, encouraging more time spent watching, which provides more data, and makes the customer more likely to continue to pay for the service.

Value delivery

The distribution channel for streamed videos is apps and smart TV’s connected to the internet, which creates a low barrier to entry by making them purely technological, requiring only an internet connection and a device for viewing. 

The target market for video streaming includes the majority of adults with an internet connection and a desire to watch movies. Netflix, for example, has 182 million active users. Given the popularity of video streaming and the increasing segregation of content into specific services as those companies attempt to own particular segments of the market, it seems likely that customers will subscribe to multiple services which will reduce competition in the market.

Value capture

Video streaming uses price versioning. Different subscription levels with different features are offered at different prices. This allows the company to segment its customers by offering a basic service that suits the needs of most individuals, along with higher priced options for customers who value watching in high definition or ultra high definition and those who want to watch on multiple devices, often because of multiple users such as a family.

Video streaming enables data capture about customers behavior, including which customers watched which movies, what time of day they watched them, how far through they watched. This data can then be used to recommend other movies 

Value communication

In the competitive video streaming services market, pricing is underplayed to signal to customers that they barely even need to consider the cost because it is so low and because the value they will receive is in being able to watch new movies when it suits them.

Analysis of the differences between business models

Video streaming and DVD rental differs on every element of Rayna & Struikova’s business model framework. The introduction of the internet revolutionised business models. Companies that provided DVD rental services and attempted to adapt their business model by moving part of it online, e.g. ordering DVDs through a website, were quickly replaced by companies that developed business models from an understanding of how the internet changes every aspect of their business. 

DVD rentalVideo streaming
Value creationAggregating assets into a physical location.Segregating assets into a virtual location.
Value propositionRent weekly and watch at home.Access anywhere, any time.
Value deliveryPhysical shops, and home-bound devices.Internet-connected home and mobile devices.
Value captureMembership and rental fees revenue model.Subscription revenue model.
Value communicationDelayed communication through trailers on DVDs and customers browsing availability in shops.New content available every time the customer opens the app.

The only aspect of consumer behaviour that doesn’t seem to have been affected by the shift in business models is that the majority of movie and TV consumption takes place as entertainment in the home.

Business model innovation came through utilising the ubiquitous adoption of internet technologies and mobile devices that enabled the asset (movies) to be shifted from being an information good to a digital good, along with the resulting change in the economics.

Information goods 

DVDs are information goods. The value is in the information contained within the good, whilst the physical item holds very little value. Information goods cannot be replicated but can be copied, albeit with some loss of information that results in a poorer quality when the movie is played. The ability to copy enabled DVD piracy driven by the high price and limited availability of DVDs. Information goods enable the transfer of information, in this case from the disc to the TV screen.

Digital goods

Video streaming uses digital goods, which are intangible, codified, transferable and replicable. Digital goods are the only type of goods to have this combination of characteristics and so create different economics than information goods rely on. Digital goods may have a high production cost but thereafter the reproduction costs are near zero, whilst also being non-rival. These unique characteristics created challenges for businesses and drove the needs for a change of business model.

Analysis of the differences between information goods and digital goods

In addition to the differences in business models, the two services also have considerable differences in the nature of the goods they provide.

DVD rentalVideo streaming
IntangibleNo – information contained in a tangible medium.Yes – information not contained in a tangible medium.
CodifiedYes – the physical medium contains codified information.Yes – contains information and is information.
TransferrableYes – information can be transferred without direct contactYes – information can be transferred in a non-rival way.
ReplicableNo – some loss of information occurs in copying.Yes – enabling retrieval without loss of information.

These differences in characteristics create different economic drives, including reproduction costs, non-rival usage and piracy reduction. Digital goods have reproduction costs at near zero. The usage of digital goods is non-rival because there is no limit on how many customers can watch at the same time. Utilising digital goods enables companies to introduce technical means to prevent piracy through replicating the movies but this drives a different consumer behaviour of sharing account details with friends and family, which causes another technical fix by services controlling the number of devices an account can be logged into.


The widespread adoption of the internet caused a shift from movies being an information good to a digital good which drove a change in business models that resulted in DVD rental services and video streaming service having considerable differences in business models and in the economics that follow from the nature of the goods.

Analysis of the business model, pricing strategies, and digital product characteristics of Shopify


This analysis of the business model that underpins the success of Shopify, the ecommerce software-as-a-service business, seeks to show that core to the business model is the idea that success comes from helping their customers be successful by providing digital products that seek to solve the challenges and meet the needs of modern retail businesses selling on the web. All aspects of Shopify’s business model are designed to acquire and retain high quality small to medium retail businesses as customers who are serious about building successful ecommerce businesses, and supporting them to succeed. This strategic approach could be summed up with the phrase, ‘your success is our success’.

Background and history of the business

Shopify started life as a single ecommerce store selling snowboards, and was built on the new (in 2004) open source software framework called Ruby on Rails. With 78% of the internet running on open source software (Vaughan-Nichols, 2015), contributing to the development of Ruby on Rails software and its community helped support the Canadain tech start-up ecommerce software-as-a-service platform that became Shopify in 2006 to develop a platform that could grow over the coming decades. The business raised $7m in 2010 and $15m in 2011 in funding, and by 2017 Shopify was hosting “over 325,000 shops for individual sellers and internet giants like Google and Tesla” (Product Habits, 2017), and by 2019 had expanded to 1,000,000 businesses in approximately 175 countries (Shopify, 2019).

Analysis of the business model

The term ‘business model’ requires definition before an analysis can be undertaken. A business model can be described as “a conceptual tool containing a set of objects, concepts and their relationships with the objective to express the business logic of a specific firm. Therefore we must consider which concepts and relationships allow a simplified description and representation of what value is provided to customers, how this is done and with which financial consequences.” (Osterwalder et al, 2005). 

Understanding business models is particularly important for businesses that rely on technology to deliver value as how transaction cost economics (Williamson, 1989) and innovation are incorporated into the model will greatly impact its chances of success. Over time, the rate of innovation in any given technology falls (Abernathy & Utterback, 1978), and so technology becomes easier to use at scale so reducing the transaction costs, which increases margin, unless and until a competitor product steals market share through new innovation that better meets the customer needs. The objective of the business model of a technology company then, is to deliver maximum value to customers to increase revenue whilst reducing transaction costs to increase margin, and continually innovating to stave off competitors in order to maintain or increase market share. 

With this understanding of a business model we are able to use the business model framework (Rayna & Struikova, 2016) to analyse Shopify’s business model to understand if it meets the objectives above.

Value creation 

Core competencies

Shopify provides hosted ecommerce solutions to enable retailers to quickly and easily launch an online business and make that business a success. Developing and maintaining the software-as-a-service platform that enables its customers to set up and run online stores is Shopfy’s core competency.

Key resources

Shopify’s key resources include:

  • Web infrastructure
  • Ecommerce platform software
  • Payment provider
  • Point-of-sale technologies
  • Marketing
  • Fulfilment & logistics
  • Partner programme

The breadth of these resources extends beyond competitor ecommerce software providers which don’t also offer physical retail technologies and fulfilment services, making Shopify unique in the market.


Shopify is an incorporated public company with a board of directors responsible to its shareholders for increasing value over the long term (Shopify, 2018).

Complementary assets

Shopify’s complementary assets include marketing capabilities, strategic partnerships, brand awareness & market share, and investment & acquisition capabilities. Having the complementary assets to commercialise the technological innovation (ecommerce platform in Shopify’s case) is essential for the success of the business (Teece, 1986).

Value networks

Shopify understands the need for commercially successful partnerships and business relationships and places itself in a network with a multitude of other modern internet businesses, including Amazon, Facebook, Snapchat, Ebay, logistics and delivery firms. These indirect network externalities all increase the power of lock-in Shopify has over its customers (Arthur, 1989). 

Value proposition 

Product offering

Shopify competes in the business-to-business ecommerce technology market, with other hosted solution providers such as BigCommerce and Volusion. Shopify could be seen as competing with consumer ecommerce marketplaces such as Ebay and Amazon, however Shopify is clear in its market positioning for small to medium business and not consumers. Shopify is increasingly moving into the large business market (Shopify, 2014) to compete with enterprise ecommerce software providers such as Magento and Salesforce Commerce Cloud.

Service offering

Shopify offers a number of services in addition to its core product offering, including:

  • Capital – provides capital investment in customer’s businesses to enable the customer to purchase merchandise to sell.
  • Partners – supports an ecosystem of partners that provide services such as custom design and development, and are rewarded for referrals to Shopify.
  • Fulfilment – offers a logistics and delivery service to customers to enable them to fulfil orders placed on their Shopify store.

These supporting services fit the ‘your success is our success’ approach of Shopify’s strategy as the capital investment encourages customer businesses to prosper and so continue selling through Shopify, the partners help to maintain and recruit Shopify’s customers, and the fulfilment services contribute to a more complete offer.

Pricing model

Shopify utilises a ‘freemium and versioning’ price model. Potential customers are offered a 90 day trial period at no cost and then a choice of three levels of features at three corresponding prices (Shopify, 2020). The pricing tiers seek to communicate Shopify’s position in the market for small to medium businesses; more expensive than selling on Ebay (as an individual might) and less expensive than running a Magento website (as a large retail business might). It also communicates that Shopify only wants customers who are committed to building their own brand and developing their business over a longer term.

Value delivery

Distribution channels

As a software-as-a-service business Shopify distributes its products to its customers over the internet, via a browser. This meets the fourth characteristic of digital goods; being aspatial (Quah, 2003), and enables customers to have near instant access from anywhere in the world and allows Shopify to update it’s software quickly and regularly to meet the needs of its customers.

Value capture

Shopify captures value from its customers through its monthly subscription model, adding to the value provided to customers through investment in developing new products, features and services and through acquisition of companies that add to its portfolio of services. This investment in technology drives increasing returns for adoption as improved capabilities serve more customer needs and so “the tendency for that which is ahead to get farther ahead” (Arthur, 1996) results in an increase in the number of customers using Shopify.

Revenue model

Shopify’s revenue model for it’s ecommerce platform and associated services is a monthly subscription fee and additional transaction fees. This model has provided revenue growth of 6475% (Table 2) and profit growth of 4457% (Table 3) over seven years.

Analysis of the digital product

Public goods

The Shopify ecommerce platform is built on open source software called Ruby on Rails. Open source software is non-rivalrous and non-excludable, making it a public good in the economic sense. Once produced there are zero extra costs associated with allowing another person to use it (Coase, 1974) whether or not they contribute to its maintenance. 

Digital goods

In Shopify’s case, the additional programming that is done to utilise the open source software and packaged up in a way for customers to use defines the ecommerce platform as a digital good. Quah argues that excludability is not an intrinsically part of the economic nature of the digital good but instead follows from additional protective mechanisms (Quah, 2003). Shopify put barriers, both technological and legal, in place to prevent copying of the software in an attempt to make the digital good excludable (Whinston et al, 1997). 

In addition to being non-rivalrous, Shopify’s ecommerce platform exhibits the other four characteristics of digital goods. The ecommerce platform is infinitely expansible – it can be copied as a means to increase the number of users or to create backups, is discrete – it only exists as a whole and will have no value if broken down, is aspatial – does not physically exist, and is recombinant – can be combined with other digital goods to form new digital goods (Quah, 2003). All of these characteristics lead to Shopify’s core digital product to enable the realisation of the benefits of digital goods, including increasing returns and decreasing average cost.

Shopify’s software product does not utilise network effects, meaning the value users receive does not increase with the number of users (Katz & Shapiro, 1994), and does not create customer lock-in, however the business model utilises complementary assets (Teece, 1986) to create commercial gain from the digital product and external partnerships that do create customer lock-in through making it difficult for customers to move to other suppliers (Vandermerwe, 2003).

The Shopify business model and digital product offering is a system of interdependent activities (Zott & Amit, 2009) of public good (open source software), digital good (ecommerce platform), and supporting services.

Analysis of the pricing strategy

Shopify has adopted a ‘freemium and versioning’ pricing model for the subscription revenue from it’s ecommerce platform.

Whilst the majority of companies utilising the freemium pricing models for software allow users to use the product for free with the expectation that a percentage will convert to a paid version (Teece, 2010. Günzel-Jensen & Holm, 2020), Spotify fixes the length of the freemium offer to ninety days resulting in reducing the barrier to entry for digital goods where the value cannot be realised until experienced (Varian, 1998) whilst also driving those customers with a serious buying intention to purchase the paid versions within a predictable time frame. This ‘try before you buy’ approach enables Spotify to understand conversion rates and make financial modelling and cash flow predictions more reliable. 

The versioning aspect of Shopify’s pricing strategy enables the business to target multiple markets (small to medium, and large) with essentially the same product and certainly the same underlying infrastructure, codebase, developers, etc. The three price tiers of $29, $79, & $299 per month have different features and benefits associated with them, and as monthly recurring revenue give Shopify a level of predictability, and for its customers costs are not tied to the success of their business in the same way a cost-per-sale model (for example) would be. 

This ‘freemium and versioning’ pricing model fits the proposed strategic approach of Shopify recognising that the success of their business is dependent on the success of their customer’s business, and so avoiding a pricing strategy that appears to penalise or prevent the growth of businesses using the Shopify platform.

Alternative pricing strategies

There are a number of alternative pricing strategies Shopify could consider:


  • Description: Cost related to the number of services in use.
  • Benefits: Can increase revenue where services are either the cheapest, highest quality, or have few competitors.
  • Disbenefits: Could cause businesses to look to competitors for services such as email marketing and fulfilment thus reducing Shopify’s lock-in.
  • Example: Stripe payments.
  • Assessment: Not be a viable option for Shopify.


  • Description: Cost related to the number of users.
  • Benefits: Increases revenue as customer business grows and requires more users.
  • Disbenefits: Only fits products that require lots of users.
  • Example: Freshdesk customer service.
  • Assessment: Not a commercially successful option for Spotify.

Active users

  • Description: Cost is related to number of users active in a given month.
  • Benefits: Is a selling point for customers as they feel that charging is fair.
  • Disbenefits: Only fits products that require lots of users.
  • Example: Slack messaging
  • Assessment: Not a commercially successful option for Spotify.


  • Description: Cost is related to quantity breaks, e.g. value of transaction processed
  • Benefits: Revenue increases with higher revenue customers.
  • Disbenefits: Makes forecasting and financial modelling difficult.
  • Example: PayPal
  • Assessment: Could potentially be successful for Shopify although favours large business customers rather than small to medium sized businesses that process lower value transactions.


  • Description: Cost is variable and related to demand.
  • Benefits: Enables revenue to be maximised depending on demand.
  • Disbenefits: Harder to be transparent with customers and maintain relationships.
  • Example:
  • Assessment: Not a commercially successful option for Spotify.

Two-Part Tariff

  • Description: Cost is lump sum and per unit.
  • Benefits: Decouples the price charged for digital goods from processing costs, enabling increases in one without affecting the other.
  • Disbenefits: Can result in uncertainty for customers and in forecasting.
  • Example: Credit cards
  • Assessment: It could be argued that Shopify uses this model as there is a fixed cost for the platform and a per unit cost for processing transactions, but equally these could be viewed as different charges for different products and services.

Shopify could perhaps adopt an alternative pricing strategy to increase revenue but a cohesive business model is one where there are “business design choices that reinforce one another” (Osterwalder, 2005) and it seems clear that Shopify’s current pricing strategy supports the wider business strategy and business model.


Given Shopify’s strategy of providing complete products and services that support retailers it is difficult to uncover any aspect of ecommerce that Shopify hasn’t already provided services for. Below are some suggestions of how Shopify could expand their offer whilst remaining true to their ‘what’s good for our customers is good for us’ approach.

Sourcing & procurement

Provide buying and merchandising services that locate suppliers and negotiate on costs on behalf of Shopify’s customers to enable them to expand their product range.

Further expansion into the enterprise market

Integrate with large enterprise ERP systems such as Microsoft Dynamics AX and Power BI, Oracle and IBM systems.

Consumer services

Move into the consumer market and leverage existing infrastructure such as Shopify Shipping to enable individuals to send packages (that they may have sold on Ebay, for example), and personal website and blog builders to compete with WordPress, Medium, Wix, SquareSpace, etc.

Partnering with adjacent business services

Support merchants to run their business by partnering with adjacent service providers such as accounting and tax returns, human resources management, etc. all part of Shopify’s strategy to help its customers run successful businesses.


Shopify’s business model  utilises the public good nature of open source software, builds digital goods that leverage the technologies of increasing returns and decreasing average cost, forms strategic partnerships that achieve customer lock-in, and provides additional services that offer the complete solution for businesses selling online. These elements achieve an effective business model made up of “business design choices that reinforce one another” (Osterwalder, 2005). 

Based on our agreed definition and objective of a business model, we can assert that Shopify has a successful business model. It is able to deliver maximum value to customers and collect on that value as revenue, seen by the increase in annual revenue between 2012 and 2019. whilst developing technology that reduces transaction costs to increase margin, and continually innovating with new services to stave off competitors in order to increase market share. 


Annual revenue

Table 2 – Source:

Annual Profits

Table 3 – Source:

Shopify plans & features

Basic ShopifyAll the basics for starting a new businessShopifyEverything you need for a growing businessAdvanced ShopifyAdvanced features for scaling your business
Monthly price$29 /mo$79 /mo$299 /mo
Online StoreIncludes ecommerce website and blog.YesYesYes
Unlimited productsYesYesYes
Staff accountsStaff members with access to the Shopify admin and Shopify POS.2515
24/7 supportYesYesYes
Sales channelsSell on online marketplaces and social media. Channel availability varies by country.YesYesYes
LocationsAssign inventory to retail stores, warehouses, pop-ups, or wherever you store products.up to 4up to 5up to 8
Manual order creationYesYesYes
Discount codesYesYesYes
Free SSL certificateYesYesYes
Abandoned cart recoveryYesYesYes
Gift cardsYesYesYes
Professional reportsNoYesYes
Advanced report builderNoNoYes
Third-party calculated shipping ratesShow calculated rates with your own account or third-party apps at checkout.NoNoYes
Fraud analysisYesYesYes
Online credit card rates2.2% + 20p1.9% + 20p1.6% + 20p
In-person credit card rates1.7% + 0p1.6% + 0p1.5% + 0p
Additional fees using all payment providers other than Shopify Payments2.0%1.0%0.5%
Table 4 – Source: Shopify website

Shopify products and services

Through development or acquisition:

  • Shopify – ecommerce platform for small and medium businesses.
  • Shopify Capital – financial loans to customers.
  • Shopify Payments – online payment processing.
  • Domain name registration and hosting – website services.
  • Business name and logo generators – branding assets.
  • Buy button – enabling non-Shopify websites to embed Shopify functionality.
  • Select Start Studios – mobile software developer.
  • Jet Cooper – design studio.
  • Shopify App Store – API platform for third party developers.
  • Build-A-Business competition – encourage entrepreneurship. 
  • Shopify Plus – ecommerce platform for large businesses with access to additional features and support.
  • Boltmade – Product design.
  • Frenzy – mobile app for flash sales.
  • Tiny Hearts – mobile products research and development.
  • Amazon integration – allow Shopify merchants to sell on Amazon marketplace.
  • Point of sale systems – a Bluetooth enabled debit and credit card reader for physical retail purchases.
  • Oberlo  – connects Shopify merchants with drop-ship suppliers.
  • Shopify Compass – Entrepreneurship guidance and learning.
  • Shopify Studios – full-service television and film content and production house.
  • Snapchat integration – manage Snapchat Story ads.
  • Facebook Messenger integration – customer communication.
  • Shopify Chat – native chat function allowing merchants to have real-time conversations with customers.
  • Handshake – business-to-business e-commerce platform for wholesale goods.
  • Shopify Fulfillment Network – shipping logistics for merchants.
  • 6 River Systems – fulfillment solutions.
  • Shopify Email – native email marketing tool.
  • Shop – personal shopping assistant app.


Steven J. Vaughan-Nichols. 2015. It’s an open-source world: ​78 percent of companies run open-source software.

Product Habits. 2017. How Shopify Grew From a Snowboard Shop to a $10B Commerce Ecosystem. 

Shopify. 2019. Now Powering Over 1 Million Merchants, Shopify Debuts Global Economic Impact Report. Shopify press release.

Osterwalder, A., Pigneur, Y., and Tucci, C. L. 2005. Clarifying business models: Origins, present, and future of the concept. Communications of the association for Information Systems, 16(1):1–25.

Oliver E.Williamson. 1989. Chapter 3 Transaction cost economics. Handbook of Industrial Organization. Volume 1, 1989, Pages 135-182. Elsevier B.V.

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Shopify. 2018. Shopify Inc, Board Charter

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Shopify. 2014. About Shopify Plus.

Shopify. 2020.

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Shopify Gross Profits & Revenue. 2019.

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Katz, M. L. & Shapiro, C. 1994. Systems Competition and Network Effects. Journal of Economic Perspectives. Vol. 8, No. 2, Spring 1994. (pp. 93-115).

Vandermerwe, Sandra. 2003. Getting Customer Lock-on Through Innovation in Services, in Service Innovation: Organizational Responses to Technological Opportunities & Market Imperatives. Ed. Joseph Tidd, Frank Hull. Imperial College Press, 2003.

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Varian, Hal R. 1998 (revised: October 16, 1998). Markets for Information Goods. University of California, Berkeley.

Will the trend towards increasing automation of production processes threaten the employability of graduates?


Mechanisation: the introduction of machines or automatic devices into a process, activity, or place (Cambridge dictionary), where the process is controlled by a human operator.

Automation: the use or introduction of automatic equipment in a manufacturing or other process or facility (Cambridge dictionary), where the process is controlled by software.

Employability: the skills and abilities that allow you to be employed (Cambridge dictionary).

Graduate: a person who has a first degree from a university or college (Cambridge dictionary).


In order to investigate the question of whether the trend towards increasing automation of production processes will threaten the employability of graduates I focus on the role of mechanisation and automation in attempting to reduce labour costs and consequently increasing the demand for high-skilled graduate workers. I examine which segments of the workforce will be most affected by automation, whether graduates are more employable than non-graduates, and whether the subject of the degree a graduate studied has any impact on employability. I also consider the increasing progression of automation and whether the threat to graduate employability will change over time.

The trend towards increasing mechanisation and automation

In order to examine the effects of mechanisation and automation on work it’s useful to look at the GDP as a measure of output per person and whether technology increases productivity. Figure 1 shows how for much of history productivity existed in a Malthusian economy (also referred to as the Malthusian Trap) of being linked to population growth (Malthus, 1798). The Malthusian Trap describes how any increase in productivity (measured by GDP) led to an increase in population, which resulted in decreasing the GDP per capita, thus sustaining a constant level of productivity. This idea is widely accepted as explaining the linear economic progress prior to the introduction of technology and social change of the industrial revolution.

Figure 1. Source: “Statistics on world population, GDP, per capita GDP, 1-2008 AD, Angus Maddison: IMF

The introduction of technology into the manufacturing processes in England and the United States of America in the eighteen century is widely accepted as being linked to the industrial revolution which took economic progress from being linear to enabling society to escape from the Malthusian trap and enabling greater output to be achieved with the same number of workers.

Contrary to the technological explanation for the onset of the industrial revolution, there is the idea that it may have been caused by social mobility (Clark, 2007). An analysis of wills showed that the wealthy had more offspring than the poor, and that this increasing upper class population disseminated it’s values across society, including education and saving for investment in capital resources (Baumol, 2002). 

Although the causes of the industrial revolution are multiple and complex, and not solely limited to technology, the introduction of technological advances had a huge effect on the workers of the time. New technologies required skilled workers to install, operate and maintain the machines, and this demand for scarce skilled labour increased labour costs between 1800 and 1900 (, which resulted in an increase in capital investment to reduce labour costs.

Machines then, became labour-saving devices and mechanisation threatened jobs, disrupted entire sectors, and caused shifts in the production processes of every industry. But the effects were not the same or equal across all industries, sectors, and roles. Frey and Osborne (2013) illustrated in the shifting of certain production processes in the nineteenth century from artisan shop to steam powered factory increased the number of workers required but deskilled those workers through breaking the work into small, specialised sequences. Where the electrification of factories was introduced, more machinery could be utilised to automate production processes, resulting in a demand high skills in the production workers and an increase in the share non-production workers also employed (Goldin and Katz, 1998). In 1913, when Henry Ford introduced continuous-flow production the assembly lines were designed to around unskilled workers (Frey and Osbourne, 2013). 

Demand for educated workers

Education and technology had to keep pace. The introduction of new technologies into the workplace resulted in demand for technologically-proficient workers to operate the new technologies. Without sufficiently educated and skilled workers the technology would fail to produce the expected productivity gains. These educated workers demand higher wages, increasing the labour costs and so driving further investment in capital and adoption of mechanisation and automation technologies. (Goldin and Katz 1995).

These examples show that the effects of mechanisation of the workplace was not as simple as machines replacing people. One interesting effect, for the purposes of this essay, is that industrial revolution technology had a profound and complex impact on productivity and employability (Baumol, 2002) through the increase in labour-saving machinery that created a demand for educated and skilled workers, and so increased labour costs fueling the introduction of further labour cost-reducing technologies.

New Growth theory, with its emphasis on knowledge creation and entrepreneurship, argues that physical assets such as capital (machinery) can only produce limited growth but that knowledge is an intellectual asset that enables increased productivity (Mankiw, Phelps & Romer. 1995) as knowledge is non-rival and non-excludable, meaning the value extracted is not restricted by the value of the asset. The endogenous model better explains how productivity can increase than the exogenous model informing Solow’s argument that productivity can increase purely through capital accumulation and technical progress (Solow. 1956), which seems to fall foul of the trap of introducing labour-saving machinery to reduce labour cost, but creating a demand for educated and skilled workers, and so increasing labour costs driving the introduction of further labour cost-reducing technologies.

Does automation affect all jobs equally?

If we equate the knowledge that Mankiw, Phelps and Romer refer to with skills and abilities of being employable (as per our definition above) we can consider how the automation of work affects workers of different skill levels. Frey and Osbourne found 47% of US employment are “at risk should these technologies materialise”.

  • Routine manual work (e.g. assembly line worker), and routine non-manual work (e.g. book-keeper) required low and middle education levels respectively, and both would suffer a decrease in demand as automation substitutes these workers.
  • Non-routine manual work (e.g. janitor) is likely to see no change in demand as automation does not perform non-routine tasks.
  • Non-routine non-manual work (e.g. lawyer) is likely to experience an increase in demand with automation being a strong complementary (Frey and Osbourne, 2013).

Equating education level with skill level, as the Department of Education does (Graduate Labour Market Statistics 2017), we can take from Frey and Osbourne’s work that high-skilled workers performing non-routine, non-manual work are least susceptible to being replaced by automation (Michaels, 2010), and those jobs involving any kind of routine work are likely to be substituted with automation technologies. These low and middle skilled workers that are substituted by automation or had their wages reduced through computerisation will move to low-skilled service occupations (Autor and Dorm 2013).

Frey and Osborne’s (2013) and Autor and Dorm’s (2013) suggestion that this changing demand is ‘squeezing the middle’ of the employment market with jobs that require “cognitive and manual tasks that can be accomplished by following explicit rules” (Autor, Levy and Murnane. 2013) being substituted by automation leaves what Goos and Mannings (2003) call ‘lousy jobs’ and ‘lovely jobs’. ‘Lovely jobs’ are those that require creative thinking and the ability to confront novel situations successfully. Automation will complement these workers in “performing non-routine problem solving and complex communications tasks” but is unlikely to replace them (Goos and Mannings. 2003).

Employability of graduates

The Graduate Labour Market Statistics 2017 report by the Department of Education showed that in 2017 for the UK graduates and postgraduates shared a similar employment rate of 87% whilst non-graduates had an employment rate of 71%. The report showed that of the 87% of graduates in employment, 66% were in high skilled jobs whilst only 22% of non-graduates were in high skilled jobs. For the purposes of this report, high skilled can be defined as “a role where the tasks typically require knowledge and skills gained through higher education”, and Autor, Levy and Murnane’s (2003) definition of non-routine work.

Looking more closely at the young population (21 to 30 year olds) which are more likely to be impacted by the automation of their role in the coming decades, the report shows that 58% of graduates were in skilled roles compared to 18% of non-graduates. Comparing the overall to young we see that non-graduates have an 18% decrease in skilled roles and whilst graduates have a 12% decrease. There could be a number of reasons for the difference between the overall working age population percentage in skilled roles compared to the young population, but one possible impact for the future. is that the percentages in the young population will move towards the overall percentages over time as they learn more creative skills relevant to non-routine work whilst in their roles, which would suggest that graduates are less threatened by the automation of work than non-graduates. Another possibility is that the percentage of the young population in skilled roles (both graduates and non-graduates) will become the trend into the future. Although there is less of a difference for graduates than non-graduates, both may experience a decline in the percentage in skilled roles in the future, possible due to the effects of automation on routine work.

Automation effects on employability depends on timeline

We can accept that being a graduate makes a person more employable, and more likely to be employed in a high-skill job, and that the subject of study has less impact on these than having a degree. However, the question of whether this is sufficient to protect graduates from the threat of automation depends very much on the timeline one considers. The list of things computers ‘can’t do’ is rapidly becoming shorter and shorter (Bakhshi, Frey & Osbourne, 2015) as “developments in Machine Learning and Mobile Robotics, associated with the rise of big data, which allows computers to substitute for labour across a wide range of non–routine tasks – both manual and cognitive. As McCormack and d’Inverno (2014) put it, “We now know how to build machines that can ‘learn’ and change their behaviour through search, optimisation, analysis or interaction, allowing them to discover new knowledge or create artefacts which exceed that of their human designers in specific contexts”” (Bakhshi, Frey & Osbourne, 2015). This view of the future of the effects of automation on work does not need to distinguish between types of work (routine or non-routine) or skill level of the workers (graduates or not), it simply implies that automation will replace all jobs eventually.

As far back as 1933 Keynes predicted technological unemployment “due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour” (Keynes, 1933, p. 3, in Frey & Osbourne, 2013) so as automation technologies improve they will undoubtedly squeeze not just the middle of the labour market but the high and low too.

Bakhshi, Frey & Osbourne’s statement that “there is nothing inevitable about the impact of automation on jobs and skills” may be historically true as they state their examples of looking back over the past few centuries, and their conclusion that the creative industries are likely to be the least disrupted by automation in the future also rings true in light of all of the thinking we have looked at, but as they say, only 24% of jobs in the UK have a high probability of being creative, which suggests a large percentage of jobs can and will be replaced by automation technologies.


In answering whether the trend towards increasing automation of production processes will threaten the employability of graduates we looked at the possible historic causes of the mechanisation of the industrial revolution era, and how mechanisation and automation had profound and complex impacts on jobs, workforce distribution, and employability since the industrial revolution and continues to today, including creating the trap of introducing technology to reduce labour costs whilst increasing the need for high-skilled workers and so increasing labour costs. We saw that the automation of work will have the greatest impact on middle and low skilled workers with high-skilled knowledge workers suffer least threat to employment. This is partly due to the need for high-skilled workers to operate new technologies and partly due to high-skilled workers being more likely to work in creative industries that are difficult to automate. We found that more graduates are in high-skilled jobs either through education and/or opportunity to learn at work, and can therefore conclude that over the next few decades graduates are least likely to suffer threats to their employability from automation.

However, over a longer time span, as computers improve their learning capabilities and become more able to tackle novel situations successfully, it’s my opinion that automation will threaten the employability of graduates. How far automation goes in changing the employability and nature of work for graduates and in fact all employees is a factor of how far we choose to look into the future. Autor, Levy and Murnane’s study looked back at a less than forty year time span but the history of mechanisation and automation goes back hundreds of years, and the future of automation has an unknown time span, making it impossible to predict how automation will affect work, jobs and employability in the future.

I can imagine a shift in how organisations invest in automation in the coming decades as they realise that digital transformation cannot be achieved by capital investment in technology alone and move to investing more in knowledge creation and turning those intellectual assets into a competitive advantage in line with the New Growth theory that will allow automation to escape the current trap of increasing automation to reduce labour costs increasing the demand for skilled workers which increases the labour costs. This change of approach in investment, along the rapidly advancing progress of Artificial Intelligence will allow automation to eventually replace all workers and completely reshape society.


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Organisations implement CSR practices and make ethical decisions primarily to increase shareholder profit as opposed to wider social considerations


Business Ethics – ‘the study of business situations, activities, and decisions where issues of right and wrong are addressed’ (Crane and Matten, 2016).

Corporate Governance – ‘the direction, management and control of an organisation’ (Cadbury, 1992).

Corporate Social Responsibility (CSR) – ‘a concept whereby companies integrate social and environmental concerns in their business operations and in the interaction with their stakeholders on a voluntary basis’ (European Commission, 2011).

Legitimacy Theory – ‘a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions’ (Suchman, 1995).

Shareholder – ‘a person who owns shares in a company and therefore gets part of the company’s profits and the right to vote on how the company is controlled’ (Cambridge dictionary).

Social Contract – a ‘moral and civil’ (Boucher & Kelly, 1994), formal and informal, tacit and explicit agreement, ‘grounded to mutual consent’ (Boucher & Kelly, 1994), by individuals, organisations and the state, under which ‘people can be expected to keep their promises and cooperate with one another (Hobbes)’, and accept authority, to constrain their behaviour, with the expectation that others will be equally compliant, at risk of sanction, ostracization, and other consequences.


The position taken in this essay is that organisations do implement CSR practices and make ethical decisions primarily to increase shareholder profit.

An understanding of the social contract between society and business helps us frame the question of whether organisations implement corporate social responsibility practices and make ethical decisions due to wider social considerations or primarily to increase shareholder profit. We consider liberal market economies and the sociological perspective on how society creates the environment for business to operate, and how business contributes to society, starting with the purpose being solely to generate profit and then through how notions of corporate social responsibility have changed over time and how the distinct boundaries between business and society were challenged to form the permeable boundaries we see today. We look at some examples of business not adhering to the social contract and society’s response through regulation, and how the internal control mechanism of corporate governance changed over time and is becoming a less reliable means of driving ethical business decisions in the twenty first century. Consideration is given to the false dichotomy of CSR or profit and how both can be aspects of a strategy that leverages CSR for competitive advantage to increase profits for shareholders.

Society’s role in providing the right environment for business

Society sets the environment for businesses to operate in through establishing laws and regulations and by forming the social contract between society (which for the purposes of this essay includes the Public Sector and Welfare State) and business (a catchall term to describe all types of commercial organisations owned by individuals).

Hall and Soskice’s concept of ‘liberal market economies’ (Hall & Soskice, 2001), which are prevalent in countries such as the UK, US and Australia, describes how the coordination of the relationship between society and business (or “economic actors and institutions of a country” in their words) relies on market forces. Examples of these market forces include society assuming responsibility for creating a supply of skilled employees by providing vocational training, and business establishing a demand for commitment from workers whilst supplying low levels of trust between the employer and employee (Blyton & Turnbull, 2004).

The sociological perspective asserts that there is “a great deal of agreement that markets are social structures characterized by extensive social relationships between firms, workers, suppliers, customers, and governments” in which “repeated exchanges occur between buyers and sellers under a set of formal and informal rules governing relations” (Fligstein & Dauter, 2006).

It is in this environment of formal and informal rules, with a multitude of supply and demand relationships between a diverse and broad range of actors, that the social contract between society and business is formed. If the environment created by society is too hard on business, through harsh regulation, or too soft on citizens, through overly-lenient social security provision, then the social contract that exists between society and business fails.

Business’ role in contributing to society

The social contract framing allows us to consider how corporate social responsibility changes over time as the view of business as having defined boundaries separate from society shifts to a view of business as having permeable boundaries that interact with society in a variety of ways. This shift did not occur in a linear fashion, and as far back as the 1800’s with Cadbury (Idowu, 2011) there are examples of businesses taking on responsibilities in society, but we can consider the interplay of different ideas about the social contract that exists between society and business as the framing the change of opinion about CSR.

People-focused CSR in the sixties

In 1962 Friedman said “there is one and only one social responsibility of business–to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game.” Since then, this quote, and Friedman’s opinions, have been a guiding principle for business that focused on returning the maximum profit for its shareholders at the cost of any wider social considerations.

It underpins the view of shareholders as self-interested individuals who “adopt constraints on their behaviour in order to maximise benefits” (Boucher & Kelly, 1994). As owners of the business the shareholders, accepting of Locke’s premise to permit “individuals to appropriate, and exercise control rights over, things in the world” applying to the assets of the business, including its workforce, exercise the unequal power balance between themselves and the worker (often through managers), to command work to be performed that returns the greatest value to themselves.

The view that business focused purely on maximising their profits expressed and reinforced the clearly defined boundaries between business and society. This is a social contract where business accepts very little responsibility towards society, and regarding workers as assets, might see no value in treating workers in ways that society might regard as fair. As society considers this ill-treatment a breach of the social contract it looks to redress the balance through regulation such as the ‘Contracts of Employment Act 1963’ which required companies to give reasonable notice before dismissal, and through an increase in the expectations society has on business to take into account their human resources as something to be looked after (Frederick, 1960).

Formal CSR in the seventies

Introduced in the seventies, Sethi’s framework for analysing corporate social responsibility formalises a means of comparing CSR within a business in three specific stages: social obligation, or response to market or legal constraints; social responsibility, or congruence with current social norms and values; and social responsiveness, or anticipation of social change and problems, with development of appropriate policies to meet these needs (Sethi, 1975). Sethi splits the social norms aspects of the social contract between business and society from the market and legal constraints allowing business to understand their corporate social responsibility in the context of their industry and place in society, giving them the means to assess whether they are complying with the laws and regulations that apply to them, adhering to the expectations from the areas of society that are meaningful to them.

This formalised approach to matching CSR practices and activities to the norms, values and expectations of society helps us begin to regard CSR as a response to changes in the social environment in which businesses are operating.

Cynical CSR in the eighties

The eighties brings cynicism to CSR. The political economic thinking at the time was that “the state should create and protect private property rights (privatisation), encourage free trade and free markets (deregulation and liberalisation) and ensure that freely negotiated contracts are enforced (rule of law), but should not do much more.” (Pillay, 2015). This rational market approach focuses on generating increased profits for shareholders, and it is against this backdrop that CSR is considered as having more of a public relations purpose than contributing to wider social considerations, which is more effective when a company has a longer history of CSR activities (Vanhamme & Grobben, (2009).

A number of scandals and tragedies occurred throughout the eighties that drove a need for business to have more public CSR programmes. The Piper Alpha disaster in July 1988 in which 167 people lost their lives as a result of inadequate safety procedures (Cullen, 1990) demonstrates how business breaches the social contract to keep workers safe by pursuing profits instead.

Inclusive CSR in the nineties

The nineties sees the Pyramid of CSR (Carroll, 1991) attempting to pull together the different views of the relationship between business and society into a single picture. The pyramid says that business has a responsibility to society to be profitable (removing the duality and adversarial nature of the profit vs. wider social concerns discussion), and then builds on that with legal responsibilities, and then ethical and philanthropic responsibilities on top.

Social contract changes over time

Looking back over four decades we can see a shift in the social contract from one of clearly defined boundaries to one of society having higher expectations and business becoming more involved in social concerns, with CSR and ethical decision making in business becoming a means for business to demonstrate how it attempts to meet the expectations of the social contract.

Business and society succeed together

The change in the nature of the social contract is driven by needs on both sides. CSR, as an expression of business trying to meet the expectations of society, has gone from a focus exclusively on people to also including concerns for the environment, to having a model that includes making profit and philanthropic activities together. As that change has happened, demonstrating how business is adhering to the social contract has become more complex.

Legitimacy theory offers business a means to show society that it is upholding it’s part of the bargain. It “is a mechanism that supports organisations in implementing and developing voluntary social and environmental disclosures in order to fulfil their social contract” (Burlea & Popa, 2013). Those disclosures range from demonstrating compliance with laws, regulations and standards, implementing corporate governance that supports ethical decision making. The forms of CSR have expanded and developed as the boundaries between business and society have become more permeable.

In looking for evidence that the success of business and the success of society are closely linked, we could consider  ‘life expectancy’ as an indicator of how society has progressed. In the UK specifically, life expectancy has risen 15% from 70.9 years in 1960 to 81.8 years in 2015 (Riley, 2005, Clio Infra, 2015, & UN Population Division 2019). Over the same period of time GDP increased 187% from £10,436 to £29,985 (Broadberry, et al., 2015 via Bank of England, 2017). Although not robustly causal, the increase in both a measure of how successful society is being in increasing life expectancy and how successful business has been in increasing GDP could be taken as an indicator of the obvious; that the success of both society and business are inextricably linked.

Change in society and business affects the social contract

The twenty first century sees repeated breaches and re-establishment of the social contract and result in both society and business trying to understand the change, and respond to it. In the case of business, how to utilise the new technologies whilst contending with the struggle to respond to the newly raised expectations, and in the case of society, how and when to regulate for these new ways of doing business. Some understanding of the push and pull of social contract negotiations is necessary for framing contemporary CSR as a means for business to demonstrate its attempt to meet the expectations of society.

Dot-com business in the early 2000’s

The dot-com era, from approximately 1994 to 2000’s saw a multitude of early internet-enabled businesses spring up and drive considerable investment causing the Nasdaq index to rise above 5000. Then, the crash saw the Nasdaq fall to just over 1000 in October in 2002. The business models, or lack of, has been suggested as a contributing factor to the dramatic failure of so many internet businesses at that time. Given that “business models play a complex range of roles and that they can even become products in their own right, often creating or transforming markets” (Hawkins, 2004) it’s useful to focus on the business model as the means by which new business has the most effect on society.

In the accepted trend of capitalism, we can accept the dot-com bubble bursting as a bust with a boom to follow in the form of internet businesses with more robust business models that utilise technology platforms to connect crowdsourced supply and demand for services. Businesses such as Uber and AirBnB provide platforms to connect those who have something to offer with those who are looking for those things.

This disruption of the usual supply and demand business model in which the business would be either providing the supply or creating the demand, these new businesses simply act as enablers for others to establish supply and demand relationships. In the case of Uber in London in 2018, Transport for London did not agree that Uber could separate itself from the responsibilities of an employer, finding them “not a fit and proper operator” and refusing to renew their license to operate until TFL had “developed a future regulatory position” (Ram, 2018). So, “ changing the way that people do business, … they created unique regulatory challenges…” (Harris, 2017) that needed to be responded to by society. These new ‘platform-as-employer’ (Prassl & Risak, 2016) businesses were breaking the social contract and triggering a response from society.

Regulating new business models

Society’s response to the new platform-as-employers business that didn’t fit the norms of how business operates was to introduce regulation. In July 2019 the European Commission introduced legislation to tackle unfair and harmful practices that affect business users of online intermediaries and search engines (European Commission, 2019), and in a blog post post about the future of ways of working the Director General of DG Connect at the European Commission said, “We need to accept that platform work is here to stay and put the right legal framework in place to make sure that workers taking advantage of its flexibility are not in turn taken advantage of by operators exploiting loopholes in labour laws from another era.” (Viola, 2019).

The idea that regulation should be the dominant means of controlling these new business models is perhaps rooted in Keynesian economics that viewed markets as not inherently self-correcting and requiring government to play a role through introducing regulation (Keynes, 1973). However, given that these businesses are technology driven it seems more appropriate to consider them from the perspective of network theories of globalization and how it is the technology that drives the change in business model. Castells’ conclusion that “Networks constitute the new social morphology of our societies, and the diffusion of network logic substantially modifies the operation and outcomes in processes of production, experience, power and culture… the new technology paradigm provides the material basis for its pervasive expansion throughout the entire social structure.” (Castells, 1996) bring into question whether regulation is the right way for society to re-establish the social contract in the twenty-first century.

Corporate governance in platform business models

Corporate governance offers another means for society to exert some influence over business and for business to demonstrate its position on the social contract. Much of the discussion around business ethics is centred on corporate governance (Crane & Matten, 2016) and the ownership versus control power relationships between shareholders and managers of the firm.

Prior to the twentieth century the governance of companies most usually followed the owner-manager pattern (Crane and Matten, 2016) where the founder of the company managed it directly. In the twentieth century corporate governance changed to became one of separation between shareholder as owner and manager as controller. This separation is important in understanding how corporate governance is used as a mechanism for enabling business to uphold its part of the social contract.

Contractual Theory offers an economic theory of the firm based on Coase’s modeling of the firm as, “a nexus of contracts in which each corporate constituency… supplies some asset in return for some gain” (Boatright, 2002). Contracts, whether formal or informal, exist between shareholder and manager, manager and employer, and firm and customer. Given that “contracting is the principal means by which we conduct our economic affairs and structure economic relations” (Boatright, 2002), Coase, through Contracting Theory, tells us that whilst markets are coordinated by exchange, within a hierarchical firm coordination takes place by direct control (Boatright, 2002). It is easy to see how that direct control would operate between manager and employee but the locus of control is shifted between shareholders and managers with shareholders having “at best indirect and impersonal control” (Crane & Matten, 2016).

The shareholder responsibility for governance and control of the business, which they exercise through the managers of the company is not without its ethical and financial issues (Jensen & Meckling, 1976). Agency theory explains the nature of the issues as arising from the idea that all individuals (agents) act in their own best interest but when part of a firm agree to act on behalf of another person, referred to as the principal (Ross, 1973). This principal-agent relationship between shareholders and managers explains why in some cases managers may choose to advance their own self-interest over acting in the best interests of the company, such as making ethical business decisions that don’t increase profits or avoid social contract damaging scandals.

Corporate governance for platform businesses in the twenty-first century sees a return to founder as owner (and major shareholder) and manager. This drastically changes the traditional power balance and leads to the conflicted perception of modern day tech CEO’s such as Mark Zuckerberg of Facebook and Elon Musk of Tesla and SpaceX being both admired and despised (Bloom & Rhodes, 2018). Bloom and Rhodes describe how these CEO’s are “characterised at one and the same time as visionaries and realists; ethical innovators and moral hazards; generous benefactors and selfish hoarders”. Zuckerberg has 53% voting rights in Facebook and Musk has a 54% stake in SpaceX, which means that the usual balances of corporate power are becoming less relevant.

This weakening of corporate governance as a mechanism to drive ethical business decisions along with society’s acceptance of the modern tech CEO indicate a further shift in the social contract, one that at first glance seems to put the power in the hands of business to lead the redefinition.

Corporate social responsibility as response to changes in society

Shifting power shifts the social contract

The success of society and success of business are tightly interwoven. They succeed together. But, in the fast-paced and rapidly changing twenty-first century world, society is unable to regulate new business models quickly and effectively enough, and corporate governance cannot be relied upon to provide sufficient control mechanism for platform businesses. In this environment of a lack of clear boundaries between society and business, both look for ways for business to demonstrate legitimacy and maintain acceptance within society to continue to operate.

With the social contract re-negotiations taking place, the question of whether corporate social responsibility activities are undertaken by the twenty-first century business to turn into a competitive advantage. Rupp, Williams & Aguilera are worth quoting at length on the matter. They said, “In the organisational behaviour literature, a great deal of research has focused on the seemingly incongruent goals of wealth maximization and social responsibility. This research has suggested that these goals are not necessarily incompatible and that CSR may actually be leveraged to serve a strategic advantage. That is, empirical research has sought to determine if there is a significant link between corporate social performance and corporate financial performance. The first of these studies was conducted by Bragdon and Marlin (1972) who found a positive relationship between these two variables.” (Rupp, Williams & Aguilera, 2010). An important point to reiterate is that making profit and doing good for society don’t have to be incongruent goals. CSR can have a positive impact on society whilst also increasing profits. This leads us to questioning the motivations of business; do they undertake CSR activities primarily for the wider social considerations or to be leveraged as a competitive advantage to increase profits?

Corporate social responsibility and ethical business as a competitive advantage

CSR as a response to social pressures

The adoption of CSR practices and ethical decisions by business is a response to changes in society (Aguilera, et al 2007). Aguilera goes on to say that “corporations are being pressured by internal and external actors to engage in CSR actions to meet rapidly changing expectations about business and its social responsibilities.” Society’s expectations around social and environmental issues have changed over the decades, reforming the social contract between business and society, and Corporate Social Responsibility activities are business’ demonstration of legitimacy and adherence to the social contract.

CSR affects financial performance

In 2016 Volkswagen’s profits fell by 20% after they admitted installing software in cars to cheat emissions tests (Kollewe, 2016). This demonstration of not being a socially responsible business breached the social contract in a number of ways; environmental impact from vehicle emissions, cheating regulatory tests, and customer trust in the product, all of which contributed to the reduced profits. This, and many other examples, suggest that scandals of this nature lead to reduced profits.

After studying thirty years of data, Orlitzky, Schmidt and Rynes concluded that Corporate Social Performance is positively correlated with Corporate Financial Performance and that the reputational benefits appear to be an important part of explaining the correlation (Orlitzky, et al. 2003).

Given the statistically validity of that study and the anecdotal information from the Volkswagen scandal we can conclude that businesses that adopt Corporate Social Responsibility practices recognise commercial benefits from doing so (Bonini, 2009).

CSR can become a competitive advantage

Corporate Social Responsibility activities can be a differentiator for businesses, providing “hard-to-duplicate competitive advantage” (Melo & Garrido‐Morgado, 2011), and having an ethical stance can become a competitive advantage (Seifert, Morris, & Bartkus, 2003). Linking Corporate Social Responsibility practices to business objectives creates a competitive advantage (Porter & Kramer, 2006). Porter and Kramer call out the fragmented and disconnected approaches many businesses take towards CSR initiatives and how the notion of business and society as separate rather than interconnected entities fuels the opinion of CSR activities as a cost to the business rather than contributing to profits. They go on to make the point that if CSR activities were aligned with a business’ strategy they would be leveraged as a competitive advantage. As with any competitive advantage, the aim for business is to use it to increase profits for shareholders.


Organisations implement CSR practices and make ethical decisions primarily to increase shareholder profit as opposed to wider social considerations. Changes in society and business are causing a redefinition of the social contract, which causes business to look for other means of demonstrating its adherence to the social contract. CSR is rarely implemented by a business because of wider social considerations, but instead as a means of demonstrating its attempts at meeting the expectations within the social contract. A business that is able to make corporate social responsibility part of its strategy and so leverage the reputational and other benefits to create a competitive advantage can improve its financial performance and increase profit for shareholders.


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