Future Studies

The AI race - China’s AI landscape

Artificial intelligence: Can China dominate this landscape by 2030?

The Steinhoff Saga Management review - University of Stellenbosch Business School

January – June 2020

The AI race - China’s AI landscape

Artificial intelligence: Can China dominate this landscape by 2030?

By Chunming Shi

  • AUG 2020

17 minutes to read

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AI: An awakening giant

Once spoken about as something belonging to the future, artificial intelligence (AI) is becoming an increasingly common feature of modern life, thanks to major advances in computing speed, big data, algorithms and deep learning. While already disrupting industry sectors and traditional employment patterns, AI has the potential to completely transform economies and societies as we know them, and even shift the geopolitical power balance. Russian President Vladimir Putin recently said that whoever becomes the leader in the AI domain will be able to design the new world order.

In a nutshell, AI is software that enables machines to simulate humans’ thoughts and actions, or even exceed them. AI is generally algorithm-based, requiring a physical interface (like a robot) to carry out its functions. The link between AI and robots is analogous to that between the human brain and limbs. The former oversees the thinking, storage of information and issuing of instructions, while the latter acts on those instructions. Although AI can function independently as software only (such as the Siri function on an iPhone), it still relies on hardware like servers and supercomputers.

The main capabilities of AI are: (i) perception (e.g. facial recognition and translation of verbal speech into other languages); (ii) prediction (e.g. forecasting of changes in traffic patterns and anticipation of natural disasters); (iii) prescription (e.g. medical diagnostics and transport planning); and (iv) integrated solutions (e.g. voice recognition and self-driving vehicles). It will take time for AI-powered goods and services to see wide-scale commercialisation, but life will be very different when AI goes mainstream and its enormous potential is more fully exploited.

Once spoken about as something belonging to the future, artificial intelligence (AI) is becoming an increasingly common feature of modern life, thanks to major advances in computing speed, big data, algorithms and deep learning.

One of the major concerns about AI is that it will deprive large numbers of people of their jobs as work becomes increasingly automated. Jobs requiring few social tasks and low dexterity, such as a truck driver or radiologist, are destined to be replaced quite quickly by AI. Jobs that require creativity and flexibility, such as a mechanic or financial analyst, are less at risk. Of course, AI should also create opportunities for new forms of human-centred work, requiring technological optimisation and problem-solving. Another area of concern is how to design a legal and regulatory framework that is suited to a high-tech, often virtual (and thus jurisdiction-free) environment.

A few years ago, the AI gaming program, AlphaGo, famously defeated one of the world’s best players of the Go board game. The fact that a machine can outwit a highly intelligent human being is intriguing but also somewhat disconcerting. In ancient China, the Go game was one of the four basic art forms that Chinese scholars were expected to master. It was thought that proficiency in the game would imbue in its players Zen-like intellectual refinement and wisdom. For thousands of years, the Chinese had led the world as Go masters, only to be defeated ultimately by a machine. Somewhat ironically, AlphaGo was the creation of Alphabet (Google’s parent company). To some observers, AlphaGo’s victory represented not just the triumph of machine over man but also that of Western tech over the rest of the world.

The dethroning of the top Go player by AlphaGo was a defining moment for China, providing the impetus for the crafting of an ambitious plan to elevate AI to the centre of the country’s national strategy to drive public and private investment, manufacturing and human capital development. The ultimate goal, said Prime Minister Li Keqiang in 2017, was “to become the global innovation centre in AI by 2030”. China sees AI having the power to reduce bottlenecks in the country’s development: an ageing population (AI could render assistance to or replace humans), rising labour costs (machines could dramatically boost productivity) and an industrial sector in need of upgrading (AI could help China reposition itself as a technology-driven economy). AI is the focus of a range of policy documents and an AI-specific national development plan has been mooted.

A strong AI drive on the part of China will influence its relationships with other countries ‒ including the USA, China’s most formidable geopolitical and technological rival. Trade disputes between the two superpowers in recent years can be traced to the Trump Administration’s concerns about China’s quest for greater competitiveness, much of which centres on innovation and technology. While the USA is the global leader in cutting-edge research and development on AI, focusing on algorithms, machine learning and deep learning, China conducts more AI research than the USA (according to numbers of published articles and patent registrations).

What will China’s AI landscape look like by 2030? And who will win the AI race between China and the USA? These questions were the focus of an MPhil Futures Studies research assignment, on which this article is based.

The link between AI and robots is analogous to that between the human brain and limbs. The former oversees the thinking, storage of information and issuing of instructions, while the latter acts on those instructions.

Broad trends in artificial intelligence

Some regions will gain more from advances in AI than others. It has estimated that some 70% of the global economic impact of AI will be concentrated in China and North America. The acknowledged top tech leaders in the world today are the USA’s Google, Facebook, Amazon and Microsoft, and China’s Baidu (similar to Google), Tencent (similar to Facebook) and Alibaba (similar to Amazon). While Europe is making strides on the AI front, regions such as South America and Africa are not yet ready to leverage the benefits of AI on a large scale.

China has huge public and private sector investment capacity and a long-term strategic outlook. The top three sectors attracting AI investment in China are transportation, healthcare and finance – well ahead of sectors such as education, logistics and manufacturing. Public funding focuses on long-term, high-risk and basic AI R&D, such as supercomputers, high-end chip manufacturing and basic algorithms. Private funding focuses on major capital investment and medium-term AI commercialisation, such as facial recognition, voice recognition, smart cities and self-driving vehicles.

It is predicted that China will be the world leader in the areas of facial and voice recognition, not only because it has large numbers of smartphone users, but also because it has mature internet infrastructure and the largest cashless payment market in the world. It is also less concerned about privacy than many other countries. Furthermore, China is almost certain to lead the autonomous drone market, with Shenzhen being home to the world’s premier drone maker, DJI. DJI, which already has about 50% of the North American drone market, makes drones for personal and industrial applications, such as crop spraying, fire-fighting, parcel delivery and search-and-rescue operations. Shenzhen is also host to the factories of leading smartphone brands (including Apple, Samsung and Huawei), self-driving vehicles (such as Momenta and UISEE) and chip manufacturers (such as Foxconn and TSMC).

China’s political system is very different from that of most other countries. Its one-party, highly centralised system of governance and lack of political transparency strongly influence the country’s economic structure and performance, and will impact its AI development trajectory in the future. Because of its centralised system of governance, China lacks spontaneous, ‘bottom-up’ innovation from individuals and private-sector entities. Nevertheless, AI is one of the most in-demand and highest paid sectors in China. In this regard, Chinese universities’ strong computer science and mathematics programmes have enabled large cohorts of engineering graduates to enter the job market each year, thereby adding to the country’s AI talent pool.

Jobs requiring few social tasks and low dexterity, such as a truck driver or radiologist, are destined to be replaced quite quickly by AI. Jobs that require creativity and flexibility, such as a mechanic or financial analyst, are less at risk.

Using quantitative methods to probe the AI potential of China and the USA

Most studies focus on past and current developments; very few look critically at what the future holds. Furthermore, most of the literature on AI is the work of Western authors whose views of China invariably have an ideological bias. In addition, comparative country studies of AI development are not common and where they have been conducted, they have rarely incorporated any form of quantitative analysis.

As a supplement to the literature review, this study used four quantitative methods to predict which of the two countries (the USA or China) is most likely to win the AI race (in other words, profitably gain the most) by 2030, based on an internal and external environmental assessment. The four methods were:

  • Trend impact analysis. This involves studying past developments and historical changes to project future outcomes.
  • Black swan identification. A black swan event has a low possibility of occurrence, but if it does occur it will have a considerable impact.
  • Scenario method. Scenarios simulate what might happen as a result of specific choices and strategies. This is one of the most frequently used methods in foresight as it allows flexibility in long-term planning.
  • SWOT analysis. This helps to reveal aspects that should be capitalised on (strengths), rapidly improved (weaknesses), further exploited (opportunities) and prepared for/adapted to (threats).

It is predicted that China will be the world leader in the areas of facial and voice recognition, not only because it has large numbers of smartphone users, but also because it has mature internet infrastructure and the largest cashless payment market in the world.

Key findings from the quantitative analysis

In the AI contest between the USA and China, black swan events might include escalating trade tensions between the two countries, the possible reunification of mainland China and Taiwan, and a possible political transformation in China (spurred by internal democratic movements and pressure from the West).

Three possible future scenarios for China are: (i) co-lead, in which China and the USA collaborate to their mutual advantage and jointly dominate the global AI landscape; (ii) stagnation, in which the USA leads the way and uses containment tactics to head off a competitive threat from China, despite the latter’s efforts to improve transparency and ethics; and (iii) cold war pattern, in which both countries adopt a zero-sum attitude, keeping their resources and technologies to themselves, and aggressively blocking the other’s attempts to get ahead in the AI game.

In terms of AI strengths, China has a vast ‘data sea’, which refers to the range of information that allows AI programs to learn independently and become smarter. Smartphones (facilitating location-based services, online payments and search engine usage) feed this data sea. The country also has a strong AI commercial footprint, with particularly high potential in the self-driving vehicle market (China is the world’s largest vehicle manufacturer and consumer). Moreover, the government is highly supportive of AI developments. In terms of AI weaknesses, China trails the USA when it comes to cutting-edge AI developments (notably algorithms and data structures), and has poor intellectual property rights protection, a reputation for dubious ethics in business and comparatively limited chip manufacturing capabilities, with the USA still set to dominate the chip market by 2030.

Notwithstanding the myriad risks and uncertainties that grip the world today, China is poised to make a significant leap into the future and adopt a leadership position in the sphere of AI.

Opportunities include China’s steady transition towards a more high-tech and capital-intensive economic structure, the expanded market and commercial linkages provided by the New Silk Road Plan, and the possibility of leveraging Taiwan’s supremacy in the chip manufacturing sector. In contrast, one of the most ominous threats to China’s AI development is the prospect of an ongoing economic and political power struggle between the USA and China. In addition, rising nationalism and protectionism in various parts of the world could well intensify, making it difficult for China to expand its AI footprint.

Conclusions

In the final analysis, the USA is predicted to dominate in the area of basic AI R&D by 2030, helped by tech giants like Google, Amazon and Facebook, which will retain the best AI brains. The USA will also lead in terms of chip manufacturing as well as the so-called ‘second wave’ of AI developments (or ‘Business AI’), covering the banking, healthcare and insurance sectors, among others.

China, in turn, is predicted to lead the so-called ‘third wave’ of AI developments (or ‘Perception AI’), covering innovations like facial and voice recognition and autonomous stores. It will likely co-lead, with the USA, the so-called ‘first wave’ (or ‘Internet AI’), covering search engines, e-commerce and social media, and the ‘fourth wave’ (or ‘Autonomous AI’), covering innovations such as unmanned warehouses, civil drones and self-driving vehicles. The latter are the epitome of AI-inspired innovation, making use of cloud data and intricate algorithms to choose routes and navigate around other moving vehicles, pedestrians and buildings.

Notwithstanding the myriad risks and uncertainties that grip the world today, China is poised to make a significant leap into the future and adopt a leadership position in the sphere of AI. Much depends on the effective implementation of its AI plans and strategies, especially in the areas of capital investment and human capital development. Moreover, if the USA loses its technological edge because it lacks a long-term AI strategy and because of its waning appetite for international collaboration, China could inch closer to AI supremacy by 2030.

  • This article is based on the MPhil in Futures Studies research assignment of USB alumnus Chunming Shi. The title of his assignment is “The AI race: China’s artificial intelligence landscape by 2030”.
  • His study leader was Prof André Roux, Senior Lecturer in Futures Studies at USB and head of the business school’s portfolio of Futures Studies programmes.

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This is how South Africa can handle the growing demand for higher education

This is how South Africa can handle the growing demand for higher education

The Steinhoff Saga Management review - University of Stellenbosch Business School

July – December 2019

This is how South Africa can handle the growing demand for higher education

This is how South Africa can handle the growing demand for higher education

By Annaliese Jeanne Badenhorst

  • DEC 2019

30 minutes to read

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Why is higher education so important?

In South Africa, higher education is mostly offered through private education, and through open and distance learning (ODL). Here, ODL refers to the amalgamation of open learning (with open referring to no specific entry qualifications) and distance education (usually digitally delivered).

South Africa’s triple challenge of poverty, inequality and unemployment has severe consequences for the economic, social and political conditions in the country. Private higher education, offered as distance education, could potentially help to address all three of these challenges.

Numerous studies have investigated the link between education and economic growth, showing that it is most likely the combination of factors resulting in sustained economic growth. However, the policy dilemma that South Africa is facing is how to spend money to best address poverty, inequality and unemployment. Should tertiary students receive free education while thousands of primary school kids are still using pit toilets and secondary learners cannot read or write?

Indeed, the expansion of tertiary education in South Africa (and in Africa) has significant potential to increase the per capita income growth rates of the county. Therefore, how can South Africa make higher education more accessible over the next ten years?

About open and distance learning

Open and distance learning (ODL) has grown in popularity due to its perceived potential to increase access to education – ultimately to gain qualifications to ease unemployment. Developing countries, especially, are turning to ODL to help solve their problems of lack of resources and access to higher education. The advantages of OLD include cost-efficiency, easier access, flexibility and lifelong learning. ODL also comes with challenges, like older students, lower completion rates, and the risk of favouring accessibility over quality.

Public versus private higher education

Private higher education has seen significant growth over the past few decades. Today, about one-third of all higher education enrolments are at private institutions.

Free higher education is seen as the best and sometimes only way out of poverty and unemployment.

The public versus private question of higher education has raised debates on social and economic policy. The debate continues around the private versus social or public benefit of education, i.e. who bears the cost and who gets the benefit from higher education. Should higher education be publicly funded if the greatest portion of the benefit will be the personal gain in terms of higher lifetime salary earned?

Developing countries like China, India, Nigeria and South Africa have the potential of a demographic dividend, where a large proportion of the population is young and potentially productive workers. However, the demographic dividend can only be realised by countries that are able to educate and train these young people and provide jobs for them. The increase in demand for higher education in these countries can arguable only be met with a combination of private and public higher education.

It is interesting to note that the remarkable growth in private higher education enrolments happened despite some formidable forces. These forces include unparalleled growth in public enrolments in the same time; partial privatisation of public institutions oftentimes helping to expand public enrolment; persistent expression of the view that higher education is a public good and a basic human right; responsibility for higher education provision assigned to the state with the resultant begrudging acceptance of private higher education; expanding regulation in terms of quality assurance and accreditation systems.

Although private higher education holds many benefits, it also has shortcomings. In a study of private higher education in Africa it was concluded that even though private higher education was successful in improving access to education, the quality of education, student experience and the recognition of qualifications, it nevertheless failed to reduce costs and retain skills on the continent.

How can higher education help South Africa to grow?

Both private education and open and distance learning (ODL) has the potential to increase access quickly for masses of students, and at lower cost than traditional face-to-face instruction that needs expensive physical infrastructure.

The private sector can arguably provide this increased access more efficiently than government. Above all, South Africa, like most developing countries, simply does not have the budget to provide higher education for all that demands it, because the country also has to meet the increased demands for better housing, health and schooling – all which are essential for economic growth.

The private sector, on its own or in partnership with the public sector, is thus perfectly positioned to provide increased higher education access where governments are unable to. This is why this study explored how the combination of private and ODL in the higher education sector will evolve over the next ten years in South Africa.

How was the study conducted?

The method used to explore the future of private, and open and distance higher education in South Africa over the next ten years was strategic foresight – the capability to craft a diversity of forward views, and to apply emerging insights in practical ways. In this study, framing, scanning and forecasting (scenarios) were used to develop future scenarios for the private and ODL higher education sector. This is how the scenarios were developed:

Step 1: Identifying the scenario field

The issue to be addressed by the scenario process is: What is the future of private ODL in higher education in South Africa over the next ten years? How can private ODL assist to solve some of the current higher education problems in the country?

Step 2: Identifying the key factors

This step comprises describing the scenario field according to the key factors (trends, variables or events) that impact the field, while also serving as the means for the field to have an impact on the world. These key factors, which were identified during the causal layered analysis and environmental scanning processes, are detailed below:

  • #Feesmustfall: The most important recent event that impacted the higher education industry is the announcement in 2018 of free higher education for poor students. The NSFAS system that provided loans and bursaries to students was changed to a system of student grants to qualifying students from poor families to study at public universities and colleges. The #feesmustfall movement has significantly increased the demand for higher education, for both public and private providers. Free higher education is seen as the best and sometimes only way out of poverty and unemployment. However, the expectation has been created that higher education must be free for poor students, and hence private providers are finding it more difficult to charge and collect tuition fees. Private providers are facing the dual challenge of increased demand but more resistance to fee payment. The reality is that the ‘excess’ students will not automatically find their way to private institutions unless free education is expanded to the private sector, or if alternative methods of financing can be found. With South Africa’s large youth population it is expected that the demand for free higher education will increase over the next ten years.
  • Poor school outcomes: Poor school outcomes in terms of literacy and mathematics skills has a significant impact on the higher education sector. Students are ill equipped for the demands of higher education. Students take longer to complete their courses and the dropout rates are very high. On the other hand, poor school outcomes also create opportunities for the higher education sector to focus on courses that help students to complete or improve their matric results, or to gain basic literacy and numeracy skills. It is expected that this trend will not change significantly over the next ten years.
  • Unemployment: The high unemployment rate in South Africa both has an impact on and is impacted by the higher education sector. The high number of unemployed youth is due in part to their poor schooling and lack of higher education. Young people with some further education are more likely to find employment. Being unemployed makes it very difficult if not impossible to access educational opportunities. It is not expected that South Africa’s unemployment rate will change significantly over the next ten years.
  • Skills shortage: South Africa is experiencing both a skills shortage and a skills mismatch. The country has a surplus of low and unskilled workers, while the modern economy needs higher skilled workers. Government has also not put policies in place to assist with the transition of workers from vanishing low-skilled jobs to the new higher skilled ones. The continued decline of industries like farming and mining is exacerbating this trend. It is anticipated that the skills shortage in the country will continue to increase over the next ten years.

The reality is that the ‘excess’ students will not automatically find their way to private institutions unless free education is expanded to the private sector, or if alternative methods of financing can be found.

  • Technology: Technology has significantly changed the face of higher education, especially in the ODL sector. The mode of delivery is now through online, individualised courses, with study materials provided electronically. In South Africa, a large part of the population still does not have access to the internet. In addition, data is still expensive compared to international standards. Thus, even though it is expected that technology will play an increasingly significant role in higher education, it is expected that in South Africa the technology adoption will be slower than internationally over the next ten years.
  • Open and international education: The trend for open higher education (without academic admission requirements) is growing internationally, but is not yet as prevalent in South Africa. Taking into account the country’s past and current poor school outcomes, open education may be the only way for previously disadvantaged groups to gain access to further education. Going hand in hand with open education is the internationalisation of higher education. Technology has made it possible for students all over the world to attend virtual classrooms via the internet (MOOCs, Getsmarter). Higher education businesses thus have to compete globally for students. It is expected that the opening up and internationalisation of higher education will continue or even speed up over the next ten years.
  • Private education: Internationally, private education is growing rapidly. In South Africa, two large listed private education companies, Advtech and Stadio, have announced ambitious expansion plans for their higher education businesses. In the light of the huge demand for higher education, which the public sector will not be able to meet in the short term, huge opportunities remain for the private sector, and for public-private partnerships. Over the next ten years, the demand and opportunities for private providers are expected to grow.
  • Changing skills demand: One of the major trends that the higher education industry in developed countries has to contend with is changes to the types of skills being demanded by the Fourth Industrial Revolution. The challenge for higher education institutions is to identify which courses to discontinue and which new courses to develop. Due to the uncertainty and the fast pace of change in the skills market, the ability to learn has become a key skill. ‘Learning how to learn’ is already one of the most popular online courses. The implication for the industry is that ‘what’ people learn will be changing, and there may be a shift from focusing on ‘specific’ job-related training to ‘general’ skills training. It is anticipated that the pace of changes in skills demand will speed up over the next ten years.
  • Lifelong learning: The trend of lifelong learning has implications for both ‘who’ is being trained and ‘what’. Higher education institutions will have to adapt to cater for older students, and possibly more educated students. Over the next ten years it is expected that the demand for lifelong learning will increase.

Step 3: Analysing the key factors

After the key factors had been identified, they were assessed according to the degree of unpredictability or uncertainty, and the degree of impact or importance. This was done to test whether the ‘key’ factors were in fact the key uncertainties impacting on the future of the industry.

Step 4: Scenario generation

The study developed four scenarios for higher education in South Africa over the next ten years. The scenarios were generated using the following two pivotal uncertainties:

  1. Will the future demand for higher education be for structured (mostly classroom based) or unstructured learning (shorter, open access, skills-based courses, taken at the learner’s own time and place, usually online, with little or no prerequisites)?
  2. Will the future supply of higher education be increasingly public or private?

Four possible scenarios were developed: the status quo, distinction, pass and fail scenarios:

Status quo scenario

The current state of higher education in South Africa will continue more or less as it is for the next ten years. Using the two pivotal uncertainties, the current state can be described as mainly public and structured.

About 80% of higher education students are studying at public institutions, with only the minority of 20% at private institutions. Courses at these public institutions are mostly structured three- or four-year diplomas and degrees, with strict entrance criteria and formalised curriculums. Even though the largest public higher education institution in the country, Unisa, is a distance only provider, the majority of students at public institutions are attending classroom-based lectures. In this scenario, looking at the next ten years in South Africa, the following factors remain more or less unchanged: high unemployment levels, poor school outcomes and skills shortage. It is anticipated that it would take much longer than ten years to significantly improve school outcomes because some of the underlying causes like poor teacher training, the lack of investment in school infrastructure in poor areas and the current below average learner performance in basic skills like reading and mathematics will not be addressed quickly and sufficiently. The recent significant increase in the budget allocation for higher education has come at the cost of basic education, where the budget for infrastructure has been reduced. If some of the increased funds for higher education can be used to train school teachers then school outcomes may show some improvement, but so far there has been no indication that the higher education funding will be used to address specific skill shortages.

With the status quo, the current high unemployment levels as well as the shortage of skills continue over the next decade. The country’s economic growth rate has been very low, and government does not seem to have plans to address either the economic growth or unemployment.

In terms of addressing the skills shortage, government has started building two new universities, but demand for higher education far exceeds supply, as can been seen from the current NSFAS applications. Once again, there are no plans to address specific skills shortages, like teachers, scientists, computer programmers and software engineers.

The #feesmustfall campaign continues to put pressure on government for free education.

Government has already increased the budget allocation for the next two years to include second and third year students (this year only covered first year students). Currently, NSFAS only covers public education, thus if this policy remains, the demand for public education will continue to increase. As long as NSFAS does not finance studies at private institutions, the growth prospects of private institutions remain limited.

With the status quo, the current split between public and private higher education provision remains more or less constant, thus the majority of students will be at public institutions. Without specific intervention, the balance between public and private higher education will remain at current levels.

Developing countries, especially, are turning to open and distance learning to help solve their problems of lack of resources and access to higher education.

Private education relies on paying customers, but the current low economic growth rate and the high number of indebted credit consumers makes it difficult to envision significant growth in the industry. The private providers can assist with the increased demand that has been created for higher education, but some form of government intervention will probably be needed, either through funding or policies to specifically promote private education.

The status quo also assumes that most higher education students attend classroom-based lectures

and that ODL remains a minor player in the higher education industry. Most courses will be structured diplomas or degrees with strict entrance requirements. The structured nature of these courses will continue to put them beyond the reach of many South Africans who did not have adequate schooling results and who may have limited time and resources for further studies.

Stringent entrance requirements will continue to put barriers in the way of ‘non-traditional’ students who are seeking to overcome past poor schooling outcomes. Higher education will remain accessible only to those who have already achieved some measure of success in good school results.

The status quo scenario shows that South Africa will make slow progress towards addressing the skills shortage, unemployment and economic growth. Higher education is delivered mainly through public, classroom-based institutions. No significant increase in access takes place and public institutions continue to expand at a slow pace. Growth in private institutions is modest. Structured courses will take preference over less structured, ODL courses.

Distinction scenario

In this scenario, South African higher education gets a distinction. This scenario projects a future for higher education over the next ten years where both public and private institutions significantly increase the number of students gaining access. At the same time, higher education courses become shorter, skills based and more unstructured. More students get access and complete their courses successfully, because courses are tailored to student and industry needs, hence reducing the skills shortage and unemployment, and potentially aiding economic growth.

An important factor in realising this scenario is that public and private institutions will work together, either informally or through public-private partnerships. The best outcome for higher education will be achieved if the best of public and the best of private higher education are combined, and the best assets and skills of each leveraged to the gain of all students. There are already a few examples of where public and private institutions are working together. For example, Unisa students attending classroom lectures at private colleges, hence private tuition used to obtain a public qualification. Other examples include government departments paying for their staff to study at both public and private institutions.

Public universities have limited infrastructure and state subsidies to deal with the growing demand, whereas private entities such as Stadio have raised millions to expand private higher education access. Stadio plans to expand into fields like medicine and engineering, which are qualifications that are expensive to develop and accredit, thus it would make more economic sense to partner with existing public institutions to share resources. Public institutions have a lot of knowledge capital that can be leveraged by private institutions through either classrooms or distance learning. By working together, access is increased significantly and cost-effectively, and some progress made to alleviate the skills shortage in the country. In this scenario, government sees the private sector as a partner in providing access to higher education to an increasing number of students.

By working together in partnership, the financing of higher education is applied where it is utilised most efficiently, for both public and private studies. Rather than ‘forcing’ students to public institutions (some that have less than stellar reputations), because only public studies are funded, students can go where it suits them best. ODL studies are traditionally less costly than classroom studies, hence by paying for private ODL studies government is assisting many more students from the same budget. Government continues to look for the most cost-effective ways of studying in order to make a significant improvement to the country’s skills shortage.

Increased access to higher education not only relies on more ‘seats’ being made available at both public and private institutions but also for access to be more ‘open’. This means that access is available to everyone, regardless of past academic performance or other selection criteria.

To cater for both the unskilled and those requiring mid-career retraining, courses that are shorter less structured and more skill focused are developed. Someone making a midlife career change does not necessarily have the time and money to complete a full three-year degree, but they can complete a one-year focused skills programme. All higher education institutions will be considering the job requirements in designing future courses. Future job seekers will acquire a portfolio of skills and courses, rather than follow a fixed degree programme. A balance is achieved between public and private institutions and between structured and unstructured higher education.

The message of the distinction scenario is that South African higher education, and hence the country, will only be successful if both private and public institutions work together in all areas of higher education.

Pass scenario

In the third scenario, South African higher education achieves a pass grade. In the pass scenario, higher education develops to be more private and less public.

Two potential wild cards or black swans for the higher education industry in South Africa have been identified; the one relating to privatisation or nationalisation, and the other relating to technology development.

Over the next ten years, private higher education increases its share from the current 20% to over 50% of students, which brings the country more in line with other developing countries. It is forecasted that enrolments at public institutions stagnate, while the growth occurs mainly at private institutions. The stagnation at public institutions is the result of increasing demands for free education, the failure of the NSFAS system or shortage of government funds. Simultaneously with increased privatisation, higher education also becomes more unstructured, open and distance based, and international. Increased privatisation of higher education happens because of either specific government intervention to promote private higher education, or through a deterioration of public education, or a combination of both. Government encourages more private institutions through deliberate policies and by making it easier to register private institutions by local and international institutions. By facilitating the funding of private studies, either directly (grants) or indirectly (bank loans), government significantly increases the supply and demand of private studies. By promoting private studies, government is using the private sector to accommodate the excess students that the public institutions do not have space for. ODL institutions are uniquely situated to cost-effectively handle the overflow of students. It is cheaper and quicker to expand capacity at ODL institutions, because there is no need for additional costly physical infrastructure.

In this scenario, it is anticipated that the enrolments at public institutions stagnate at current levels

for two reasons. Firstly, due to the high cost of free public higher education, it is unlikely that government can afford to expand access significantly. Secondly, since government has only built two new universities in the last 20 years, it is unlikely that it will have the capacity or the budget to build many new public institutions over the next ten years.

Government has not indicated that it plans to either build more facilities or significantly increase the number of places at current institutions. Therefore, if the country wants to significantly improve the skills shortage and unemployment, it would be up to the private sector to supply such skills.

If the #feesmustfall campaign continues to cause disturbances at public institutions, students may be disinclined to go there and many may prefer private institutions. Similarly, the problems at NSFAS, which has been put under administrative management recently, may cripple public institutions, if funds are not dispersed to them timeously. Students may prefer to attend private institutions that are not affected by political problems.

Private institutions are better at adjusting to the trends of changing skills demand and lifelong

learning. Some of the international ODL institutions, especially the online educators, have been much quicker in adjusting their course offerings in response to the changes in skills demand than

the traditional public providers. The demand for shorter skills-based programmes is increasing, especially for workers that do not have the time or money for a full course or degree.

This scenario is better than the status quo, because supply (access) in the private higher education

sector is expanded and it plays a bigger role in alleviating the skills shortage in the country, and possibly also unemployment. At the same time, less structured, shorter, open-access programmes make higher education more accessible and affordable to more students.

Fail scenario

In the last scenario, South African higher education fails. This scenario envisages a decline in both

public and private higher education, thus fewer students at both public and private institutions.

The failure of the NSFAS student financing scheme leads to fewer students being assisted financially and hence fewer students studying at public institutions. Continuing administration and financing problems at NSFAS result in delays of payments to public institutions, which puts them under severe financial strain. Questions are also arising around government’s ability to fund the increased budget allocations for higher education as the scheme is extended to second and third year students over the next few years.

As the scheme continues into future years, the institutions’ funding is more and more based on government funding. Should government not be able to meet all the funding requirements, or if government decides to reprioritise spending (like to primary education or national health care), public institutions are forced to reduce their student intake.

This scenario anticipates that private higher education will simultaneously decline, or at best

remain static. If there is no significant change to the way private studies are financed, private institutions will not expand significantly to take up the slack from public institutions. Similarly, without government policies specifically promoting private higher education, growth in the sector will remain subdued.

It is expected that the opening up and internationalisation of higher education will continue or even speed up over the next ten years.

This scenario expects that the problems of high unemployment, skills shortage and poor school outcomes will continue, or possibly even worsen over the next decade. In this scenario both education and the country fail.

Step 5: Scenario transfer

What can one do with these scenarios? Some of the recommended scenario transfer options for this study are impact analysis, actor analysis, sectoral analysis, strategy development and policy evaluation, and backcasting.

Wild cards and black swans

Wild cards are events that have a low probability but that can have a high impact. Similarly, black swans are random, unsystematic and unforeseen events that could have a high impact on a business or sector. They are often described as the ‘unimaginable’. Two potential wild cards or black swans for the higher education industry in South Africa have been identified; the one relating to privatisation or nationalisation, and the other relating to technology development.

What did the study find?

To recap, this study wanted to find out if and how the combination of private and ODL higher education can help to address South Africa’s education challenges over the next ten years.

The study has shown that both private and ODL higher education providers are uniquely situated to address some of the main challenges that higher education will face. Indeed, the increasing demand for access to higher education can be met with an increasing supply of both private and ODL institutions.

ODL institutions already have the capacity to enrol many more students, and private groups have raised funding to expand their capacities. ODL specifically addresses some of the trends in higher education like changing skills demands and the need for lifelong learning. ODL also opens access for those who did not have the opportunity in the past or with inadequate schooling, a significant constituency in South Africa.

The scenario exercise showed that the best outcome for higher education in South Africa will be if public and private institutions both form part of the future. This creates the opportunity for private-public partnerships to deliver the best education outcomes.

The scenarios show where higher education policy in the country needs to be focused. Policy makers need to decide on the best balance between public and private higher education, and also on the balance between structured programmes and open, unstructured programmes. Appropriate policy will encourage private investment and partnerships in the industry. Government should also expand financing to students at private institutions.

It is recommended that the expansion of private higher education is encouraged through relevant government policies. Private capital should be encouraged to invest in higher education, both as new ventures and the expansion of current institutions.

In terms of future strategies, it is recommended that both private and public institutions become more ‘open’ in terms of entrance requirements, are less structured, and develop shorter, skills-based modular courses. The transferability of credits and qualifications between institutions also needs to be made easier. In addition, private institutions should focus their efforts on areas where skills shortages have been identified.

A further recommendation is that both government and the private sector explore ways to work together to create the best outcome for higher education in South Africa through public-private partnerships, joint ventures and other collaborative efforts.

  • This article is based on the research assignment of Annaliese Jeanne Badenhorst – a PGDip in Futures Studies alumnus of USB. The title of her research assignment is: The future of private, open and distance higher education in South Africa over the next ten years.
  • Her study leader was Prof André Roux, programme head of USB’s portfolio of Futures Studies programme. Prof Roux lectures in Management Economics and Africa Country Risk Analysis at USB

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Developing a nowcasting algorithm that can work without big data

The Steinhoff Saga Management review - University of Stellenbosch Business School

July – December 2018

Developing a nowcasting algorithm that can work without big data

Prof George Djolov

  • OCT 2018
  • Tags Insights, Futures Studies, nowcasting

18 minutes to read

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Prof George Djolov

The need for short-range forecasting

The constant demand for a faster release of official statistics has fuelled a growing need by statistical offices for accurate nowcasts based on reliable nowcasting techniques aimed at advance estimation of key components of their headline statistics. To respond to this challenge, in 2016 Eurostat – the EU’s Central Statistics Office – initiated the Eurostat Nowcasting Competition for the nowcasting of official statistics of EU member states. In the sphere of official statistics, nowcasting refers to the short-range forecasting of an officially published statistic with short time lines of release.

The Eurostat Nowcasting Competition is also known as the Big Data for Official Statistics Competition or BDCOMP for short, due to encouraging the use of big data in nowcasting. Big data are data sources composed of high volumes in the sense of having large scale; high variety in that they exist in many different forms; and high velocity in the sense of streaming swiftly. All of these components demand cost-effective and innovative forms of information processing to enhance insight as well as decision-making. However, the use of big data was not an explicit requirement of the competition. Incorporating big data was part of a formal attempt by the competition’s Scientific Committee to establish what the accuracy of nowcasts will be with and without the use of such data.

… nowcasting refers to the short-range forecasting of an officially published statistic with short time lines of release.

About the Eurostat Nowcasting Competition

A seven-member Scientific Committee drawn from Eurostat itself, the Organisation for Economic Co-operation and Development (OECD), the German Central Bank (Deutsche Bundesbank), and the Statistical Offices of Italy (ISTAT), Slovenia (SURS) and Romania (INS) adjudicated the 2016 instalment of the competition.

Calls for participation in the competition were publicised on the Eurostat website as well as the newsletter of the Institute of Mathematical Statistics, i.e. the IMS Bulletin. Participation in the competition entailed 12 submission rounds – for every month in 2016 – according to the participant’s choice of track as well as the country for which the nowcasts are to be made. Anyone of the 28 member countries could be paired with a choice of seven tracks referring to the monthly indicators for nowcasting, these being:

  • Unemployment levels
  • Harmonised Index of Consumer Prices (HICP) – all items
  • HICP excluding energy
  • Tourism – nights spent at tourist accommodation establishments
  • Tourism – nights spent at hotels
  • Volume of retail trade
  • Volume of retail trade excluding automotive fuel.

… the accuracy of nowcasts does not necessarily improve with the increased use of big data.

Participation in the competition is anonymised, and administered by a two-stage elimination process. The main requirements of participation were that no participants could change their methodology after its disclosure at the time of entry, and all nowcasts were to be submitted ahead of the release of the official statistics for which they are being made. Failing to meet these requirements led to elimination at the first stage. In the second stage, the surviving participants were further trimmed to determine the top five entries. From among these, the best-performing entry in the respective track of participation is then chosen. The second stage selections were done after the last submission round, according to advance criteria established by the competition’s Scientific Committee. The three criteria used in the 2016 competition were:

  • Average error of the nowcasts, in terms of how far they are overall from their actual, i.e. official, counterparts;
  • Directional accuracy, in terms of whether the nowcasts correctly predict the direction of change of their official counterparts; and
  • Their likelihood of being generally representative, in terms of the extent to which the nowcasted estimates resemble their official counterparts.

The top five entries as well as the best performers of the competition, announced by Eurostat in March 2017 at its biennial statistics conference, New Techniques and Technologies for Statistics, held in Brussels, Belgium, were:

  • Team ETLA of the Research Institute of the Finnish Economy in collaboration with the Massachusetts Institute of Technology
  • Team JRC of the European Commission’s Joint Research Centre
  • University of Warwick Forecast Team
  • Dr Roland Weigand of the Research Institute of the German Federal Employment Agency
  • Prof George Djolov of the University of Stellenbosch Business School and Stats SA.

Prof Djolov’s entry – which was based on a newly developed Robust Nowcasting Algorithm or RNA for short – finished first in Directional accuracy in the track of nowcasting Ireland’s monthly unemployment levels.

… in relative terms, techniques short of big data can perform similarly or just as well, especially if they use data sources that fit directly into their nowcasting context while also being made adaptive.

Reflecting on the competition’s results

In commenting on the competition’s results, its Scientific Committee reflected that the accuracy of nowcasts does not necessarily improve with the increased use of big data. The results suggested that, in relative terms, techniques short of big data can perform similarly or just as well, especially if they use data sources that fit directly into their nowcasting context while also being made adaptive.

The RNA resonates with the Scientific Committee’s findings. It is an algorithm that functions without a precondition for having big data or knowing anything about the data’s distribution properties. However, when available, such data are bonus to the algorithm.

The RNA is developed from established methods whose seeming remoteness is brought together by the “plug-and-play” principle. Formally, Harrison’s smoothing procedure is “plugged” into the Kolmogorov-Zurbenko filter, and in turn their combination is implemented by a mix between Tukey’s and Hann’s filters. The RNA emerging from this blending has the advantages of technical simplicity, reliability, and ease of use in practice. Furthermore, to isolate seasonality from obscuring the principal behaviour of an examined series, the effects of seasonality are first chained to determine how they change this behaviour from one to another period before factoring them out according to the periods they come from. In the RNA, this is achieved by plugging-in the Persons method of seasonal adjustment to the algorithm’s blended filter.

The RNA … an algorithm that functions without a precondition for having big data or knowing anything about the data’s distribution properties, focuses on the filtration of a series by signal extraction and noise reduction.

It’s like making wine, in a way

The RNA’s premise is that the best nowcast of a series is most likely to come from the series itself, thus ensuring that any noise in estimation can be traced only to a single source, i.e. the series itself. The RNA’s goal then becomes extracting the series signal, or what we commonly refer to as its trend, by recursively drawing it out from the coarseness of the data. This is repetitively done much like filtering out impurities from wine with a funnel until a satisfactory finish is reached. By this analogy, the wine is the data; the funnel is the RNA algorithm; and its filters for removing impurities, i.e. the noise, are the smoothing techniques that make up its inner mechanism.

With each filtration, noise is cleared out of the data until clarity of taste is reached, i.e. the trend is revealed and improved. Key to this improvement is controlling for the trend’s extraction from the start, by beginning with establishing the encountered boundaries of variation in the data, formally called the control limits. They are derived from Tukey’s control chart using the interquartile range as a measure of spread. The boundaries or limits so derived serve as guideposts to continuously refine the extractions of prior rounds with the objective either to minimise or at least not to grow the encountered variability. As part of this, the limits dictate either the recasting of abnormal points in the processed data to its more usual ones or their replacement with the next-in-line less abnormal ones, which is a procedure known as Winsorisation. This control monitoring and enforcement in the RNA kicks-off with a sample size, which is mechanically determined to contain the least amount of wine, i.e. data, from what is available in stock. Technically speaking, Dodge’s sampling rule is applied. In this way, trial and error about the needed amount of data to initialise the algorithm by manual guesses is eliminated. Once the sample size has been established, the corresponding data are collected “prospectively” up to the most recent or newest available point, given the aim of collection is to operationalise the RNA as a forward-looking (nowcasting) instrument capable of determining the immediate future of a series.

In terms of the wine analogy used earlier, the main output from the RNA’s clean-up is a triple-distilled wine, which is further distilled for a seasonally neutral taste, so that the end product is not shaped by a specific harvesting period. In technical terms, the output is a stably-smoothed nominal series that is projected, with or without the retention of seasonality, by the Hann filter as the projection rule. In the case without seasonality, the smoothed nominal series is deseasonalised by having its inter-seasonal movements removed, based on their periods of origin. For this, the inter-seasonal movements are localised at their mid-points in order to determine their representative periodic (or originating) values, which are then filtered thrice for noise reduction before being separated from the smoothed nominal series in turns. Afterwards, the left-over deseasonalised series is redistilled twice more to cut down any left-over impurities, i.e. noise, before being extrapolated in the same manner as its nominal counterpart. As mentioned, this is done by a mechanical rule drawing on the weights of the Hann filter.

The RNA’s extra strength comes from it being a sequential method in the sense that it operates by accumulating information. It reboots, i.e. reruns its computational sequence every time a new observation is added, resulting in its estimates being progressively refined as new data are drafted in. This systematic inclusion of the latest information for purposes of recalculation gives the RNA its adaptive ability to update as well as to cast forward a series during its processing. Based on our wine analogy, wine fortification is performed where the estimation of the series is strengthened by taking on extra spirits, i.e. additional observations. The exception is the rebooting of the calculations for the seasonal effects, which are updated once off, at the beginning of every nowcasting period. This is done in order to build up the evidence of the seasons’ effects whose fluctuating and extended nature exposes their influence only post their occurrence. When they come to an end a picture emerges of how the seasonalities prevail, shedding light on how the stockpile of such prevalence would shape up a series development until the next occurrence. That is why in the RNA the stockpiled effects from the seasons are prospectively removed based on what is observed about them from their prior cycles.

It’s like making wine in a way – with each filtration, noise is cleared out of the data until clarity of taste is reached, i.e. the trend is revealed and improved.

Visual diagnostics make things better

A picture is worth a thousand words, and certainly, this is true of the RNA’s diagnostic toolkit where the overlay plot and the diagonal plot are the two visual diagnostics by which the algorithm’s generated or nowcasted series are juxtaposed along their official counterparts. The first of them, i.e. the overlay plot, gives a visual confirmation as to whether the trend is extracted successfully in the sense of being clear and traceable in path following the RNA’s noise compression. The second of them, i.e. the diagonal plot, visually confirms whether there is similarity between the RNA’s generated series and their official counterparts in the sense of how well the former imitate the latter, namely as to whether they are limited or free from inventing a direction that does not exist in the original (in this case official) series. If there are any shortcomings, these plots will expose them graphically. If such were the case the RNA would then be the inappropriate nowcasting algorithm to use, implying that the use of an alternative filter or approach would be better suited for the nowcast.

As with all visual checks, optical illusion is possible. To minimise the chances of this, the RNA’s diagnostic toolkit includes three numerical measures, which summarise into single numbers what is observed in the overlay and diagonal plots. They include the extent of the dissimilarity between the generated and the official series; the degree of association between them; and the extent to which the generated series systematically deviates from its official counterpart. All are expressed as percent for purposes of standardising their evaluation. Technically, the relative mean absolute error, the Pearson correlation, and Lin’s bias coefficient are computed respectively. The benefit of these numerical measures is to reinforce verification of the visuals by intuitively encouraging secondary check-ups before deciding on the suitability of the nowcasted RNA series. In the end, this consolidates interaction with the RNA and serves to improve confidence about its results in terms of the immediate future of the series it is applied to.

The RNA’s extra strength comes from … being a sequential method in the sense that it operates by accumulating information … resulting in its estimates being progressively refined as new data are drafted in. This systematic inclusion of the latest information … gives the RNA its adaptive ability to update as well as to cast forward a series during … processing.

RNA – an algorithm that can handle the nowcasting of big data with speed

The RNA’s computational simplicity makes it attractive for handling the high volumes that characterise big data. By default, it also gives it the speed to work quickly with the high variety of such data, and the fastness to absorb it as it streams. Its use to nowcast Ireland’s monthly unemployment levels demonstrates this. Its blending of established techniques gives it familiarity and also promotes their integrated as opposed to isolated use.

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Fin Tech

Is Fin Tech the fix for financial inclusion in Africa?

The Steinhoff Saga Management review - University of Stellenbosch Business School

January – June 2018

Is FinTech the fix for financial inclusion in Africa?

Fin Tech

  • Stephanus J de Bruin
  • MAY 2018
  • Tags Insights, Futures Studies

15 minutes to read

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Article written by USB MPhil in Futures Studies alumnus Stephanus J de Bruin

About financial services, growth, and technology for all

Around the world, financial inclusion strategies and principles are directly linked to economic growth and employment statistics.

Not all financial inclusion initiatives have worked so far. There are cases where financial inclusion initiatives have failed even though similar measures were introduced in the different territories. A generalised model can therefore not be used to meet financial inclusion targets because success depends on the idiosyncrasies of each country.

Around the world, financial inclusion strategies and principles are directly linked to economic growth and employment statistics.

In this context, it is also essential to understand the financial inclusion landscape, which is characterised by:

  • The ongoing evolution in the financial industry
  • Exponential advances in internet-based technologies
  • Lower entry barriers to the financial industry
  • Diminished or poorly defined boundaries in some financial services’ eco-systems.

In the past, the financial services industry was dominated by banks, insurers and investment houses. Now technology companies are also making their mark in this landscape. What’s more, the splicing of finance and technology, known as FinTech, is broadening the financial industry. While new competitors in the market might impact adversely on traditional banks, FinTech could be a powerful tool to create financially inclusive societies.

The growing importance of financial inclusion

Complete financial inclusion is a state in which all people have convenient access to a full suite of quality financial services at affordable prices. Financial inclusion allows access to a wide variety of products and services to ensure positive outcomes for individuals, households, micro and macro enterprises, and regional economies.

Although the concept of financial inclusion has been topical for several decades, global financial inclusion only recently became essential political and strategic building blocks in most countries.

A surge of findings over the past decade made it clear that financial inclusion is not just an emerging markets issue. It also affects advanced economies. Even in developed countries, large segments of global populations still do not have bank accounts. In fact, in 2015, around 2 billion individuals in developed countries still did not have their own bank accounts. These people are financially excluded from economic resources, access to basic services, property ownership, inheritance, natural resources, appropriate new technology, financial services and microfinance.

Financial inclusion can help to alleviate poverty and stimulate economic growth. It can help to eradicate famine, support health and well-being, ensure quality education, resolve gender inequality, safeguard pure water supplies, provide hygienic sanitation, supply affordable and clean energy, create employment opportunities, inspire innovation, secure infrastructure, and generate justice and peace for all.

Technologies at work or not at work

History is littered with cautionary tales involving adoption rates and the applicability of new technologies. New technology does not necessarily lead to acceptance and mass implementation. In fact, mass acceptance depends on the technology itself and the way in which stakeholders perceive its added value.

For example, in 2001, Segways scooters were heralded as the future of individual transportation. It was thought that these new self-balancing, two-wheeled personal transport devices would change the way humanity thinks about personal transportation. It is now almost two decades later, with the original scooters still going strong while the uptake of Segways remains limited.

However, other technologies have massively altered human behaviour. In just over a decade, in June 2016, Facebook exceeded all expectations and acquired 1.71 billion globally active monthly users.

The financial services industry also has evidence of mass adoption rates. M-Pesa, the mobile money provider in Kenya, is part and parcel of the Kenyan economy. In 2013, the M-Pesa user base of more than 18 million illustrated the significant social and economic impact of technological innovation. Kenya is the most financially inclusive emerging market economy, with South Africa in the overall global fourth position, and in second place on the African continent (see Figure 1 below). Yet, the uptake of the M-Pesa failed in South Africa. It was launched in the country in 2010 and shut down in July 2016.

These contrasting outcomes of the same technology-based solutions is a stark reminder that technology itself is not a panacea. Various underlying variables lead to success or failure.

FinTech provides a powerful, readily available and effective mechanism to help eradicate poverty and achieve global financial inclusion.

Enter FinTech

FinTech is a combination of the words financial and technology, and the latest portmanteau to grace the covers of leading business and technology publications. The concept of technology in the finance world has been around for decades, but exponential advances and lower entry barriers are increasing the rate at which technology is being used to provide global financial services and products. So, FinTech is a moniker for the combination of technology and any area remotely related to finance. Its scope has gone beyond its origins of bank transactions and into adjacent areas such as insurance, lending, investments, digital crypto-currencies and personal digital identity.

Can FinTech help to facilitate financial inclusion?

This study investigated the barriers to a financially inclusive society and wanted to find out if financial technology could be used as a mechanism to address financial exclusion on the African continent. The objective of the research was to postulate on various outcomes of a technological approach to solve the lack of financial inclusion in Africa and to understand which characteristics will ensure the use of sustainable financial technology over the long term.

Four distinct yet interrelated variables were identified:

  • Providers: These are the institutions providing financial services and products.
  • Products and services: These are the financial products and services offered by institutions, and products and services needed by consumers.
  • Channel: This refers to the mechanism or conduit distributing products and services to consumers, or the method through which consumers prefer access to financial products and services.
  • Consumers: These are the end users who benefit from access to and the usage of products and services.

The research design used a scenario approach. Scenarios are used to influence decisions by illustrating the consequences of those decisions over time. The scenarios weave together different concepts enabling participants to gain a better understanding of the building blocks, their interaction and the eventual outcomes. Scenarios are therefore ideal to illustrate the impact of choices, decisions, events and consequences.

The scenario field consisted of two areas, namely financial inclusion and FinTech. The variables associated with these two areas were identified. The purpose was to measure the impact of the variables on the rate of financial inclusion across the African continent.

Affordability for consumers … includes access to more funds, personalised interest rates and lower administration costs.

The four research scenarios and their nutshell explanations

  • The Usual Exclusion Scenario: This is the reference scenario. The status quo remains intact and no change is implemented nor expected. Traditional providers are burdened by systems and processes which affect their ability to provide consumers with an appropriate range of products and services through suitable channels. FinTech providers are acknowledged but remain a systemic externality.
  • Potentially Eventually Scenario: FinTech is hailed as a mechanism to facilitate financial inclusion. Traditional providers pivot parts of their business to create financial inclusion. Multi-national providers cross-subsidise consumer segments and geographical territories. These principles are combined with incentives to increase the reach of financial products and services. Non-traditional enterprises partner with traditional financial service providers to amplify market presence and log consumer data.
  • Fast For a Few Scenario: FinTech technology is applied and rapidly accepted in geographical areas. Providers who deploy products and services to consumers in order to create a more inclusive society become a dominant consumer force.
  • Africa Incorporated Scenario: Collaboration between various stakeholders ensures increasing financial inclusion for the excluded population. FinTech providers can innovate as long as they adhere to LASIC principles (Low margin, Asset light, Scalable, Innovative and Compliance easy).

Heed was given to these thematic barriers to entry:

  • Affordability for consumers: This includes access to more funds, personalised interest rates and lower administration costs.
  • Affordability for providers: This includes access to consumer data and profiles to offer appropriate products and services, the availability of products and services without expenditure on business premises, and products and services pinned at attractive price points.
  • Access for consumers: This includes the availability of financial services in consumers’ immediate location, increased mobile penetration rates, access to bank transactions and a broad range of products and services, pay-point technology and connectivity reducing the need for cash in hand, and government authorities paying citizens electronically.
  • Access for providers: FinTech can eliminate the need to build or run a business in areas with questionable economic viability. In addition, FinTech can leverage access points and increase product distribution without additional capital investment.
  • Regulatory requirements for consumers: Documentation is reduced because government and financial service providers share data. Also, transactional records of financial services and products allow consumers to capitalise on credit with personalised interest rates.
  • Regulatory requirements for providers: Consumer data allows providers to offer accurate price points and share risk. Also, government incentives and reduced compliance burdens encourage providers to offer financial services and products. The sharing of client data between providers lead to lower initiation costs and higher consumer acquisition rates.
  • Financial education and literacy for consumers: National government, traditional and FinTech providers as well as third-party agents network and use campaigns to educate consumers about the value of formal financial services and products. Consumers learn about the correct usage of credit and how different ancillary financial services or products work so that they can take control of their financial journey.

Financial education and literacy for providers: Providers are usually faced with time-consuming education processes and little revenue during the process. Incentives are provided to third-party banking agents to educate consumers in order to compensate for the lack of revenue. Government supports initiatives and manages stakeholder expectations and responsibilities.

If FinTech is applied correctly, it could address provider and consumer concerns about affordability, access, regulation and financial education.

What does the future hold for FinTech?

Financial exclusion is a result of barriers limiting consumers’ access to financial products and services, and barriers limiting the ability of providers to supply products and services. Financial inclusion is a systemic problem requiring collaboration from multiple stakeholders to gain the expansion of financial inclusion and nurture continental growth.

FinTech provides a powerful, readily available and effective mechanism to help eradicate poverty and achieve global financial inclusion. It provides an opportunity which could contribute significantly to create a continent where most individuals are financially included. If FinTech is applied correctly, it could address provider and consumer concerns about affordability, access, regulation and financial education.

  • Original research: De Bruin, S.J. 2017. Scenarios for the excluded: A technological approach to financial inclusion in Africa. Unpublished MPhil in Futures Studies research report. Bellville: University of Stellenbosch Business School.

Stephanus J de Bruin completed his research report under the supervision of Prof André Roux as part of his MPhil in Futures Studies at the University of Stellenbosch. Prof Roux is the head of USB’s Futures Studies programmes.

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Airbnb – corporate entrepreneurship served up on digital platter

The Steinhoff Saga Management review - University of Stellenbosch Business School

January – June 2018

Airbnb – corporate entrepreneurship served up on a digital platter

  • Labeeqah Schuurman
  • MAY 2018
  • Tags Insights, Futures Studies

13 minutes to read

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Article written by USB MPhil in Futures Studies alumnus Labeeqah Schuurman

The Fourth Industrial Revolution – a game changer

Economic growth and industrial development have been the building blocks of all four the industrial revolutions up till now. The First Industrial Revolution (1760 to 1840) was characterised by machine manufacturing while the Second Industrial Revolution (1870 to 1914) boosted rapid industrial developments and mass production and the Third Industrial Revolution (also called the digital revolution) introduced computers and the internet in the 1960s.

The Fourth Industrial Revolution started at the beginning of the 21st century by building on the technological achievements of the previous industrial revolution. This new revolution differs fundamentally from the previous three because of the combination of, and interface between, the emerging technologies predetermining exponential growth and continuous change to create a new world and a new future.

… the online exchange of goods and services opened up a completely new trade platform to re-use existing and under-utilised goods and services at affordable prices.

The Fourth Industrial Revolution will not only change the patterns of consumption, production and employment, it will also challenge businesses, governments and individuals to adapt proactively in order to remain at the cutting edge in this perpetually changing world.

Advanced technologies have a major impact on businesses across all industries. This means that the established industry value chains are disrupted because technology-enabled platforms drive, among others, demand and supply. This is evident in the sharing economy which creates new ways of buying and consumption, and lowered barriers for entrepreneurs and individuals to enter the economic landscape in order to claim their proverbial slice of the economic pie.

Here, Airbnb serves as an excellent example of the Fourth Industrial Revolution type of company. Airbnb, established in San Francisco in 2008, is a digital hosting platform on which available accommodation in homes, flats and privately owned suites in hotels are advertised and rented out.

The unique quality of these online platforms allows for worldwide trading without any assets being owned by the operating platforms.

What is the sharing economy?

The conclusion of local and international transactions has changed drastically over the past few years. New terms and phrases – such as collaborative consumption, access economy and sharing economy – have been coined to describe new trading trends. Collaborative consumption is defined as peer-to-peer-based exchanging of goods and services online, while the access economy focuses on technology-based platforms that cater commercially for individuals as well as businesses. This study uses the term sharing economy, as it is more widely used across the board, and regularly features in academic literature and the broader media environment.

In general, sharing economy refers to a new way of going about transactions using technological advancements that develop at a disruptive and exponential pace. It allows everyone with access to the internet to participate in the exchange of goods and services. What’s more, the online exchange of goods and services opened up a completely new trade platform to re-use existing and under-utilised goods and services at affordable prices.

Using Airbnb to explore the sharing economy

This study examined the role and impact of the sharing economy on the tourism industry. For the purposes of this study, it was decided to select a specific company in the sharing economy space, namely Airbnb. The study therefore covered Airbnb’s contribution to the tourism industry, assessed its business model and sustainability, and provided a futures perspective on Airbnb’s likely role and contribution to the tourism industry by 2030. This research also took into account the tourism ecosystem, which includes transport, hospitality, accommodation, dining and personal experiences.

Looking at Airbnb from various angles

The following combination of methodologies was used to examine Airbnb:

  • An environmental scan of the sharing economy, tourism and hospitality industry, and Airbnb
  • A business model evaluation of Airbnb
  • A scenario planning exercise.

Motivation for selecting these applications included the following: The methodologies are complementary in nature and appeared to be the best options to examine and understand the role of Airbnb and to evaluate its contribution to the tourism industry. In addition, the methodologies were considered to be the best way to evaluate Airbnb’s business model with regard to corporate entrepreneurship and sustainability in the tourism industry. Also, the methodologies allowed for a futures perspective on Airbnb.

Airbnb can be seen as the biggest disruptor of the hospitality industry. Airbnb’s listings grew from 200 000 in 2012 to approximately 1 million in 2015, and 3 million in 2017.

The connection between the sharing economy and Airbnb

The sharing economy refers to the online transactional space that has been created for goods and services to be exchanged between individuals and businesses. The unique quality of these online platforms allows for worldwide trading without any assets being owned by the operating platforms.

By their very nature, tourism and hospitality form part of the sharing economy because they exist and function in the global space. Airbnb can be seen as the biggest disruptor of the hospitality industry with its phenomenal exponential growth and no sign of slacking in its upward trajectory. Airbnb therefore influences consumers’ buying behaviour, can be labelled as a disruptive innovation, has had an impact on the hotel industry, and has brought regulatory and legal issues to the fore.

Airbnb facts and figures

The sharing economy has had an impact on four key areas of the tourism landscape:

  • Transport (car pool, car lending and car parking at private homes)
  • Accommodation (sub-letting in private homes)
  • Hospitality (sharing a meal and social reviews of restaurants)
  • Guides and tours (locals as tour guides and online guidebooks).

Using the Airbnb platform, accommodation owners can create accommodation profiles on this website after Airbnb has confirmed that the potential hosts comply with particular terms and conditions. One or more accommodation offerings per host can be uploaded as listings, featuring photographs, availability, rental costs, etc. Hosts may view the profiles of potential clients and decide if they want to accept the accommodation request, while potential guests may communicate with the host via Airbnb’s website if answers are needed in respect of bookings or other issues.

Airbnb’s listings grew from 200 000 in 2012 to approximately 1 million in 2015, and 3 million in 2017. In June 2012, Airbnb’s bookings added up to a total of 10 million nights, with 25 million nights booked in 2015, and 52 million nights booked in 2016. Airbnb’s value grew from $24 billion in 2015 to $31 billion in 2017.

What does the future hold for Airbnb?

This study investigated the following: What is Airbnb’s role in, and contribution to, the tourism industry, and does this enterprise have a sustainable business model that will keep on growing exponentially in the next few years? The research findings led to the following insights:

  • Airbnb’s external task environment is fundamentally different to that of a conventional business model, because it has two interdependent user groups (hosts and guests) as its customer base.
  • Airbnb’s business model is a multi-sided platform consisting of hosts and guests who are dependent on Airbnb’s website to enter into and conclude transactions. At the same time, the hosts are the suppliers.
  • Airbnb’s internal environment consists of small teams focused on entrepreneurial activity and innovation. Through strategic entrepreneurship and sustained regeneration, the organisation is progressively diversifying its products and services into new and existing markets. In this way, the organisation displays high levels of corporate entrepreneurship.
  • Airbnb’s business model has completely disrupted the traditional distribution channel of the tourism industry. As a multi-sided platform, Airbnb is central to a newly evolved distribution channel enabling the flow of transactions between hosts and guests, and guests and hosts.
  • A comparative analysis of the traditional tourism industry’s distribution channel with Airbnb’s distribution channel illustrates that Airbnb has opportunities to further expand into the categories of carriers and attractions.

Airbnb’s internal environment consists of small teams focused on entrepreneurial activity and innovation … the organisation displays high levels of corporate entrepreneurship.

What next?

Airbnb’s exponential growth has had a fundamental impact on the traditional travel, tourism and hospitality industries. Some legislative reforms are needed to accommodate and respond to Airbnb’s operational structures. Additional quantitative and evidence-based research is needed to assess Airbnb’s full impact on the travel, tourism and hospitality industries, especially on hotels and cities.

  • Original research: Schuurman, L. 2017. To Airbnb or not to be: A global futures perspective on Airbnb. Unpublished MPhil in Futures Studies research report. Bellville: University of Stellenbosch Business School.

Labeeqah Schuurman is an MPhil in Futures Studies student at the University of Stellenbosch Business School. She completed her research report under the supervision of Prof André Roux, head of USB’s Futures Studies programmes.

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