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Digital platforms and university strategies: tensions and synergies

by Sam Sellar

This blog is based on a presentation to the 2021 SRHE Research Conference, as part of a Symposium on Universities and Unicorns: Building Digital Assets in the Higher Education Industry organised by the project’s principal investigator, Janja Komljenovic (Lancaster). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The project introduces new ways to think about and examine the digitalising of the higher education sector. It investigates new forms of value creation and suggests that value in the sector increasingly lies in the creation of digital assets.

In a lecture delivered to Stanford University in 2014, which was provocatively titled Competition is for losers, Peter Thiel argued that ‘[m]onopoly is the condition of every successful business.’ Thiel’s endorsement of monopoly over competition has become business strategy orthodoxy for Big Tech firms, which, as Birch, Cochrane and Ward (2021, p6) argue, have ‘often been willing to accept low revenues in the short- to medium-term with the longer term goal of capturing markets and monopoly rents through their expected future control over data’. Assets are replacing commodities in contemporary capitalism, and an asset can be defined as ‘something that can be owned or controlled, traded, and capitalised as a revenue stream … [and] the point is to get a durable economic rent from them’ by limiting access to the asset (Birch and Muniesa, 2020, p2). We can see these assetisation dynamics emerging in EdTech markets serving UK higher education, and in this article I offer early insights into how these dynamics, driven by the growth in use of digital platforms during the Covid-19 pandemic, are shaping university strategies and practices.

This article reports on findings from the second phase of the Universities and Unicorns: building digital assets in the higher education industry project. The project is led by Dr Janja Komljenovic at Lancaster University and it aims to investigate new processes of value creation and extraction-assetisation-in the HE sector as it increasingly digitalises its operations. In Phase 2 of our project, we are conducting a series of university case studies in the UK, along with the investor and the company case studies. The university case studies are designed to help us understand how universities work with their commercial partners and what are the synergies and tensions. We are also curious about how universities view changing business models that focus on assetisation.

Importantly, we are not evaluating the use of EdTech in the context of teaching and learning or evaluating the strategies of individual institutions. Our concern is with how the HE sector is evolving in connection with EdTech markets. We are interviewing senior leaders, academic staff, directors of IT departments, IT developers and staff working in procurement, commercialisation and legal departments. We are also collecting a range of documents relating to digital strategy, business and data management plans, technical reports, financial records, and contracts with EdTech companies.

Our fieldwork with universities is a work in progress, and in this blog post I will outline three of our emerging findings, which relate to: (1) the ways that universities think about digital strategy; (2) the value of data from a university perspective; and (3) emerging processes of assetisation.

Digital strategy

None of the universities that we have studied so far have had formal and distinct digital strategies. Rather, digital strategy is embedded in IT, teaching and learning (T&L) and library strategies. In most cases, universities appear to be ‘between’ official strategy documents that cover this area. COVID-19 clearly shifted the short-term focus to tactics – working urgently to adjust and develop digital ecosystems to accommodate new demands of large-scale shifts online – and these universities are just now catching their breath and starting to update their strategies. However, despite this lack of formal strategy, some universities are very clear regarding the use of digital platforms to lead the sector and create value. In these cases, there clearly is an overarching strategy, it just isn’t described or formally presented as such.

Universities see themselves as developing institutional digital ecosystems by joining up platforms and focusing on the interoperability of their systems. Decisions about specific platforms are increasingly shaped by their potential integration into these ecosystems, and how data can be managed and integrated across platforms.

Interestingly, digital strategy is being driven by teaching and research strategy rather than shaping it. In one case, the point was made very strongly that digital is not separate, but rather a way of delivering the core business. Digital platforms are largely being used to deliver existing activity in digital form, rather than to create new forms of economic activity and new sources of value. However, questions are being raised about the relationship between IT and teaching and learning. For example, should IT departments simply support other business functions, or might they lead on digital strategy to enable new possibilities for the university?

The value of digital data

The primary value of digital data for universities appears to be reputational, and responses from our participants thus far have been remarkably consistent in this regard. Digital platforms can help to enhance the university’s brand and extend the business over a wider geographic range. This primacy of reputational, rather than financial, value is a distinctive feature of university perspectives on digital platforms, in contrast to companies.

Engagement with digital platforms was also seen to be valuable insofar as it generates market intelligence, supports student recruitment, changes perceptions of teaching and learning (eg blended approaches); and change perceptions of students (eg enabling particular cohorts to engage in new ways with benefits for their learning outcomes). Most interviewees are not thinking about the data generated by digital platforms as an asset, but it is clear that digital content (eg recorded lectures) are being seen in these terms insofar as they can be controlled by intellectual property rights and re-used over time.

Interestingly, our participants clearly hold the view that there is more potential for universities to make use of the digital data generated by platforms they use. However, in the case of learning analytics there is also scepticism regarding what it promises and its true value at this time. Despite a number of trials and experiments, many in UK universities are yet to see the benefits beyond what can be achieved using more prosaic approaches to data analytics.

Assetisation

The universities that we have studied so far do not appear to be using data to develop new products or services that generate value through economic rents; this kind of activity appears limited to commercial providers of digital platforms. However, universities increasingly understand the potential value of the data generated by their staff and students, and they are actively pursuing access to these data in their contractual negotiations with partners.

This is where we are seeing the emergence of assetisation dynamics in EdTech markets, which reflect the business strategies that Thiel promotes in his celebration of monopolies. Even if universities are able to negotiate favourable terms in individual contracts, providing rights to access and use data generated by university users on a given platform, they do not have access to aggregated data collected by companies through the use of this platform by other universities.

There is thus concern about the assetisation of data by commercial providers, for example, in relation to the use of aggregated data sets to develop new products and services that automate aspects of academic work (eg assessment). Turnitin is a primary example that came up in many of our discussions. The monopoly created by Turnitin leaves universities with little choice but to use their platform and pay whatever is asked, and relationships with Turnitin have become strained in many cases. The value of Turnitin is based on the data they have collected, and this data could be used to develop new services that automate, and thus substitute for, aspects of teaching currently delivered by lecturers. Work is being pursued through industry bodies to negotiate fairer distribution of the potential value generated by digital platforms in such cases.

Conclusion

While our university case studies are a work in progress, these three themes are already emerging quite consistently across our research sites. The value of data for universities is primarily reputational, extending the reach of teaching and learning functions, enhancing recruitment and supporting innovation in teaching and learning. Universities see digital strategy and the use of digital platforms as a way to extend their core business, not as a means to create new kinds of economic activity. In this respect, tech sector business strategies focused on creating value from data as an asset are not yet evident in the strategies of universities. However, we are seeing early signs that data is being assetised by EdTech companies, in an effort to extract monopoly rents by locking-in users through subscriptions to digital platforms. In this sense, we are curious to see whether monopoly will be a condition of every successful business in the burgeoning HE EdTech space.

Sam Sellar is Dean of Research (Education Futures) and Professor of Education Policy at the University of South Australia. Sam’s research focuses on education policy, large-scale assessments and the datafication of education. Sam also works closely with teacher organisations around the world to understand the impact of digitalisation on teachers’ work. His most recent book is titled Algorithms of education: How datafication and artificial intelligence shape policy (University of Minnesota Press), co-authored with Kalervo N Gulson and P Taylor Webb. Contact here: sam.sellar@unisa.edu.au

References

Birch, K, Cochrane, DT, and Ward, C (2021) ‘Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech’ Big Data & Society, 8(1), 20539517211017308.

Birch, K, and Muniesa, F (eds) (2020). Assetization: turning things into assets in technoscientific capitalism Boston: MIT Press

Thiel, P (2014) ‘Competition is for losers’ The Wall Street Journal Available from: https://www.wsj.com/articles/peter-thiel-competition-is-for-losers-1410535536


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Mapping financial investment flows in digital higher education: a focus on data-rich operations

by Janja Komljenovic

This blog is based on a presentation to the 2021 SRHE Research Conference, as part of a Symposium on Universities and Unicorns: Building Digital Assets in the Higher Education Industry organised by the project’s principal investigator, Janja Komljenovic (Lancaster). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The project introduces new ways to think about and examine the digitalising of the higher education sector. It investigates new forms of value creation and suggests that value in the sector increasingly lies in the creation of digital assets.

Universities worldwide are increasingly interested in digital technologies and how they can support higher education. A recent study by the European University Association found that most European universities are already using or planning to use data-rich products and services, such as artificial intelligence, machine learning, learning analytics, big data, and the internet of things (see Figure 18 on page 36). Indeed, it is precisely these data-rich operations that are central to the idea of the disruptive potential of education technology (edtech), as argued by my colleague, Javier Mármol Queraltó, in the recent UU project report. The discourse of investors and edtech companies promises thoroughly improved higher education based on personalisation, automation and efficiency. But how deliverable are these promises? Who innovates in the space of data-rich operations, for which services and for which users? Who profits? These are some of the questions we address in the Universities and Unicorns project, which aims to understand forms of value and ways of creating it in digital higher education. In this blog post, I will address three possible trends that can be identified from the interim findings of our quantitative analysis. But before proceeding to discuss these trends, I will contextualise our analysis.

We used Crunchbase to build three databases covering 2,012 edtech companies, 1,120 investors in edtech, and 1,962 edtech investment deals. We identified those relevant to the higher education sector, and our data reflects the state of the sector as of July 2021. Based on this analysis, we identified four key service models in the higher education edtech industry. First, the business to business (B2B) model includes digital platforms serving universities and companies, such as virtual learning environments. Second, the business to customer (B2C) model includes platforms targeting individuals directly. Third, the business to business to customer (B2B2C) model serves institutions that use or further develop the platform to reach individuals, such as Massive Open Online Courses (MOOC) or Online Programme Management platforms (OPM). Finally, the business to the customer to customer (B2C2C) model includes platforms that connect individuals, such as skills and knowledge sharing platforms. B2B2C and B2C2C platforms, in particular, act as the kind of infrastructural intermediaries that are so popular in other sectors of our social and economic lives.

Our analysis found that half of all investment went into B2B platforms, followed by investment into B2C, while B2C2C and B2B2C together received just under a quarter of all investment. However, platforms with the fastest pace of increasing investment are those targeting individuals directly or through intermediation, ie B2C and B2C2C models. This might indicate emerging parallel or alternative higher education products and services that compete with traditional university provision, especially in the context of lifelong learning.

Digital platforms that say they incorporate data-rich operations in their products and services are not the priority area for investors. While we noticed an increasing investment in data-rich platforms, it was still only less than a quarter of all investment going into innovating such products. Nevertheless, we identified three possible trends that are especially worthy of our attention: (1) data-rich operations are being innovated largely in B2B platforms; (2) there is notable unevenness in terms of the location of edtech companies and investments in those platforms who innovate in data-rich operations; and (3) there might be potential for monopolies in data-rich innovation. Let’s delve into each of these possible trends.

Almost all investment in the companies developing data-rich operations in their platforms went to the B2B service model. Looking only at higher education institutions as the target customer, already half of the investment supports data-rich innovation. Most of that went into platforms that act as the institutional digital backbone, indicating that the intention might be to support all institutional functions beyond teaching with data-rich operations, such as artificial intelligence, machine learning and various kinds of analytics beyond learning analytics. There seems to be a trend towards data-rich digital ecosystems at universities that harvest all user and other data in the near future.

There is high unevenness in where the investment in data-rich platforms is allocated. Regarding the number of companies, 239 in our database declare that they offer data-rich operations on their platforms. Almost half of those (101) are based in the USA, 21 in the UK and 19 in India. Companies based in Africa are entirely missing from the list. In terms of investment amounts, 88% of all investment in companies offering data-rich services in their platforms went into companies based in the USA, 3% each to those based in Norway and the UK, and 6% to the rest of the world. The discrepancy between the number of companies and investment size indicates that investment amounts are higher in the USA than elsewhere in the world.

Finally, if we compare different indicators of investment in companies that innovate data-rich solutions for higher education institutions, we notice interesting dynamics. Looking at the money raised, half of B2B investment went into those companies with a platform that included data-rich operations. But this is only 30% of deals and 25% of companies. This indicates that the concentration of investment in data-rich operation platforms for higher education institutions goes into a smaller number of companies who get higher investments. We wonder if this signals potential for monopolies in the future. Moreover, if we compare granted patents, we notice that a higher percentage of companies offering data-rich solution platforms own patents (30%) versus those offering other kinds of service or product platforms (10%). Digital platforms are typically still protected by a licence, but that differs from a more restrictive patent protection. We wonder if such discrepancy in patent share might indicate black-boxing of data-rich operations in higher education?

Our research on digitalising higher education is showing the complex impact of digital technology and datafication on the sector. This impact includes potential positive and supportive measures, but also many potentially worrying trends. However, further research is needed into these trends and the role of different actors, particularly financial investors and edtech companies. Please follow our project in which we will share the findings from this further work as it unfolds.

Janja Komljenovic is a Senior Lecturer and co-Director of the Higher Education Research and Evaluation at Lancaster University in the UK. She is also a Research Management Committee member of the Global Centre for Higher Education with headquarters at the University of Oxford. Janja’s research focuses on the political economy of knowledge production and higher education markets. She is especially interested in the relationship between the digital economy and the higher education sector; and in digitalisation, datafication and platformisation of knowledge production and dissemination. Janja is published internationally on higher education policy, markets and education technology.


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Challenges of multilingual studies

The SRHE Blog is now read in more than 100 countries worldwide, and we have therefore decided to introduce publications in more than one language. Click on ‘Versão em Português‘ below to jump to the Portuguese language version of this post. In the next few months we hope to post blogs in French, Russian, Chinese and more. SRHE members worldwide are encouraged to forward this notification, especially to non-English-speaking colleagues.

New contributions are welcome, especially if they address topical issues of policy or practice in countries other than England and the USA. Submissions may be written either in English or in the author’s native language. Please send all contributions to the Editor, rob.cuthbert@uwe.ac.uk. 

Desafios de realizar pesquisas multilíngues Versão em Português

by Aliandra Barlete

I have been intrigued – and somehow fascinated, too – by the ethical implications of conducting international research. As an international student in the UK, ethical dilemmas have surfaced many times, in spite of preparation during the course of studies. Continue reading →