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New business models for research and development with affordability requirements are needed to achieve fair pricing of medicines

Read our achieving fair pricing of medicines collection.

• Related content
• Peer review
• Fatima Suleman , professor 1 2 ,
• Marcus Low , postgraduate student 2 ,
• Suerie Moon , director of research 3 ,
• Steven G Morgan , professor 4
• 1 Prince Claus Chair of Development and Equity, Affordable (Bio) Therapeutics for Public Health, Utrecht University, Netherlands
• 2 Discipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
• 3 Global Health Centre, Graduate Institute of International and Development Studies, Geneva, Switzerland
• 4 School of Population in Public Health, University of British Columbia, Vancouver, Canada
• Correspondence to F Suleman sulemanf{at}ukzn.ac.za

For research and development to systematically deliver fairly priced medicines, new approaches to financing and organisation are needed, and affordability must be integrated into push, pull, and pooling mechanisms, say Fatima Suleman and colleagues

The health of populations depends, in part, on the development and appropriate use of new drugs, diagnostics, vaccines, and other biological medicines (broadly referred to as medicines). 1 Realising the social value of pharmaceutical innovation, however, is difficult. Policies must promote investment in research and development in areas of significant unmet health need while also ensuring access to resulting innovations. 2

Pharmaceutical R&D relies heavily on the monopoly pricing enabled by patents or other forms of market exclusivity. This threatens the goals of innovation and access and can result in “unfair” prices. 2 A fair price for medicines is one that is affordable for health systems and patients while providing sufficient market incentive for industry to invest. 3

Concerns about the high and rising prices of new medicines 4 have prompted increased interest in the possibility that changing the way in which R&D is financed and organised might result in fairer prices for innovative medicines. In particular, “delinkage,” where the financing of R&D is decoupled from the price of medicines by removing market exclusivity as a driving incentive, has attracted growing attention as an alternative business model for pharmaceutical R&D. This idea was recently endorsed in the political declaration of the United Nations high level meeting on tuberculosis. 5

The range of policy tools that can facilitate fair pricing falls into three broad categories: (1) “push” mechanisms, which typically provide grants for research projects in advance; (2) “pull” mechanisms, which provide rewards for research accomplishments at various stages of the drug development process; and (3) “pooling” mechanisms, which facilitate access to knowledge to advance scientific progress, thereby shortening timelines and reducing development costs. Below we provide an overview of these mechanisms and argue that without adequately enforced affordability requirements they may not lead to fair pricing.

Push mechanisms

Push mechanisms offer direct funding for various stages of drug R&D projects in advance, usually in the form of grants. These payments can incentivise (push) research by product developers when there is an unmet need but limited commercial potential or a high risk of failure. 4 6

Conditions tied to R&D grants can include requirements that product developers price the resulting medicines affordably. A patented technology may be transferred to a body other than the grant recipient—for example, an academic institution may grant licences to a private company. In such cases, funders can require the grant recipient to include affordability guarantees in any such agreements.

By subsidising the costs of R&D, grant funding reduces the need for developers to recoup investments through higher prices. 7 For neglected diseases or other recognised areas of market failure, public or philanthropic funding accounts for all or nearly all the direct costs of R&D. 8 In these cases, charging the lowest sustainable prices for medicines is a reasonable expectation.

Traditionally, product development at a later stage has been financed by the pharmaceutical industry responding to pull incentives, with no pricing conditions attached. An exception, however, is neglected diseases, for which public or philanthropic grants have financed late stage product development. These often come with affordability requirements. Such grants are increasingly being considered for new antibiotics, medicines needed for disease outbreaks, and some paediatric formulations.

Early stage research is largely funded through public grants. Early stage grants from the US National Institutes for Health (NIH), the world’s single largest funder of biomedical research, give the funder the right to “march in” and take control of intellectual property if medicines are not made available on “reasonable terms.” The NIH, however, has never made use of this right, despite repeated petitions asking it to do so in response to high medicine prices. 9

There is some evidence that push mechanisms can steer investments, reduce barriers to entry by small and medium sized enterprises, and absorb early stage risks of failure. 10 A disadvantage of push mechanisms is the incentive for developers to oversell investments in their particular projects. 8 11 Push mechanisms may also create a tension between the desire to steer investments and giving developers insufficient flexibility to be efficient and innovative in their research. Without adequate enforceable affordability requirements push incentives will not lead to fair prices.

Pull mechanisms

Pull mechanisms deliver rewards after a research and development objective or milestone is reached. These rewards may include incentives such as tax breaks, cash prizes, patents or data exclusivity, or advance market commitments where procurers commit to buy a certain amount of medicines. In contrast to push mechanisms, pull incentives based on outcome only compensate successful achievement of milestones or end products meeting specific criteria.

Pull mechanisms can contribute to fair pricing if the rewards are designed to do so, but this has not generally been the case. To date, most pull mechanisms have aimed at promoting innovation but not affordability. For example, the US priority review voucher programme provides a tradeable voucher as a reward for priority Food and Drug Administration review of a potentially lucrative medicine for a neglected or outbreak prone disease. The programme, however, does not require the voucher recipient to set affordable prices or to supply the relevant medicine to the market. 12

In addition, not all pull mechanisms are compatible with achieving affordability goals. Monopolies, whether based on patents or data exclusivity, enable a company to price the product at relatively high levels for a certain period. Furthermore, policy makers have sometimes even declined to include affordability requirements in pull mechanisms, as in the case of the priority review voucher. Some of these mechanisms have been criticised for “socialising the risks and privatising the profits” 13 of the drug development process.

Nevertheless, it is possible to craft some pull incentives to promote affordability. For example, large scale prizes, such as the antibiotics prize fund proposed in the US, 14 would reward inventors of new medicines; in exchange, the inventor would relinquish their patent monopoly and allow new medicines to be sold close to the cost of production.

Because developers bear the development costs in advance, pull mechanisms provide a greater incentive to maximise efficiency and innovation than push mechanisms. 9 One disadvantage is that the financial risk and uncertainty inherent in pull mechanisms may deter participation. This is particularly true for smaller companies that may lack the resources to finance lengthy R&D processes. Other challenges include determining the size of the incentive needed to motivate developers while remaining cost effective, and defining drug characteristics linked to the pull incentive that are neither too specific nor too general. Finally, an effective outcome based pull system relies on a funder that will credibly commit to long term funding guarantees. 15

Pooling mechanisms

Information sharing through the pooling of data or intellectual property can expedite innovation by removing the barriers to R&D created by secrecy, patents, and data exclusivity, and by minimising wasteful duplication of effort. By doing so, pooling can lower the cost of innovation and thereby enable more affordable pricing. For example, the Medicines Patent Pool, established in 2010 with support from Unitaid, pools patents relating to medicines for HIV/AIDS, hepatitis C, and tuberculosis. This accelerates the development of fixed dose combinations and facilitates testing of multiple drugs together to develop regimens rather than individual molecules.

Data pooling is being promoted through open source innovation initiatives, in which interested stakeholders place knowledge, data, and technology in the public domain. A number of open initiatives are in operation, including the Indian Open Source Drug Discovery initiative, the Librassay initiative, and the WIPO Re:Search consortium. All these allow scientists to share information and access intellectual property to search for new treatments.

The central idea behind these initiatives is that open collaboration and exchange of information will both expedite, and lower the cost of, the development of desired innovations, leading to more affordable prices. It should be noted, however, that specific enforceable conditions requiring fair pricing are needed to ensure that lower costs do indeed result in lower prices, rather than just producing wider profit margins.

Strategic use of push, pull, and knowledge pooling mechanisms can build affordability into the R&D process. Existing push mechanisms generally function well and enjoy wide support, but more must be done to ensure that medicines resulting from such push funding are fairly priced. This includes ensuring strong pricing and access provisions in funding agreements, and better enforcement of such provisions that already exist.

Interest in pull mechanisms or those that combine push and pull has risen in recent years. According to a recent mapping exercise, at least 49 alternative R&D funding initiatives are in operation, and 32 are being planned. 4 However, many alternative models remain underused and insufficiently tested by governments and other research funders. These include prize funds, advance purchase agreements, patent buy-outs, innovative taxes, conditional licences, and pricing guarantees.

Implementing alternative R&D models requires new sources of financing, particularly when the use of high prices and market exclusivities as drivers of R&D investments are deliberately limited. Because alternative R&D business models have largely been employed in areas of market failure, the financing has come from public and philanthropic sources. For example, most of the funding for research into neglected disease (if the NIH is excluded) comes from the two largest philanthropic investors—namely, the Bill and Melinda Gates Foundation and the Wellcome Trust. Together they contributed $660m (£520m; €580m) in 2014. 16

A wide variety of both state and non-state actors also contribute significantly to alternative funding mechanisms for dealing with neglected diseases, antimicrobial resistance, and diseases with epidemic potential. However, compared with the estimated US$240bn spent on biomedical R&D annually, investment in alternative business models is a drop in the ocean, probably <1% of total investment. Efforts to build affordable prices into the R&D process itself remain the exception to the rule. 17 18

Thus an important question is whether existing examples can be replicated or scaled up. This would ensure that R&D activities systematically result in affordably priced medicines for a broader set of diseases and public health challenges, beyond the handful of areas recognised as market failures. An intriguing example has been provided by the Drugs for Neglected Diseases project to develop an affordable hepatitis C drug. The first clinical trial results were highly promising. If successful, the medicine could be sold for less than $300 per treatment course, compared with $12 500 to $100 000 for hepatitis C drugs in the same class developed through traditional models. 19

A key barrier to replication of such efforts and to testing of alternative innovation models is a lack of funding. Some WHO member states have supported the creation of a fund housed at the Special Programme for Research and Training in Tropical Diseases, hosted at WHO. They propose a voluntary financing model based on the principles of delinkage, the use of open knowledge innovation, and open licensing for access. 20 21 None of these proposals has attracted major financial support, underlining the general difficulty in generating funding for such initiatives. Significant sums have been mobilised, however, to deal with R&D for antimicrobial resistance and epidemic threats, suggesting that it is feasible. 16

It is also notable that political will is not always present. In WHO and UN processes, some influential countries have not supported promotion of alternative R&D models that may challenge the dominant market exclusivity based system.

Consensus for alternatives to the status quo is growing, 22 and calls for reform are becoming more insistent. 2 Health systems have never been so financially challenged, partly because the demands on them have never been so great as many drive towards universal health coverage. Meanwhile, the demographic and epidemiological transformation of global populations continues rapidly, with a seemingly inexorable increase in non-communicable disease and the looming threat of generalised antimicrobial resistance. 23

The potential of alternative models to facilitate more efficient R&D and lower prices is now widely recognised. It is now up to states and other funders of research to insist upon affordability requirements in all R&D funding, to enforce them, and to increase investment in alternative models.

Key messages

Governments and other research funders remain slow to invest in alternative research and development models, though the need is well recognised

Governments and other research funders should insist on binding affordability requirements as a condition of all research and development funding to ensure fair pricing of medicines

Governments and other research funders should invest in models that delink the cost of research and development from the cost of production, and invest in research that measures the efficiency of such alternative models

Contributors and sources:FS drafted the article and finalised it with contributions from ML, SM, and SGM, who provided input and critical feedback for important intellectual content. FS is the guarantor. This manuscript is based on a narrative review of the literature and the authors’ experience and expertise in working in pharmaceutical policy, pricing, and reimbursement in different settings worldwide.

Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: Commissioned; externally peer reviewed.

This article is part of a series proposed by WHO and commissioned by The BMJ . The BMJ retained full editorial control over external peer review, editing, and publication of these articles. Open access fees are funded by WHO.

This is an Open Access article distributed under the terms of the Creative Commons Attribution IGO License (https://creativecommons.org/licenses/by-nc/3.0/igo/), which permits use, distribution, and reproduction for non-commercial purposes in any medium, provided the original work is properly cited.

• United Nations Secretary-General’s High-level Panel on Access to Medicines
• ↵ World Health Organization. Essential medicines and health products. 2017. https://www.who.int/medicines/access/fair_pricing/en/
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• ↵ United Nations. Political declaration of the United Nations high-level meeting on tuberculosis. 2018. https://www.un.org/pga/72/wp-content/uploads/sites/51/2018/09/Co-facilitators-Revised-text-Political-Declaraion-on-the-Fight-against-Tuberculosis.pdf
• ↵ Novartis. Novartis expands partnership with Medicines for Malaria Venture to develop next-generation antimalarial treatment. 2016. https://www.iol.co.za/business-report/companies/novartis-expands-malaria-research-2034875
• ↵ GSK. First African country introduces GSK’s pneumococcal vaccine through innovative financing mechanism. 2011. https://www.gsk.com/en-gb/media/press-releases/first-african-country-introduces-gsk-s-pneumococcal-vaccine-through-innovative-financing-mechanism/
• ↵ Policy Cures Research. Neglected disease research and development: reflecting on a decade of global investment. G-FINDER Report, 2017. https://www.policycuresresearch.org/g-finder-2017/
• Treasure CL ,
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• ↵ Mossialos E, Morel CM, Edwards S, Berenson J, Gemmill-Toyama M, Brogan D. Policies and incentives for promoting innovation in antibiotic research. European Observatory on Health Systems and Policies, 2010. http://www.euro.who.int/__data/assets/pdf_file/0011/120143/E94241.pdf
• ↵ Vouching for access . Nat Med 2016 ; 22 : 693 . doi: 10.1038/nm.4151   pmid: 27387878 OpenUrl CrossRef PubMed
• ↵ S.771—Improving Access To Affordable Prescription Drugs Act. 115th Congress, 2017-18. https://www.congress.gov/bill/115th-congress/senate-bill/771 .
• Renwick MJ ,
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• Mossialos E
• ↵ Policy Cures. Neglected disease research and development: the Ebola effect. G-FINDER Report, 2015. http://www.policycures.org/downloads/Y8%20GFINDER%20full%20report%20web.pdf
• ↵ Decision of the 67th World Health Assembly, WHA67.15. In: Sixty-seventh World Health Assembly, Geneva, 19-24 May 2014. World Health Organization, 2014. http://apps.who.int/gb/ebwha/pdf_files/WHA67-REC1/A67_2014_REC1-en.pdf
• Balasegaram M ,
• Bréchot C ,
• ↵ Kollewe J. Non-profit’s $300 hepatitis C cure as effective as $84 000 alternative. Guardian 2018 Apr 12. https://www.theguardian.com/science/2018/apr/12/non-profits-300-hepatitis-c-cure-as-effective-as-84000-alternative
• ↵ World Health Assembly. Follow-up of the report on the Consultative Expert Working Group on Research and Development: financing and coordination. WHO, 2014. http://apps.who.int/gb/ebwha/pdf_files/WHA67/A67_28Add1-en.pdf
• Røttingen JA ,
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• Wirtz VJ, Hogerzeil HV, Gray AL, ,
• ↵ Chan M. Remarks at the G7 health ministers meeting. Antimicrobial resistance: realizing the “one health” approach. Berlin, Germany. 8 October 2015. https://www.who.int/dg/speeches/2015/g7-antimicrobial-resistance/en/

Business model innovation: a review and research agenda

New England Journal of Entrepreneurship

ISSN : 2574-8904

Article publication date: 16 October 2019

Issue publication date: 13 November 2019

The aim of this paper is to review and synthesise the recent advancements in the business model literature and explore how firms approach business model innovation.

Design/methodology/approach

A systematic review of business model innovation literature was carried out by analysing 219 papers published between 2010 and 2016.

Evidence reviewed suggests that rather than taking either an evolutionary process of continuous revision, adaptation and fine-tuning of the existing business model or a revolutionary process of replacing the existing business model, firms can explore alternative business models through experimentation, open and disruptive innovations. It was also found that changing business models encompasses modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements in four areas of innovation: value proposition, operational value, human capital and financial value.

Research limitations/implications

Although this review highlights the different avenues to business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain unexplored.

Practical implications

The business model innovation framework can be used by practitioners as a “navigation map” to determine where and how to change their existing business models.

Originality/value

Because conflicting approaches exist in the literature on how firms change their business models, the review synthesises these approaches and provides a clear guidance as to the ways through which business model innovation can be undertaken.

• Business model
• Value proposition
• Value creation
• Value capture

Ramdani, B. , Binsaif, A. and Boukrami, E. (2019), "Business model innovation: a review and research agenda", New England Journal of Entrepreneurship , Vol. 22 No. 2, pp. 89-108. https://doi.org/10.1108/NEJE-06-2019-0030

Emerald Publishing Limited

Copyright © 2019, Boumediene Ramdani, Ahmed Binsaif and Elias Boukrami

Published in New England Journal of Entrepreneurship . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Firms pursue business model innovation by exploring new ways to define value proposition, create and capture value for customers, suppliers and partners ( Gambardella and McGahan, 2010 ; Teece, 2010 ; Bock et al. , 2012 ; Casadesus-Masanell and Zhu, 2013 ). An extensive body of the literature asserts that innovation in business models is of vital importance to firm survival, business performance and as a source of competitive advantage ( Demil and Lecocq, 2010 ; Chesbrough, 2010 ; Amit and Zott, 2012 ; Baden-Fuller and Haefliger, 2013 ; Casadesus-Masanell and Zhu, 2013 ). It is starting to attract a growing attention, given the increasing opportunities for new business models enabled by changing customer expectations, technological advances and deregulation ( Casadesus-Masanell and Llanes, 2011 ; Casadesus-Masanell and Zhu, 2013 ). This is evident from the recent scholarly outputs ( Figure 1 ). Thus, it is essential to comprehend this literature and uncover where alternative business models can be explored.

Conflicting approaches exist in the literature on how firms change their business models. One approach suggests that alternative business models can be explored through an evolutionary process of incremental changes to business model elements (e.g. Demil and Lecocq, 2010 ; Dunford et al. , 2010 ; Amit and Zott, 2012 ; Landau et al. , 2016 ; Velu, 2016 ). The other approach, mainly practice-oriented, advocates that innovative business models can be developed through a revolutionary process by replacing existing business models (e.g. Bock et al. , 2012 ; Iansiti and Lakhani, 2014 ). The fragmentation of prior research is due to the variety of disciplinary and theoretical foundations through which business model innovation is examined. Scholars have drawn on perspectives from entrepreneurship (e.g. George and Bock, 2011 ), information systems (e.g. Al-debei and Avison, 2010 ), innovation management (e.g. Dmitriev et al. , 2014 ), marketing (e.g. Sorescu et al. , 2011 ) and strategy (e.g. Demil and Lecocq, 2010 ). Also, this fragmentation is deepened by focusing on different types of business models in different industries. Studies have explored different types of business models such as digital business models (e.g. Weill and Woerner, 2013 ), service business models (e.g. Kastalli et al. , 2013 ), social business models (e.g. Hlady-Rispal and Servantie, 2016 ) and sustainability-driven business models ( Esslinger, 2011 ). Besides, studies have examined different industries such as airline ( Lange et al. , 2015 ), manufacturing ( Landau et al. , 2016 ), newspaper ( Karimi and Walter, 2016 ), retail ( Brea-Solís et al. , 2015 ) and telemedicine ( Peters et al. , 2015 ).

Since the first comprehensive review of business model literature was carried out by Zott et al. (2011) , several reviews were published recently (as highlighted in Table I ). Our review builds on and extends the extant literature in at least three ways. First, unlike previous reviews that mainly focused on the general construct of “Business Model” ( George and Bock, 2011 ; Zott et al. , 2011 ; Wirtz et al. , 2016 ), our review focuses on uncovering how firms change their existing business model(s) by including terms that reflect business model innovation, namely, value proposition, value creation and value capture. Second, previous reviews do not provide a clear answer as to how firms change their business models. Our review aims to provide a clear guidance on how firms carry out business model innovation by synthesising the different perspectives existing in the literature. Third, compared to recent reviews on business model innovation ( Schneider and Spieth, 2013 ; Spieth et al. , 2014 ), which have touched lightly on some innovation aspects such as streams and motivations of business model innovation research, our review will uncover the innovation areas where alternative business models can be explored. Taking Teece’s (2010) suggestion, “A helpful analytic approach for management is likely to involve systematic deconstruction/unpacking of existing business models, and an evaluation of each element with an idea toward refinement or replacement” (p. 188), this paper aims to develop a theoretical framework of business model innovation.

Our review first explains the scope and the process of the literature review. This is followed by a synthesis of the findings of the review into a theoretical framework of business model innovation. Finally, avenues for future research will be discussed in relation to the approaches, degree and mechanisms of business model innovation.

2. Scope and method of the literature review

Given the diverse body of business models literature, a systematic literature review was carried out to minimise research bias ( Transfield et al. , 2003 ). Compared to the previous business model literature, our review criteria are summarised in Table I . The journal papers considered were published between January 2010 and December 2016. As highlighted in Figure 1 , most contributions in this field have been issued within this period since previous developments in the literature were comprehensively reviewed up to the end of 2009 ( Zott et al. , 2011 ). Using four databases (EBSCO Business Complete, ABI/INFORM, JSTOR and ScienceDirect), we searched peer-reviewed papers with terms such as business model(s), innovation value proposition, value creation and value capture appearing in the title, abstract or subject terms. As a result, 8,642 peer-reviewed papers were obtained.

Studies were included in our review if they specifically address business models and were top-rated according to The UK Association of Business Schools list ( ABS, 2010 ). This rating has been used not only because it takes into account the journal “Impact Factor” as a measure for journal quality, but also uses in conjunction other measures making it one of the most comprehensive journal ratings. By applying these criteria, 1,682 entries were retrieved from 122 journals. By excluding duplications, 831 papers were identified. As Harvard Business Review is not listed among the peer-reviewed journals in any of the chosen databases and was included in the ABS list, we used the earlier criteria and found 112 additional entries. The reviewed papers and their subject fields are highlighted in Table II . Since the focus of this paper is on business model innovation, we selected studies that discuss value proposition, value creation and value capture as sub-themes. This is not only because the definition of business model innovation mentioned earlier spans all three sub-themes, but also because all three sub-themes have been included in recent studies (e.g. Landau et al. , 2016 ; Velu and Jacob, 2014 ). To confirm whether the papers addressed business model innovation, we examined the main body of the papers to ensure they were properly coded and classified. At the end of the process, 219 papers were included in this review. Table III lists the source of our sample.

The authors reviewed the 219 papers using a protocol that included areas of innovation (i.e. components, elements, and activities), theoretical perspectives and key findings. In order to identify the main themes of business model innovation research, all papers were coded in relation to our research focus as to where alternative business models can be explored (i.e. value proposition, value creation and value capture). Coding was cross checked among the authors on a random sample suggesting high accuracy between them. Having compared and discussed the results, the authors were able to identify the main themes.

3. Prior conceptualisations of business model innovation

Some scholars have articulated the need to build the business model innovation on a more solid theoretical ground ( Sosna et al. , 2010 ; George and Bock, 2011 ). Although many studies are not explicitly theory-based, some studies partially used well-established theories such as the resource-based view (e.g. Al-Debei and Avison, 2010 ) and transaction cost economics (e.g. DaSilva and Trkman, 2014 ) to conceptualise business model innovation. Other theories such as activity systems perspective, dynamic capabilities and practice theory have been used to help answer the question of how firms change their existing business models.

Using the activity systems perspective, Zott and Amit (2010) demonstrated how innovative business models can be developed through the design themes that describe the source of value creation (novelty, lock-in, complementarities and efficiency) and design elements that describe the architecture (content, structure and governance). This work, however, overlooks value capture which limits the explanation of the advocated system’s view (holistic). Moreover, Chatterjee (2013) used this perspective to reveal that firms can design innovative business models that translate value capture logic to core objectives, which can be delivered through the activity system.

Dynamic capability perspective frames business model innovation as an initial experiment followed by continuous revision, adaptation and fine-tuning based on trial-and-error learning ( Sosna et al. , 2010 ). Using this perspective, Demil and Lecocq (2010) showed that “dynamic consistency” is a capability that allows firms to sustain their performance while innovating their business models through voluntary and emergent changes. Also, Mezger (2014) conceptualised business model innovation as a distinct dynamic capability. He argued that this capability is the firm’s capacity to sense opportunities, seize them through the development of valuable and unique business models, and accordingly reconfigure the firms’ competences and resources. Using aspects of practice theory, Mason and Spring (2011) looked at business model innovation in the recorded sound industry and found that it can be achieved through various combinations of managerial practices.

Static and transformational approaches have been used to depict business models ( Demil and Lecocq, 2010 ). The former refers to viewing business models as constituting core elements that influence business performance at a particular point in time. This approach offers a snapshot of the business model elements and how they are assembled, which can help in understanding and communicating a business model (e.g. Eyring et al. , 2011 ; Mason and Spring, 2011 ; Yunus et al. , 2010). The latter, however, focuses on innovation and how to address the changes in business models over time (e.g. Sinfield et al. , 2012 ; Girotra and Netessine, 2014 ; Landau et al. , 2016 ). Some researchers have identified the core elements of business models ex ante (e.g. Demil and Lecocq, 2010 ; Wu et al. , 2010 ; Huarng, 2013 ; Dmitriev et al. , 2014 ), while others argued that considering a priori elements can be restrictive (e.g. Casadesus-Masanell and Ricart, 2010 ). Unsurprisingly, some researchers found a middle ground where elements are loosely defined allowing flexibility in depicting business models (e.g. Zott and Amit, 2010 ; Sinfield et al. , 2012 ; Kiron et al. , 2013 ).

Prior to 2010, conceptual frameworks focused on the business model concept in general (e.g. Chesbrough and Rosenbloom, 2002 ; Osterwalder et al. , 2005 ; Shafer et al. , 2005 ) apart from Johnson et al. ’s (2008 ), which is one of the early contributions to business model innovation. To determine whether a change in existing business model is necessary, Johnson et al. (2008) suggested three steps: “Identify an important unmet job a target customer needs done; blueprint a model that can accomplish that job profitably for a price the customer is willing to pay; and carefully implement and evolve the model by testing essential assumptions and adjusting as you learn” ( Eyring et al. , 2011 , p. 90). Although several frameworks have been developed since then, our understanding of business model innovation is still limited due to the static nature of the majority of these frameworks. Some representations ignore the elements and/or activities where alternative business models can be explored (e.g. Sinfield et al. , 2012 ; Chatterjee, 2013 ; Huarng, 2013 ; Morris et al. , 2013 ; Dmitriev et al. , 2014 ; Girotra and Netessine, 2014 ). Other frameworks ignore value proposition (e.g. Zott and Amit, 2010 ), ignore value creation (e.g. Dmitriev et al. , 2014 ; Michel, 2014 ) and/or ignore value capture (e.g. Mason and Spring, 2011 ; Sorescu et al. , 2011 ; Storbacka, 2011 ). Some conceptualisations do not identify who is responsible for the innovation (e.g. Casadesus-Masanell and Ricart, 2010 ; Sinfield et al. , 2012 ; Chatterjee, 2013 ; Kiron et al. , 2013 ). Synthesising the different contributions into a theoretical framework of business model innovation will enable a better understanding of how firms undertake business model innovation.

4. Business model innovation framework

Our framework ( Figure 2 ) integrates all the elements where alternative business models can be explored. This framework does not claim that the listed elements are definitive for high-performing business models, but is an attempt to outline the elements associated with business model innovation. This framework builds on the previous work of Johnson et al. (2008) and Zott and Amit (2010) by signifying the elements associated with business model innovation. Unlike previous frameworks that mainly consider the constituting elements of business models, this framework focuses on areas of innovation where alternative business models can be explored. Moreover, this is not a static view of the constituting elements of a business model, but rather a view enabling firms to explore alternative business models by continually refining these elements. Arrows in the framework indicate the continuous interaction of business model elements. This framework consists of 4 areas of innovation and 16 elements (more details are shown in Table IV ). Each will be discussed below.

4.1 Value proposition

The first area of innovation refers to elements associated with answering the “Why” questions. While most of the previously established models in the literature include at least one of the value proposition elements (e.g. Brea-Solís et al. , 2015 ; Christensen et al. , 2016 ), other frameworks included two elements (e.g. Dahan et al. , 2010 ; Cortimiglia et al. , 2016 ) and three elements (e.g. Eyring et al. , 2011 ; Sinfield et al. , 2012 ). These elements include rethinking what a company sells, exploring new customer needs, acquiring target customers and determining whether the benefits offered are perceived by customers. Modern organisations are highly concerned with innovation relating to value proposition in order to attract and retain a large portion of their customer base ( Al-Debei and Avison, 2010 ). Developing new business models usually starts with articulating a new customer value proposition ( Eyring et al. , 2011 ). According to Sinfield et al. (2012) , firms are encouraged to explore various alternatives of core offering in more depth by examining type of offering (product or service), its features (custom or off-the-shelf), offered benefits (tangible or intangible), brand (generic or branded) and lifetime of the offering (consumable or durable).

In order to exploit the “middle market” in emerging economies, Eyring et al. (2011) suggested that companies need to design new business models that aim to meet unsatisfied needs and evolve these models by continually testing assumptions and making adjustments. To uncover unmet needs, Eyring et al. (2011) suggested answering four questions: what are customers doing with the offering? What alternative offerings consumers buy? What jobs consumers are satisfying poorly? and what consumers are trying to accomplish with existing offerings? Furthermore, Baden-Fuller and Haefliger (2013) made a distinction between customers and users in two-sided platforms, where users search for products online, and customers (firms) place ads to attract users. They also made a distinction between “pre-designed (scale) based offerings” and “project based offerings”. While the former focuses on “one-size-fits-all”, the latter focuses on specific client solving specific problem.

Established firms entering emerging markets should identify unmet needs “the job to be done” rather than extending their geographical base for existing offerings ( Eyring et al. , 2011 ). Because customers in these markets cannot afford the cheapest of the high-end offerings, firms with innovative business models that meet these customers’ needs affordably will have opportunities for growth ( Eyring et al. , 2011 ). Moreover, secondary business model innovation has been advocated by Wu et al. (2010) as a way for latecomer firms to create and capture value from disruptive technologies in emerging markets. This can be achieved through tailoring the original business model to fit price-sensitive mass customers by articulating a value proposition that is attractive for local customers.

4.2 Operational value

The second area of innovation focuses on elements associated with answering the “What” questions. Many of the established frameworks included either one element (e.g. Sinfield et al. , 2012 ; Taran et al. , 2015 ), two elements (e.g. Mason and Spring, 2011 ; Dmitriev et al. , 2014 ). However, very few included three or more elements (e.g. Mehrizi and Lashkarbolouki, 2016 ; Cortimiglia et al. , 2016 ). These elements include configuring key assets and sequencing activities to deliver the value proposition, exposing the various means by which a company reaches out to customers, and establishing links with key partners and suppliers. Focusing on value creation, Zott and Amit (2010) argued that business model innovation can be achieved through reorganising activities to reduce transaction costs. However, Al-Debei and Avison (2010) argued that innovation relating to this dimension can be achieved through resource configuration, which demonstrates a firm’s ability to integrate various assets in a way that delivers its value proposition. Cavalcante et al. (2011) proposed four ways to change business models: business model creation, extension, revision and termination by creating or adding new processes, and changing or terminating existing processes.

Western firms have had difficulty competing in emerging markets due to importing their existing business models with unchanged operating model ( Eyring et al. , 2011 ). Alternative business models can be uncovered when firms explore the different roles they might play in the industry value chain ( Sinfield et al. , 2012 ). Al-Debei and Avison (2010) suggested achieving this through answering questions such as: what is the position of our firm in the value system? and what mode of collaboration (open or close) would we choose to reach out in a business network? Dahan et al. (2010) found cross-sector partnerships as a way to co-create new multi-organisational business models. They argued that multinational enterprises (MNEs) can collaborate with nongovernmental organisations (NGOs) to create products/or services that neither can create on their own. Collaboration allows access to resources that firms would otherwise need to solely develop or purchase ( Yunus et al. , 2010 ). According to Wu et al. (2010) , secondary business model innovation can be achieved when latecomer firms fully utilise strategic partners’ complementary assets to overcome their latecomer disadvantages and build a unique value network specific to emerging economies context.

4.3 Human capital

The third area of innovation refers to elements associated with answering the “Who” questions. Most of the established frameworks in this field tend to focus less on human capital and include one element at most (e.g. Wu et al. , 2010 ; Kohler, 2015 ). However, our framework highlights four elements, which include experimenting with new ways of doing business, tapping into the skills and competencies needed for the new business model through motivating and involving individuals in the innovation process. According to Belenzon and Schankerman (2015) , “the ability to tap into a pool of talent is strongly related to the specific business model chosen by managers” (p. 795). They claimed that managers can strategically influence individuals’ contributions and their impact on project performance.

Organisational learning can be maximised though continuous experimentation and making changes when actions result in failure ( Yunus et al. , 2010 ). Challenging and questioning the existing rules and assumptions and imagining new ways of doing business will help develop new business models. Another essential element of business model design is governance, which refers to who performs the activities ( Zott and Amit, 2010 ). According to Sorescu et al. (2011) , innovation in retail business models can occur as a result of changes in the level of participation by actors engaged in performing the activities. An essential element of retailing governance is the incentive structure or the mechanisms that motivate those involved in carrying out their roles to meet customer demands ( Sorescu et al. , 2011 ). For example, discount retailers tend to establish different compensation and incentive policies ( Brea-Solís et al. , 2015 ). Revising the incentive system can have a major impact on new ventures’ performance by aligning organisational goals at each stage of growth ( Roberge, 2015 ). Zott and Amit (2010) argued that alternative business models can be explored through adopting innovative governance or changing one or more parties that perform any activities. Sinfield et al. (2012) suggested that business model innovation only requires time from a small team over a short period of time to move a company beyond incremental improvements and generate new opportunities for growth. This is supported by Michel’s (2014) finding that cross-functional teams were able to quickly achieve business model innovation in workshops through deriving new ways to capture value.

4.4 Financial value

The final area of innovation focuses on elements associated with answering the “How” questions. Previously developed frameworks tend to prioritise this area of innovation by three elements (e.g. Eyring et al. , 2011 ; Huang et al. , 2013 ), and in one instance four elements (e.g. Yunus et al. , 2010 ). These elements include activities linked with how to capture value through revenue streams, changing the price-setting mechanisms, and assessing the financial viability and profitability of a business. According to Demil and Lecocq (2010) , changes in cost and/or revenue structures are the consequences of both continuous and radical changes. They also argued that costs relate to different activities run by organisations to acquire, integrate, combine or develop resources. Michel (2014) suggested that alternative business models can be explored through: changing the price-setting mechanism, changing the payer, and changing the price carrier. Different innovation forms are associated with each of these categories.

Business model innovation can be achieved through exploring new ways to generate cash flows ( Sorescu et al. , 2011 ), where the organisation has to consider (and potentially change) when the money is collected: prior to the sale, at the point of sale, or after the sale ( Baden-Fuller and Haefliger, 2013 ). Furthermore, Demil and Lecocq (2010) suggested that changes in business models affect margins. This is apparent in the retail business models, which generate more profit through business model innovation compared to other types of innovation ( Sorescu et al. , 2011 ).

5. Ways to change business models

From reviewing the recent developments in the business model literature, alternative business models can be explored through modifying a single business model element, altering multiple elements simultaneously and/or changing the interactions between elements of a business model.

Changing one of the business model elements (i.e. content, structure or governance) is enough to achieve business model innovation ( Amit and Zott, 2012 ). This means that firms can have a new activity system by performing only one new activity. However, Amit and Zott (2012) clearly outlined a systemic view of business models which entails a holistic change. This is evident from Demil and Lecocq’s (2010) work suggesting that the study of business model innovation should not focus on isolated activities since changing a core element will not only impact other elements but also the interactions between these elements.

Another way to change business models is through altering multiple business model elements simultaneously. Kiron et al. (2013) found that companies combining target customers with value chain innovations and changing one or two other elements of their business models tend to profit from their sustainability activities. They also found that firms changing three to four elements of their business models tend to profit more from their sustainability activities compared to those changing only one element. Moreover, Dahan et al. (2010) found that a new business model was developed as a result of MNEs and NGOs collaboration by redefining value proposition, target customers, governance of activities and distribution channels. Companies can explore multiple combinations by listing different business model options they could undertake (desirable, discussable and unthinkable) and evaluate new combinations that would not have been considered otherwise ( Sinfield et al. , 2012 ).

Changing business models is argued to be demanding as it requires a systemic and holistic view ( Amit and Zott, 2012 ) by considering the relationships between core business model elements ( Demil and Lecocq, 2010 ). As mentioned earlier, changing one element will not only impact other elements but also the interactions between these elements. A firm’s resources and competencies, value proposition and organisational system are continuously interacting and this will in turn impact business performance either positively or negatively ( Demil and Lecocq, 2010 ). According to Zott and Amit (2010) , innovative business models can be developed through linking activities in a novel way that generates more value. They argued that alternative business models can be explored by configuring business model design elements (e.g. governance) and connecting them to distinct themes (e.g. novelty). Supporting this, Eyring et al. (2011) suggested that core business model elements need to be integrated in order to create and capture value ( Eyring et al. , 2011 ).

6. Discussion and future research directions

From the above synthesis of the recent development in the literature, several gaps remain unfilled. To advance the literature, possible future research directions will be discussed in relation to approaches, degrees and mechanisms of business model innovation.

6.1 Approaches of business model innovation

Experimentation, open innovation and disruption have been advocated as approaches to business model innovation. Experimentation has been emphasised as a way to exploit opportunities and develop alternative business models before committing additional investments ( McGrath, 2010 ). Several approaches have been developed to assist in business model experimentation including mapping approach, discovery-driven planning and trail-and-error learning ( Chesbrough, 2010 ; McGrath, 2010 ; Sosna et al. , 2010 ; Andries and Debackere, 2013 ). Little is known about the effectiveness of these approaches. It will be worth investigating which elements of the business model innovation framework are more susceptible to experimentation and which elements should be held unchanged. Although business model innovation tends to be characterised with failure ( Christensen et al. , 2016 ), not much has been established on failing business models. It is interesting to explore how firms determine a failing business model and what organisational processes exist (if any) to evaluate and discard these failed business models. Empirical studies could examine which elements of business model innovation framework are associated with failing business models.

Another way to develop alternative business models is through open innovation. Although different categories of open business models have been identified by researchers (e.g. Frankenberger et al. , 2014 ; Taran et al. , 2015 ; Kortmann and Piller, 2016 ), their effectiveness is yet to be established. Further research is needed to examine when can a firm open and/or close element(s) of the business model innovation framework. Future studies could also examine the characteristics of open and/or close business models.

In responding to disruptive business models, how companies extend their existing business model, introduce additional business model(s) and/or replace their existing business model altogether remains underexplored. Future research is needed to unravel the strategies deployed by firms to extend their existing business models as a response to disruptive business models. In introducing additional business models, Markides (2013) suggested that a company will be presented with several options to manage the two businesses at the same time: create a completely separate business unit, integrate the two business models from the beginning or integrate the second business model after a certain period of time. Finding the balance between separation and integration is of vital importance. Further research could identify which of these choices are most common among successful firms introducing additional business models, how is the balance between integration and separation achieved, and which choice(s) prove more profitable. Moreover, very little is known on how firms replace their existing business model. Longitudinal studies could provide insights into how a firm adopts an alternative model and discard the old business model over time. It may also be worth examining the factors associated with the adoption of business model innovation as a response to disruptive business models. Moreover, new developments in digital technologies such as blockchain, Internet of Things and artificial intelligence are disrupting existing business models and providing firms with alternative avenues to create new business models. Thus far, very little is known on digital business models, the nature of their disruption, and how firms create digital business models and make them disruptive. Future research is needed to fill these important gaps in our knowledge.

6.2 Degrees of business model innovation

Business models can be developed through varying degrees of innovation from an evolutionary process of continuous fine-tuning to a revolutionary process of replacing existing business models. Recent research shows that survival of firms is dependent on the degree of their business model innovation ( Velu, 2015, 2016 ). This review classifies these degrees of innovation into modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements of the business model innovation framework.

In changing a single element, further research is needed to examine which business model element(s) is (are) associated with business model innovation. It is not clear whether firms intentionally make changes to a single element when carrying out business model innovation or stumble at it when experimenting with new ways of doing things. It may also be worth investigating the entry (or starting) points in the innovation process. There is no consensus in the literature on which element do companies start with when carrying out their business model innovation. While some studies suggest starting with the value proposition ( Eyring et al. , 2011 ; Landau et al. , 2016 ), others suggest starting the innovation process with identifying risks in the value chain ( Girotra and Netessine, 2011 ). Dmitriev et al. (2014) suggested two entry points, namely, value proposition and target customers. In commercialising innovations, the former refers to technology-push innovation while the latter refers to market-pull innovation. Also, it is not clear whether the entry point is the same as the single element associated with changing the business model. Further research can explore the different paths to business model innovation by identifying the entry point and subsequent changes needed to achieve business model innovation.

There is little guidance in the literature on how firms change multiple business model elements simultaneously. Landau et al. (2016) claimed that firms entering emerging markets tend to focus on adjusting specific business model components. It is unclear which elements need configuring, combining and/or integrating to achieve a company’s value proposition. Furthermore, the question of which elements can be “bought” on the market or internally “implemented” and their interplay remains unanswered ( DaSilva and Trkman, 2014 ). Casadesus-Masanell and Ricart (2010) argued that “[…] there is (as yet) no agreement as to the distinctive features of superior business models” (p. 196). Further research is needed to explore these distinctive elements of high-performing business models.

In changing the interactions between business model elements, further research is needed to explore how these elements are linked and what interactions’ changes are necessary to achieve business model innovation. Moreover, the question of how firms sequence these elements remains poorly understood. Future research can explore the synergies created over time between these elements. According to Dmitriev et al. (2014) , we need to improve our understanding of the connective mechanisms and dynamics involved in business model development. More work is needed to explore the different modalities of interdependencies among these elements and empirically testing such interdependencies and their effect on business performance ( Sorescu et al. , 2011 ).

It is surprising that the link between business model innovation and organisational performance has rarely been examined. Changing business models has been found to negatively influence business performance even if it is temporary ( McNamara et al. , 2013 ; Visnjic et al. , 2016 ). Contrary to this, evidence show that modifying business models is positively associated with organisational performance ( Cucculelli and Bettinelli, 2015 ). Empirical research is needed to operationalise the various degrees of innovation in business models and examine their link to organisational performance. Longitudinal studies can also be used to explore this association since it may be the case that business model innovation has a negative influence on performance in the short run and that may change subsequently. Moreover, it is not clear whether high-performing firms change their business models or innovation in business models is a result from superior performance ( Sorescu et al. , 2011 ). Further studies are needed to determine the direction of causality. Another link that is worth exploring is business model innovation and social value, which has only been explored in a few studies looking at social business models (e.g. Yunus et al. , 2010 ; Wilson and Post, 2013 ). Further research is needed to examine this link and possibly examine both financial and non-financial business performance.

6.3 Mechanisms of business model innovation

Although we know more about how firms define value proposition, create and capture value ( Landau et al. , 2016 ; Velu and Jacob, 2014 ), what remains as a blind spot is the mechanism of business model innovation. This is due to the fact that much of the literature seems to focus on value creation. To better understand the various mechanisms of business model innovation, future studies must integrate value proposition, value creation and value capture elements. Empirical studies could use the business model innovation framework to examine the various mechanisms of business model innovation. Also, the literature lacks the integration of internal and external perspectives of business model innovation. Very few studies look at the external drivers of business model innovation and the associated internal changes. The external drivers are referred to as “emerging changes”, which are usually beyond manager’s control ( Demil and Lecocq, 2010 ). Inconclusive findings exist as to how firms develop innovative business models in response to changes in the external environment. Future studies could examine the external factors associated with the changes in the business model innovation framework. Active and reactive responses need to be explored not only to understand the external influences, but also what business model changes are necessary for such responses. A better understanding of the mechanisms of business model innovation can be achieved by not only exploring the external drivers, but also linking them to specific internal changes. Although earlier contributions linking studies to established theories such as the resource-based view, transaction cost economics, activity systems perspective, dynamic capabilities and practice theory have proven to be vital in advancing the literature, developing a theory that elaborates on the antecedents, consequences and different facets of business model innovation is still needed ( Sorescu et al. , 2011 ). Theory can be advanced by depicting the mechanisms of business model innovation through the integration of both internal and external perspectives. Also, we call for more empirical work to uncover these mechanisms and provide managers with the necessary insights to carry out business model innovation.

7. Conclusions

The aim of this review was to explore how firms approach business model innovation. The current literature suggests that business model innovation approaches can either be evolutionary or revolutionary. However, the evidence reviewed points to a more complex picture beyond the simple binary approach, in that, firms can explore alternative business models through experimentation, open and disruptive innovations. Moreover, the evidence highlights further complexity to these approaches as we find that they are in fact a spectrum of various degrees of innovation ranging from modifying a single element, altering multiple elements simultaneously, to changing the interactions between elements of the business model innovation framework. This framework was developed as a navigation map for managers and researchers interested in how to change existing business models. It highlights the key areas of innovation, namely, value proposition, operational value, human capital and financial value. Researchers interested in this area can explore and examine the different paths firms can undertake to change their business models. Although this review pinpoints the different avenues for firm to undertake business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain underexplored.

The evolution of business model literature (pre-2000 to 2016)

Business model innovation framework

Previous reviews of business model literature

Reviewed papers and their subject fields

Source of our sample

Business model innovation areas and elements

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Spieth , P. , Schneckenberg , D. and Ricart , J.E. ( 2014 ), “ Business model innovation – state of the art and future challenges for the field ”, R&D Management , Vol. 44 No. 3 , pp. 237 - 247 .

Sorescu , A. , Frambach , R.T. , Singh , J. , Rangaswamy , A. and Bridges , C. ( 2011 ), “ Innovations in retail business models ”, Journal of Retailing , Vol. 87 No. 1 , pp. S3 - S16 .

Sosna , M. , Trevinyo-Rodríguez , R.N. and Velamuri , S.R. ( 2010 ), “ Business model innovation through trial-and-error learning: the naturhouse case ”, Long Range Planning , Vol. 43 Nos. 2-3 , pp. 383 - 407 .

Storbacka , K. ( 2011 ), “ A solution business model: capabilities and management practices for integrated solutions ”, Industrial Marketing Management , Vol. 40 No. 5 , pp. 699 - 711 .

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Transfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review ”, British Journal of Management , Vol. 14 No. 3 , pp. 207 - 222 .

Teece , D.J. ( 2010 ), “ Business models, business strategy and innovation ”, Long Range Planning , Vol. 43 Nos 2-3 , pp. 172 - 194 .

Velu , C. ( 2015 ), “ Business model innovation and third-party alliance on the survival of new firms ”, Technovation , Vol. 35 No. 1 , pp. 1 - 11 .

Velu , C. ( 2016 ), “ Evolutionary or revolutionary business model innovation through coopetition? The role of dominance in network markets ”, Industrial Marketing Management , Vol. 53 No. 1 , pp. 124 - 135 .

Velu , C. and Jacob , A. ( 2014 ), “ Business model innovation and owner–managers: the moderating role of competition ”, R&D Management , Vol. 46 No. 3 , pp. 451 - 463 .

Visnjic , I. , Wiengarten , F. and Neely , A. ( 2016 ), “ Only the brave: product innovation, service business model innovation, and their impact on performance ”, Journal of Product Innovation Management , Vol. 33 No. 1 , pp. 36 - 52 .

Weill , P. and Woerner , S.L. ( 2013 ), “ Optimizing your digital business model ”, MIT Sloan Management Review , Vol. 54 No. 3 , pp. 71 - 78 .

Wilson , F. and Post , J.E. ( 2013 ), “ Business models for people, planet (& profits): exploring the phenomena of social business, a market-based approach to social value creation ”, Small Business Economics , Vol. 40 No. 3 , pp. 715 - 737 .

Wirtz , B.W. , Pistoia , A. , Ullrich , S. and Göttel , V. ( 2016 ), “ Business models: origin, development and future research perspectives ”, Long Range Planning , Vol. 49 No. 1 , pp. 36 - 54 .

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Yunus , M. , Moingeon , B. and Lehmann-Ortega , L. ( 2010 ), “ Building social business models: lessons from the grameen experience ”, Long Range Planning , Vol. 43 Nos 2-3 , pp. 308 - 325 .

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Further reading

Weill , P. , Malone , T.W. and Apel , T.G. ( 2011 ), “ The business models investors prefer ”, MIT Sloan Management Review , Vol. 52 No. 4 , pp. 17 - 19 .

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Business Development for Researchers

Sebastian Adolphy

About this video

Business benefits with deep insights from research, but is it possible for researchers to benefit from business models? Is it relevant for researcher to know how to find business ideas and build a business model around those? The answer is yes. Business-savvy researchers can combine academia and industry, and for that they need motivation and a little guidance.

This module is a part of the Researcher Academy’s Innovation for Researchers series, delivered in collaboration with experts Christina Stehr and Sebastian Adolphy from Humboldt-Innovation   at Humboldt University. It is designed specifically for researchers, who want to make an impact with their research and are looking for ways to transfer their research results into marketable applications.

After watching this module, you will come away with a better understanding of the fundamental elements of business models. You will understand the path from having a business idea to building a business model using innovative methods like “Business Model Canvas”.

About the presenter

Humboldt-Universität zu Berlin

Sebastian Adolphy teaches Entrepreneurship and Innovation Management at Humboldt-Universität zu Berlin.  As educational program manager at the startup incubator he enables students and researchers to develop their business ideas and guides their journey in starting companies. An engineer by trade, he built his expertise as lecturer, scientist and consultant in engineering design methodology and product development at various Berlin research institutions. His current research focus is on personalities, competences and roles in startup teams.

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The Business Model of a University Research Lab

By: Henry W. Chesbrough, Ze'ev R. Abrams

This case study teaches students how to think through the management of open innovation in the context of a different setting such as a university research laboratory. The goal of the case is to show…

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This case study teaches students how to think through the management of open innovation in the context of a different setting such as a university research laboratory. The goal of the case is to show students that there are significant management issues in the organization and selection of models for academic research. Open innovation, in turn, may require students to bend and flex the model in different contexts or situations. In the process of analyzing this case, students must take into consideration the context of the organization, the culture, and the drivers and metrics for success.

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To teach students how to think through the management of open innovation in the context of a different setting such as a university research laboratory

Sep 1, 2011

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The business model of research is winner-take-all. Is it killing higher ed?

By: Alana Dunagan

The international perception of US higher education can be pared down to three words: Harvard, Stanford, and MIT.

But that’s not the reality of US higher education. These institutions serve a tiny fraction—0.258%, to be exact—of the nearly 20 million students enrolled in higher education across the country. These three schools have world-class research production, exceptional graduation rates, and strong student outcomes in terms of graduation and job placement. The rest of higher education has copied their business model—to ill effect.

Why is the business model of elite institutions breaking down for most of the country’s colleges and universities? And does this breakdown signal a need to shift priorities both in how a majority of institutions serve students and in how systems evaluate the value of those institutions?

Higher education is a conflation of business models

As Michael B. Horn, Clayton M. Christensen, Louis Soares, and Louis Caldera first argued in Disrupting College nearly a decade ago, higher education has conflated multiple businesses. Simply put, the business model of knowledge creation (research) coexists with the business model of knowledge transmission (teaching)—to say nothing of ancillary activities like social networking and alumni fundraising that have business models of their own. 

At elite schools, this coexistence is synergistic: ultra-elite academics conduct prize-winning research which attracts top students, as well as research grants and donations from wealthy alumni. It’s a virtuous, coherent model that enables top institutions to be generous with financial aid, ostensibly meritocratic, and attractive to the world’s best thinkers and learners. 

Looking downstream, to a wider, more typical set of postsecondary institutions, the coexistence of these models is more fraught. The world’s best students that often attend elite institutions are academically prepared for college and hypermotivated. But this isn’t representative of the overall college-going population. Only a tiny fraction of 12th-grade students— 25% , to be exact—are proficient in math and only 37% are proficient in reading. Remediating these gaps once students enroll in college is intense and expensive—and means that non-elite institutions have to focus more, not less, on high-quality teaching. 

In higher education overall, hiring and promotion practices for professors often don’t favor teaching, however. There is no “instructional quality” analogue to “publish or perish”. Professors at many institutions receive relatively little training in effective instructional practices; advising is often not evaluated at all. Teaching and researching are essentially two different jobs ; professors must do both. If professors at elite schools are unfocused or untalented at teaching, well-prepared, hyper-motivated students can still fare well. But at non-elite schools, students bear the costs of bad teaching. 

Knowledge creation is winner-take-all

While many institutions attempt to balance research and teaching, the gains of research are not spread evenly. There are only six Nobel prizes every year—Harvard-affiliated researchers have won 60 of them since 2000. Half of total R&D expenditures are made by a mere 45 schools. Over 80% of business school cases used worldwide are produced by Harvard Business School faculty. Knowledge creation is a business model with many participants, but only a few winners.

“Winner-take-all” markets were first described by Sherwin Rosen in a 1981 article for the American Economic Review , observing that “In certain kinds of economic activity there is concentration of output among a few individuals, marked skewness in the associated distributions of income and very large rewards at the top.” In these types of markets, small differences in abilities or talent mean big differences in returns or income—Rosen titled his paper, “The economics of superstars.” Academic research fits this market dynamic, both in the flow of dollars, as well as in the flow of information. The most cited research paper —a 1955 piece on proteins in solution by Oliver Lowry—has been cited over 300,000 times, but much of academic research is never cited at all .

Winner-take-all markets are facilitated in part by technologies that enable the work of one person to be consumed by many. The printing press is one such technology—textbooks were one of the examples cited in Rosen’s first paper. The internet is another, further commoditizing information of all types—including this blog, which competes for eyeballs with the four million hours of content uploaded to YouTube every day . Some evidence— like this data from the NSF —shows that research dollars are not only concentrated in the hands of a few, but that the market for R&D dollars is becoming steadily more concentrated over time. Because elite schools concentrate talent, facilities, and dollars, research revenues continue to flow largely to elite institutions. 

Thousands of schools are replicating a model that doesn’t work for them—or for their students

Knowledge creation remains critical for driving advances in science and technology and pushing the human conversation forward. And for a small fraction of institutions, knowledge creation is a critical element of a highly synergistic business model. But at most institutions following the leaders—or what higher ed nerds refer to as “isomorphism”— creates complexity and costs, rather than significant revenues. At most institutions, trying to do both knowledge creation and knowledge transmission is bad for business—and bad for students. 

This means that when it comes to long-term viability, most institutions are solidly in the knowledge transmission business, regardless of whether they aspire to something else. Their revenues come from students, not the NSF. Our current sense of “world class” institutions is largely based on which institutions excel at research. But perhaps a new ranking system is in order, one that evaluates schools based on their excellence in transmitting knowledge. This focus on teaching and learning would be a new competitive trajectory for higher education, which has historically been focused on prestige. Competing to excel at teaching and learning would demand an increased focus on assessment, and on using data to iteratively improve curriculum and the classroom experience. 

These institutions would be well served if they simplified their business model, and prioritized what they really do: teaching students. But focusing in on knowledge transmission calls radical questions onto the table, for institutions, as well as for accreditors. Who should teach? Should they have tenure? Are PhDs necessary? What training in educational methods should professors have? How should schools make promotion decisions? Do schools need to develop their own curriculum or are they better off buying it?

These questions are fraught, but they are important, especially in the current climate of anxiety around the cost—and value—of college, as well as troubling skills gaps. Institutions that grapple with them and innovate effectively can start optimizing their resources in a time when the university business model is breaking down. But more importantly, these institutions can better serve the millions of students who are signing up to have knowledge transmitted to them. 

Alana Dunagan

Alana’s higher education research works to find solutions for a more affordable system that better serves both students and employers.

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Getting started: Business model research

• Getting started: Business model research - tutorial transcript [Google Doc]
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Cambridge Business Model Innovation Research Group (CBiG)

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Key research projects:

• Business Model Innovation for Digital Technologies and Productivity
• Business Model Innovation in Digital Fabrication
• Business Model Innovation and Entrepreneurship
• Business Model Innovation and Business Ecosystems
• Business Model Innovation and Economic Development

The progress of globalisation, the intensity of technological change, and shifts in industry borders have all created opportunities for new business models. Indeed, business model innovation can create huge opportunities while threatening traditional means of generating revenue. Such innovations can, consequently, make the fortunes of some firms while killing the market positions of others.

A business model articulates the customer value proposition, the means to create that value, the network of partners needed and the approach to capture some of the value for the firm. The business model can be seen as a complex system that acts as the mechanism to enable the technology’s core technical properties to be transferred as benefits to the customer via markets. This research programme seeks to explore further the role of business model innovation in connecting new technology with the market in order to deliver new customer value propositions and spur growth. In particular, understanding the interface between technology, business and policy can help determine whether new businesses will be viable, how established businesses can sustain their leadership position and whether policies related to industry and technology can be implemented effectively.

The Cambridge Business Model Innovation Research Group (CBiG) carries out research by applying economic and management theories to innovation issues. The programme has a specific focus on exploring the antecedents and consequences of business model innovation. The Group is investigating the implications of business model innovation on productivity resulting from the adoption of digital technologies. In addition to its own initiatives, the programme engages and builds on research on business model innovation across a number of research centres at the IfM covering technology, management and policy.

The programme also run various courses on business model innovation based on the ongoing research.

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Driven by the high practical relevance of the topic, business model innovation has developed into its own discipline in research. 

The vibrant new field at the crossroads of innovation and strategy offers many unanswered questions to be explored. based at the university of st. gallen, one of europe’s leading business schools, our team is committed to being at the forefront of business model innovation research. our academic network includes leading scholars from other universities such as of stanford (design school), berkeley (haas school of business), and harvard (harvard business school)., our current focus topics include:, process, methods, and tools of business model innovation (construction methodology), networks and ecosystems as a driver for bmi, software/technology and their relationship to bmi, anchoring of bmi within the organization, business model patterns and their diffusion across multiple industries, peer-to-peer business models, open business models, business model innovation in emerging markets, the business model navigator.

The BMI-Book explains the St. Gallen Business Model Innovation Navigator and the 55 Business Model Patterns. Read more about the background and many practical examples of successful business model innovators.

«… a sensation.» Frankfurter Allgemeine

Hanser Verlag 2013

Exploring the Field of Business Model Innovation

This book analyses 50 management theories in the context of business model innovation to yield valuable new insights. It presents ‘grand theories’ that will help researchers and refl ective practitioners to approach business model innovation through a di erent angle and to understand their patterns and mechanisms. The authors aim to open up a new debate on the fascinating phenomenon of business models.

The BMI Video

We regularly publish our findings in academic journals and present them at scientific conferences, where we connect with the scientific community and expand our horizons. recent publications on business model innovation:, gassmann, o., frankenberger, k., sauer, r. (2016): exploring the field of business model innovation – new theoretical perspectives. palgrave macmillan: london, gassmann, o., sauer, r., hofmann, f. (2015) geschäftsmodelle radikal innovieren: der st.galler business model navigator, in: innovationscontrolling bd. 37, gleich, r., schimank, c. (eds.), haufe: münchen. , frankenberger, k., gassmann, o., sauer, r., lee, jy., meister, c. (2015): cewe: business model innovation – when disruptive technologies hit you. thecasecenter., gassmann, o., frankenberger, k., csik, m., sauer, r., lee, jy (2015): holcim: paving the way towards radical business model innovation. thecasecenter., friesike, s,; widenmayer, bastian ; gassmann, oliver ; schildhauer, thomas: opening science: towards an agenda of open science. in: journal of technology transfer, (2015), nr. 40, s. 581-601. , schweitzer, f., rau, c., gassmann, o., hende, e. (2015): technologically reflective individuals as enablers of social innovation. in:journal of product innovation management, 32(6), (2015), s. 847-860. , krech, c. a.; rüther, f.; gassmann, o. (2015): profiting from invention: business models of patent aggregating companies. in:international journal of innovation management, 19 (2015), nr. 3, s. 1-26.   , winterhalter, s.; zeschky, m.; gassmann, o. (2015): dual business models in emerging markets: an ambidexterity perspective. in: r & d management (2015), nr. forthcoming, s. 1-33.   , schweitzer, f.; gassmann, o.; rau, c. (2014).: lessons from ideation: where does user involvement         lead us. in: creativity and innovation management, 23 (2014), nr. 2, s. 155-167. , gassmann, o. (2014): the danger in missing the innovation moment : companies fail to identify future opportunities because they do not have fresh business models. in: financial times (2014) , zeschky, m.; winterhalter, s.; gassmann, o. (2014): from cost to frugal and reverse innovation : mapping the field and implications for global competitiveness. in: research technology management, 57 (2014), nr. 4, s. 1-8.   , palmié, m.; keupp, m. m.; gassmann, o.(2014): pull the right levers: creating internationally ‘useful’ subsidiary competence by organizational architecture. in: long range planning, 47 (2014), nr. 1-2, s. 32-48 , frankenberger, k.; weiblen, t.; gassmann, o. (2014): the antecedents of open business models : an exploratory study of incumbent firms. in: r&d management, 44 (2014), nr. 2, s. 173-188.  , zeschky, m.; widenmayer, b.; gassmann, o. (2014): organizing for reverse innovation in western mncs: the role of frugal product innovation capabilities. in: international journal of technology management, 64 (2014), nr. 2-4, s. 255-275 , frankenberger, k.; weiblen, t.; gassmann, o. (2013): network configuration, customer centricity, and performance of open business models : a solution provider perspective. in: industrial marketing management, nr. imm6869 , gassmann, o. (2013): keine halben sachen. in: harvard business manager, (2013), nr. 2, s. 32-33. , frankenberger, k.; weiblen, t.; csik, m.; gassmann, o. (2013): the 4i-framework of business model innovation : a structured view on process phases and challenges. in: international journal of product development, 18, nr. 3/4, s. 249-273. , keupp, m. m.; gassmann, o. (2013): resource constraints as triggers of radical innovation: longitudinal evidence from the manufacturing sector. in: research policy 42 (2013), nr. 8, s. 1457-1468., gassmann, o.; schweitzer, f. (hrsg., 2013): management of the fuzzy front end of innovation, springer,  339 s. , gassmann, o.; frankenberger, k.; csik, m. (2014): the business model navigator, 55 models that will revolutionise your business, ft publishing pearson: harlow, uk, 387 s. .

For more publications on strategic management of innovation, click here .

Developing national cancer survivorship standards to inform quality of care in the United States using a consensus approach

• Open access
• Published: 13 May 2024

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• Michelle A. Mollica 1 ,
• Gina McWhirter 2 ,
• Emily Tonorezos 1 ,
• Joshua Fenderson 3 , 4 ,
• David R. Freyer 5 , 6 ,
• Michael Jefford 7 , 8 , 9 ,
• Christopher J. Luevano 10 ,
• Timothy Mullett 11 ,
• Shelley Fuld Nasso 12 ,
• Ethan Schilling 13 ,
• Vida Almario Passero 14 , 15 , 16 &

the National Cancer Survivorship Standards Subject Matter Expert Group

To develop United States (US) standards for survivorship care that informs (1) essential health system policy and process components and (2) evaluation of the quality of survivorship care.

The National Cancer Institute and the Department of Veterans Affairs led a review to identify indicators of quality cancer survivorship care in the domains of health system policy, process, and evaluation/assessment. A series of three virtual consensus meetings with survivorship care and research experts and advocates was conducted to rate the importance of the indicators and refine the top indicators. The final set of standards was developed, including ten indicators in each domain.

Prioritized items were survivor-focused, including processes to both assess and manage physical, psychological, and social issues, and evaluation of patient outcomes and experiences. Specific indicators focused on developing a business model for sustaining survivorship care and collecting relevant business metrics (e.g., healthcare utilization, downstream revenue) to show value of survivorship care to health systems.

Conclusions

The National Standards for Cancer Survivorship Care can be used by health systems to guide development of new survivorship care programs or services or to assess alignment and enhance services in existing survivorship programs. Given the variety of settings providing care to survivors, it is necessary for health systems to adapt these standards based on factors including age-specific needs, cancer types, treatments received, and health system resources.

Implications for Cancer Survivors

With over 18 million cancer survivors in the United States, many of whom experience varied symptoms and unmet needs, it is essential for health systems to have a comprehensive strategy to provide ongoing care. The US National Standards for Survivorship Care should serve as a blueprint for what survivors and their families can anticipate after a cancer diagnosis to address their needs.

Avoid common mistakes on your manuscript.

Introduction

A cancer survivor is any individual from the point of diagnosis through the balance of life [ 1 ]. There are over 18 million cancer survivors in the United States [ 2 ], and with advances in diagnostic and treatment capabilities and the aging population, this number is expected to grow. People with cancer have unique survivorship needs, including physical and psychological symptoms both during and after their treatment, risk for recurrence and subsequent cancers, and social needs. As a result, most survivors require long-term follow-up care.

Survivorship care is multifaceted, and recommendations have included surveillance for recurrence and new cancers, prevention and management of physical and psychosocial symptoms, and promoting healthy behaviors [ 3 , 4 ]. While survivorship guidelines exist [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], the delivery of survivorship care, including what care is delivered, to whom it is delivered, and who delivers the care, varies greatly based on factors including care setting, geographical area, and personal resources. Survivorship care is often fragmented, depending on survivors to seek care from multiple providers without a coordinated system. This is further exacerbated by differing philosophies concerning when survivorship care should be delivered (e.g., post-treatment for those treated with curative intent versus post-diagnosis for anyone with a cancer diagnosis). Survivorship care for many people in the United States is suboptimal, leaving survivors with persistent symptoms, unmet needs, and lack of access to comprehensive care.

There have been several previous efforts to define survivorship care. The LIVESTRONG Essential Elements of Survivorship Care were developed in 2011 with the goal of building consensus in the survivorship community around how best to address the needs of post-treatment survivors [ 14 ]. The American College of Surgeons’ Commission on Cancer (CoC) Survivorship Standard 4.8, recently updated in 2019, defined requirements for CoC accredited programs [ 15 ]. The updated survivorship standards require a survivorship coordinator, a survivorship program documenting a minimum of three services offered each year to support patients, and a focus on enhancing existing and developing new services. This revised standard was an update from the 2016 survivorship standard that required documentation of a survivorship care plan for patients with early-stage cancer treated with curative intent [ 16 ]. In addition, the Quality of Survivorship Care Framework was developed to define the key components of quality survivorship care that are applicable to diverse populations of adult cancer survivors and was intended to inform clinical care, research, and policy [ 3 ].

Given that people with cancer are treated in diverse settings, including cancer centers, academic medical centers, and community sites, there is a need for developing a comprehensive set of national standards for health systems to provide quality survivorship care. The overall goal of this project was to build upon existing efforts to develop national standards for survivorship care that can be utilized by all healthcare systems to assess the quality of existing survivorship care and guide the development of new programs and services. Standards of care represent recommendations for health systems that apply to the patients they serve. Specifically, we sought to define standards for (1) essential health system policy and process components of survivorship care programs and (2) the evaluation of the quality of survivorship care.

The Biden Cancer Moonshot, President Biden’s whole of government response to accelerate progress against cancer and end cancer as we know it, established a goal to develop standards for survivorship care. This project was led by the National Cancer Institute and United States Department of Veterans Affairs, in collaboration with several other Health and Human Services Agencies. Methods were adapted from a previous effort in Australia, where an online modified reactive Delphi survey was completed, followed by a consensus meeting of survivorship experts to inform the Victorian Quality Cancer Survivorship Framework [ 17 ].

Key definitions

For the purpose of this project, we defined a cancer survivor as any individual from the time of diagnosis through the balance of life, diagnosed at any age or stage. We also adapted definitions from Lisy et al. for health system policy, health system process, and evaluation/assessment [ 17 ]. Health system policies were defined as principles and procedures guiding an organization’s capacity and structure to provide survivorship care; health system processes were an organization’s capacity to deliver care through its embedded practices and procedures; and evaluation/assessment were how to measure the impacts of survivorship care within an organization.

Identification of possible indicators

A list of potential indicators in the three domains of health system policy, process, and evaluation/assessment were identified through a review of survivorship and cancer-specific guidelines, the CoC survivorship standard [ 15 ], existing survivorship quality frameworks [ 3 , 17 ], US cancer control plans [ 18 ], and relevant literature. These resources were gathered based on the recommendations of the Task Force and subject matter experts.

Subject matter expert consensus meetings

In 2023, three virtual meetings with survivorship subject matter experts were held to prioritize the most important and feasible indicators to include in the standards. The three meetings were iterative and invited subject matter experts included leading national and international experts in clinical survivorship care, survivorship research, implementation science, health policy, and survivor advocates. Subject matter experts were chosen based on their knowledge of the evidence related to survivorship care and/or their experience in providing care, informing health policy, and/or conducting survivorship care delivery research. We utilized a snowball approach to identify experts and accepted additional recommendations from invited experts, with the overall goal of collectively representing diverse perspectives and experiences related to survivorship. A total of 35 experts participated in the meetings. Additionally, these meetings were open for public viewing and attendees were able to submit comments and questions for consideration and comment.

Meeting 1 focused on providing background to the project, an open discussion among the experts, and individual polling where experts rated the importance of each possible survivorship indicator and identified other indicators for consideration in the next round. Importance was defined using the definition from Lisy and colleagues, as “a core component in achieving survivorship care and can be used to measure the quality of survivorship care” [ 17 ]. For the first meeting, experts were asked only to consider the importance of each indicator rather than also considering the feasibility of implementing and collecting this information. Experts could also suggest edits to the indicators. Questions from Meetings 1–3 can be found in the supplementary information (Appendix A ).

Responses from the Meeting 1 poll were aggregated to identify those rated most important and those rated least important. Based on those results and suggestions from experts and public viewers on edits and additional indicators, an updated list of 15–20 indicators in each domain (policy, process, evaluation/assessment) was developed. Meeting 2 was then held one week later, where results from Meeting 1 were shared, including the indicators rated most important and those rated least important. Following was an open discussion of the results among the experts, including a discussion of feasibility. Experts were then asked to select the top 10 most important and feasible (to implement and/or collect) indicators within each domain; they could also suggest edits to the indicators.

Responses from Meeting 2 were then aggregated to identify the top 10 rated most important and feasible indicators in each domain. Results were shared with experts during Meeting 3, followed by an open discussion of the results. A final poll was conducted where experts were asked to suggest edits to the top 10 indicators in domain and to identify indicators that did not make the top 10 but should be considered for inclusion in the final standards.

Based on suggested edits and additions during the Meeting 3 poll and through refinement by the co-chairs, a final set of standards was developed that includes 10 indicators in health system policy, processes, and evaluation/assessment.

Meeting 1 results

The poll for Meeting 1 included 18 indicators for health system policy, 33 indicators for processes, and 20 indicators for evaluation/assessment. Based on polling results, the policy indicators rated highest importance were a policy requiring establishment of a survivorship program, outlining a team of multidisciplinary health professionals included in the survivorship program, collection of data on survivors’ experience of care and patient-reported outcomes, stratifying survivors to appropriate models of care, provision of support services to survivors based on needs, consideration of approach and timing of transitions in survivorship care, training for healthcare providers, and designation of an organizational survivorship care leader. The policy indicators rated lowest importance were a policy for documenting survivorship care reporting requirements to a government agency, public reporting and dissemination of survivorship outcomes, documenting a minimum of three services offered each year to support patients and survivors, and providing access to prescription produce programs using existing systems/programs. The process indicators rated highest importance were assessment of emotional and psychological effects of cancer and its treatment, physical effects during and following cancer treatment, risk of recurrence or new cancers, practical and social effects (e.g., financial challenges), lifestyle behaviors, and provided with treatment, referrals, and advice to manage physical, emotional, and social effects. The process indicators rated lowest importance were providing the opportunity for participating in research including clinical trials, providing support or referrals for other medical or chronic conditions that are non-cancer related, providing access to advice on vaccinations, providing a meeting to plan survivorship care at the time of diagnosis, providing medically tailored food and nutrition services, providing information and access to complementary health services to support overall health and well-being, and providing a consultation with palliative care. For evaluation , indicators rated highest importance were survivors’ patient-reported outcomes, quality of life, patient-reported experiences of care, return to work, and functional capacity. The evaluation indicators rated lowest importance were overall cost of survivorship care to the health system, number of survivors provided with a survivorship care plan, health professionals’ view of survivorship care, survivors’ hospital admissions, number of referrals made for survivors, and number of primary care providers who are sent a survivorship care plan.

Meeting 2 results

The poll for Meeting 2 included the top 15 indicators for health system policy, 20 indicators for processes, and 20 indicators for evaluation/assessment. The indicators that were ranked in the top 10 for policy included a policy that requires establishment or existence of a survivorship program either on-site or by referral; that describes framework for the provision of survivorship care informed by relevant survivorship guidelines (e.g., ASCO, NCCN, ACS), on stratifying survivors to appropriate models of care; that designates an organizational survivorship care lead who evaluates compliance with standards, has senior role in healthcare system, and includes succession plan for the role, outlining team of multidisciplinary health professionals included in survivorship program; and that considers approach and timing of transitions in survivorship care (e.g., pediatric to adult, acute to primary care, oncology team to survivorship team), for the provision of support services to survivors with special needs and from diverse backgrounds (e.g., navigators, interpreters), for training healthcare providers to deliver survivorship care, for collection of data on survivors’ experience of survivorship care and patient-reported outcomes, and for outlining business case/plan with funding allocated for survivorship care (to include budget). Policy indicators not ranked in the top 10 included outlining the provision of needs assessment tools for survivors at certain time points post-treatment; requiring survivorship-focused information available in other languages or different formats for low-literacy readers; outlining the role of survivors in design, evaluation, and reporting of progress; documenting survivorship care reporting requirements to relevant organizational executive committee; and collecting data on caregivers’ experiences of survivorship care.

For processes , the indicators that were ranked in the top 10 were that cancer survivors were provided access to a survivorship program which addresses the needs of cancer survivors either on-site or by referral; assessed for physical effects during and following cancer treatment, including monitoring for late effects and chronic conditions, and provided with treatment and/or referrals; assessed for emotional and psychological effects of cancer and its treatment and provided with treatment and/or referrals; assessed for practical and social effects of cancer and its treatment (e.g., relationship difficulties, financial challenges, education and employment/return to work) and provided with resources and/or referrals, provided with recommendations regarding surveillance for recurrent or new cancers; assessed for their risk of recurrence or new cancers, including family history and genetic testing; assessed for lifestyle behaviors with recommended management and/or provided with appropriate referral (e.g., smoking cessation, promoting physical activity); provided with access to allied health services (e.g., nutrition, physical therapy, sexual health, fertility services, rehabilitation, dental and podiatry services); provided with access to specialty care services to manage potential late effects (e.g., cardiology); assessed for financial hardship/toxicity and provided with resources and support; and provided with care planning conversations including coordination of care with primary care provider and/or other multidisciplinary health professionals involved in their care. The process indicators not ranked in the top 10 were providing care consistent with their goals, providing access to care to manage fertility and reproductive concerns, providing access to age-specific survivorship care, providing access to primary care services, providing access to age- and gender-appropriate cancer screening or referrals to appropriate screening services, and providing access to tobacco cessation services.

In the domain of evaluation/assessment, indicators ranked in the top 10 were survivors’ and caregivers’ patient-reported outcomes, including quality of life, functional capacity, survival rates (1, 5, and 10 years), experiences of care, return to work, rate of recurrence and new cancers, number and characteristics of survivors lost to follow-up, number of survivors with subsequent chronic condition, rate of survivor service referrals and completions, and relevant business metrics to show return on investment of survivorship care to the healthcare system. Evaluation indicators that were not ranked in the top 10 were collecting data on the number of health professionals trained to provide survivorship care, the number of survivors who have their needs assessed at certain times post-treatment, overall cost of care to survivors and caregivers, survivors’ emergency care and urgent care utilization, number of survivors stratified to different models of care, and oncology providers’ view of the role of nurses and advanced practice providers in survivorship care.

Meeting 3 results

The poll for Meeting 3 included an updated list of the top 10 highest-rated indicators in each domain, as well as a list of the indicators that did not make the top 10. Experts were asked to suggest edits, including suggestions to combine indicators, as well as identify lower-rated indicators to consider for inclusion. Lower-rated indicators that were identified by at least 40% of experts to consider for inclusion were then either combined with other indicators or added to the final set. For health system policy, this included a policy outlining the role of survivors in design, evaluation, and reporting of progress. For processes, this included providing care that is consistent with goals, and consideration of age-specific care. For evaluation/assessment, this included the number of health professionals trained to provide survivorship care.

Final cancer survivorship standards

The National Cancer Standards for Survivorship Care are presented below and in Table  1 and include the top 10 indicators in each of the three domains of health system policy, process, and evaluation/assessment.

Health system policy

The organization has a policy that includes:

Establishment or existence of a survivorship program either on-site, through telehealth, or by referral

A framework for the provision of survivorship care informed by survivor stakeholders and relevant survivorship guidelines (e.g., American Society of Clinical Oncology, National Comprehensive Cancer Network, Children’s Oncology Group)

A description of multidisciplinary care, including each team member’s specific roles and responsibilities and workflow(s) for referrals to team members

An overview of how to stratify and refer survivors to appropriate models of care based on age, treatments, and risk factors

Description of the approach and timing of transitions in survivorship care and shared care (e.g., pediatric to adult providers and settings, oncology team to survivorship team and/or primary care) and efforts to prevent/mitigate loss to follow-up care

An outline for the provision of information for support services (e.g., navigators, social work, interpreters) for survivors based on their needs (including but not limited to health, insurance, and financial literacy, disability status), including survivors from diverse and underserved backgrounds

Identification of an executive-level survivorship care lead (with succession plan) whose role is to ensure compliance with standards, with reporting to an appropriate executive committee

Collection of longitudinal data on survivors’ experience of survivorship care and patient-reported outcomes

Requirements and methods for training healthcare providers (either on-site or through an external training program) to deliver survivorship care within their scope of practice

A business case/plan, including budget, with funding allocated for survivorship care

Health system processes

Cancer survivors are…

Provided with access and referral to a survivorship program that addresses the needs of cancer survivors either on-site, through telehealth, or by referral

Assessed at multiple points in their follow-up care for physical effects during and following cancer treatment, including monitoring for late effects and chronic conditions, and provided with treatment and/or referrals

Provided with access to appropriate specialty care services to manage potential late effects (e.g., cardiovascular issues) either on-site, through telehealth, or by referral

Assessed at multiple points in their follow-up care for emotional and psychological effects of cancer and its treatment and provided with treatment and/or referrals

Assessed for practical and social effects of cancer and its treatment (e.g., social risks, health-related social needs, education and employment/return to work or school) and provided with resources and/or referrals

Assessed for their risk of recurrence or new cancers, including family history and genetic testing, and provided with recommendations and referrals regarding surveillance for recurrence or new cancers

Assessed for lifestyle behaviors and provided with recommended strategies for management and appropriate referrals or education as needed (e.g., smoking cessation, diet/nutrition counseling, promoting physical activity)

Provided with access and referrals to appropriate supportive health services (e.g., nutrition, occupational and physical therapy, rehabilitation, sexual health, fertility services, dental and podiatry services)

Assessed for financial hardship/toxicity and concerns regarding insurance coverage, and provided with resources and support as needed

Engaged in the care planning process including discussion of shared goals of care, advanced care planning, and coordination of care with providers and services (e.g., primary care provider, other health professionals, and community-based services) as needed

Health system evaluation/assessment

The organization has a process to collect data on…

Survivors’ patient-reported outcomes, including quality of life, and experiences of survivorship care

Survivors’ functional capacity

Survivors' return to previous participation in paid and unpaid work/ school/ productive activities of living

Survival rates (1, 5, and 10 years) from the time of diagnosis

Rate of recurrence

Rate of subsequent cancers

Number and relevant characteristics (demographics, clinical factors) of survivors lost to follow-up

Caregivers’ experiences and unmet needs

Number of health professionals trained to provide survivorship care

Relevant business metrics to show return on investment of survivorship care to the healthcare system (e.g., healthcare utilization, rate of referrals and completion, downstream revenue)

Efforts to advance survivorship care have largely been focused on development of evidence-based guidelines and defining the key components of quality survivorship care. Survivorship care services vary greatly among cancer centers and in the community [ 19 ]. Given the growing population of survivors treated in a variety of care settings, it is essential to define a standard for health systems to care for survivors. This current effort aimed to address this gap by developing national standards to define and prioritize key health system policy, process, and evaluation/assessment indicators. While evidence-based guidelines inform provider practices [ 5 ], and the Nekhlyudov framework identifies key components to survivorship care [ 3 ], the standards presented herein build on this previous work. They are intended to be utilized to assess survivorship programs within a health system or organization to address the comprehensive needs of cancer survivors during and after treatment.

The methods for this project were adapted from the Victorian Quality Cancer Survivorship Framework [ 17 ], and the resulting indicators differed in several ways. First, consensus meeting discussions included the need to de-emphasize survivorship care plan documentation, given limited evidence on improving survivor outcomes [ 20 ]. In addition, these standards incorporate all modalities to offer survivorship care services, including telehealth. Experts also identified the need to emphasize support for care transitions across the continuum from diagnosis forward, to include a policy requiring training of healthcare professionals to deliver survivorship care, and subsequently to assess the number of providers trained. Though it was recognized that assessment of survival would be challenging, experts also recognized the need to include and aspire to collect long-term survival data after diagnosis with cancer (1, 5, and 10 years). Finally, US standards include a policy to develop a business case/plan with funding allocated for survivorship care, as well as relevant business metrics to show return on investment for survivorship care. Experts in the consensus meeting stressed the need for a sustainable business model for delivering survivorship care services that is evaluated longitudinally using appropriate metrics including (but not limited to) overall healthcare utilization, rate of referrals and completion, and downstream revenue to the organization or healthcare system. If organizations are to provide quality care for the growing number of survivors, it will be critical to show financial impacts for the healthcare system. An additional process indicator focused specifically on assessing and mitigating survivors’ financial hardship and concerns regarding insurance coverage. This is not surprising given the high proportion of cancer survivors who reporting experiencing financial challenges in the United States [ 21 ].

Consensus meetings also discussed considerations that health systems should take when implementing these standards. In the area of health system policy, experts and advocates noted that these indicators could be combined into one survivorship policy that informs care system-wide. One advantage of separating these indicators, however, is that key informants or stakeholders could be included in development or writing the individual policies. Furthermore, the impact of separate policy changes could be evaluated individually. Additionally, several experts noted that process indicators related to physical, psychological, and social impacts of cancer and its treatment should go beyond only assessment for late effects and should include management and specialty referral, as indicated. Experts and advocates also noted that in the area of evaluation/assessment, validated measures should be utilized whenever possible. While this process was not intended to endorse specific measures or tools, participants emphasized an expectation that validated, patient-centered measures would be used. Finally, health systems should ensure consent has been obtained from survivors and caregivers before assessment.

The final set of standards represents input from survivorship experts and advocates and can be implemented in a variety of settings. A key next step in this work is to implement the standards within healthcare systems that are developing new survivorship programs or have existing programs or services. Healthcare systems that provide care for people after a cancer diagnosis, including but not limited to cancer centers, may use these standards to assess organizational alignment and enhance their survivorship care services. After aligning with the standards, there will be a need to evaluate for feasibility, potential for sustainability, and impact on survivor outcomes. It is important to note that use of these standards by health systems is voluntary, and components of care may or may not be covered by public or private health insurance.

There are several key considerations when implementing these survivorship standards to inform survivorship program development or assess alignment with the indicators. First, while these standards were intended to inform health systems caring for cancer survivors diagnosed at any age, with any cancer, and at any stage, there is a need to tailor care services based on specific factors, including age, setting, and specific cancer types and treatment. For example, while we utilized the NCI recognized definition of a cancer survivor from the time of diagnosis through the balance of life [ 1 ], the standards could also be applied to post-treatment survivors. In addition, survivors of pediatric cancers diagnosed between birth and 15 years may have markedly different needs from survivors of adolescent and young adult (AYA) or older adult survivors [ 22 , 23 ]. For patients diagnosed as young children, survivorship care and research are already highly developed with effective, existing clinical models that constitute “standard of care” for this population. Indeed, in many ways pediatric survivorship care and research have inspired adult-focused efforts. But in pediatric cancer survivorship, particularly for well-established treatments, the evidence base for late effects and their trajectory is well-established and comprehensive. For most childhood cancer survivors, recommended survivorship care for late effects monitoring and management is generally annual, lifelong follow-up, including transition to adult-focused care during young adulthood [ 24 ]. There is still significant work to be done to improve transition services and outcomes [ 25 ], which is one area where the standards could be very informative. Existing guidelines for childhood cancer survivorship care should continue to be utilized [ 13 ].

For survivors of AYA cancers (diagnosed between 15 and 39 years old), health systems and providers must pay close attention to the unique needs of this population. While the components of their survivorship care may be similar to older adults, AYAs are particularly vulnerable to adverse impacts of cancer on education, career development, work, financial status, and psychosocial needs. Fertility is a particular medical concern for this cancer population. Although a separate, parallel set of survivorship standards for survivors of AYA cancer may not be necessary, it is crucial these standards be applied in a manner responsive to their needs. For older adult survivors, geriatric assessment and focused provider training could be incorporated to address the unique considerations of older adults with a history of cancer [ 26 ]. Overall, the standards should be used as a guide for health systems to adapt based on the known needs of populations served.

Though the results of this work represent national standards for survivorship care, these standards can also be utilized to inform survivorship research. The NCI has supported key efforts in survivorship care, including funding opportunities focused on addressing primary care for cancer survivors [ 27 ] and optimizing survivorship care for survivors transitioning between oncology and non-oncology providers [ 28 ]. A challenge in delivering and evaluating survivorship care, however, is that there has not been an accepted national standard. This project represents consensus agreement of national experts on essential policy, process, and evaluation components to survivorship care that health systems should utilize based on the available evidence. Rigorous evaluation of the implementation and outcomes of these standards will be critical to show continued value to follow-up care for people after a cancer diagnosis [ 29 ]. Additionally, the evaluation/assessment indicators of these standards may be used as meaningful endpoints for survivorship care interventions to show impact on survivor and health system outcomes.

These standards represent a key foundation for improving the delivery of survivorship care across the United States; however, there are limitations to this work. First, though a robust review was conducted to identify potential indicators, it is possible that specific literature may have been inadvertently missed. In addition, the consensus meetings represented a diverse panel of experts and survivor advocates who provided feedback and input. It is possible, however, that some perspectives were not represented in the expert group. One important perspective missing is that of a healthcare business administrator, who will be essential in converting these standards to implementation of survivorship programs. A key next step in this work will be collaboration with healthcare administrators and payors to translate these recommendations into action. The selection of experts also may lead to limitations in the prioritization of indicators. Future empirical support is needed to provide evidence of the outcomes of implementing the standards.

Data availability

No datasets were generated or analyzed during the current study.

This study has not been previously presented.

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Michelle A. Mollica & Emily Tonorezos

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Gina McWhirter

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Mollica, M.A., McWhirter, G., Tonorezos, E. et al. Developing national cancer survivorship standards to inform quality of care in the United States using a consensus approach. J Cancer Surviv (2024). https://doi.org/10.1007/s11764-024-01602-6

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Release of “Fugaku-LLM” – a large language model trained on the supercomputer “Fugaku”

Enhanced Japanese language ability, for use in research and business

Tokyo institute of technology, tohoku university, fujitsu limited, riken, nagoya university, cyberagent inc., kotoba technologies inc..

Kawasaki, May 10, 2024

• Large language model with enhanced Japanese language ability was developed using Japanese supercomputing technology
• Distributed parallel learning by maximizing the performance of the supercomputer “Fugaku”
• Commercial use is permitted, which will lead to innovative research and business applications such as AI for Science

A team of researchers in Japan released Fugaku-LLM, a large language model ( 1 ) with enhanced Japanese language capability, using the RIKEN supercomputer Fugaku. The team is led by Professor Rio Yokota of Tokyo Institute of Technology, Associate Professor Keisuke Sakaguchi of Tohoku University, Koichi Shirahata of Fujitsu Limited, Team Leader Mohamed Wahib of RIKEN, Associate Professor Koji Nishiguchi of Nagoya University, Shota Sasaki of CyberAgent, Inc, and Noriyuki Kojima of Kotoba Technologies Inc.

To train large language models on Fugaku, the researchers developed distributed training methods, including porting the deep learning framework Megatron-DeepSpeed to Fugaku in order to optimize the performance of Transformers on Fugaku. They accelerated the dense matrix multiplication library for Transformers, and optimized communication performance for Fugaku by combining three types of parallelization techniques and accelerated the collective communication library on the Tofu interconnect D.

Fugaku-LLM has 13 billion parameters ( 2 ) and is larger than the 7-billion-parameter models that have been developed widely in Japan. Fugaku-LLM has enhanced Japanese capabilities, with an average score of 5.5 on the Japanese MT-Bench ( 3 ) , the highest performance among open models that are trained using original data produced in Japan. In particular, the benchmark performance for humanities and social sciences tasks reached a remarkably high score of 9.18.

Fugaku-LLM was trained on proprietary Japanese data collected by CyberAgent, along with English data, and other data. The source code of Fugaku-LLM is available on GitHub ( 4 ) and the model is available on Hugging Face ( 5 ) . Fugaku-LLM can be used for research and commercial purposes as long as users comply with the license.

In the future, as more researchers and engineers participate in improving the models and their applications, the efficiency of training will be improved, leading to next-generation innovative research and business applications, such as the linkage of scientific simulation and generative AI, and social simulation of virtual communities with thousands of AIs.

In recent years, the development of large language models (LLMs) has been active, especially in the United States. In particular, the rapid spread of ChatGPT ( 6 ) , developed by OpenAI, has profoundly impacted research and development, economic systems, and national security. Countries other than the U.S. are also investing enormous human and computational resources to develop LLMs in their own countries. Japan, too, needs to secure computational resources for AI research so as not to fall behind in this global race. There are high expectations for Fugaku, the flagship supercomputer system in Japan, and it is necessary to improve the computational environment for large-scale distributed training on Fugaku to meet these expectations.

Therefore, Tokyo Institute of Technology, Tohoku University, Fujitsu, RIKEN, Nagoya University, CyberAgent, and Kotoba Technologies have started a joint research project on the development of large language models.

Role of each institution/company

Tokyo Institute of Technology: General oversight, parallelization and communication acceleration of large language models (optimization of communication performance by combining three types of parallelization, acceleration of collective communication on the Tofu interconnect D)

Tohoku University: Collection of training data and model selection

Fujitsu: Acceleration of computation and communication (acceleration of collective communication on Tofu interconnect D, performance optimization of pipeline parallelization) and implementation of pre-training and fine-tuning after training

RIKEN: Distributed parallelization and communication acceleration of large-scale language models (acceleration of collective communication on Tofu interconnect D)

Nagoya University: Study on application methods of Fugaku-LLM to 3D generative AI

CyberAgent: Provision of training data

Kotoba Technologies: Porting of deep learning framework to Fugaku

Research outcome

1. significantly improved the computational performance of training large language models on the supercomputer fugaku.

GPUs ( 7 ) are the common choice of hardware for training large language models. However, there is a global shortage of GPUs due to the large investment from many countries to train LLMs. Under such circumstances, it is important to show that large language models can be trained using Fugaku, which uses CPUs instead of GPUs. The CPUs used in Fugaku are Japanese CPUs manufactured by Fujitsu, and play an important role in terms of revitalizing Japanese semiconductor technology.

By extracting the full potential of Fugaku, this study succeeded in increasing the computation speed of the matrix multiplication by a factor of 6, and the communication speed by a factor of 3. To maximize the distributed training performance on Fugaku, the deep learning framework Megatron-DeepSpeed was ported to Fugaku, and the dense matrix multiplication library was accelerated for Transformer. For communication acceleration, the researchers optimized communication performance for Fugaku by combining three types of parallelization techniques and accelerated the collective communication on the Tofu interconnect D. The knowledge gained from these efforts can be utilized in the design of the next-generation computing infrastructure after Fugaku and will greatly enhance Japan's future advantage in the field of AI.

2. An easy-to-use, open, and secure, large language model with 13 billion parameters

In 2023, many large language models were developed by Japanese companies, but most of them have less than 7 billion parameters. Since the performance of large-scale language models generally improves as the number of parameters increases, the 13-billion-parameter model the research team developed is likely to be more powerful than other Japanese models. Although larger models have been developed outside of Japan, large language models also require large computational resources, making it difficult to use models with too many parameters. Fugaku-LLM is both high performance and well-balanced.

In addition, most models developed by Japanese companies employ continual learning ( 8 ) , in which open models developed outside of Japan are continually trained on Japanese data. In contrast, Fugaku-LLM is trained from scratch using the team’s own data, so the entire learning process can be understood, which is superior in terms of transparency and safety.

Fugaku-LLM was trained on 380 billion tokens using 13,824 nodes of Fugaku, with about 60% of the training data being Japanese, combined with English, mathematics, and code. Compared to models that continually train on Japanese, Fugaku-LLM learned much of its information in Japanese. Fugaku-LLM is the best model among open models that are produced in Japan and trained with original data. In particular, it was confirmed that the model shows a high benchmark score of 9.18 in the humanities and social sciences tasks. It is expected that the model will be able to perform natural dialogue based on keigo (honorific speech) and other features of the Japanese language.

Future Development

The results from this research are being made public through GitHub and Hugging Face so that other researchers and engineers can use them to further develop large language models. Fugaku-LLM can be used for research and commercial purposes as long as users comply with the license. Fugaku-LLM will be also offered to users via the Fujitsu Research Portal from May 10th, 2024.

Acknowledgement

This research was supported by the Fugaku policy-supporting proposal "Development of Distributed Parallel Training for Large Language Models Using Fugaku" (proposal number: hp230254).

• [1] Large language model : Models the probability with which text appears and can predict the text (response) that follows a given context (query).
• [2] Parameter : A measure of the size of a neural network. The more parameters, the higher the performance of the model, but the more data is required for training.
• [3] Japanese MT-Bench : Benchmark test provided by Stability AI
• [4] GitHub : Platform used to publish open source software
• [5] Hugging Face : Platforms used to publish AI datasets
• [6] ChatGPT : A large language model developed by OpenAI, which has brought about a major social change, surpassing 100 million users in about two months after its release.
• [7] GPU : Originally produced as an accelerator for graphics, but has recently been used to accelerate deep learning
• [8] Continual learning : A method for performing additional training on a large language model that has already been trained. Used for training language models in different languages or domains.

About Fujitsu

Fujitsu’s purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers in over 100 countries, our 124,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: Computing, Networks, AI, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.7 trillion yen (US$26 billion) for the fiscal year ended March 31, 2024 and remains the top digital services company in Japan by market share. Find out more: www.fujitsu.com .

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Carnegie Mellon, UC San Diego top US grant winners for AI research

Science|Business ranks recipients of National Science Foundation grants in AI - and finds an Italian at MIT is the biggest winner

Photo credits:  Auburn University / Flickr

The US has been leading global development of artificial intelligence – but which universities are biggest in the sciences underlying the field? A Science|Business analysis of grants by the US National Science Foundation ranks Carnegie Mellon, UC San Diego, University of Illinois’ Urbana-Champagne campus, and MIT as its top four AI-related science grant recipients since 2010.

Over the past 14 years, Pittsburgh-based Carnegie Mellon University has been awarded $225 million (€209 million) in NSF grants for AI-related research – more than any other American university. Close behind is the University of California San Diego with $219 million, the University of Illinois Urbana-Champagne at $196 million, and the Massachusetts Institute of Technology with $183 million.

Taken together, those four universities have won 10.6% of the agency’s $7.7 billion total AI-related science grants. But the funding is more broadly distributed than that might suggest. In all, 93 universities across the US have received AI-related NSF grants exceeding $20 million each, and 145 have received more than $10 million each. That diversity may be part of the reason the US tech industry, which relies on academic breakthroughs for much of its own AI development, has been so strong in the field: today, eight out of the ten largest tech companies in the world by market cap are US-based .

Also important to the US strength, however, is that many of its top researchers are foreign-born – a feature of American science since before the wartime Manhattan Project.

MIT’s Italian grant winner

The single most successful principal investigator, in terms of NSF money received, is European: Tomaso Poggio. He is a computational neuroscientist at MIT, focusing on the mathematics of deep learning and the visual cortex – both areas of fundamental science underpinning today’s AI systems. He was educated in Italy and worked at the Max Planck Society in Germany, before moving to MIT in Cambridge, Mass. His name is on $48.6 million in AI research grants from NSF, and is co-director of MIT’s Center for Brains, Minds and Machines . Poggio also contributed over the years to the creation of many AI companies, including Google-owned DeepMind.

Of course, there are lots of AI funders in the US, public and private; and money alone can’t buy scientific success. So our analysis of the NSF’s public database of grants is only one possible indicator of who matters most in AI-related science. But the NSF, founded in 1950, is the country’s premiere funder of fundamental research, and most of today’s commercial AI developments are based on decades of basic research on how brains work and computers can “learn”. Universities and their public funders, across the globe, have been critically important for that. 

In Brussels, NSF’s analogue is the European Research Council. In a March report , the ERC said it had awarded about €2 billion in frontier research on AI-related topics, across 1,048 projects, since 2007. Its funding has reached 25 countries, with a majority of the projects in the field of computer science and informatics. The topic has also grown in importance: while AI projects represented 3% of the ERC’s funding in 2013, they made up 14% in 2021.

By contrast, the NSF has awarded $7.7 billion in grants since 2010 that, according to its database taxonomy, is AI-related. Of that, $5 billion was awarded in the last seven years, with 2021 the first year when its total AI-related funding topped $1 billion. Last year, it funded 1,549 new projects for $870 million.

According to its website , the NSF has been a funder of AI-relevant projects since the 1960s, from neural networks to large language models, the technology powering today’s ChatGPT and other “generative AI” systems. In 2020 the NSF launched its National Artificial Intelligence Research Institutes programme, under which universities get $16 million to $20 million to set up special institutes on specific AI themes. The current 2024 call , for example, invites applications for  astronomical sciences, materials research and new methods for strengthening AI.

Over the years, the biggest single NSF project was in 2013 for a “Center for Brains, Minds and Machines: the Science and the Technology of Intelligence” – the project that led to Poggio’s lab at MIT – with $48.1 million in funding. The second biggest project, establishing a Center for the Study of Evolution in Action combining bioscience and computation, got Michigan State University another $48 million in 2010.

Looking at NSF AI grants by region, California has come first with more than $1 billion received – and of that, $481 million went to three state universities, UC San Diego, UC Berkeley and UCLA. New York and Massachusetts are close behind California. Measured by funding per project, however, Oklahoma ranks first with an average of $825,000 over 80 projects.

In all, the NSF has awarded AI-related grants to 9,710 principal investigators over 13,548 projects. After Poggio, the biggest individual winners are Erik Goodman at Michigan State, and Jeffrey Bokor at UC Berkeley’s Center for E3S (Energy Efficient Electronics Science).

Among foreign-born researchers, the biggest winner after Poggio is Dhabaleswar Panda, originally from India, who has already received five grants as principal investigator in the field of AI. His main project, ICICLE, is focused on AI technology applied to environmental issues. Another foreign-born researcher is Stuart Rowan at the University of Chicago, originally from Scotland. He helped establish the university’s Materials Research Science and Engineering Center.

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• UAE releases new AI model to compete with big tech

Emirati AI firm G42 pulled out Chinese hardware and divested stakes in Chinese companies before securing a $1.5 billion investment from Microsoft that was coordinated with Washington. Advanced Technology Research Council Secretary General Faisal Al Bannai, who is also an adviser to the president on strategic research and advanced technology, said the UAE was demonstrating it can be a major player in artificial intelligence.

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May Advertisement Model Brand Reputation Rankings Announced

The Korean Business Research Institute has revealed this month’s brand reputation rankings for advertisement models!

The rankings were determined through an analysis of consumer behavior, using big data collected from April 3 to May 3. The Korean Business Research Institute evaluated the participation, communication, media, and social values of popular advertisement models in order to calculate each star’s total brand reputation index for May.

Kim Soo Hyun shot to the top of the list this month, scoring a brand reputation index of 2,388,253 for May, while Lim Young Woong took second place with a score of 1,525,447.

Ma Dong Seok came in at a close third with a brand reputation index of 1,432,721, marking an impressive 60.71 percent rise in his score since April.

Son Heung Min ranked fourth for the month with a brand reputation index of 930,693, and Son Suk Ku rounded out the top five with a score of 850,648.

Check out the top 30 for this month below!

• Kim Soo Hyun
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• Park Seo Joon
• Jo Jung Suk
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UAE releases new AI model to compete with big tech

A GOVERNMENT research institute in the United Arab Emirates on Monday (May 13) released a new open source GenAI model, which could rival the ones from big technology companies.

Abu Dhabi’s Technology Innovation Institute (TII) said it was releasing the Falcon 2 series: Falcon 2 11B, a text-based model, and Falcon 2 11B VLM, a vision-to-language model that can generate a text description of an uploaded image.

TII is a research centre within Abu Dhabi’s Advanced Technology Research Council.

The UAE, a major oil exporter and influential Middle East power, is making huge investments in artificial intelligence (AI). But that bet has also drawn scrutiny from the US officials who last year issued an ultimatum: American or Chinese technology.

Emirati AI firm G42 pulled out Chinese hardware and divested stakes in Chinese companies before securing a US$1.5 billion investment from Microsoft that was coordinated with Washington.

Advanced Technology Research Council secretary general Faisal Al Bannai, who is also an adviser to the president on strategic research and advanced technology, said the UAE was demonstrating it can be a major player in artificial intelligence.

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The Falcon 2 series come as companies and countries are racing to develop their own large language models following the 2022 release of ChatGPT by OpenAI. While some have opted to keep their AI code proprietary, others, such as UAE’s Falcon and Meta’s Llama, have made their code publicly available for anyone to use.

Al Bannai said he was optimistic about Falcon 2’s performance and that they were working on “Falcon 3 generation”.

“We are very proud that we can still punch way above our weight, really compete with the best players globally,” he said. REUTERS

KEYWORDS IN THIS ARTICLE

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