The scientific review panel of the Council for the Lindau Nobel Laureate Meetings has selected the FINDER Fellow Barbara Voelkl to participate in the 7th Lindau Meeting on Economic Sciences. Nobel Laureates in Economic Sciences and young scientists from around the globe attend the Meeting to exchange knowledge and discuss current social and economic matters as well as recent discoveries in their field. Barbara together with 372 young scientists from 60 countries will meet Laureates of the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel in August 2021. Due to the spread of Covid-19, this year’s activities circle around the Online Science Days 2020 and the Online Sciathon this summer.
Max Tegmark, professor of physics at the MIT, points out in his latest book (Life 3.0 – Being human in the age of artificial intelligence) that “in the future, the only traded resource will be knowledge” because knowledge-driven technology will be able to create all sorts of matter just by reassembling atoms. That sounds far-fetched but even today companies that harvest and utilize data, the precursor of knowledge, are among the highest-valued firms globally. However, you don’t see price tags on all the data that is floating on the internet, because markets have not yet developed. I will inform you from an economic perspective why that hasn’t and why it actually should.
Of course, already today you trade your data in exchange for services. If that hits you by surprise, I recommend a short reading of the terms of service on your Facebook account (if you still have one). Yet, given the margins of big tech companies, the deal could be more profitable for producers and also more transparent. Markets could help to distribute data’s benefits more equally and make data trade less opaque.
Researchers at the University of Amsterdam approached the issue of lacking data markets from a scientific perspective. They defined data commoditization and market mechanisms by proposing six data properties that could pave the way for building solid data markets.
Data sovereignty addresses data ownership. Compared to oil, data, as an economic good, is non-rivalry, meaning that data can be copied and shared infinitely.
Trustworthiness (or trusted data) refers to data being verifiable and auditable. Data is becoming a decisive element of automated decision-making and therefore it has to be trusted. A consumer at a gas station does not have to check the gas for its quality because all parties involved in the gas value chain have agreed on industry standards.
Data reusability ensures that data is stored and can be gathered for future projects and applications. Well, oil doesn’t make such a great job here either.
Actionability demands that data purchased by a company is directly applicable to its value chain. Meaning companies should be able to assess the economic gains or savings through data acquisition before purchasing the data. In the oil industry, companies have estimates for the returns they can expect from acquiring a specific amount of oil through a trade market and established value chains.
Finally, measurability refers to the valuation of data. There are different approaches to conduct the pricing of data.
First, there is the cost-based method, which is based on the idea that data creation, sharing, storage, and analysis are costly and therefore should determine the price.
Second, there is problem-based pricing where the consumer sets a price and the providers react upon. Current examples for the latter are tournaments on data science platforms such as Kaggle.
Also, the price depends on the data quality which hinges on multiple factors. For instance, the number of variables and cases, the precision, the accuracy, the actuality, and the temporal resolution of a data set. In an existing market for data, pricing would be much easier through processes of comparison with similar data assets.
What is the current status across significant industries? A look into the (European) perspective.
Currently, data commoditization is mostly pursued by huge tech companies. As they do both the data harvesting and monetization, there is little incentive for them to engage in the creation of transparent data markets. In Europe, the European Union has set the agenda for the creation of open data markets to facilitate digital transformation. To not fall further behind the curve, industries sitting on huge piles of data, e.g. incumbent banks, should embrace the establishment of open data markets as an opportunity.
Demchenko, Y., Los, W., & de Laat, C. (2018). Data as Economic Goods: Definitions, Properties, Challenges, Enabling Technologies for Future Data Markets. ITU Journal: ICT Discoveries, Special Issue “Data for Goods”.
Mike Schavemaker, Innovation Transformation Lead and senior innovation consultant at Royal Philips, and member of the FINDER Advisory Board, and Barbara Voelkl, FINDER PhD, share their thoughts on the aftermath of COVID-19. Disclaimer: The content of the FINDER blog is not an expression of Royal Philips, nor created on behalf of Royal Philips. The content is created and contributed by private persons.
COVID-19 is first and foremost a tragedy affecting the life, health and existence of hundreds of thousands of people all over the globe. From the perspective of today, beginning of April 2020, the outbreak moreover severely impacts small, medium and multinational businesses. With governments implementing strict or soft lock-downs, physical distancing measures and closures of non-essential businesses, the way of working for those lucky enough to continue to do so changed drastically. COVID-19 forced us to make digital the new normal.
The FINDER program deals with digitalization in the business world, more specific in the financial sector. As such, we start to cautiously think about a post-crisis time given that safeguarding lives is the utmost priority. Will COVID-19 drastically change the way we work and life? Will digital solutions, now starting with all forms of collaboration tools, start to thrive? As BCG (2020) report, the tremendous success of Alibaba and JD.com is often credited to the SARS outbreak in 2003. It is highly likely that COVID-19 functions at least as a catalyst for slow processes to scale up solutions around data, Artificial Intelligence and digitalization.
Digital Only as the New Normal
Working remotely, teaching online, digital conferences and meetings require existing technology to be tailored, powerful and agile. In addition, existing academic research shows the benefits of face-to-face meetings (Kirkman et al., 2004, Andres, 2002) and different requirements for leaders of virtual teams (Malhotra et al., 2005) to be taken into account to guarantee productivity, collaboration and innovation in digital settings.
COVID-19 not only changes the way we work, but also the way we consume. The current state of “Digital Only” increases the significance for incumbents and techs to access and accompany customers online. Consumption patterns changed already and will continue to do so. With lock-downs and #stayhome being the reality, e-consumption in the food, health and wellbeing industry are growing to an incomparable extent. Post-crisis, consumption patterns could continue in a way that the initial algorithm or e-commerce aversion by a systematic share of the population is replaced by favoring digital over real-life solutions. Artificial Intelligences support businesses in detecting consumption patterns, predicting human needs and personalizing offerings to sustain their customers. Given drastic job losses around the globe, particularly nonessential goods and services can benefit heavily from Artificial Intelligences, given that a high competition for revenue after the crisis is likely. COVID-19 therefore can be regarding as a stimulus for flux in the economic, and social system – for better and for worse – but surely will create a flux of innovation spree.
The Power of Digitalization in the Value Chain
Following the supply shortages and tremendous impacts of local lockdowns, multinational companies are likely to rely less on just-in-sequence supply chains based on selective production in countries with lower labor costs. Rather, scaling up existing technologies – 3D printing, Artificial Intelligence, specifically Machine Learning– helps to create safety through redundancy while at the same time increasing predictive precision and balancing costs. COVID-19 will therefore likely impact the surge of insourcing: creating simpler – and in part , more national bounded – supply chains which are less prone to political or biologic triggered quarantines and more automated to off-set the costs of labor using robotic process hyper automation and virtual assistant robots. Investing and developing solutions to adjust resources, offerings and supply chains in real-time supports companies in coming back strongly and thrive with new business models.
Again, here we argue that businesses that are focused on the bottom righthand side of the business model canvass will surge, i.e. focus on designing their capabilities in service of unlocking revenue models sustainably and not looking to the concept of a revenue model as a ‘bumper sticker’. In our earlier blog we did not elaborate how these revenue models are in their turn indirectly subject to the political -and economic biotopes and directly to legal frameworks and its subsequent institutional agents of distributions of wealth (or lack thereof), like for example national health systems, national energy systems and the like.
While we are hoping for the best that governmental and health institutions progress in combating the virus, we hope for strategic business progress in the post-crisis rather than businesses seeing digitalization as the crisis mode while in lock-down. Drastic changes towards digital business and revenue models are needed to use of the power of data, digitization and digitalization to create sustainable revenue models for aftermath.
Andres, H. P. (2002). A comparison of face‐to‐face and virtual software development teams. Team Performance Management: An International Journal.
Kirkman, B. L., Rosen, B., Tesluk, P. E., & Gibson, C. B. (2004). The impact of team empowerment on virtual team performance: The moderating role of face-to-face interaction. Academy of management journal, 47(2), 175-192.
Malhotra, A., Majchrzak, A., & Rosen, B. (2007). Leading virtual teams. Academy of Management perspectives, 21(1), 60-70.
In these challenging times the FINDER team hopes you are all safe and sound.
Though committed as always to the implementation of the FINDER Project, in line with recent developments of the coronavirus COVID-19 outbreak, we too had to put certain measures in place and were in return also confronted with measures put in place by other countries as well.
As you may recall, as part of the SMS Special Conference Berkeley “Designing the Future: Strategy, Technology, and Society in the 4th Industrial Revolution”, the Strategic Management Society was to host a Doctoral Workshop on March 25th, 2020. This event was an initiative of the FINDER program and would be hosted by Rick Aalbers (Radboud), Saeed Khanagha (VU) and Krsto Pandza (Univ Leeds). The main objectives of this Doctoral Workshop, focusing on strategy and innovation in a digital era, would be to foster interaction among leading faculty scholars and doctoral students on various aspects of research and on preparing for a professional career in academia. The doctoral student participants will broaden their academic network with senior faculty from around the world and develop a better understanding of the particularities of the academic career.
As the impact of the COVID-19 outbreak has continued to expand however, the University of California, Berkeley, took steps designed to help limit coronavirus risk to the campus community. This included the cancellation of all campus events, which means that SMS will no longer hold the Special Conference at Berkeley as planned March 25-27.
At the moment alternatives for this conference are explored and we are waiting on more information. We remain committed to and excited about this program are looking forward to the moment we can announce when it will take place.
So please stay tuned as we will inform you in the time to come.
Data privacy is a hot topic affecting numerous people around the globe – if not every single individual. While the public debate often revolves around the un-ethical retrieval and use of personal data I am going to shed some light on the societal ramifications of people deliberately sharing their data.
In 2009, Meglena Kuneva, European Commissioner for Consumer Protection at that time, said that “personal data is the new oil of the Internet and the currency of the digital world”. Although personal data has become its own asset class and markets for personal data have been developed, it is often traded in grey zones or used in exchange for free services, making its precise valuation complicated.
These days, companies utilize personal data for a variety of purposes: reducing search costs for products via personalized and collaborative filtering of offerings, lowering transaction costs for themselves and for consumers, increasing advertising returns through better targeting of advertisements, and conducting risk analysis on customers.
Let’s focus on the last aspect of conducting risk analysis on customers and illustrate its application in the financial industry. For instance, accurately predicting the default risk of a borrower or an insurance policyholder’s risk of having a car accident can be a competitive advantage and save you money. But how does this development look from a customer’s perspective? So-called usage-based insurances (e.g. Drivewise from Allstate), for instance, are using driver behavior to calculate insurance premiums. Customers who are not willing to share their driving behavior are obviously not amongst the clientele of these insurances and that does not impose a problem at this point. But this only holds as long as there are enough alternative insurance companies that do not require customers to share their driving behavior. However, the market for usage-based insurances is expected to reach a global market size of $115 billion by 2026. Things could change tremendously once insurers and customers realize how much money they can save by using and sharing data. At this point not sharing your data becomes costly and the sole fact that data is not shared already conveys information that could make companies suspicious. What does he or she have to hide?
Going back in history: Germany ratified the “General Act of Equal Treatment” in 2006 which aimed at avoiding discrimination based on race, ethnicity, gender, age, religion, disabilities, and sexual identity. An example is the disclosed information in German CVs: employees do not have to provide any information on aspects mentioned in the General Act of Equal Treatment. However, equality is only ensured if all applicants follow the recommendations and do not share this information in their application. There lies the rub: people who can expect favorable treatment by a system (positive discrimination) could be more forthcoming and willing to share their data, whereas people who have to fear a negative treatment (negative discrimination) could be more likely to withhold it.
But if a critical mass is sharing its data, data privacy-sensitive people might be caught between a rock and hard place because of the phenomenon called information unraveling. Meaning the information disclosure of others pushes you towards disclosing your information if you want to avoid negative discrimination.
The following is an example of information unraveling told by Prof. Ben Polak during his lecture on game theory at Yale University. He describes that the hygiene in restaurants in Los Angeles in the 1990s had become so alarmingly bad that the government introduced a new quality control that checked the restaurants and distributed health certificates from A to D. Despite the fact that companies were not obliged to display their certificate to the public those restaurants receiving an A started to put their certificate in the window. What did this do to the other restaurants? Well, those who received a B started hanging up their certificate because they did not want to be considered only having a C or D. Guess what C-certificated restaurants did? They followed the logic of B-certificated places and hung up their certificates as well. Only those receiving a D did not engage in the practice of showcasing their certificate. However, from a customer’s perspective, the interpretation is clear: if you do not show your certificate you are most likely part of the lowest assessment and therefore, not a good place to dine. By the way, information unraveling is only effective if the receivers know about it. Tourists usually did not which made displayed certificates ineffective in touristy areas.
So where does this leave us? The bottom line is if people are sharing their data deliberately it can start cascades of information disclosure that make markets extremely efficient. However, it also holds the potential to discriminate against people who are not willing to share their data. So, while the public debate has been revolving around protecting customers from companies harvesting and utilizing personal data against their will, the debate on which data companies are not allowed to use despite the customers’ consent should get more attention. Evidently, that debate is a very industry- and service-specific discussion but one that has to go with the current developments.
We are happy to introduce Mike Schavemaker, Innovation Transformation Lead and senior innovation consultant at Royal Philips, and member of the FINDER Advisory Board, on the FINDER blog! Mike guides the fellows with his academic and industry experience. Together with Barbara Voelkl, he shares his opinions, exciting developments and future revolutions in the world of business models in a blog series, so stay tuned.
Disclaimer: The content of the FINDER blog is not an expression of Royal Philips, nor created on behalf of Royal Philips. The content is created and contributed by private persons.
Last year, on March 28th, Amazon announced to move into health care space. The company, founded in Seattle on the concept of delivering books at the most convenient way possible, now a tech-giant that delivers anything from overstock toys to data lakes through AWS makes it way to an arguably complete new venture space: health care. Why does Amazon think moving into health care space is the next place to be? Amazon is renowned to move into red ocean industries where traditional suppliers and supply chain rein such as publishing (with acquiring a.o. the Washington Post), catalog sales and ubiquitous data center applications and turn them into blue oceans. And leading the pack.
Amazon does this profoundly by understanding a fundamental question in business: who owns the customer. It enters spaces where providers of goods and services conveniently sell in a status-quo market. Where these same incumbent providers do not question any more how to bring additional value through combinations of innovations to capture the attention of the customer and retaining them; at least not in a paranoid sense. Rather they tend to relish themselves the comfort of their existing business models and only incrementally improve the propositions that they bring to market.
Navigate Uncharted Waters – Streamline Your Business Model
We argue that the simplest way to uncover industry leaders – or industry revolutionaries – are to find those companies who push their revenue models whilst fully aligning their value chain, from innovation, operations to sales, in their obsessed sense to stay close to their customers, or even fully align their customers’ interest with their own. The revolution therefore starts by focusing on the bottom right of the business model canvas and understand how to move your ship and your crew in line with this next purpose. For traditional product oriented companies, this means to move from a capital expenditure model to an OPEX-delivery model in the first place.
Essentially this means that as a product company your start to develop capabilities to address the needs of your customer according to their life cycle – and let them pay accordingly across this life cycle. Typically as a service, not as a mere product sell. In ‘product-sell country’, market share is your ultima. This accounts for hardware products, for ‘productized’ software where you buy a license per release. Appreciation by the customer presents itself by a transaction; thereafter product companies typically direct the attention to the next interested party.
In ‘solutions country’, wallet-share is your ultima. Wallet-share resembles how relevant you are as a company in the eyes of the customer. If your customer only brings 3% of their income to you, then you are not likely to be invited to the proverbial birthday party. If you manage to your customer to bring 30% of their income to you, then you are certainly invited to your customer’s birthday party: in fact, the party wouldn’t be complete without your presence. In a business sense, relevancy is connected linearly with the dollar-amount running from your customer to you. It is connected to your ability to address your customer’s preparation, planning, design and implementation of a solution, and being able to sustain the solution operationally for your customer and to enrich the solution optimally to your customer’s needs. The other currency acting as a proxy for relevancy is time: how well you are able to address their imminent need and invest time to persistently and longitudinally in making their lives easier, achieve their goals more effectively and raise the bar from satisfaction to delight. Taking your customer by the hand across these steps in the life cycle means that you can now shift from product based CAPEX-sell, to the game-play of providing a solution.
Get Your Customer On Board – Leverage The Relationship
The first stepping stone of providing a solution is to extend a product or license sell with a performance based revenue model. Particularly business-to-business oriented firms have extended their portfolio of offering to this model in the nineties. Nowadays any self-respecting product company in B2B-space has a service organization to support their ‘productized’ maintenance services, even if they deliver components to a solution. In this context, the firm commits itself to ensure business continuity and resilience for their customer base and leverages their contracts to substantiate the commitment.
The contract itself becomes the embodiment of how thick, or how thin the umbilical cordis between the firm and the customer is. And how simple it is to do business; as simple it is to deepen out the relationship. In performance based revenue models, the needle still hinges towards ‘transaction’ rather than ‘relation’.
Firms who push the needle further away from transaction, will typically start to develop usage-based revenue models. Moving towards this model will yet require, or actually demand the firm to understand how their product is consumed in the hands of the customers. Ensuring to provide richer functionality and solutions to answer for the customer’s ever evolving needs. Data becomes the inevitable carrier to understand how/when/who/what/where/how the customer’s needs continuously evolve. Addressing a richer set of offerings requires on the one hand a clear contract, on the other hand data mapping translated in integrated lifetime-serving offerings, being enabled by a digital platform that accommodates an ecosystem of solution and channel partners. The prior unlocks a leading position, the latter unlocks to sustain that leadership position. Not the other way around. Leadership, defined in its nature not by means of market share in a total addressable market; the traditional line of thinking. Contrary, leadership, defined by remaining relevant in terms of the wallet-share you manage to address at your customer, complemented by your natural role to orchestrate the connections and probabilities in your ecosystems.
Just look at players as Salesforce.com and Microsoft. By first building a comprehensive portfolio of products that captures (and captivates) the value of the customer, they then stretch their portfolio to additional adjacent applications – which on their term are offered in a partnership program. The composite of this approach allows these industry leaders to create a fine network of application partners, whilst retaining the central orchestrating role around addressing the life cycle their customers. Cisco referred to this as Customer Advocacy, Microsoft perfected the approach by introducing the practice of Customer Success Management, a concept that takes relationship to a next level.
Orchestrate Your Customer’s Reality – Build Up Joint Relations
In history, the strongest relationships are based on trust and a sense of co-investment. An investment in time, an investment in money – or both. This brings us to the ultimate revenue model, being the outcome-based class. Providing services that allow a win, or a loss to your customer – and yourself – if you fail to address the need correctly, if you do not reach the opted result. This type of risk sharing requires your company’s capabilities to fully plug-in in your customer’s reality. This is not for the faint-hearted, especially for those companies that have full focus on establishing shareholder value. Company risk is often associated with volatility. Volatility requires a premium. Another reason why this revenue model is not often seen in any industry, is simply that economic or even political context is not ready yet. For instance, in the case of healthcare space, solution providers who try to offer these solutions to hospitals often face that the economic reimbursement model does not entice the hospital to opt-in: the investment costs would arguable be lower, however this defeats the purpose of the hospital trying to sustain their allocated annual budget to run their facilities. However, in the United States, the health care reimbursement is much more liberal.
What if you could, simply spoken, put your organization’s capabilities to use that you have garnered whilst developing yourself towards an outcome based service provider. Can you turn red oceans to blue, or even to purple on a global basis? This is arguable exactly what happens with Amazon Health. Amazon takes its organizational capabilities to use to provide improved health care services to its employees. What stops them to make the full hospital equipment floor completely digital, reading out vital signs first on assets to weave in the hospitals as outcome based partners, then elevate their partnership with these same hospitals to create meaningful outcome based treatment based on clinical vital signs. Who owns the customer? Making hospital operations fully digital and fully life cycle immersed is just one step to turn the red ocean a little bit more purple. Just apply this simple thought experiment: offering incumbent field service staff an extra raise and tools to be more effective in handling operations would create a massive shift in the existing U.S. healthcare service landscape. Healthcare provided as a financial service by a new entry tech leader: to any actor in the value chain.
Whereas FinTechs and digital financial applications are labeled “disruptive forces” and “game changers” shaking up the existing world of finance and beyond within industry and even politics, academics tend to hold the view that by a bare change of the platform or transaction setting of our financial decisions, existing theoretical frameworks are not challenged too intensively.
However, not only does digitalization allow for more collaboration – between humans, distributed humans as well as between humans and technological entities – but also for different ways of collaboration. Imagine you consider buying Apple stocks in four different situations:
i) Analyzing your finances, you consider you are liquid enough now to invest and Apple seems a solid start for that. You open your online depot and fulfil the transaction.
ii) When opening your interactive depot, you just saw your boss sold his 120 Apple stocks just a minute ago. You still continue your transaction?
iii) When opening your online depot which you share with your baseball mates, you need to get the majority of them on board before the buying trade is possible. Do you consider researching a bit more? Are your mates going to agree to this transaction?
iv) While surfing on your phone, a push-up from your online trader pops up – their chatbot informs you it is a good time to buy Apple stocks now. Do you follow this advice on the go?
Considering these, a mere selection of possible scenarios of a trading situation, it becomes obvious that human financial decisions are shaped through contact, if online or offline, direct or indirect, if in the form of advice, communication or the pure existence of a social group within which an individual makes a decision. Keeping in mind the vast financial and strategic decision-making literature on nudges with the numerous examples of how framing a decision context changes our decisions, FinTech applications with their diverse setups, designs and defaults are definitely worth having a second glance from an academic perspective. FinTech applications give a new angle to financial decision-making transforming the way of collaboration. Does online and task-related communication such as in a collaborative investment app free individuals from halo effects? Does advice from AI remove or strengthen critical thinking? It remains the joint task of practitioners and academics to understand and design these applications as frames for inclusive, unbiased decisions so that research can serve its purpose – society.