Business models in the digital age

In this research digest, ‘business model’ is understood as the way in which private and public sector employers structure and organise their activities. It refers, for example, to aspects such as design and production or service provision (in house, outsourced or in collaboration with other entities), innovation, internationalisation and involvement in supply chains. Business models are closely linked to another aspect of work discussed in this project: work organisation.

A full list of references used to compile this research digest can be found at the end of the page. 

Author: Irene Mandl

 

Overview

According to the Organisation for Economic Co-operation and Development (OECD), the COVID-19 shock has accelerated the digitalisation of public and private sector activities in many countries, including in the form of improved broadband connectivity, the adoption of online business models, the promotion of online payments and the enhancement of digital skills.

According to a McKinsey Global Institute survey of executives, the COVID-19 crisis has also accelerated the digitisation of customer and supply chain interactions and of internal operations by three to four years. The share of digital or digitally enabled products in their portfolios has been accelerated by seven years.

Since most national governments in the EU urged workers to stay at home as much as possible during the pandemic, one of the most visible impacts of the COVID-19 crisis has been the huge increase in teleworking. Companies have been obliged to quickly invest in software platforms that facilitate communication and meetings (such as Zoom and Microsoft Teams) while making changes in production and service provision processes to reduce face-to-face interaction. The adoption of automation and digitisation technologies, as well as some types of platform work, has increased substantially during the pandemic.

The interaction between the health crisis and technological development has been noticeable, since the adoption of digital resources to prevent and better manage the effects of the pandemic has been considerable. From efforts in researching vaccines to the rise in telemedicine and the use of additive manufacturing – not to mention the controversial contact tracing apps – digital technology has shown its capacity to contribute to mitigating and fighting the pandemic.

 


 

 

Policy pointers

  • Integrated support for innovation, internationalisation and digitalisation – rather than three isolated support strands – could result in enhanced competitiveness and sustainability for European businesses.

  • To help companies determine and implement the most suitable business model in the transition to the digital age, advice, consultancy and exchange of good practices, as well as financial support, could be beneficial.

  • Public support for supply chain integration and management – such as identification of business partners in the country or abroad, or management training in negotiating and implementing cross-organisational cooperation – could benefit organisations with digitalised business models. Furthermore, policymakers could monitor outsourcing and insourcing developments driven by automation and, if needed, take action to ensure a level playing field in terms of power relations along the supply chain. As regards increasingly digitised supply chains, policy could support the transition from traditional business models to ensure the sustainable involvement of businesses in cross-company cooperation.

  • As digitised business models and supply chains are based on the creation and use of a large amount of data, policymakers could further regulate, monitor and enforce the adequate handling of data. This relates, for example, to data protection and cybersecurity but also to management training on the use of data for business decisions.

  • Policy needs to ensure a level playing field between business models in the platform economy and in the traditional economy. Furthermore, platform workers should be supported to take advantage of the benefits inherent to the platform business model, while it should also be ensured that those in need receive the protection and support required to guarantee decent employment and working conditions.

 


Digitalisation: General and comparative perspectives

Introduction

Digitalisation is a broad concept that can be interpreted in a wide variety of ways by organisations. For example, analysis of the European Company Survey (ECS) 2019, which covers establishments with at least 10 employees, distinguishes four types of establishments – which could be considered to have four different business models – based on the combination of their use of different technologies.

  • Highly digitalised (28% of establishments in the EU): a high share of employees use computers daily, and the establishments are likely to have purchased customised software. Almost all highly digitalised establishments use data analytics for process improvement, the use of robots is slightly above average, and e-commerce is relatively widespread.
  • High computer use, limited use of other digital technology (26%): in these establishments, a high share of staff also use computers daily, but customised software, e-commerce and the use of robots are less common. The use of data analytics is marginal.
  • High use of robots and other digital technology, limited computer use (19%): in these establishments, a rather low share of employees use computers daily, but customised software and the use of robots and data analytics are common.
  • Limited digitalisation (27%): in these establishments, the use of all of the technologies covered by the survey is below average.

The available data show a higher share of digitalised establishments engaged in design activities than of establishments with limited digitalisation (about 40–50% compared with less than 30%) (Figure 1). This holds particularly true for design conducted in house (26–34% compared with 20%) and in collaboration with other companies (7–11% compared with 5%). As regards production activities, the differences are less pronounced. The highest share of establishments engaged in production is in the group with high use of robots and limited computer use (73%) and the lowest is in the group with high computer use but limited other digitalisation (62%). As in the case of design, the share of establishments conducting production in cooperation with other companies is highest among the highly digitalised establishments.

Figure 1: Design and production activities by digitalisation intensity of establishments, EU27 and the UK, 2019 (%)

 

The shares of establishments that introduced product, process or marketing innovations between 2016 and 2019 are highest among highly digitalised establishments (43–47%, depending on the type of innovation) and lowest among establishments with limited digitalisation (15–19%) (Figure 2). The largest difference between these two groups is found in the introduction of a marketing innovation new to the establishment (20 percentage points). Overall, the shares of establishments with limited digitalisation that developed innovations new to the market are very low (2–6%).

Figure 2: Introduction of innovation since 2016 by type of innovation and digitalisation intensity of establishments, EU27 and the UK, 2019 (%)

 

International business activities are also more common among digitalised establishments (about 50–60% sell to customers in other countries) than among establishments with limited digitalisation (about 40%) (Figure 3). Establishments with high use of robots but limited computer use show the highest share of intensive internationalisation (more than 50% of sales to customers abroad).

Figure 3: Level of sales to customers in other countries by digitalisation intensity of establishments, EU27 and the UK, 2016–2019 (%)

 

Opportunities

  • Higher competitiveness and sustainability of digitalised establishments
  • Positive economic ‘spillover’ effects on other establishments due to supply chain involvement
  • Increased job and employment security in digitalised establishments
  • High and increasing employability of staff in digitalised establishments

While no direction of causality can be established, the data from the ECS 2019 show that higher digitalisation intensity tends to be related to innovation and internationalisation, cooperation with other organisations, and design activities. This brings about the following opportunities in the economy and in work and employment:

  • higher competitiveness and sustainability of digitalised establishments due to their inclination towards innovation and internationalisation
  • positive economic spillover effects of digitalised establishments on more traditional parts of the economy due to supply chain involvement
  • job and employment security in digitalised establishments due to their expected higher competitiveness and sustainability
  • high and increasing employability of staff in digitalised establishments, notably white-collar workers involved in innovation, design and cross-organisational networking activities

Risks

  • Limited access to finance for digitalisation, innovation and internationalisation
  • Administrative and regulatory burden in the phase of transferring the digital innovation to the market
  • Lack of harmonised protocols and standards for digital applications, resulting in disruptions in the supply chain
  • Limited access to the staff needed for design, innovation and internationalisation
  • Labour market polarisation

The particularities of organisations with digitalised business models – that is, their tendency to be innovative, internationalised and involved in supply chains – also pose some risks:

  • The financial burden on a company in the innovation and design phase – when investments are needed but returns may materialise only years later – can endanger its survival. This is a particular challenge for small and medium-sized enterprises, including start-ups, which have more limited resources.
  • The administrative and regulatory burden in the phase of transferring the digital innovation to the market can be a risk if the ‘newness’ of the product or process results in a situation in which it is unclear how it should be handled, for example with regard to standards, registration, insurance or liability issues. In the worst-case scenario for companies, this can mean that they have a market-ready and in-demand product or process that they cannot make available or can make available only with delays and additional costs, thus endangering its economic viability.
  • A related point is that, particularly in the context of cross-border cooperation, a lack of harmonised protocols and standards for digital applications can result in disruptions in the supply chain, causing additional costs and human resource requirements, and, in the worst-case scenario, loss of business opportunities due to dissatisfied clients.
  • Companies may suffer from limited access to the staff needed to carry out design, innovation, internationalisation and supply chain management tasks for a number of reasons: the small size or young age of the enterprise, which limits the incentives that can be provided to staff; a workplace location that is deemed unattractive; skills shortages on the labour market; or regulatory burdens, for example, when hiring third-country nationals. This can hinder or at least delay the full exploitation of the economic potential of digitalised enterprises.
  • Labour market polarisation can arise if higher-skilled white-collar workers are required for design, innovation, internationalisation and supply chain management activities, while lower-skilled tasks can be automated.

Concluding commentary

Across the EU, about one-quarter of establishments with at least 10 employees are characterised by limited digitalisation. These establishments tend to be less engaged in innovation and internationalisation – two business activities that are generally considered to positively affect competitiveness, growth and sustainability. Accordingly, policymakers could further enhance their efforts to assist businesses in their transition to the digital age.

That said, a wide variety of innovation, internationalisation and digitalisation support already exists. Policymakers could review whether the measures in place are aligned and coordinated to ensure that digitalised enterprises have access to the support that is most suitable for their particular business model. This also requires a special focus on small and medium-sized enterprises, including start-ups, as they are expected to face more challenges in the digital transition than larger or more established businesses, owing to their more limited human and financial resources.

The business models of digitalised establishments tend to be beneficial for high-skilled, white-collar workers, whose job and employment security is likely to increase, as well as their employability. Lower-skilled, blue-collar workers, however, potentially face the risk of being confronted with task automation. Both developments highlight the importance of anticipating skills requirements and ensuring delivery mechanisms for the types of education and training that are most suitable for the different target groups.
 


Automation

Automation is one of the ‘vectors of change’ identified as part of the broader notion of ‘digitalisation’ in Eurofound’s conceptual framework. It is the replacement of human input, in full or in part, by machine or software input. Advanced robotics, both for services and for manufacturing, is grouped with autonomous vehicles under the automation vector, since the ultimate aim of their application is to substitute machine for human input.

Introduction

Automation can influence the business models of existing companies or foster the establishment of new businesses with new business models, owing to the potential it offers for developing and providing new products and services. Examples of new business models that automation enables include the following:

  • providers leasing out robots so companies can use them without having to make a one-off investment, or renting them out if companies need them only temporarily (for example, in agriculture for harvesting). These providers tend to also take care of the maintenance and repair of the robot, hence offering additional services to the client companies
  • systems integration firms that facilitate the integration of robots into an existing work environment by conducting feasibility studies, providing consultancy, supplying the robot and implementing it in the workplace
  • specialist industries that offer analysis of robot-generated data for process and performance improvement purposes; this can be done either for an individual company or by collecting information on multiple use cases to aggregate data and generate cross-company insights

Furthermore, supply chain cooperation may change as businesses increase outsourcing or insourcing as a result of automation:

  • Automation can lead to a greater need for information and communications technology resources, which are sourced externally because of internal skills or capacity shortages.
  • Some tasks that were previously outsourced are internalised, as they can be automated. An example is professional service robots that integrate production machinery, warehousing systems and production facilities into single cyber-physical systems. By blurring the traditional boundaries between production and logistical tasks, they enable manufacturing to be insourced into services companies and vice versa. In some cases, such forward or backward integration is driven not exclusively by automation but by automation in combination with digitisation (IoT).

Opportunities

  • Mass customisation and servitisation as a competitive edge
  • New product and service offers
  • Slimmer organisational structure and improved communication and coordination in the case of enhanced outsourcing
  • Less dependency on business partners in the case of enhanced insourcing

Automation offers a variety of opportunities from a business model perspective.

  • The possibilities for mass customisation and servitisation lend themselves to different ways of identifying and approaching customers, which might relate to changes in the overall business strategy and corporate set-up (for example, changes in the existence and responsibilities of functional units, divisions or departments, and their interactions).
  • New product and service offers enabled by automation give rise to new businesses with new business models. At the same time, established companies can benefit from adapting their business models to compete with these new businesses, thus increasing their sustainability.
  • Increased outsourcing driven by automation can result in improved business models in terms of slimmer organisations that can better focus on core activities and benefit from better information, communication and coordination processes.
  • Increased insourcing driven by automation can result in improved business models owing to greater autonomy in business processes due to reduced dependency on external actors. This can make an organisation more agile.

Risks

  • Reduced productivity and customer satisfaction in the early phase of a new business model
  • Increased dependency on external business partners in the case of enhanced outsourcing
  • Increased customer concerns regarding specialisation and product/service quality in the case of enhanced insourcing

The positive impact of automation on business models does not come without risks. New business models driven by automation can, particularly in their early phases, be subject to teething problems that negatively affect productivity and customer satisfaction, thus endangering the company’s survival. This can be a severe problem, especially for start-ups, which do not yet have an established market reputation and client portfolio, as such issues can create an unfavourable brand reputation that is hard to overcome.

Enhanced outsourcing required by automation can result in a high level of dependency on external business partners. This can be problematic if the business partner turns out to be unreliable or underperforms in terms of quality. Furthermore, if there are few providers of the required services, the client’s ability to negotiate prices and other conditions can be limited.

Automation-induced insourcing can result in a blurring of the company’s profile, thus raising concerns among potential customers about the level of specialisation and the quality of the products and services provided. In house, an increased portfolio of tasks can lead to a more complex organisational structure and a loss of sense of belonging and commitment on the part of staff.

Concluding commentary

Automation can result in new product and service offers as well as increased insourcing and outsourcing, which can affect business models. Whether the establishment of a new business model or the adaptation of an existing one to take advantage of automation results in a positive or negative outcome for the organisation and its employees will vary from case to case, depending on how successful they are in capitalising on the opportunities and avoiding or mitigating the risks.

From a policymaker’s perspective, it may be worth considering support for innovative business models exploiting the opportunities of offering new products and services or adapting previous processes by insourcing or outsourcing. This might relate to advice, consultancy and exchange of good practices, as well as financial support. It is not only start-ups that require assistance, but also established companies seeking to transform their business models. As regards supply chains, policymakers may want to pay attention to whether a tendency towards increased outsourcing of specific tasks emerges, and, if it does, to ensuring a level playing field for the actors in the supply chain.
 


Digitisation

Digitisation is one of the ‘vectors of change’ forming part of ‘digitalisation’ in Eurofound’s conceptual framework. It refers to the process through which aspects of the physical world are rendered into data and virtual models, and vice versa. Three main technologies fall under this vector of change: 3D printing; Augmented Reality (AR)/Virtual Reality (VR); and the internet of things (IoT).

In mid-2021, no information was available on the impact of AR and VR on business models.

Introduction

3D printing

Mass customisation through 3D printing is expected to be an increasingly common feature of production processes. This can enable business models based on sharing product designs and connecting designers, as well as manufacturers, in a form of co-creation. This could go as far as to also involve customers in the production process. Some research anticipates the emergence of distributed local and global supply chains, with production taking place closer to the point of consumption.

At the same time, 3D printing encourages supply chain optimisation by reducing the number of actors involved. Rapid and self-organised production, enabled by this technology, reduces the need for intermediaries such as suppliers specialising in spare parts.

The economies of scale inherent to 3D printing may also give rise to new market entrants with innovative business models, offering shorter delivery times and lower production costs. An example of this is ‘bridge manufacturers’ who use 3D printing to fulfil first orders while large-scale production processes are being set up.

Internet of things

IoT is expected to make supply chains more complex and flexible, as it has the potential to transform them from linear chains into value networks or ecosystems, thus triggering new business models (in mid-2021, however, concrete examples were limited).

Horizontal integration – aligning the information technology (IT) systems involved in the different stages of the production or service delivery process across organisations – and vertical networking across companies will be facilitated by digital connections, and boundaries between individual companies and stages in the production process are likely to blur. It is probable that the number of actors involved in supply chains will increase as production processes become more decentralised. To better allow for customer-specific adaptations, clients will also be connected to the supply chain at an earlier stage.

Companies are expected to focus on their core activities and outsource other tasks in their value network, driven by the need for higher specialisation due to customer demand. Sharing, instead of owning assets, could be a key feature of new business models linked to IoT, with data becoming the competitive asset. Some authors refer to this business model as ‘digitally charged products’, as it merges physical products with sensor-based digital services. Another feature of IoT-driven business models is a combination of production and servitisation, with the expectation that production will take place closer to consumption. That said, supply chain partners can more easily be more spatially dispersed than in the past, as they will be digitally connected.

Opportunities

  • Mass customisation, involvement of the client and production closer to consumption, creating added value for the customer
  • Data generated through the internet of things (IoT), which can be used to improve management decisions
  • Supply chain optimisation through digitisation

Both 3D printing and IoT are expected to facilitate mass customisation, which encourages business models that involve the client more and move production closer to consumption. This is an opportunity for companies, notably at this early stage in the deployment of the technology, to establish close customer relationships and create added value for their clients, thus exploiting a unique selling proposition.

As data are at the core of IoT technologies, business models exploiting data as effectively as possible can benefit from better-informed management decisions. Anecdotal evidence from case studies hints at increased profitability and the identification of new business opportunities as outcomes of this approach (see the case studies of Sanmina and Est-Agar.

The technologies also foster supply chain optimisation. In the case of 3D printing, the expected reduction in the number of actors can result in less dependency on business partners (see the case studies of companies: Bächer Bergmann and Bosch Rexroth, in quicker turnarounds and hence in higher customer satisfaction. In the case of IoT, the anticipated transformation from linear to network structures would allow businesses to become more specialised, which could result in a better quality of production and service provision.

Risks

  • Slow or insufficient adaptation of the business model to the needs of digitised supply chains, reducing business activities
  • Intentional or unintentional misuse of data having a detrimental effect on digitised business models

As a consequence of the supply chain optimisation that is expected to result from both 3D printing and IoT, companies that do not follow the trend risk being eliminated from the supply chain, as they will slow down the process of production and service delivery. It is likely that small and medium-sized enterprises will be more exposed to this risk because of their more limited market power compared with larger companies and the greater challenges that they face in the digital transition.

By nature, digitisation technologies are related to the creation and use of a large amount of data. Business models driven by or adapted to digitisation need to find a balance between exploiting the data as much as possible – including for management decisions – and at the same time respecting data protection regulations and ethical standards. Incorrect behaviour along the supply chain, as well as insufficient cybersecurity measures, can be detrimental to companies with digitisation-based business models.

Concluding commentary

Digitisation is likely to impact business models as a result of increased mass customisation, greater involvement of customers, production closer to consumption and optimisation of supply chains. Digitised connections across actors facilitate cooperation but only if standardisation of products, services and processes is implemented along the supply chain, to allow the various programming languages and applications to communicate with each other. Furthermore, data security and cybersecurity must be guaranteed to enable effective supply chain management among digitised businesses. Policy could support this through further regulation, monitoring and enforcement.

Companies built on traditional business models involved in increasingly digitised supply chains should be supported by policymakers to master the transition, to ensure their sustainable involvement in cross-company business activities. This could be done through raising awareness, upskilling and investment support.
 


Platforms

Platform work is a form of employment in which organisations or individuals use an online platform to access other organisations or individuals to solve problems or provide services in exchange for payment.

In this specific context, ‘business model’ is understood as the main characteristic of how a platform mediates the supply of, and demand for, paid labour.

Introduction

As is natural for actors in a still young form of economic activity – platform work emerged in Europe only about 15 years ago – the business models of digital labour platforms tend to adapt flexibly to changing market developments and regulations. As a result, there is an increasing variety of platform business models. That said, in mid-2021, the large platforms were converging on similar business models through mergers and acquisitions, collaboration, and adoption of successful business models.

The business model of a platform is composed of a combination of different elements, such as the following:

  • Type of tasks mediated through the platform: these can be delivered online (for example, translations, database cleaning or IT services) or on location (for example, ride hailing, food delivery or cleaning); they can vary in scale from microtasks (‘click work’) to large tasks (‘projects’); and they differ in terms of the skills required to fulfil them. Small-scale, on-location routine tasks were the most prominent type in mid-2021.
  • Selection mechanism: this can be fully algorithmic (that is, supported by artificial intelligence) or supported, to varying degrees, by human input. Furthermore, selection can be based on an offer made by a client or a worker and a response by the other party. Or it can take the form of a contest in which workers submit their contributions and the client chooses which to reward. In mid-2021, algorithms were the most common selection mechanism.
  • Degree of intervention of the platform: this can amount to merely matching supply and demand, taking on the management of the task by prescribing the conditions and organisation, or any variation in between these two extremes. For the time being, platforms determining how the work is done are those most prominent in the public debate.
  • Rating mechanisms, reward and monitoring systems, and gamification approaches: platforms use different approaches to rate workers and in some cases also clients. This can be done algorithmically through the platform or manually by the client and worker. In most cases, the rating influences access to specific types of tasks mediated through the platform. Some platforms apply gamification approaches to induce greater motivation and engagement among platform workers by making them compete against each other.
  • Pricing model: some platforms charge fees of their workers, others of their clients. Fees can be collected on registration or on successful service delivery. Some platforms also charge third parties through advertisements. In mid-2021, the most common source of revenue for platforms was commission taken from payments made by clients.
  • Additional services provided by the platform to workers (for example, training or insurance) or clients (for example, assistance in drafting a request for services): while as of 2021 the provision of such additional services was rather limited, it can be expected that in the longer run they will gain importance as a criterion that workers and clients will use to choose from among the growing number of platforms.
  • Data collection and use: the data collected by the platform on tasks, workers and clients can be used to improve the platform’s business model (for example, through analysis of user behaviour) or to diversify and expand it (for example, by selling the data to third parties).
  • Contractual relationship with workers: in mid-2021, most platforms considered their affiliated workers to be self-employed, but there were also examples of employment relationships in accordance with labour law. Given the lack of clear regulation, an increasing number of court cases seek to clarify the most appropriate employment status for platform workers.
  • Ownership/governance structure: most of the older platforms are profit oriented and follow a shareholder-value model. Nevertheless, different approaches are emerging, such as platforms run as worker cooperatives, employer-owned platforms internal to an individual organisation and platforms run by public institutions.

Opportunities

  • Increased innovation and competitiveness in the economy
  • Access to the labour market and income, particularly for vulnerable groups
  • Fostering of self-employment and entrepreneurialism

As a relatively new type of business activity with increasing demand, the platform economy offers good opportunities for start-ups to offer their services on the market. Those that can identify a niche not yet dominated by longer-established market actors have particular potential to be successful. This points towards the value of innovative business models that are beneficial for overall innovation and competitiveness in the economy.

From a labour market perspective, the overall business model of platform work offers workers rather easy access to work and income generation opportunities. In addition to the unbureaucratic processes for assigning work, the fact that matching is dominated by algorithms, which – at least in theory – are objective, can be particularly beneficial for vulnerable groups on the labour market that might suffer from human selection bias in more traditional forms of work. This holds true particularly for those types of platforms that mediate low-skilled, small-scale tasks.

Those business models that mediate moderately to highly skilled tasks and do not intervene in the management of the tasks can also contribute to stimulating self-employment and entrepreneurialism. These models also tend to result in a high degree of worker autonomy and decent rates of pay.

Risks

  • Dominance of a few large platforms
  • Undue exploitation of workers’ and clients’ data
  • Misclassification of workers’ employment status
  • Contribution to labour market segmentation and deskilling
  • Limited worker autonomy due to algorithmic management and surveillance
  • Unpaid working time, unpredictable income and pay below market rates
  • Regulation not fit for purpose and / or difficult to enforce

Given the particularities of the platform economy, there is a risk that platforms that enter the market early and quickly will attract a critical mass of workers and clients, dominate the market and make it difficult, if not impossible, for new platforms to sustainably establish their business. This can result in an oligopoly situation in which a few market actors can dictate their conditions to workers and clients. This problem is aggravated when platforms compete not only with each other but also with providers in the traditional economy, but can offer different terms and conditions from those providers due to their regulatory situation.

Another risk is that platforms will exploit the data gathered from clients and workers for purposes other than matching supply and demand. Data collection, use and ownership tend to lack transparency, and monitoring of platforms’ activities, as well as enforcement of existing regulations, is challenging because of the particularities of platforms’ business models.

From a labour market perspective, most risks are associated with platform business models that involve small-scale, low-skilled tasks and in which the platform not only mediates supply and demand but also determines how the tasks are carried out. In an increasing number of such cases, it can be argued that the treatment of the workers as self-employed is a misclassification of their employment status, resulting in unjustified limitations on their employment rights and entitlements, including social protection coverage. Furthermore, these business models also have the potential to contribute to labour market segmentation, deskilling of the workforce and limited autonomy for workers due to algorithmic management and surveillance.

Business models that focus on mediating online tasks are found to result in unpaid working time, unpredictable income and pay below market rates.

From a regulatory perspective – as regards both business and employment legislation – there is a risk that current regulations are not fit for purpose when it comes to the innovative business models of the platform economy. Specific legislation on platforms faces the challenges of the heterogeneity within platform work and the speed with which platforms adapt their business models, which could render legislation obsolete by the time of its publication, as platforms might use the time it takes to develop legislation to change their approaches. Finally, there is a risk that regulations will not be properly enforced in relation to platforms, particularly as regards online and cross-border activities, owing to limitations in the capacities of enforcement bodies.

Concluding commentary

Platform work is a form of employment and a category of business model that offers a wide range of opportunities for the economy, the labour market and society, but also comes with numerous risks. From a policy perspective, it is important to be aware of both and to find a way to capitalise on the benefits and reduce and mitigate the risks.

From an economic perspective, innovative business models of young or diversifying platforms should be supported not only to reap the benefits of their creativity for overall competitiveness, but also to help them grow to a level at which they can sustainably compete with more established market players to avoid the risk of oligopoly. Specific attention could, in this context, be devoted to helping stakeholder-value business models reach market maturity, as these also show good potential to improve the employment and working conditions of platform workers. In general, policy needs to ensure a level playing field between the platform economy and the traditional economy by ensuring that platforms cannot bypass regulations or standards to which traditional providers must adhere.

From a labour market perspective, workers should be supported to benefit from the flexibility offered by providing services through platforms, including by exploring self-employment options, while receiving the protection they deserve if their work resembles dependent employment rather than self-employment, particularly in the context of the algorithmic management and control inherent to most platforms’ business models. Legislative approaches can help in this regard, as can ensuring that platform workers have a collective voice, through traditional trade unions, grassroots organisations or alternative mechanisms, or a combination thereof.

Related material
 

Related policy pointers Related research digests

 


References

Eurofound sources

Eurofound (2018), Additive manufacturing: A layered revolution , Eurofound working paper, Dublin.

Eurofound (2018), Advanced industrial robotics: Taking human-robot collaboration to the next level , Eurofound working paper, Dublin.

Eurofound (2018), Industrial internet of things: Digitisation, value networks and changes in work , Eurofound working paper, Dublin.

Eurofound (2019), Advanced robotics: Implications of game-changing technologies in the services sector in Europe , Eurofound working paper, Dublin.

Eurofound (2019), Platform work: Maximising the potential while safeguarding standards? , Publications Office of the European Union, Luxembourg.

Eurofound (2020), ‘ Platform work: Platforms’ business model ’, dossier, 13 March.

Eurofound (2021), Digitisation in the workplace: Uptake, drivers and impact on work organisation and job quality , Publications Office of the European Union, Luxembourg.

Other sources

Acatech and Forschungsunion (2013), Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , Plattform Industrie 4.0, Frankfurt.

Antonova, A. (2013), ‘ Emerging ubiquitous technologies and requirements for developing complex business competences ’, conference paper, Vanguard Scientific Instruments in Management, 10–14 September, Ravda, Bulgaria.

A.T. Kearney (2015), 3D printing: A manufacturing revolution , New York.

Brettel, M., Friederichsen, N., Keller, M. and Rosenberg, M. (2014), ‘ How virtualization, decentralisation and network building change the manufacturing landscape: An industry 4.0 perspective ’, International Journal of Information and Communication Engineering , Vol. 8, No. 1, pp. 37–44.

Cohen, D., Sargeant, M. and Somers, K. (2014), ‘ 3-D printing takes shape ’, McKinsey Quarterly, January, pp. 1–6.

Deloitte (2014), 3D opportunity in tooling: Additive manufacturing shapes the future , New York.

Deloitte (2015), Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies , Zurich.

European Commission (2021), Digital labour platforms in the EU: Mapping and business models , Publications Office of the European Union, Luxembourg.

Fleisch, E., Weinberger, M. and Wortmann, F. (2014), Business models and the internet of things , Bosch IoT Lab white paper, Zurich.

Foresight (2013), The future of manufacturing: A new era of opportunity and challenge for the UK , Government Office for Science, London.

International Federation of Robotics (2018), Robots and the workplace of the future , Frankfurt.

McKinsey Global Institute (2015), The internet of things: Mapping the value beyond the hype , Toronto.

Royal Academy of Engineering (2013), Additive manufacturing: Opportunities and constraints , London.

Van Houten, F. and Scholten, C. (2016), ‘CPS for manufacturing’, in Scientific Foresight Unit, European Parliamentary Research Service, Ethical aspects of cyber-physical systems , Brussels, pp. 14.

Weller, C., Kleer, R. and Frank, P. T. (2015), ‘ Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited ’, International Journal of Production Economics, Vol. 164, pp. 43–56.

World Economic Forum (2015), Industrial internet of things: Unleashing the potential of connected products and services , Geneva.

 

 

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