Research and analysis

Factors influencing firms’ adoption of advanced technologies: A rapid evidence review

Published 2 June 2025

Halima Jibril and Stephen Roper (Innovation and Research Caucus)

Executive Summary

This rapid literature review focuses on firms’ decisions about whether to adopt advanced technologies. The review is intended to inform subsequent business interviews focused on advanced technology adoption decisions across a range of UK sectors. The wider project aims to identify the key determinants of firms’ adoption decisions and how these may vary across firm types and technologies.

The terms ‘adoption’ and ‘diffusion’ of technologies are often used interchangeably. Here, we use these terms as follows:

• Adoption decision – is the investment decision firms make to adopt or not adopt a specific technology. This focuses on the go or no-go decision point and the factors shaping that determination.

• Diffusion – represents the level of take-up of a specific technology among a population (or sub-population) of firms; as such diffusion is the outcome of individual firms’ adoption decisions.

There is no universally accepted definition of ‘advanced technology’. Our working definition of ‘advanced technologies’ is that they are:

cutting-edge technologies at the boundaries of existing scientific, engineering and technological knowledge which are likely to integrate elements from domains such as digital technology, data science and analytics, AI, robotics and material science

Here, we focus on evidence on the adoption decision in general for advanced technologies, and in 5 technology clusters: Information and Communication Technologies, Advanced Computing Technologies, Advanced Manufacturing and Materials, Energy and Environmental Technologies and Life Sciences and Healthcare.

Conceptual perspectives

System-wide perspectives on technology diffusion are often systematised using Diffusion of Innovation (DoI) theory and/or by using the Technology-Organisation-Environment framework (TOE). The TOE framework identifies 3 main categories of contextual factors: technological, organisational, and environmental. The technological context refers to (perceived) technology characteristics and existing technological capabilities and skills. The organisational context refers to factors like company size, structure, strategy, and resources. The environmental context involves the business environment, such as the industry, nature of competitors, customers, partners, and regulations.

While TOE/DoI is an overarching framework that helps to understand technology diffusion, the specificities of each technology need to be considered in order to understand adoption and non-adoption in specific groups of firms such as SMEs.

The adoption decision itself has been considered through various ‘lenses’, each of which has implications for the planned interviews relating to firms’ adoption decisions. These lenses include:

• Behavioural models focus on the psychological aspects of the adoption decision and the factors that influence that decision, including the Technology Acceptance Model (TAM). The related ORGANISER model provides a more generalised framework for managerial decisions.

• Investment decision frameworks focus on the nature of the investment firms make and differentiate between intangible and tangible assets. The factors governing each type of investment decision may vary significantly.

• Strategic decision frameworks bring temporal considerations into the adoption decision, particularly regarding the pros and cons of first- and second-mover strategies. Here, the risk-reward trade-off reflects the uncertainty implicit in advanced technologies.

Barriers and enablers of advanced technology adoption

Evidence from a wide range of academic papers and policy reports suggest broadly similar types of barriers and enablers identified across technology clusters, with broad consistency in findings with studies which consider a wider range of advanced technologies.

It is possible to summarise the 11 categories of barriers and enablers of advanced and emerging technology adoption, organised as internal, external and technological, to be consistent with the TOE framework and to align with the ORGANISER model which form the basis for planned interviews. These 11 factors, each consisting of a series of enablers and barriers, are:

Internal factors:

• Behavioural factors, including managerial and employee willingness, vision, and motivations.
• Skills, including difficulties in developing employee skills and employee absorptive capacity.
• Risks and uncertainty relating to potential benefits and returns on investment, inadequate technical knowledge, and the extent of preparatory activities that reduce risks
• Financial factors relating to high costs of adoption, anticipated low return on investment, or inadequate access to external finance
• Innovativeness, relating to the desire or commitment to reduce costs or improve existing products and processes

External factors

• Regulation, standards and government support
• Market factors reflecting competitor pressures and the extent to which customer demand supports adoption or supplier relationships allow adoption.
• Networks and external advice

Technological factors

• Technological functionality and risks, including perceptions of functional adequacy.
• Technological alignment and ease of use, relating to the degree of complexity of the technology and the extent of compatibility or complementarity with existing technologies or systems, as well as the ease of use.
• Business and strategic alignment related to the extent to which technologies are consistent with wider business strategies.

It is interesting to note that many of the same factors also appear important for the adoption of more established technologies, but with some important differences. In particular, external factors related to regulation, standards and government support, as well as market factors related to supply-chain relationships, appear much more relevant for advanced technologies. Cost-related barriers also appear stronger. These contrasts reflect the cutting-edge, riskier, and costlier nature of advanced technologies relative to established digital technologies.

Adoption in different types of firms

Evidence on the effects of firm size on the adoption of advanced technologies suggests a  ‘a ‘hierarchy of technological sophistication’, with smaller firms evidencing less technological sophistication and lower adoption rates, and larger firms evidencing higher technological sophistication and higher adoption rates. Studies variously attribute these effects to the stronger human and technological resources in larger firms, their wider market access, and higher commercial viability of technologies afforded by a larger scale of output.

Research suggests that manufacturing firms are not necessarily more likely to adopt advanced technologies than service firms. Larger, more productive firms which tend to be exporters are more likely to be adopters of advanced technologies across both sectors.

Advanced technologies – particularly digital technologies – are reshaping retailing by influencing various aspects of the customer journey, from pre-purchase to post-purchase stages. Different enablers and barriers influence adoption at each stage of the customer journey and different types of advanced technologies.

In creative industries and finance the TOE model has been widely used to examine adoption. Creative industries are dominated by smaller firms, so resource and expertise constraints are often critical drivers of the adoption of advanced technologies. In finance, organisational characteristics, leadership, and industry traits also play crucial roles.

There is a positive correlation between being a member of a group of companies and the adoption of advanced technologies. This may be because common ownership enables access to the knowledge base of the parent firm resulting in higher performance and Advanced Manufacturing Technologies (AMT) adoption.

Interventions supporting adoption

There is little direct evidence on the effectiveness of policies designed to support the adoption of advanced cutting-edge technologies. However, much can be learned from the series of interventions conducted under the Government’s Business Basics programme, a policy experiment designed to generate robust insights into effective interventions for digital adoption and management practices, as well as from international evidence on digital adoption. Phipps and Fuller (2022) synthesise these policy-relevant findings, which we categorise based on effective intervention areas and intervention methods for digital adoption.

A range of policy intervention areas which may support adoption include:

• Market related interventions: creating incentives to adopt technologies through altering demand side factors may work to increase adoption, as can the introduction of standards which enhance interoperability and trust with other market actors e.g., suppliers. Economic models of adoption also suggest the potential effectiveness of policies that facilitate cost spreading and revenue risk reduction, for instance through the provision of shared facilities or Advance Market Commitments,  such as through government procurement. Advance Market Commitments, or AMCs, are binding commitments to make a market for an innovative product/service that is not yet commercially developed and is intended to incentivise innovation.
• Infrastructure for technology adoption: Digital infrastructure, such as broadband access, has been found to enable SMEs to adopt digital technologies. For advanced technologies like AI and AR/VR high-speed connectivity will be critical.
• Informational interventions: Raising awareness of the benefits of technologies may be effective in helping adoption but is rarely sufficient on its own. Benchmarking information, particularly when personalised, has proven more effective in motivating businesses to act.
• Capability and capacity enhancing interventions: Various interventions for digital adoption take the form of training for businesses to improve managers’ digital awareness and attitudes to adoption or to enhance digital skills. Others have sought to enhance business capacity by using students and apprentices to reduce resource burdens and address skills gaps in SMEs
• Financial incentives and subsidies: The cost barrier to adoption, its perceived riskiness and uncertainty about benefits make providing subsidies an attractive policy tool to help overcome these barriers. However, evidence on effectiveness of subsidies is mixed.

Effectiveness of different approaches to intervention

• **Structured training: The evidence supports using structured training to encourage adoption.  Structured training can address behavioural and strategic barriers to adoption, and is more successful if expert facilitation and scheduled sessions are provided for businesses. Benefits have been found even from online training programmes.
• Peer group involvement: Incorporating a peer group element in technology adoption programmes has generally been effective in both UK and international contexts.
• Business adviser or mentor: Some evidence suggests that external advisers or mentors may help businesses adopt digital technologies.
• Behavioural insights: The Behavioural Insights Team propose that business interventions, broadly defined, should aim to be easy, attractive, social, and timely if they are to encourage small business to adopt new processes.

It is instructive to note that much of the evidence on effective policy interventions relate to the adoption of more established technologies. Given the more nascent, riskier, and costlier nature of the advanced technologies considered here, market related policies that influence demand and reduce risks (such as advance market commitments) or reduce costs (such as through shared facilities), as well as clearer standards and regulatory environments are likely to be important. However, there is little evidence in the literature on the effectiveness of these kinds of interventions in the context of technologies considered here.

1. Aims, objectives and definitions

1.1 Aims and objectives

This rapid literature review focuses on firms’ decisions about whether to adopt or not adopt advanced technologies. As such, it focuses on firms’ initial investment decisions and not on subsequent implementation challenges. The review is intended to inform a series of business interviews on advanced technology adoption decisions across a range of UK sectors. The wider project aims to identify the key determinants of firms’ adoption decisions and how these may vary across firm types and technologies.

Adoption decisions are often complex and may represent the culmination of an evaluation process which reflects the technological and commercial risks involved in adopting advanced technologies. This reflects the inherent risks of embracing advanced technologies, with recent studies suggesting that the proportion of innovation projects abandoned in part or wholly is between 40% and 90% (Rhaiem and Amara, 2021).

The commercial and technological risks are minimised where adoption decisions relate to proven and mature technologies. For advanced and emerging technologies, where technology is developing rapidly, and market opportunities may be emergent and uncertain, adoption risks are magnified. However, the rewards from early adoption may be greater, emphasising the contextual aspect of adoption by one firm relative to its competitors. The trade-offs between early adoption and a ‘wait-and-see’ approach are reflected in the discussion of first-mover and second-mover adoption strategies (Nafizah et al. 2024) and economic studies of technology diffusion (Karshenas and Stoneman, 1993).

Adoption decisions will also reflect firms’ internal resources and existing technological capabilities. Both matter as they shape the expected costs and benefits from implementing new technologies alongside factors external to the firm. Skill levels may for instance, influence the anticipated returns and therefore, adoption of digital technologies (Crillo et al. (2023).

1.2 Adoption and diffusion

The terms ‘adoption’ and ‘diffusion’ of technologies are often used interchangeably in contexts such as digital technologies. Here, we use these terms as follows:

• Adoption decision: the investment decision firms make to adopt or not adopt a specific technology. This focuses attention on the go or no-go decision point and the factors which shape that determination.

• Diffusion: the level of take-up of a specific technology among a population (or sub-population) of firms, as diffusion is the outcome of individual firms’ adoption decisions.

1.3 Defining ‘advanced technologies’

There is no universally accepted definition of ‘advanced technology’. Indeed, given the pace of technological change, it is uncertain whether any universal definition is feasible. Where business surveys ask about advanced technologies – such as Statistics Canada’s ‘Survey of Advanced Technologies’ - no generic definition of ‘advanced technology’ is adopted. Instead, the notion of advanced technologies is approached more directly by specifying a series of specific technologies which may be adopted by Canadian firms.

Here, we adopt a general working definition with a secondary focus on a group of specific technological areas. Our working definition of an ‘advanced technology’ is as follows:

cutting-edge technologies at the boundaries of existing scientific, engineering and technological knowledge which are likely to integrate elements from domains such as digital technology, data science and analytics, AI, robotics and material science

These technologies (see examples below) offer new opportunities for business growth and productivity and pose specific challenges for businesses in their adoption and implementation. Implementation issues are beyond the scope of the current project but may be important in the adoption decision itself as anticipated challenges may influence expected post-adoption returns. Key elements of the risk-reward balance in adopting Advanced Technologies (ATs) relate to:

• High Level of Innovation: ATs are at the forefront of their respective fields and represent significant advancements over previous technologies. They are developing rapidly, creating first-mover risks and opportunities for firms.
• Complexity: ATs often involve sophisticated scientific principles, intricate engineering, and interdisciplinary knowledge. They may require specialised expertise that is not always readily available, particularly in smaller firms.
• Integration of Multiple Fields: Many ATs combine elements from various technology areas requiring diverse expertise and implementation teams. Again, this requirement may pose particular challenges for smaller firms.
• Implementation costs: ATs may require significant capital and organisational (business model) investment. Combined with the associated risks, this poses significant financial challenges, particularly for cash-constrained firms.

As in the Canadian survey cited above, the suggestion here is to focus on specific groups of advanced technologies, derived from the UK Department for Science, Innovation and Technology (DSIT)’s Innovation Diffusion and Adoption (IDA) Survey. These form the focus for material reviewed later in this literature survey:

Cluster 1: Information and communication technologies

• Artificial Intelligence (AI) (including, but not limited to, Machine Learning)
• Future Computing & Data Management Technology (including, but not limited to, Future Computing Paradigms, Big Data Infrastructure, Privacy Enhancing Technology)
• Extended Reality, Immersive and Synthetic Environment Technologies (including, but not limited to, AR, VR, Digital Twins)
• Future telecoms (including, but not limited to, 5G/6G, Wireless Communications, Cloud Communications, Satellite Communications)

Cluster 2: Advanced computing technologies

• Robotics, Drones and Autonomous Systems
• Quantum Technology (including, but not limited to, Sensing and Metrology)
• Photonics and non-Quantum Sensors (including, but not limited to Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), Quantum Photonics, Biophotonics)

Cluster 3: Advanced Manufacturing and Materials

• Manufacturing technologies including additive manufacturing (including, but not limited to, 3D, 4D printing)
• Advanced Materials (including, but not limited to, Novel or Complex Metal Alloys, Advanced Composites, Engineering and Technical Polymers and Ceramics)
• Novel Electronics and Position, Navigation and Timing Technology (including, but not limited to, Flexible Electronics, Printed Electronics)

Cluster 4: Energy and Environmental Technologies

• Low Carbon Energy, Heating and Propulsion Technologies (including, but not limited to, Nuclear, Renewables)
• Recycling and waste technology (including, but not limited to, CC(U)S)
• Battery and energy storage technologies

Cluster 5: Life Sciences and Healthcare

• Medical technologies (including, but not limited to, Devices, Therapeutics)
• Biotechnologies (including, but not limited to Engineering Biology)

2. Conceptual perspectives on technology adoption

2.1 Introduction

Existing research evidence suggests that a complex interplay of factors drives the adoption of advanced technologies—and, inversely, their non-adoption. Motivations, skills, and resources inside the firm may enable or restrict adoption. Likewise, external factors linked to markets or the supply side may either allow or hinder adoption. Infrastructure issues, such as broadband availability in rural areas, may also limit adoption. Conceptual models often structure these factors into precedents or antecedents, decision factors and outcomes or results. See for example, Figure 1 developed by the Portuguese Digital Transformation Observatory.

Figure 1: Factors influencing the success of digital transformation

Source: Digital Transformation Observatory

This section provides a brief overview of conceptual approaches to understanding technology diffusion at the system-wide level and adoption decisions at the company level. System-wide or systemic perspectives on technology diffusion help us understand the levels of take-up of digital technologies across the population of firms. However, they are less helpful in understanding how and why individual firms make adoption decisions. This is the focus of the adoption decision models discussed later in this section.

2.2 System-wide perspectives on technology diffusion^{[footnote 1]}

Diffusion of Innovation (DoI) Theory

These factors are often systematised using the Diffusion of Innovation (DoI) theory and by using the Technology-Organisation-Environment Framework (TOE). The TOE framework identifies 3 main categories of contextual factors: technological, organisational, and environmental. The technological context refers to (perceived) technology characteristics and existing technological capabilities and skills. The organisational context refers to factors like company size, structure, strategy, resources, etc. The environmental context involves the business environment, such as the industry, nature of competitors, customers, partners, and regulations.

While TOE/DoI is an overarching framework that helps to understand technology diffusion, the specificities of each technology need to be considered to understand adoption and non-adoption in specific groups of firms such as SMEs. In particular, not all advanced technologies are universally relevant to all businesses. For technologies to be perceived as useful, they should align with the business strategy and stage of development (Olivera et al., 2014; Hasani et al., 2017). Finally, adopting technology is not an end goal – it also needs to be assimilated and routinised by the firm. Some beneficial factors for adoption can negatively affect further stages of the innovation process. For example, Zhu et al., (2006) find that too much competition is bad for the routinisation of adopted technologies.

Technology diffusion from the perspective of neoclassical economic growth models

Neoclassical growth models of adoption and diffusion typically emphasise the cost element of adoption and expected returns. Comin and Hobjin (2010) develop a model of technology adoption and diffusion based on growth models which incorporate technology diffusion trajectories across countries. A key determinant of widespread adoption here is the adoption lag, i.e., the time it takes between the invention of a new technology and its eventual widespread adoption by individual firms. The adoption lag is shaped by the initial (fixed) cost of adoption and has significant implications for first mover productivity advantages across countries.

New technologies here are assumed to embody higher efficiency and productivity than existing technologies, such that new technologies are linked to higher Total Factor Productivity (TFP) at the firm level through increasing average level of productivity across technologies (the embodiment effect) as well as through increasing the range of technologies in use (the variety effect). As more and more technologies are added to the production process, the additional impact on productivity diminishes, suggesting an optimal level of technology adoption which may vary by firm, product and market characteristics. Comin and Hobjin (2010) empirically test their stylised model using data on 166 countries and 15 technologies over the 1820-2003 period. They find widespread adoption of technologies typically occurs with a significant lag, with an average adoption lag of about 45 years. This varies across technologies, however, with newer technologies consistently adopted faster than older ones.

Stokey (2020) also emphasises the importance of technology costs in adoption and diffusion, distinguishing between changes in fixed adoption costs and the implications of technology costs for relative input prices. The fixed costs of adoption here is expected to decline over time as more and more of the same technology is adopted, such that first mover technology adopters are likely to pay higher fixed costs than a broader set of subsequent adopters. As such, large scale adoption (diffusion) becomes more likely over time. This, in part, may explain why technologies diffuse with long adoption lags. In terms of relative input prices, Stokey (2020) argues that since technologies are by nature labour saving and capital using, and adoption is intended to reduce costs and raise efficiency, firms will evaluate the value of technologies based on relative prices of other inputs i.e., labour (wages) and capital costs. Adoption is thus more likely in countries where relative labour prices are higher.

2.3 Behavioural perspectives on technology adoption

Technology Acceptance Model

The most commonly used framework to examine digital adoption is the Technology Acceptance Model (TAM) (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989). TAM is an adaptation of the Theory of Planned Behaviour (Ajzen, 1991) for technology adoption and use. According to TAM, the behavioural intention to use a technology, which should be the best predictor of subsequent technology adoption, depends on a positive attitude towards this technology (see Figure 2).

This positive attitude, in turn, is driven by 2 further psychological antecedents. First, perceived usefulness is an essential precondition for developing a positive attitude towards technology use. Perceived usefulness is defined as the potential user’s perception of a technology as useful to increase business success (Davis et al., 1989).  The more valuable a technology appears to a business owner, the more positive the attitude towards this technology, and the stronger the intention to use the technology in the business. Second, the perceived ease of use of technology influences the attitude towards it. Perceived ease of use describes the extent to which a technology can be adapted without investing significant effort (Davis et al., 1989).

In more recent versions of TAM, technology-related self-efficacy, which refers to an individual’s judgment about their ability to apply digital technology, has been suggested as a critical antecedent of perceived ease of use (Roca, Chiu, & Martinez, 2006). According to TAM, perceived ease of use should also positively influence the perceived usefulness of a technology.

Figure 2: The Technology Acceptance Model

Source: Roca, Chiu and Martinez (2006)

Meta-analytic results support the usefulness of TAM for the prediction of technology adoption in various contexts, such as e-service adoption, physician’s acceptance of telemedicine technology, and use of internet banking services (King & He, 2006). Interventions stimulating technology use may affect the perceived usefulness and ease of technology adoption and thereby the attitude towards and intentions to use technology.

The ORGANISER model

The ORGANISER model is a generalised behavioural framework which aims to capture the external and internal factors which shape business decisions along with aspects of the organisational process Department of Energy and Climate Change (2016). This closely reflects the structure of the TOE model discussed earlier.  The main themes of the ORGANISER framework are outlined below.

Table 1: Key aspects of the ORGANISER framework

Theme	Brief description	Overarching suggested action
External
Operating environment	Organisations operate in an environment characterised by a complex set of laws, regulations, taxes, and other influences that shape behaviour.	Map how the operating environment is enabling or constraining organisational behaviour and how this can affect your policy or issue.
Relationships	Organisations exist within a network of relationships – with suppliers, customers, shareholders and others – that influence behaviour.	Identify most important network members and assess how your respective interests align and identify potential entry points for influence.
Gaining advantage & reputation	Organisations pursue some sort of comparative or competitive advantage relative to others with which they either compete or compare themselves.	Assess whether your policy or issue can benefit from benchmarking similar organisations against each other.
Internal
Aims	Organisations always have an aim or a goal or a purpose; and this aim shapes and helps to explain behaviour.	Frame your policy so it aligns with known organisational aims and capitalise on how your policy may positively affect or enhance organisational aims
Norms & organisational culture	Organisations have norms – a culture, rules, an ethical framework, a sense of their own identity – that explain and influence behaviour.	Frame policy to align with internal organisational norms/values; and assess what shift in values/norms might be necessary to enable your policy to be more successful
Internal structures	Organisations have an internal structure – leadership, teams, a more-or-less explicit distribution of power, a varied mechanism of making decisions – that shapes behaviour.	Target policies at the right level of decision makers focusing where responsibility for implementation resides within target organisations
Decision-making processes
Strategic processes	Organisations tend to be more strategic and slower in their decision-making than an individual; they are slower. This can be positive, by softening extreme positions; or can be negative, creating a ‘group-think’ situation that reinforces an extreme position.	Gather evidence on decision-making by the organisation(s) and assess whether it is possible to influence any factors of negative or biased strategic decision making via your policy or issue.
Estimation	Organisations are constrained by time and resources and use heuristics and rules of thumb – best estimates – to make decisions.	Make things easier and design implementation of policies to go with the grain of behaviour by accounting for organisational constraints and short-cuts.
Relying on trusted sources	Organisations rely on trusted sources, in particular, to provide information, insight and judgment when making decisions.	Secure the buy-in from and use, where possible, trusted sources to deliver messages relating to your policy or issue.

Source: Department for Energy and Climate Change (2016)

While the ORGANISER framework provides a robust model within which to capture the behavioural aspects of decision making, it does not fully capture the inherent characteristics of the technologies on which decisions are being made. In decisions around advanced technologies, their acceptability and ease of use – as reflected in the TAM – may also need to be considered as part of any application of this approach.

2.4 Adoption as an investment decision

Adopting advanced technologies is likely to require significant investment by most firms. The inherent risks and uncertainties along with the potential rewards, suggest the potential value of considering adoption as an investment decision. In conceptual terms, this has led researchers to combine real options theory (derived from finance) with the TOE framework discussed earlier to consider investment decisions related to AI. For example, Ameye et al. (2023) highlight the critical impact of uncertainty around potential applications on AI adoption decisions and also point to the effects of collective learning on reducing uncertainty. This reflects discussions about first-mover and second-mover advantages, as later adopters can benefit from the learning and experience of earlier adopters.

A recent review of business investment decisions provides further clues as to the range of factors which may influence advanced technology adoption decisions (Golubova , 2024). Here, a key distinction is between tangible (also often referred to as physical capital investment) and intangible investments. Tangible investment consists of physical assets such as machinery, equipment, vehicles, buildings, plants. Intangible investment refers to non-monetary assets such as research and development (R&D), intellectual property, branding, marketing, staff training and education, organisational efficiency, service design. British firms have yet to invest as much into capital as could have been expected based on standard economic theory, in particular given the recorded decline in the risk-free rate over the past decades without a corresponding decline in the rate of return on capital. This is known as a ‘missing investment puzzle’ and can be partly explained by firms making intangible investments instead of tangible ones (Bailey et al., 2022).

The review highlights 3 main groups of factors which may shape investment decisions:

• First, the primary driver of business investment is a positive assessment of return on investment (ROI) - perceived or evaluated - mainly financial return. The key barrier to investment is uncertainty: at firm-level on ROI and wider macroeconomic uncertainty, which various shocks can cause. Uncertainty appears to have a stronger negative impact on intangible investments and investments in advanced technologies that have higher associated risks and less known or certain returns.

• Second, business leaders’ perceptions and motivations affect various types of investment. This can be summarised as a positive attitude towards business growth and specific investments making decision-makers more likely to invest and vice versa. Furthermore, a number of different internal and external stakeholders feed into investment decision-making processes.

• Lastly, external influences affect business investment decisions. Public policy support, such as financial support (e.g., R&D subsidies), and environmental regulation (for green and energy efficiency investments) tends to increase investment. Fiscal and monetary policies and public investment in general also influence business investment. Whether they incentivise, disincentivise or have unintended consequences, such as crowding out certain investment types, depends on policy specifics. However, intangible investments are less sensitive to fiscal and monetary policies because the latter primarily affect the cost of capital.

These factors work differently in different groups of companies. Larger firms and those that export are more likely to invest. However, the evidence is not conclusive or uniform in the case of other firm characteristics.

2.5 Strategic considerations in adoption

Adopting advanced technologies can provide firms with clear strategic advantages. The literature generally distinguishes 2 types of adoption strategies based on the timing of adoption: first-mover and second-mover strategies. In each case, the choice of strategy reflects the balance of risks and rewards associated with each approach.

In the first-mover strategy, firms are the early adopters of new technology. They may gain first-mover advantages by exploiting their technological superiority to achieve larger market shares and higher returns. As such, a first-mover adoption strategy reflects a pre-emptive competitive strategy assuming that firms win when they introduce better innovations. Suarez and Lanzolla (2007) identify 3 sources of first-mover advantage:

• Economic advantages from monopolising production resources, achieving economies of scale, or capitalising on the patents of their innovations
• Resource-based advantages - First movers enhance their internal competence and capabilities earlier and, therefore, may have specialised or superior capabilities to those of their competitors.
• Market environment advantages arise from initiating market evolution and technology evolution and, therefore, benefit from technology leadership.

On the other hand, a first-mover adoption strategy is higher risk due to the cost of investments and technological uncertainty. These first-mover disadvantages become the advantages captured by second-movers. In the second-mover strategy, firms delay adoption to benefit from information spillovers and lower adoption costs. Later adopters aim to gain information about technology adoption by observing early adopters’ experience, to reduce technological uncertainty and adoption risks.

Nafizah et al. (2024) examine the first-mover and second-mover advantages of adopting AI and Machine Learning. Working with data on micro-businesses (with 1-9 employees), they show that each technology has a different optimal adoption strategy linked to the characteristics of each advanced digital technology. Adopting Artificial Intelligence by both first and second movers increases micro-businesses’ propensity to innovate. This finding shows that Artificial Intelligence improves the innovation capability of micro-businesses regardless of the adoption strategy. On the contrary, the adoption of Machine Learning by first movers leads to a more significant and positive impact on innovation capability than the adoption by second movers.

3. Enablers and barriers of technology adoption

The literature on technology adoption is dominated by studies that focus on the adoption of well-established Digital Technologies (DTs). This literature suggests the importance of Technological, Organisational, and Environmental factors in determining the adoption of different technologies, consistent with the TOE framework.

It is instructive to consider briefly the findings from this literature as a starting point for understanding the barriers and enablers to adopting more advanced technologies, mainly to understand the main differences in factors influencing adoption.

• Technological context: In most studies that consider established technologies, the perceived benefits is one of the key determinants of adoption. Technology’s (perceived) complexity can also hinder its adoption, with simpler ‘easy-to-use’ systems being more likely to be adopted (Ramdani & Kawalek, 2007). The observability (visibility) of a technology can positively influence its adoption. If many firms in a sector are using a particular technology, it can encourage others to follow. Trialability as part of DT design and delivery may be a key factor in fostering adoption.
• Organisational context: Adoption requires senior management support. In addition, DT adoption is considered useful when it is aligned with business strategy and resources (organisational readiness). Size and market scope also play an important role: growing businesses and businesses present in multiple markets are more likely to adopt digital technologies (Ramdani & Kawalek, 2007).
• Environmental context: While some studies find a positive influence of competitive pressure, customers, and trading partners on digital adoption, others are more cautious. There is limited evidence on the role played by external support, particularly from technology vendors, in driving DT adoption.

The subsequent sections examine the evidence on barriers and enablers of adoption for advanced and emerging technologies.

3.1 Barriers to advanced technology adoption

This section provides an overview of the general issues that have been found to impede the adoption of a wide range of advanced and emerging technologies. It draws primarily on 3 reports which consider adoption of advanced technologies across the UK: a recent Government Office for Science (GOS) report on the implications of emerging technologies for future UK productivity (PwC, 2025); the Made Smarter Review, published in 2017, which examined the potential for Industrial Digitalisation Technologies in the UK (Made Smarter, 2017); and a recent national survey on the adoption of ADTs and platforms (Massini et. al, 2025). Subsequent sections highlight specific barriers within technology clusters, drawing from a wide range of academic and grey literature.

The recent report from PwC (20254, unpublished)  examined the potential of 15 emerging technologies to contribute to productivity gains in the UK over the next decade; many of these technologies form part of the group of advanced technologies we consider here^{[footnote 2]}. The research included a survey of about 500 companies (with more than 15 employees), aimed at understanding current and projected levels of investment in these technologies. The characteristics of businesses in the survey suggest a context of successful, established, larger businesses; 39% of surveyed firms have over £10m in revenue with 13% having over £100m revenue, and more than half of firms have over ten years of operating history. Nevertheless, the report represents one of only a few sources of information on the adoption and potential impact of these advanced technologies in the UK context.

The report estimates that the most significant future investments in the UK could be in AI and Machine Learning, Synthetic/Engineering Biology, Augmented Reality, Therapeutics, and Robotics and Autonomous systems. However, in many cases, these investments are likely to come from a few firms making significant investments, as opposed to the diffusion of these technologies across many firms. AI and Machine Learning are expected to have greater diffusion, with 44% of respondents expecting to use the technology within the next year, fuelled by a motivation to improve efficiency. Other technologies projected to have a relatively high impact on business innovation are AR/VR/ER, Robotics and Autonomous Systems (RAS) and Quantum technology.

In terms of adoption barriers, the report found that the 2 primary barriers regarded as ‘significant’, across technologies are the financial costs of adoption (33% of respondents) and gaps in skills of the workforce (25% of respondents). This is followed by concerns about access to finance (24%) the regulatory climate (17%), uncertainty about the potential benefits of the technology (15%), lack of supplier relationships (14%) and uncertainty about current or future business performance (13%) (Table 2).

There are variations in the extent of perceived barriers across technology areas (see Table 3). The regulatory climate and technology infrastructure is expected to be a significant barrier for adopting Advanced Sensing, Quantum technologies, and Robotics. Quantum technology and Advanced Sensing, in particular, appear to have a wider range of frequently cited barriers to adoption.

The Made Smarter Review, conducted in 2017, examined the barriers to the adoption of advanced Industrial Digitalisation Technologies (IDT). Through a survey of manufacturing businesses and a series of workshops, the Review identified a range of issues, organised into seven key barriers: strategic barriers, security and standards, legacy (infrastructure) issues, skills shortages, access to funding, high costs of adoption, and the availability of trusted advice or external support (see Figure 3). The most significant barriers identified were related to cyber security concerns, lack of skills to design and implement new systems, uncertainty about the benefits of adoption and difficulties in finding the right partners to support adoption.

Massini et al., (2025) conducted a national survey of ADT adoption in 2024, covering over 3,000 UK businesses. The focus was on 5 technologies: AI, Big Data, Cloud Computing, 3D Printing, Internet of Things and Robotics. The main barriers to adoption among non-adopters related to maturity and cost of the technology, concerns with safety and security, inadequate skills, and the need for significant business changes to facilitate adoption. The latter barrier suggests that anticipated implementation challenges do serve as an adoption barrier among UK businesses.

Table 2: Prevalence of barriers to adopting a range of 15 emerging technologies

Extent of the impact	A significant barrier	A slight barrier	No barrier	Don’t know
The financial cost	33%	42%	22%	4%
Workforce skills gap	25%	46%	24%	5%
The access/availability of finance	24%	37%	35%	4%
Inadequate technology infrastructure	21%	41%	33%	6%
The regulatory climate	17%	47%	32%	4%
Uncertainty in potential benefits	15%	48%	31%	6%
Lack of supplier relationships	14%	46%	36%	4%
Uncertainty in current or projected company performance	13%	45%	37%	5%

Source: PwC (2025)

Table 3: Extent of perceived barriers to adoption by technology area

Barriers to adoption	The financial cost	Workforce skills gap	The regulatory climate	Inadequate technology infrastructure	Lack of supplier relationship	The access/availability of finance	Uncertainty in potential benefit	Uncertainty in current or projected company performance
Agritech	84%	70%	73%	70%	66%	77%	64%	64%
Advanced Sensing	82%	81%	81%	68%	76%	65%	73%	64%
Quantum Technology	81%	88%	78%	75%	81%	78%	75%	66%
Future Telecoms	80%	77%	70%	73%	67%	63%	65%	64%
Photonics	79%	79%	76%	68%	79%	62%	65%	59%
Synthetic/Engineering Biology	78%	78%	74%	63%	67%	74%	48%	65%
Therapeutics	78%	70%	76%	75%	67%	65%	66%	67%
AR/VR/ER	78%	77%	72%	72%	65%	70%	70%	69%
Digital Twins	78%	75%	78%	75%	64%	69%	61%	64%
AI and Machine Learning	78%	75%	72%	72%	69%	68%	69%	66%
Autonomous Vehicles	77%	77%	74%	71%	65%	68%	61%	52%
Robotics and Autonomous Systems	77%	82%	76%	70%	69%	68%	68%	64%
Advanced Materials	75%	75%	70%	65%	75%	80%	65%	70%
Future Computing	75%	84%	72%	65%	73%	67%	69%	72%
Semiconductors	74%	75%	76%	72%	71%	65%	69%	68%

Source: PwC (2025)

Figure 3: Seven key barriers to industrial digitalisation identified in the Made Smarter Review

Source: Made Smarter (2017). Technologies considered here are: Additive manufacturing, Artificial Intelligence/Machine Learning/Data Analytics, Robotics and automation; The Industrial Internet of Things (IIOT) and Connectivity (5G, LPWAN etc.), and Virtual Reality & Augmented Reality.

Barriers to adoption by technology clusters

In recent years, systematic literature reviews have been published in peer-reviewed academic journals which look at the factors that determine take-up of groups of advanced technologies which either align with the technology clusters considered here or else form a significant part of clusters. There are striking similarities in the barriers identified across technology groups, and there is broad consistency in findings with the studies above which consider a wide range of advanced technologies.

Cluster 1: Information and Communication Technologies

Key challenges identified in a systematic literature review on the adoption of advanced ICT technologies, such as Industry 4.0 technologies, include the degree of compatibility and complexity of these technologies, the costs and overall investment risks as well as perceptions of cyber security risks (Ghobakhloo et al., 2022). For specific technologies such as AI, the continued desire for one-on-one human interactions in organisational decision-making, especially in organisational contexts requiring creativity, spontaneity, and intuition, can discourage adoption (Oldemeyer et al., 2024). Regulatory uncertainty and the desire to maintain human control over organisational processes can also hinder adoption (Oldemeyer et al., 2024). Massini et al., (2025) found that, among UK firms, the biggest barrier to AI adoption is the perceived low maturity of the technology (24% of firms) followed by security risks (17%) and issues with accessing skilled talent (15%). The biggest motivators among AI adopters were the desire to automate tasks performed by workers (55%), and to improve quality and reliability of processes (46%) and products/ services (43%).

Svanberg et. al., (2023) consider the factors governing AI adoption using economic growth models which incorporate the technical feasibility and economic viability of deploying AI systems in the context of a specific organisational task: computer vision. Their model shows that firms will adopt AI to automate tasks only if the potential labour-saving benefits of AI exceed the costs of adoption. The attractiveness of adopting automation technologies like AI is shown to increase where automation is likely to yield significant competitive advantages to the firm, and where automated tasks represent a large share of overall tasks. This has potential implications for the size of firms likely to adopt; this is discussed in subsequent sections.

Cluster 2: Advanced Computing Technologies

These technologies, such as Robotics and Autonomous Systems (RAS) have the potential to be adopted by a wide range of sectors through several use-cases, for example, through automated guided vehicles, mobile retail robots, and humanoid customer service robots, with the Warehouse and Logistics and Food and Drink Manufacturing sectors having a substantial opportunity for uptake. Department for Business, Energy & Industrial Strategy (2021) identified several barriers that mean there is huge gap between actual and potential uptake of RAS across sectors, notably regulatory barriers, financial barriers and skills-related barriers.

Massini et. al., (2025) find that, among UK firms, the most frequently cited barrier related to technology costs, followed by significant business changes that would be required for adoption.

Cluster 3: Advanced Manufacturing and Materials

Stornelli et al., (2021) undertook an extensive systematic literature review of barriers and enablers of Advanced Manufacturing Technology adoption. The review included 87 peer-reviewed academic papers on the topic. Advanced Manufacturing Technologies (AMTs) are defined here as ‘components of the computer-integrated manufacturing (CIM) paradigm, enabling the control and optimisation of organisational and manufacturing processes’ (Stornelli et al., 2021, p.2). The review identified 5 main barriers across studies, all serving to limit adoption either at the earlier stages where businesses are evaluating the technology or at the set-up and installation stages of the technology. The most commonly cited barriers to AMT adoption are high capital costs, a lack of a vision to adopt, personnel-related skills and resistance, and technology system issues. More specific barriers include:

• Financial barriers related to the high costs of capital required to invest in advanced manufacturing technologies and difficulties justifying investment decisions due to uncertain returns, especially where the technology’s benefits have not yet been well proven (low maturity).
• Organisational constraints related to a lack of vision for managers to adopt and difficulties in relationships with suppliers (including lack of trust) act as significant barriers at the initial evaluation stages of adoption. At the installation stage, adoption may be stalled by set-up preparation difficulties (such as a lack of an implementation plan).
• Personnel-related issues related to skills gaps and difficulties in skills development, as well as behavioural aspects such as employee resistance to adopting advanced manufacturing technologies, given entrenchment in existing processes.
• Technological barriers related to shop floor disruption caused by new technologies, since these may require a reconfiguration or overhaul of existing systems, differences between a prototype and final product, and concerns about data security. All of these technological constraints are more significant at the set-up or installation stage i.e., after a business had made a positive evaluation of the technology and a decision to adopt, but adoption may still be stalled by these factors.
• Policy and regulatory barriers: These barriers, external to the firm, are found to be significant at the evaluation stage, before a decision to adopt. Factors that inhibit adoption here relate to a lack of standardisation of technologies, inadequate government support for adoption, and inadequate information about the benefits of technologies or about specific technical information.

Cluster 4: Energy and Environmental Technologies

Alayón et al. (2022) conducted a systematic review of 32 academic papers which examined the barriers and enablers to adopting a broad group of environmentally friendly and sustainable manufacturing technologies. They found a variety of cultural and attitudinal barriers related to: organisational cultures inconducive to adoption, uncertainty or lack of information about the benefits of technologies, costs of adoption, skills gaps, supply-chain relationships unsupportive of adoption, inadequate government incentives, and technological barriers related to, for example, the need for additional infrastructure modifications. Other recent studies have found many of the same barriers for similar technologies (e.g., Shoaib Abdul Basit et al., 2024).

Cluster 5: Life Sciences and Healthcare

Another systematic review of 33 academic papers published between 1999 and 2019 examined the barriers to adopting medical technologies in the healthcare sector (Warty et al., 2021). The most common barriers found across the individual studies were technology-specific challenges, uncertainty of the effectiveness of technologies due to a lack of clinical evidence, regulatory constraints, and difficulties for healthcare providers getting reimbursed for the costs of using technologies to deliver care. The recent Medtech Strategy published by the Department for Health and Social Care in 2024 acknowledged that adoption in the NHS is made difficult because the multitude of competing innovations such that there are many medical devices with similar functions, making procurement challenging.

3.2 Enablers of advanced technology adoption

Looking again at studies which examined a wide range of advanced technologies, PwC (2025) found that, across technologies, the most cited motivators for adoption are the desire to improve efficiency, respond to competition, and respond to customer needs.

The Made Smarter Review (2017) found three key enablers of Industrial Digitalisation Technologies (IDTs) adoption to be addressed through government policy. These are:

• Standards: Standards that enhance the interoperability of IDTs are expected to increase adoption by building confidence and assurance among stakeholders. These Standards should include both generic and sector-specific standards, as well as the establishment of cyber security guidelines and best practices.
• Financial incentives: Given the cost barriers to adoption, targeted financial incentives are likely to enable the uptake of advanced digital technologies. These may include enhanced capital allowances in the first year of IDT investments, broadening the R&D tax credit scheme to include IDT investments, increasing the write-off allowances for specific technologies, and developing policies and programmes to facilitate the financing of suitable projects.
• Access to data: Implementing advanced digital technology requires data sharing, so trusted frameworks and agreements that ensure secure data exchanges are likely to encourage adoption.

In a recent national survey of ADT adoption, Massini et al., (2025) found that, among adopters, the most significant motivations were related to the need to upgrade outdated processes, improve the quality of existing processes and products, and to enable task automation. This suggests process and product innovations as important drivers of adoption.

Enablers of adoption by technology clusters

Cluster 1: Information and Communication Technologies

Key enablers for the adoption of advanced ICT technologies include perceived strategic benefits as well as user-friendliness of technologies (Ghobakhloo et al., 2022). AI adoption, for example, is found to be driven by the pursuit of increased productivity and improved software and hardware capabilities (Cisterna et al., 2024). Massini et al., (2025) found that, among UK AI adopters, the biggest motivators were the desire to automate tasks performed by workers (55%), and to improve quality and reliability of processes (46%) and of products/ services (43%).

Cluster 2: Advanced Computing Technologies

Boston Consulting Group, (2019) found that successful adoption of technologies such as Robotics and Autonomous Systems required adopters to: i) have a clear target vision of the future factory which incorporates technologies such as robots, ii) develop the necessary organisational competencies, and iii) create an appropriate system architecture. Thus, in line with the organisational preparatory enablers identified by Stornelli et al., (2021) for Advanced Manufacturing Technologies, this suggests the need for businesses to develop appropriate organisational visions, put in place plans for change, and reconfigure existing processes to pave the way for successful adoption.

Among UK adopters of Robotics, Massini et. al., (2025) found that the most prevalent motivator was task automation (61%), improving the quality of processes (52%) and improving the quality of products and services (45%). These are also the top motivators for AI adoption and suggest process and product innovation as well as labour cost reduction as drivers of adoption in both contexts. Labour cost-saving as a driver is consistent with findings from aggregate economic growth models (Stokey, 2020).

Cluster 3: Advance Manufacturing and Materials

For Advanced Manufacturing Technologies (AMT), Stornelli et al., (2021)’s systematic review found the following enablers:

• Technological alignment, such as where manufacturing priorities and strategies align with technology adoption, and where technologies are adopted sequentially or are complementary to existing technologies. This suggests that companies more likely to adopt are those that already have complementary capabilities.
• Policies and government programmes which support organisational changes, training, and the formulation of an adoption plan play a role in supporting uptake. For additive manufacturing, for example, successful programmes facilitated the development of capabilities to introduce product, service, and process innovations. In addition, the relocation of manufacturing plants from offshore countries, stimulated by both government and private entities, allowed businesses to leverage market advantages and local resources for adoption. Government support to SMEs was also found to encourage AMT implementation.
• Effective AMT project management was found to enable adoption. This included effective planning of change processes and various preparatory activities, including preliminary contacts with suppliers, pilot projects to reduce uncertainty, involvement of manufacturing managers in strategy formulation, attendance at related networking events. In addition, higher absorptive capacity (such as employee skills and education), employee training, and complementary process redesign were found to enable adoption.
• Corporate structure: The review found that larger firms, firms that are part of a group, and firms with an innovation culture are more likely to adopt AMT. This finding on innovation culture is consistent with that of Massini et al., (2025) which hights the desire for new and better processes and products as key drivers of adoption among UK firms.

Cluster 4: Energy and Environmental Technologies

In their systematic review of barriers and enablers of sustainable manufacturing technologies, Alayon et al. (2022) found that the most frequently cited enablers are knowledge networks and social networks, support from larger customers in the supply chain, external collaboration or advice, and the provision of government-sponsored platforms that support businesses, particularly SMEs, in adopting these technologies.

Cluster 5: Life Sciences and Healthcare

There is limited evidence of adoption enablers in this space. However, in its Medtech strategy published in 2024, the previous government suggested that the NHS adoption challenge regarding medical technologies may be addressed through a coherent, end-to-end market environment that provides a higher degree of clarity and a more reasonable risk apportionment for innovators, clinical professionals, and commercial partners.

Summary

Our review of the literature reveals many of the same types of barriers and enablers of adoption across advanced technology clusters and for a wider group of advanced technologies. Table 4 synthesises these into 11 main categories of factors, organised as internal, external and technological to be consistent with the TOE framework and to align with the ORGANISER model. The 11 main factors we identify, each consisting of a series of enablers and barriers, are:

Internal factors:

• Behavioural factors, including managerial and employee willingness, vision, and motivations.
• Skills, including difficulties in developing employee skills and employee absorptive capacity.
• Risks and uncertainty relating to potential benefits and returns on investment, inadequate technical knowledge, and the extent of preparatory activities that reduce risks
• Financial factors relating to high costs of adoption, anticipated low return on investment, or inadequate access to external finance
• Innovativeness, relating to the desire or commitment to reduce costs or improve existing products and processes

External factors

• Regulation, standards and government support
• Market factors reflecting competitor pressures and the extent to which customer demand supports adoption or supplier relationships allow adoption.
• Networks and external advice

Technological factors

• Technological functionality and risks, including perceptions of functional adequacy.
• Technological alignment and ease of use, relating to the degree of complexity of the technology and the extent of compatibility or complementarity with existing technologies or systems, as well as the ease of use.
• Business and strategic alignment related to the extent to which technologies are consistent with wider business strategies.

It is interesting to note that many of the same factors also appear important for the adoption of more established technologies, but with some important differences. In particular, external factors related to regulation, standards, government support, and market factors (supply-chain), as well as cost related barriers, appear much stronger for advanced technologies. These contrasts reflect the cutting-edge, riskier nature of advanced technologies and their higher costs relative to established digital technologies. This does not mean that other factors, such as skills, are less important for the adoption of advanced technologies; rather, in addition to these factors, advanced technologies are even more acutely affected by external environmental factors and cost barriers relative to established technologies.

Table 4: Summary of barriers and enablers to the adoption of advanced and emerging technologies: Internal, external and technological

Factor Types	Factors	Barriers	Enablers
Internal	Behavioural factors	Employee resistance	Desire to improve efficiency
Internal	Behavioural factors	Lack of managerial vision or motivation	A clear vision of a future with adoption
Internal	Behavioural factors	Cultural/attitudinal barriers	High perceived strategic benefits
Internal	Skills	Skills shortages	Involving technical staff in strategy formulation
Internal	Skills	Difficulties in skills development	Absorptive capacity
Internal	Risks and uncertainty	Uncertainty about benefits	Preparatory activities e.g., planned change processes, pilot projects to reduce uncertainty
Internal	Risks and uncertainty	Inadequate technical knowledge about the technology	N/A
Internal	Risks and uncertainty	Actual/ perceived riskiness of investment	N/A
Internal	Costs	Financial costs of adoption	N/A
Internal	Costs	Inadequate access to finance	N/A
Internal	Innovativeness	N/A	The desire or commitment to improve existing products and processes
Internal	Innovativeness	N/A	The pursuit of efficiencies
External	Regulation, standards and government support	Uncertain or unsupportive regulatory climate	Standards which enhance trust and technology interoperability
External	Regulation, standards and government support	Inadequate standardisation of technologies	Trusted data sharing frameworks
External	Regulation, standards and government support	Inadequate government incentives	Provision of financial incentives
External	Regulation, standards and government support	N/A	Government programmes e.g., train, implementation plan development
External	Market factors	Unsupportive supply chain relationships	Supportive supply chain relationships
External	Market factors	N/A	Competitor pressures
External	Market factors	N/A	Responding to customer needs
External	Networks and external advice	N/A	Social and business networks
Technological	Functionality and risk	Functional inadequacy (e.g., human input still required for Robotics or AI solutions)	N/A
Technological	Functionality and risk	Low maturity of technology	N/A
Technological	Functionality and risk	Cyber security risks	N/A
Technological	Technological alignment and ease of use	Shop floor disruption	Appropriate system architecture to support adoption
Technological	Technological alignment and ease of use	Technological complexity	Sequential or complementary to existing technologies
Technological	Technological alignment and ease of use	N/A	User friendliness
Technological	Business and strategic alignment	Incompatibility with existing business models	Alignment with wider organisational strategies

3.3 Enablers and barriers to tech adoption in different business segments

Size band

Evidence on the effects of firm size on the adoption of advanced technologies paints a consistent picture. There is typically what has been termed a ‘hierarchy of technological sophistication’, with smaller firms showing less technological sophistication and lower adoption rates, while larger firms demonstrate higher technological sophistication and greater adoption rates. A recent UK wide survey of more than 3,000 businesses found that smaller firms have lower adoption rates across a range of Advanced Digital Technologies including AI and Robotics (Massini et al., 2025). Studies variously attribute these effects to the superior human and technological resources available in larger firms and their broader market access. 

Evidence on the adoption of each type of advanced technology is summarised below, with many studies focusing on individual countries:

Cluster 1: Information and Communication Technologies

International evidence indicates that firm size significantly influences the adoption of information and communication technologies (ICTs). Larger firms tend to adopt more advanced ICT solutions and employ individuals with higher ICT skills compared to smaller firms (Dang Thi Viet Duc & P. Nguyen, 2022). However, smaller firms have made progress in narrowing the technology adoption gap (Santos, 2023). Internal IT modularity influences cloud computing adoption, with varying effects for small and medium-sized enterprises (SMEs) versus large enterprises (Guo et al., 2023). 

US evidence reveals essentially similar adoption patterns. While digitalisation, such as cloud computing usage, is widespread, the use of advanced computing technologies like AI and robotics remains relatively rare and is more common among larger and older firms. ‘Adoption patterns are consistent with a hierarchy of increasing technological sophistication, where most firms that adopt AI or other advanced business technologies also utilise the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large, resulting in significantly higher technology exposure for the average worker’ (Zolas et al. 2020, p. 1). 

These findings are consistent with Svanberg et. al., (2023) who find the attractiveness and commercial viability of automation tools such as AI is related to the scale of tasks to which they are applied. In small firms, therefore, costly AI and other automation systems may be less commercially viable, although decreases in adoption costs or increases in the scale of adoption can help spread costs and risks (e.g., through ‘AI as a Platform’ service). Svanberg et. al., (2023) show that adoption is less cost effective where automated tasks form only a small proportion of overall labour tasks; such that larger firms are more likely to adopt AI because the scale of automated tasks enhance cost effectiveness. Still, they find that even a firm with more than 5,000 employees, larger than 99% of US firms, could only cost effectively automate on average up to 10% of their computer vision tasks. Computer vision helps automate tasks that require the processing of visual information for decision making, such as visually detecting quality of food ingredients in a restaurant through a trained camera system.

Bessen et. al. (2023) highlight implications of firm size through a capability perspective. They find that larger firms have better capabilities to develop and deploy complex internal systems using advanced information technologies such as AI. Once developed internally, these technologies are typically not made available to smaller firms, slowing diffusion. However, where these technologies are made available to smaller firms for a fee, such as Amazon’s AWS cloud service, this can accelerate innovation in small firms. The analysis in Bessen et. al. (2023) appears to suggest inefficiencies in the supply side factors influencing adoption and diffusion, which could be addressed through encouraging larger firms to make their technologies available to others. However, the development of open-source platforms can mitigate some of these inefficiencies.

Cluster 2: Advanced Computing Technologies

There is little evidence on adoption barriers and enablers of advanced computing technologies based on firm size, although findings in Massini et al., (2025) indicate that Robotics is among the technologies least likely to be adopted among smaller firms in the UK.

Cluster 3: Advanced Manufacturing and Materials

Larger firms are more likely to invest in AMTs, reflecting resource advantages and the experience associated with age and organisational capabilities (Arvanitis and Hollenstein, 2001; Burcher et al., 1999; Stornelli et al. 2021)

Cluster 4: Energy and Environmental Technologies

Larger firms are more likely to implement pro-environmental behaviours (Bodjongo et al., 2023). Internal factors, such as financial advantage and ethical responsibility, are more important than external factors in eco-innovation adoption, with differences observed between small and large firms (Chappin et al., 2020). Ownership differences may also play a role here with adoption in smaller firms more strongly linked to entrepreneurial attitudes and adoption in larger firms more dependent on stakeholder pressure (Seroka‐Stolka & Fijorek, 2020)

Cluster 5: Life Sciences and Healthcare

There is perhaps less evidence on firm size differences in the adoption of life sciences and healthcare related technologies.

Sector

In this section, we focus on the factors influencing the adoption of advanced technologies across the 5 focal sectors: Manufacturing, Retail, Finance, Creative Industries and Scientific and Professional Services. However, the relevant advanced technologies differ for each sector, which is likely to be reflected in the sector-specific evidence:

Manufacturing (versus Services)

Research suggests that manufacturing firms are not necessarily more likely to adopt advanced technologies than service firms. While Industry 4.0 technologies enhance innovation in both sectors, service firms demonstrate a higher intensity of service innovation (Sarbu, 2020). Generally, advanced technology adoption is infrequent and skewed towards larger, older firms across sectors (Zolas et al., 2020). Key organisational factors include digital skills, company size, and R&D intensity (Kinkel et al., 2021).

Differences in adoption patterns reflect broader disparities in innovation between the 2 sectors—manufacturing is more technologically focused, while services emphasise creativity and systems improvement. Factors influencing technology adoption in manufacturing include investment reversibility, demand uncertainty, and technological uncertainty (Luque, 2002). In contrast, service firms prioritise human resource strategies for innovation, whereas manufacturers focus on product innovation and quality (Atuahene-Gima, 1996). In highly tradable sectors, the most productive firms tend to be high-tech exporters in both manufacturing and services (Bertschek et al., 2013).

Retail

Advanced technologies—especially digital technologies—are transforming retailing by impacting various aspects of the customer journey, from pre-purchase to post-purchase stages (Shankar et al., 2021; Roggeveen & Sethuraman, 2020). Various enablers and barriers influence adoption at each stage of the customer journey and across different types of advanced technologies. For augmented reality adoption, perceived usefulness, attitude, competitive pressure, customer pressure, perceived cost, and technological knowledge are significant factors (Alam et al., 2021). Customer demand and convenience drive the adoption of mobile payment and procurement apps among small retailers (Aithal et al., 2022). Technological factors, particularly technology availability, significantly affect the adoption of emerging technologies in the context of last-mile delivery, suggesting a potential supply-side constraint in this niche retail segment (Ismail & Jokonya, 2022).

Finance

Similar to Creative Industries, Financial Services encompass a broad range of activities with diverse potential for adoption. The Technology Acceptance Model (TAM) has been extensively utilised to examine the adoption of new (typically digital) technologies in the finance sector (Firmansyah et al., 2022). Self-efficacy closely correlates with TAM in the banking sector (Santini et al., 2020). Organiszational characteristics, leadership, and industry traits also play crucial roles (Matsepe & Van der Lingen, 2022). OrganiszationalOrganisational culture, management attitudes, and strategic partnerships further impact fintech adoption (Khuan, 2024).

Creative Industries

Typically, these sectors are dominated by smaller firms, so resource and expertise constraints are often critical drivers of the adoption of advanced technologies. It is also important to acknowledge the diversity of the Creative Industries themselves in terms of the relevance of advanced technologies across different sectors. This suggests the importance of issues related to employee readiness (Susitha, 2021) and the need for tailored support for SMEs (Zahra et al., 2021). Previous studies have applied the technology-organisation-environment (TOE) framework discussed earlier to understanding IT adoption in creative industry clusters (Dhewanto et al., 2020).

More advanced digital technologies such as blockchain technology and NFTs offer new opportunities for creators to access consumers directly (Malik et al., 2022). Organisational factors, such as top management support and firm size, are critical drivers of technology adoption (Parvand & Rasiah, 2021; Zahra et al., 2021). Digital literacy significantly affects the intention to use digital technology among creative professionals (Cavalheiro et al., 2020).

Professional services

Professional Services firms are potential adopters primarily of Cluster 1 technologies. Brooks et al., (2020) found that find that, among UK legal services firms, cultural and structural challenges hamper AI adoption and effective implementation. These businesses were found to face market pressures to adopt, e.g., pressures to increase efficiencies in order to deliver services at low costs to clients. New entrants into the sector also introduce competitive pressures to adopt. However, a culture of resistance to change, conservatism and risk aversion within legal services firms constrain widespread adoption. In addition, the dominance of LLP management structures in legal firms was found to constrain the implementation of innovative ideas due to limited chains of command and the need for all partners to agree to the innovation (Brooks et al., 2020).

In a qualitative study of auditing firms in Australia, Yang et al., (2022) found that AI adoption is influenced by firms’ size. Larger firms have more to gain from adoption, have fewer resource constraints, and have better organisational processes to facilitate adoption. However, regulatory constraints may limit effective implementation. Conversely, smaller firms are primarily driven by industry competition to adopt AI, but resource and organisational constraints limit adoption and effective implementation.

A recent report from the Association of Chartered Certified Accountants (ACCA, 2023) examined AI adoption among accounting firms and found that efficiency and process improvements were the major drivers of adoption, a finding consistent across regions globally and across firm size. The largest barriers to adoption were high costs, employee resistance to adoption (and other cultural factors) and technological issues such as complementarity to existing technologies, poor IT systems and lack of technical leadership.

Ownership

A positive correlation exists between being a member of a group of companies and adopting advanced technologies. This may be because common ownership enables access to the parent firm’s knowledge base; for instance, in one study by Szalavetz (2019), common ownership resulted in higher performance and AMT adoption.

Multinational companies can also benefit from constructive tensions, open innovation, and knowledge standardisation, which accelerate technology adoption (Schmidt et al., 2023).  Participation in global value chains also facilitates the adoption of Industry 4.0 technologies in developing countries (Delera et al., 2022). The extent to which multi-national businesses are advantaged in adoption may depend on the organisation of the multinational itself. Where advanced technology development centres exist, this may help firms to leverage technological competencies and create synergies (Haghbin et al., 2023).

4. Interventions supporting adoption

4.1 Introduction

In this section, we review studies that have examined the effectiveness of interventions designed to improve the adoption of digital or advanced/emerging technology or have reviewed the existing evidence. We consider both thematic areas of interventions, often related to specific barriers identified above, and effective methods of interventions, i.e., how to intervene.

Phipps and Fuller (2022) identify policy-relevant findings from experimental research into the effectiveness of different policy approaches to foster digital transformation among SMEs. This focuses on learnings from the Government’s Business Basics programme, a policy experiment designed to generate robust insights into effective interventions for digital adoption and management practices^{[footnote 3]}, as well as international evidence on the effectiveness of different government interventions based on highest quality assessments i.e., Randomised Controlled Trials (RCTs, Maryland Scientific Scale of 5). The focus here is on broad digital technologies instead of advanced or emerging technologies. Evidence on interventions for advanced or emerging technology adoption is rare.

Phipps and Fuller (2022) emphasise the importance of policy specificity in interventions for digital technology adoption; the complex barriers to adoption mean that a detailed and specific diagnosis of the most important barriers is required to avoid redundant or ineffective policies. For example, an awareness-raising campaign about available technologies is unlikely to improve adoption if the main obstacles are related to costs, risk aversion, or skills shortages within businesses.

It is instructive to note that the economic growth models of adoption previously discussed, which emphasise adoption costs and benefits, have potential policy implications. This could include spreading costs through standards agreements or shared facilities and infrastructure, (Svanberg et. al., 2023) or perhaps by reducing revenue risks through advance market commitments and public procurement. Bessen et. al., (2023) also suggest potential scope for policy to accelerate technology diffusion by encouraging larger firms to share their increasingly advanced and complex technologies, built inhouse, with small businesses for a fee. Larger firms will need to be convinced to risk their competitive advantage in pursuit of the uncertain reward of a larger market size and an additional income stream (e.g., as with Amazon AWS cloud services).

4.2 Evidence on policy effectiveness by areas of intervention

• Market related interventions: International evidence suggests that creating incentives to adopt technologies through altering demand-side factors may increase adoption. Through an RCT, the Mexican government distributed debit cards to households, which induced businesses to adopt digital payment methods, raising overall sales volumes. Although this is a different national context, this example highlights how interventions that alter the pattern of customer demand may induce adoption behaviours in response to customer needs.

• In the context of advanced and emerging technologies, policies likely to be effective are the establishment of technological standards which may alleviate implementation barriers related to concerns about switching technology providers, thus addressing supply-side market related barriers (Be the Business, 2021).

• Infrastructure for technology adoption: Phipps and Fuller (2022) identify various experimental studies that have shown the benefits of digital infrastructure, such as good broadband access, in enabling SMEs to adopt digital technologies. In the context of more advanced or emerging technologies, infrastructure supporting specific technology families would be essential. Technologies in Cluster 1, i.e., Information and Communication Technologies, such as AI and VR/AR, would require high-speed internet connectivity. Technology standards will also ensure compatibility across technologies for SMEs who adopt.

• Informational interventions: Raising awareness of the benefits of technologies may be effective in helping adoption but is rarely sufficient on its own (Phipps and Fuller, 2022). Additionally, there are risks of informational overload which may raise the perceived complexity of adoption.

• However, the type of information provided matters. The evidence suggests that benchmarking information provided to businesses has successfully spurred businesses to take action.  Kneller et al, (2022) found that UK SMEs which were provided information about the performance of their websites relative to other businesses were driven to undertake changes that improved their website performance within a month of the information being provided. Here, the information was personalised which may have raised its effectiveness. Several Business Basics projects piloted similar schemes with anecdotal evidence that this was well-received by businesses. Evidence from Brazil suggests benchmarking is most effective in increasing uptake if the benchmarking report is short, specific areas of improvement are suggested and the risk of business failure is highlighted (Phipps and Fuller, 2024).

• Capability and capacity-enhancing interventions: Various interventions for digital adoption involve training businesses to improve managers’ digital awareness and attitudes to adoption or to enhance digital skills. For instance, Jibril et al. (2022) found that training small family businesses on the usefulness of digital technologies and ways to encourage other members of the business to support adoption increased managers’ confidence in adopting new technologies.

• Other interventions aim to enhance businesses’ capacity to adopt by using students to aid them in learning about and implementing new technologies, for example, through apprentices. Canada’s Digital Adoption Programme aims to support 200,000 SMEs in their adoption journeys, including by creating opportunities for 28,000 young people to work with these businesses. This additional capacity reduces the resource burden on businesses and may address skills challenges previously highlighted.

• Financial incentives and subsidies: The cost barrier to adoption, the perceived riskiness of technologies and uncertainty about benefits make providing subsidies an attractive policy tool to address these barriers.

• However, the evidence on the effectiveness of subsidies on the take up of digital technologies in the UK is mixed. Various digital adoption programmes conducted under Business Basics offered to subsidise the cost of technologies but there was little demand from SMEs. Subsidies offered to rural businesses by Devon County Council had only a 5% take-up rate, and another provided by the Greater London Authority for adopting basic AI tools found that none of the vouchers were used (Phipps and Fuller, 2022). The UK’s flagship digital programme, Help to Grow: Digital, also offered subsidies but it was met with low demand. This has been partly attributed to the initial eligibility criteria which excluded the business segment most likely to take-up the support (the smallest businesses). The provision of subsidies as the main policy instrument assumes businesses are willing to adopt, overlooking important behavioural and informational barriers outlined above.

• The lesson here is that subsidies offered alone, as the main offer of an intervention, are unlikely to induce adoption. Rather, subsidies should be part of a package which motivates businesses, identifies a need, trains them on digital adoption, supports implementation planning and then makes an offer to subsidise costs as a final push to take up an adoption opportunity. Subsidies are perhaps better targeted not at the general population of firms but at those that have overcome behavioural barriers, are digitally ready, and are at the cusp of adoption.

Overall, the evidence here appears stronger for capability and capacity enhancing interventions as well as for benchmarking related informational interventions, although again these studies relate mainly to the adoption of more established technologies.

4.3 Evidence on the effectiveness of different approaches to intervention

Apart from aspects of barriers to be addressed, the mode of delivery also has implications for the take-up and effectiveness of support. Modes of delivery examined in the literature include:

• Online campaigns: Online marketing campaigns can be used to increase awareness of digital technologies to address informational barriers, and to promote available government support. Although commonly employed, it is difficult to evaluate the effectiveness of these types of interventions, because it is challenging to attribute any adoption decision to public campaigns since a reliable control group is difficult to construct (Phipps and Fuller, 2022)

• Structured training: The evidence is more positive for using structured training to encourage adoption. This is likely to be more successful if there is expert facilitation and scheduled sessions for businesses to attend, and benefits have been found even from online training programmes.

• As part of the Business Basics policy experiments, the Evolve Digital experiment aimed to combine formal guidance on digital technologies and a strong element of peer learning to enhance firms’ confidence to adopt new digital technologies (Jibril et al., 2022). It was delivered fully online using a range of digital platforms and portals. The intervention improved firms’ confidence in their ability to identify relevant digital technologies, to create the conditions necessary for using digital technologies in their firms, and to use these technologies. It also had a positive influence on firm’s attitudes towards using technologies. By contrast, firms that only had access to online materials for self-study, without expert facilitation and peer group interactions, did not have these positive outcomes. Similar programmes have seen better participation rates where employers were asked to pay a small fee for their managers to participate in the online sessions (Tinelli & Ashley-Timms, 2022), perhaps reflecting a desire to get value for money from the program (Phipps and Fuller).

• There is also evidence training programmes aiming to address behavioural barriers to adoption may complement traditional business training programmes by unlocking growth-oriented mindsets within decision makers (Phipps and Fuller, 2022)

• Overall, the evidence suggests that training programmes for technology adoption constitute a particularly promising intervention. A critical metric for any training intervention is the extent to which training contents are transferred to the workplace and lead to a change in work behavior (e.g., actual adoption of a technology in the business). Ensuring such ‘training transfer’ is therefore critical for training success. A number of factors may moderate the strength of the influence of any training programme related either to individual or workplace characteristics with the literature on training transfer (e.g., Blume et. al., 2010) describing different individual-level (trainee characteristics) and workplace-level (work environment) determinants of training transfer.

• Peer group involvement: Incorporating a peer group element in business support programmes has generally been found to be effective in both UK and international contexts. Some Business Basics programmes incorporated a strong peer learning element with evidence that firms involved had better confidence in adopting digital technologies (Jibril et all, 2022) and better participation rates (see Phipps and Fuller, 2022) than those that had no peer learning element in their digital adoption training programmes. Qualitative evidence from programme participants highlighted the peer network elements as particularly helpful, citing positive demonstration effects from businesses already adopting technologies. Peers should be similar in terms of sector, company size and hold similar positions in their organisations, but a trusting environment will need to be created to allow businesses to open up to potential competitors.

Business adviser or mentor: There is some evidence that the use of external advisers may help businesses adopt digital technologies. For example, in one policy experiment (outside the UK), businesses that engaged with external advisers were more likely to adopt digital marketing tools compared to those offered training or consultancy services (Anderson and McKenzie, 2021). Anecdotal evidence from the Growth Vouchers programme in the UK, which offered a 50% subsidy on strategic external advice, suggests that businesses that sought advice related to ‘making the most of digital technology’ were more likely to optimise their websites for improved customer experience and to explore the adoption of additional hardware and software (Phipps and Fuller, 2022). Overall, the evidence here is supportive of a positive influence of a business adviser or mentor for technology adoption.

Behavioural insights: Using the EAST framework

Ultimately, decisions to adopt technologies or to engage with interventions and support programmes aiming to boost technology adoption are made by individuals within businesses. These individuals have a myriad of psychological, behavioural, and mindset traits that may affect these decisions. These are highlighted in the behavioural perspectives to technology adoption discussed in Section 2.

The Behavioural Insights Team (Broughton et al., 2019) examined the application of behavioural insights to enhance the effectiveness of business policies and programmes. They propose that business interventions, broadly defined, should aim to align with the EAST framework: interventions should be made Easy, Attractive, Social and Timely.

• Make it easy: Since businesses often have limited cognitive bandwidth and time constraints, interventions should be designed to make it easy for businesses to take action by simplifying messages, providing clear next steps, and reducing friction costs. ​This can be achieved through measures simplifying processes for eligibility checks and providing pre-filled forms to minimize the effort required from businesses. ​
• Make it attractive: Businesses are more likely to engage with key messages and government incentives if these are salient, attractive, and relevant. ​Interventions should tailor information in a way that clearly signals that the business has been specifically chosen to receive the information, and ensure the message reaches the appropriate decision-maker within the business. ​
• Make it social: Business decision-makers are influenced by the actions and behaviours of others around them, such as competitors, suppliers, and professional contacts. ​Thus, interventions can encourage desired behaviours by showing that other businesses are performing these behaviours (similar to benchmarking-related informational interventions). In addition, intervention should seek to leverage personal and professional networks to enhance learning and adoption of best practices. ​
• Make it timely: Business managers often fail to follow through on their best intentions due to time constraints and competing priorities. ​Simple, timely prompts and reminders can be therefore becan therefore be powerful tools for encouraging businesses to take action. ​Policymakers should aim to target key moments of change and disruption because businesses are more receptive to external advice and new strategies during these periods. ​

Related to this, the Behavioural Insights Team (Wu and, Broughton, 2019) also conducted a review of the evidence on behavioural factors that induce firms to take action towards adopting productivity enhancing technologies and management practices. Here the focus was on key characteristics of specific prompts or messaging likely to have an impact, such as:

• The prompt’s framing should appeal to the target business segment. For example, a framing around national pride appeals to social norms. Based on evidence from multiple RCT experiments, simpler framing consistently had more success.
• The messenger, i.e., the person or organisation delivering the intervention, such as the government, business support organisations of other businesses, may determine how the message is received. The evidence suggests that businesses are more receptive to trusted and familiar sources but it is not always clear who this is; it is likely to be context specific.
• The timing of the prompt is also important. Interventions are most likely to be taken up and implemented when a business is at a ‘trigger point’ such as when undergoing leadership changes or when a new competitor enters the market. From a practical point of view, it is not always feasible to identify these trigger points for individual organisations, but timing interventions to coincide with wider policy changes such as the introduction of regulations, standards or other policies affecting certain industries may help with better targeted intervention timings.
• The form of the prompt, i.e., whether delivered via email, phone call, or face-to-face, is important. The evidence suggests that interventions that involve 2-way human interactions are more effective. This is in line with the evidence highlighted above on the relative ineffectiveness of online campaigns for digital adoption.
• The recipient of the prompt within an organisation may influence take up; senior decision makers are more likely to champion the adoption and implementation of technologies.

Overall, the evidence on effective intervention approaches appears stronger for the use of structured training programmes, incorporating an element of peer group learning and business advisors or mentors. As before, the studies here consider mainly the adoption of more established technologies; policy interventions that specifically target economic barriers and mitigate investment uncertainties may potentially yield greater efficacy in promoting the adoption of cutting-edge technologies compared to their observed impact on more established technologies. This distinction warrants further investigation to shed light on the potential differential effects of policy interventions for technologies of different maturity.

4.4 Challenges of Intervention

Phipps and Fuller (2022) reflected on learnings from over 40 Business Basics trials in the UK. The main issues they identified in delivering effective interventions for digital adoption are:

• Recruitment Challenges: Engaging SMEs that would benefit from support is often harder than expected. ​ SMEs with low current adoption are less likely to engage since they may be disconnected from support networks. ​Thus, effective recruitment strategies often depend on leveraging existing networks and making direct, one-to-one contact with SMEs, an approach which may be time consuming and expensive. ​
• Issues with programme design: Even after recruiting businesses, many projects find that engagement levels are lower than expected. ​This could be due to a misalignment between the support provided and the needs of the participants, or because the support is seen as too generic or basic. Providing more information before registration could help businesses and support providers to quickly identify if the programme aligns with business expectations.
• Issues with implementing adopted technologies: Many projects find that businesses get stuck at the implementation stage. ​This could be due to uncertainty about how to introduce wider changes or difficulties accessing resources to embed new practices. ​

Overall, these lessons suggest the importance of tailored support which is designed and implemented in a way that encourages sustained engagement and progression through to successful implementation.

References

ACCA. (2023). Digital Horizons: Technology, Innovation, and the Future of Accounting. Association of Chartered Certified Accountants.

Aithal, R. K., Choudhary, V., Maurya, H., Pradhan, D., & Sarkar, D. N. (2022) Factors influencing technology adoption amongst small retailers: insights from thematic analysis. International Journal of Retail & Distribution Management, 51(1), 81-102.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

Alam, S. S., Susmit, S., Lin, C.-Y., Masukujjaman, M., & Ho, Y.-H. (2021) Factors Affecting Augmented Reality Adoption in the Retail Industry. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 142.

Alayón, C.L., Säfsten, K. and Johansson, G., 2022. Barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs. Sustainability, 14(4), p.2364.

Ameye, N., Bughin, J., & van Zeebroeck, N. (2023). How uncertainty shapes herding in the corporate use of artificial intelligence technology. Technovation, 127, Article 102846.

Anderson, S. J., & McKenzie, D. (2021). Improving business practices and the boundary of the entrepreneur: A randomized experiment comparing training, consulting, insourcing, and outsourcing. Journal of Political Economy.

Arvanitis, S., Hollenstein, H. (2001). The determinants of the adoption of advanced manufacturing technology: an empirical analysis based on firm-level data for Swiss manufacturing. Econ. Innov. New Technol. 10 (5), 377–414.

Atuahene‐Gima, K. (1996) Differential Potency of Factors Affecting Innovation Performance in Manufacturing and Services Firms in Australia. Journal of Product Innovation Management, 13(1), 35-52.

Bank of England. (2022). Structural change, global R* and the missing investment puzzle (Working Paper No. 1010).

Basit, S.A., Gharleghi, B., Batool, K., Hassan, S.S., Jahanshahi, A.A. and Kliem, M.E. (2024). Review of enablers and barriers of sustainable business practices in SMEs. Journal of Economy and Technology, 2, pp.79-94.

Bertschek, I., Hogrefe, J., & Rasel, F. (2013) Trade and technology: new evidence on the productivity sorting of firm s. Review of World Economics, 151(1), 53-72.

Bertschek, I., Hogrefe, J., & Rasel, F. (2013) Trade and technology: new evidence on the productivity sorting of firms. Review of World Economics, 151(1), 53-72.

Bessen, J., 2023. How Software Stifles Competition and Innovation. Communications of the ACM, 66(10), pp.34-36.

Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A meta-analytic review. Journal of Management, 36(4), 1065-1105.

Boston Consulting Group (2019). Advanced Robotics in the Factory of the Future.

Brooks, C., Gherhes, C. and Vorley, T., 2020. Artificial intelligence in the legal sector: pressures and challenges of transformation. Cambridge Journal of Regions, Economy and Society, 13(1), pp.135-152.

Broughton, N., Costa, E., Pickering, J., Shakhina, N., Tilleard, R., & Wu, H. (2019). Boosting

Burcher, P., Lee, G., Sohal, A., 1999. Lessons for implementing AMT. Int. J. Oper. Prod. Manage. 19 (5/6), 515–527.

Chappin, M. M. H., van den Oever, M. V. D., & Negro, S. O. An overview of factors for the adoption of energy efficient eco-innovation: The cases of the Dutch brewing and paper industry. Journal of Cleaner Production, 275, 124122.

Cirillo, V., Fanti, L., Mina, A., & Ricci, A. (2023) The Adoption of Digital Technologies: Investment, Skills, Work Organisation. SSRN Electronic Journal.

Cisterna, D., Gloser, F.F., Martinez, E. and Lauble, S., 2024. Understanding Professional Perspectives about AI Adoption in the Construction Industry: A Survey in Germany. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 41, pp. 347-354). IAARC Publications.

Comin, D. and Hobijn, B., 2010. An exploration of technology diffusion. American economic review, 100(5), pp.2031-2059.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of 2 theoretical models. Management Science, 35(8), 982-1003.

Delera, M., Pietrobelli, C., Calza, E., & Lavopa, A. (2022) Does value chain participation facilitate the adoption of Industry 4.0 technologies in developing countries? World Development, 152, 105788.

Department for Business, Energy & Industrial Strategy. (2021). The economic impact of Robotics & Autonomous Systems across UK sectors.

Department of Energy and Climate Change (2016) ORGANISER: A behavioural approach for influencing organisations.

Department for Health and Social Care. (2024). Medical Technology Strategy.

Dhewanto, W., Lestari, Y. D., Herliana, S., & Kania, R. (2020) Information technology adoption model in Indonesian creative industry clusters: toward strengthening competitive advantages. International Journal of Technology Transfer and Commercialisation, 17(1), 19.

Duc, D. T. V., & Nguyen, P. V. ICT impact and firm size: Empirical results from Vietnam. African Journal of Science, Technology, Innovation and Development, 15(3), 337-348.

Firmansyah, E. A., Masri, M., Anshari, M., & Besar, M. H. A. (2022) Factors Affecting Fintech Adoption: A Systematic Literature Review. FinTech, 2(1), 21-33.

Ghobakhloo, M., Iranmanesh, M., Vilkas, M., Grybauskas, A. and Amran, A., 2022. Drivers and barriers of industry 4.0 technology adoption among manufacturing SMEs: a systematic review and transformation roadmap. Journal of Manufacturing Technology Management, 33(6), pp.1029-1058.

Golubova, E. (2024). What do we know about factors that affect business investment decisions? State of the Art Review No. 62. Enterprise Research Centre.

PwC (2025). ​The Wider Economic Impacts of Emerging Technologies in the UK. ​ A report prepared for the Government Office for Science by Price Waterhouse Coopers LLP.

Guo, R., Tafti, A., & Subramanyam, R. (2023) Internal IT modularity, firm size, and adoption of cloud computing. Electronic Commerce Research.

Haghbin, A., Hamedi, M., & Haghifam, M. (2023) A Model for Setting up an Advanced Technology Development Center in Multi-division Industrial Groups, 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)

Hasani, T., Bojei, J., & Dehghantanha, A. (2017). Investigating the antecedents to the adoption of SCRM technologies by start-up companies. Telematics and Informatics, 34(5), 655-675.

Ismail, R.-E., & Jokonya, O. (2022) Factors Affecting the Adoption of Emerging Technologies in Last-Mile Delivery in the Retail Industry. Procedia Computer Science, 219, 2084-2092.

Jibril, H., Mensmann, M., Roper, S., & Scott, D. (2022). Evaluation of the Evolve Digital programme to promote digital adoption in family firms: A randomised control trial. Enterprise Research Centre.

Juliot Mpabe Bodjongo, M., Fofana, M., & Kabwe Omoyiepse Essomme, F. (2023) Firm size and pro-environmental behavior in Cameroon. Environmental Economics, 14(1), 47-60.

Karshenas, M., & Stoneman, P. L. (1993). The rank, stock order and epidemic effects in the diffusion o fnew process technologies - an empirical model Rand Journal of Economics, 24(4), 503-528.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.

Kinkel, S., Baumgartner, M., & Cherubini, E. (2021) Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375.

Kinkel, S., Baumgartner, M., & Cherubini, E. (2021) Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375.

Kneller, R., Li, C., & Adem, A. (2022). Looking beyond digital adoption: A new business support experiment to open the window to a company’s digital soul. Innovation Growth Lab.

Luque, A. (2002) An option-value approach to technology adoption in U.S. manufacturing: Evidence from microdata. Economics of Innovation and New Technology, 11(6), 543-568.

Made Smarter. (2017). The Made Smarter Review.

Massini, S. Sanchez-Barrioluengo, M., Yu, X., Kim, M., Chen, P., and Velu, C. (2025) Adoption of Advanced Digital Technologies and Platforms: Insights from a UK national survey. Working Paper No. 049, The Productivity Institute.

Matsepe, N. T., & Van der Lingen, E. (2022) Determinants of emerging technologies adoption in the South African financial sector. SOUTH AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 53(1).

Nafizah, U. Y., Roper, S., & Mole, K. (2024). Estimating the innovation benefits of first-mover and second-mover strategies when micro-businesses adopt artificial intelligence and machine learning. Small Business Economics, 62(1), 411-434.

Oldemeyer, L., Jede, A. and Teuteberg, F., 2024. Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, pp.1-43.

Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510.

Parvand, S., & Rasiah, R. (2021) Adoption of Advanced Technologies in Palm Oil Milling Firms in Malaysia: The Role of Technology Attributes, and Environmental and Organizational Factors. Sustainability, 14(1), 260.

Phipps, J. and Fuller, R. (2022). Developing policies to promote SME digital adoption: A rapid evidence review. IGL Working Paper No. 22/01. Innovation Growth Lab.

Ramdani, B., & Kawalek, P. (2007). SME Adoption of Enterprise Systems in the Northwest of England. Paper presented at the Organizational Dynamics of Technology-Based Innovation: Diversifying the Research Agenda, Boston, MA.

Rhaiem, K., & Amara, N. (2021). Learning from innovation failures: a systematic review of the literature and research agenda. Review of Managerial Science, 15(2), 189-234.

Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696.

Roggeveen, A. L., & Sethuraman, R. (2020) Customer-Interfacing Retail Technologies in 2020 & Beyond: An Integrative Framework and Research Directions. Journal of Retailing, 96(3), 299-309.

Santini, F. d. O., Ladeira, W. J., Sampaio, C. H., Perin, M. G., & Dolci, P. C. (2020) Propensity for technological adoption: an analysis of effects size in the banking sector. Behaviour & Information Technology, 39(12), 1341-1355.

Santos, E. (2023) Tech-driven transformation: Investigating digitalization dynamics across varying firm sizes. Journal of Information Economics, 1(3).

Sarbu, M. (2020) The Impact of Industry 4.0 on Innovation Performance: Insights from Ge rman Manufacturing and Service Firms. SSRN Electronic Journal.

Schmidt, A., Mom, T. J. M., & Volberda, H. W. Factors (2023) Influencing Technology Adoption in a Traditional Multinational Energy Company. Research-Technology Management, 66(5), 36-43.

Seroka‐Stolka, O., & Fijorek, K. Enhancing corporate sustainable development: Proactive environmental strategy, stakeholder pressure and the moderating effect of firm size. Business Strategy and the Environment, 29(6), 2338-2354.

Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J., Bull, J. S., & Waddoups, R. (2021) How Technology is Changing Retail. Journal of Retailing, 97(1), 13-27.

Stokey, N. (2020). Technology Diffusion (Working Paper No. 27466). National Bureau of Economic Research.

Stornelli, A., Ozcan, S., & Simms, C. (2021). Advanced manufacturing technology adoption and innovation: A systematic literature review on barriers, enablers, and innovation types. Research Policy, 50(6), 104229.

Susitha, S. D. E. (2021) Influencing factors of employee readiness to adopt advanced manufacturing technology (AMT) on apparel shop floor in Sri Lanka. International Journal of Multidisciplinary Studies, 8(3), 1.

Svanberg, M. S., Li, W., Fleming, M., Goehring, B. C., & Thompson, N. C. (2024). Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision? [Working Paper]. Massachusetts Institute of Technology and The Productivity Institute.

Szalavetz, A. (2019). Industry 4.0 and capability development in manufacturing subsidiaries. Technol. Forecast. Social Change 145 (August 2019), 384–395.

Tinelli, M., & Ashley-Timms, D. (2022). Adopting ‘Operational Coaching’ as a management style to drive SME productivity. Highlights Report. Notion Limited.

Warty, R.R., Smith, V., Salih, M., Fox, D., Mcarthur, S.L. and Mol, B.W., 2021. Barriers to the diffusion of medical technologies within healthcare: a systematic review. IEEE Access, 9, pp.139043-139058.

Wu, H., & Broughton, N. (2019). Nudging firms to improve productivity: rapid literature review. Department for Business, Energy, and Industrial Strategy. BEIS Research Paper Number 2019/17.

Yang, J., Blount, Y. and Amrollahi, A., 2024. Artificial intelligence adoption in a professional service industry: A multiple case study. Technological Forecasting and Social Change, 201, p.123251.

Zahra, A. M., Dhewanto, W., & Utama, A. A. (2021) Boosting Emerging Technology Adoption in SMEs: A Case Study of the Fashion Industry. International Journal of Applied Business Research, 81-96.

Zhu, K., Kraemer, K. L., & Xu, S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52(10), 1557-1576.

Zolas, N. J., Kroff, Z., Brynjolfsson, E., McElheran, K., Beede, D. N., Buffington, C., Goldschlag, N., Foster, L., & Dinlersoz, E. M. (2020). Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey. NBER Working Paper Series.

1. This section draws on a briefing note prepared for the Department for Business and Trade (September 2024) by Anastasia Ri, Eugenie Golubova and Vicki Belt, Enterprise Research Centre ↩

2. Technologies considered in the Government office for Science report are: Artificial Intelligence (AI) / Machine Learning (ML), Synthetic Biology/Engineering Biology, Therapeutics, Augmented Reality (AR) / Virtual Reality (VR) / Extended Reality (ER), Future Telecoms, Digital Twins, Advanced Sensing, Semiconductors, Future Computing, Robotics and Autonomous Systems, Autonomous Vehicles, Advanced Materials, Quantum Technology, Agritech, and Photonics ↩

3. A more recent report, Fuller and Phipps (2024), summarises learnings from the wider suite of programmes which include those on management practices. Here we review Phipps and Fuller (2022) due to the more direct focus on digital technologies. ↩