British businesses have access to more powerful technology than at almost any point in modern commercial history.<\/p>\n\n\n\n
Cloud computing is widespread. Generative artificial intelligence can draft documents, analyse information, write software and support customer interactions. Automation platforms can connect business processes that once required extensive manual work. Advanced analytics are becoming available even to relatively small firms.<\/p>\n\n\n\n
The adoption statistics reflect this technological acceleration.<\/p>\n\n\n\n
The Office for National Statistics reported in July 2026 that around 35% of UK businesses with 10 or more employees were using at least one artificial intelligence technology<\/strong>, compared with approximately 12% in late 2023. In information and communication businesses, the proportion had reached 58%.<\/p>\n\n\n\n Yet another set of official statistics presents a less spectacular picture.<\/p>\n\n\n\n The UK’s 2025 Innovation Survey found that only 34% of businesses were innovation-active during 2022\u20132024<\/strong>, down from 36% during 2020\u20132022. Large businesses were considerably more likely to be innovation-active than small and medium-sized enterprises: 47% compared with 34%.<\/p>\n\n\n\n There is an important timing qualification. These statistics do not demonstrate that increased AI adoption caused innovation to decline. The innovation survey largely predates the sharpest acceleration in business AI adoption.<\/p>\n\n\n\n Nevertheless, together they expose an important management question:<\/p>\n\n\n\n Why does greater access to technology not automatically translate into more innovation?<\/strong><\/p>\n\n\n\n The answer may lie in the difference between adopting technology and transforming a business<\/strong>.<\/p>\n\n\n\n Businesses frequently use the words digitalisation, technology adoption and innovation as though they describe the same activity.<\/p>\n\n\n\n They do not.<\/p>\n\n\n\n A company adopts technology when it introduces a tool into its operations. A business innovates when it successfully introduces a new or significantly improved product, service, process or method of doing business.<\/p>\n\n\n\n Under the UK Innovation Survey, the definition of innovation is based on the Organisation for Economic Co-operation and Development’s Oslo Manual framework. A business can be considered innovation-active through activities including introducing new or improved products, implementing business-process innovation or undertaking relevant innovation projects and investments.<\/p>\n\n\n\n Consider two accounting firms.<\/p>\n\n\n\n Both purchase the same generative AI platform.<\/p>\n\n\n\n The first allows employees to use it occasionally to summarise emails and rewrite reports.<\/p>\n\n\n\n The second redesigns its client onboarding process around automation, builds an AI-supported document review workflow, creates a new advisory service from the resulting data and retrains employees to perform more analytical work.<\/p>\n\n\n\n Both firms have adopted AI.<\/p>\n\n\n\n Only one may have fundamentally changed how it creates value.<\/p>\n\n\n\n That distinction is central to understanding the UK’s emerging innovation challenge.<\/p>\n\n\n\n The headline AI adoption figure of 35% is impressive. The depth of that adoption is less dramatic.<\/p>\n\n\n\n Among UK businesses with at least 10 employees already using AI, only 10% described their AI use as extensive<\/strong>. Just 15% reported that more than half of their employees used AI as part of their daily work.<\/p>\n\n\n\n The average number of AI technologies used by adopting businesses also increased only modestly, from around 1.4 in late 2023 to approximately 1.6 by June 2026.<\/p>\n\n\n\n That produces a very different picture from the idea of businesses undergoing wholesale AI transformation.<\/p>\n\n\n\n AI is spreading rapidly across organisations, but much of the adoption remains relatively shallow.<\/p>\n\n\n\n A marketing department might use ChatGPT.<\/p>\n\n\n\n A finance employee might experiment with an AI assistant.<\/p>\n\n\n\n A customer-service team may introduce automated responses.<\/p>\n\n\n\n These activities matter, but isolated use cases do not necessarily constitute organisational innovation.<\/p>\n\n\n\n The strategic challenge begins when businesses ask how technology changes the entire system through which value is created<\/strong>.<\/p>\n\n\n\n The UK is not alone.<\/p>\n\n\n\n A 2026 US Census Bureau study using nationally representative business data found that 18% of American firms were using AI in at least one business function during the November 2025 to January 2026 reference period. Weighted by employment, the figure rose to 32%.<\/p>\n\n\n\n But again, the depth of adoption was limited.<\/p>\n\n\n\n Among businesses using AI, 57% had integrated it into three or fewer business functions<\/strong>. At worker level, 65% of AI-using firms reported AI use in three or fewer task categories.<\/p>\n\n\n\n Sales and marketing, strategy and business development, and information technology were among the leading areas of adoption.<\/p>\n\n\n\n This matters because the economic returns from technology may depend not merely on whether a company owns or accesses a technology, but on how deeply it reorganises activities around it.<\/p>\n\n\n\n The Census researchers found a positive association between commercial performance and the breadth of AI integration<\/strong>, including deployment across business functions, worker tasks and operational investment.<\/p>\n\n\n\n In other words, the number of companies experimenting with AI may tell us less than the number successfully integrating it into their business model.<\/p>\n\n\n\n Technology discussions frequently focus on software capability.<\/p>\n\n\n\n Business performance depends just as heavily on management capability.<\/p>\n\n\n\n Earlier analysis from the Office for National Statistics found a striking relationship between the quality of management practices and the adoption of advanced technologies in UK firms.<\/p>\n\n\n\n Among firms in the highest decile for management-practice scores, 88% had adopted at least one advanced technology<\/strong>, compared with only 51% among firms in the bottom decile. AI adoption was also substantially more likely among better-managed firms.<\/p>\n\n\n\n When ONS controlled for characteristics such as industry, size, age and region, management quality remained associated with technology adoption.<\/p>\n\n\n\n The relationship may work in both directions.<\/p>\n\n\n\n Better-managed firms may be more capable of identifying valuable technologies. They may also be better at reorganising themselves after adopting them.<\/p>\n\n\n\n This is significant because technologies such as artificial intelligence are often described as general-purpose technologies<\/strong>.<\/p>\n\n\n\n Their value does not arise only from the tool itself.<\/p>\n\n\n\n A company may also need to redesign workflows, change performance measures, train employees, establish governance, restructure responsibilities and invest in complementary systems.<\/p>\n\n\n\n Technology therefore does not remove the need for good management.<\/p>\n\n\n\n It may actually increase it.<\/p>\n\n\n\n Perhaps the most revealing statistic in the ONS management research concerns the reasons companies did not adopt AI.<\/p>\n\n\n\n In the 2023 data, the most commonly reported barrier was not regulation, employee resistance or even technology cost.<\/p>\n\n\n\n It was difficulty identifying relevant activities or business use cases<\/strong>, reported by 39% of firms. Cost followed at 21%, while 16% identified AI expertise or skills as a barrier.<\/p>\n\n\n\n This is a strategy problem.<\/p>\n\n\n\n Buying technology before identifying a valuable business problem reverses the normal logic of innovation.<\/p>\n\n\n\n Successful innovation generally begins with a need: reducing production time, serving customers differently, eliminating an expensive bottleneck, entering a new market or developing a new product.<\/p>\n\n\n\n Technology is then selected because it helps solve that problem.<\/p>\n\n\n\n The current AI boom can encourage the opposite sequence:<\/p>\n\n\n\n \u201cWe need AI. Now, where can we use it?\u201d<\/strong><\/p>\n\n\n\n That approach can produce demonstrations and pilots without generating lasting competitive advantage.<\/p>\n\n\n\n Technology cannot transform an organisation if employees lack the capability to use it effectively.<\/p>\n\n\n\n The 2026 ONS analysis shows that businesses identifying a lack of AI expertise as a barrier were substantially more likely to respond by retraining employees. Around 62% reported training or retraining existing staff, compared with roughly 26% among businesses reporting no AI-adoption barriers.<\/p>\n\n\n\n Yet training penetration remains limited.<\/p>\n\n\n\n Only 11% of businesses with 10 or more employees reported that more than half of their workforce had received AI-related training<\/strong>.<\/p>\n\n\n\n This is important because innovation rarely comes from technology operating independently of human knowledge.<\/p>\n\n\n\n Employees need to understand customers, processes and organisational problems well enough to recognise where technology can create value.<\/p>\n\n\n\n The UK Innovation Survey reinforces the relationship between human capital and innovative firms. In 2024, 16% of employees in broader innovator businesses held science or engineering qualifications, compared with 7% among non-innovators. Employees with non-science degree-level qualifications were also more prevalent among broader innovators.<\/p>\n\n\n\n Technology capability and workforce capability therefore cannot easily be separated.<\/p>\n\n\n\n The UK’s innovation figures reveal a persistent size gap.<\/p>\n\n\n\n During 2022\u20132024, 47% of large businesses were innovation-active compared with 34% of SMEs<\/strong>. Product innovation also showed a gap: 26% of large businesses introduced product innovations compared with 19% of SMEs.<\/p>\n\n\n\n The digital transition risks widening that difference.<\/p>\n\n\n\n Larger businesses generally have greater access to capital, specialised staff, data infrastructure, consultants, legal expertise and dedicated innovation functions.<\/p>\n\n\n\n A multinational corporation can experiment with five AI platforms, abandon four and still benefit from the fifth.<\/p>\n\n\n\n A small company may not have the financial capacity for repeated failed experiments.<\/p>\n\n\n\n The same pattern is visible internationally.<\/p>\n\n\n\n The OECD’s 2025 survey of SME digitalisation across countries including the UK, US, Canada, Germany, Japan and Australia highlighted both the competitiveness opportunities created by digitalisation and continuing barriers to converting digital tools into business value.<\/p>\n\n\n\n This suggests that the next digital divide may not simply separate firms that have technology from those that do not<\/strong>.<\/p>\n\n\n\n It may separate firms capable of converting technology into innovation from those that merely consume digital tools.<\/p>\n\n\n\n Recent European research indicates that artificial intelligence can generate measurable productivity gains\u2014but those gains are not automatic.<\/p>\n\n\n\n A 2026 European Investment Bank study using data from roughly 12,000 European non-financial firms found that AI adoption was associated with an average 4% increase in labour productivity<\/strong> after the researchers addressed selection and endogeneity concerns.<\/p>\n\n\n\n But the gains were not evenly distributed.<\/p>\n\n\n\n They were concentrated primarily among medium-sized and large firms.<\/p>\n\n\n\n More importantly, productivity gains were greater when AI adoption was accompanied by complementary investment in areas including software, data and employee training<\/strong>.<\/p>\n\n\n\n This is perhaps the most important evidence for managers.<\/p>\n\n\n\n AI does not operate in an organisational vacuum.<\/p>\n\n\n\n A business may need reliable data before an AI model can generate reliable insight.<\/p>\n\n\n\n It may need software integration before recommendations can influence operations.<\/p>\n\n\n\n Employees may require training before they can use the technology effectively.<\/p>\n\n\n\n Managers may need to alter processes before time savings turn into productivity gains.<\/p>\n\n\n\n The technology may therefore be only one component of innovation.<\/p>\n\n\n\n There is no simple story of British businesses refusing to spend.<\/p>\n\n\n\n The latest ONS data show that UK business investment increased by 1.8% in the second quarter of 2026<\/strong> and stood 5.2% above its level in the same quarter a year earlier. Investment in intellectual-property products, ICT equipment and machinery contributed to the overall picture.<\/p>\n\n\n\n But expenditure should not be confused with outcomes.<\/p>\n\n\n\n A company can invest heavily in software without improving customer experience.<\/p>\n\n\n\n It can purchase automation without redesigning inefficient workflows.<\/p>\n\n\n\n It can acquire data platforms without developing analytical capability.<\/p>\n\n\n\n It can license an AI system and never embed it into day-to-day decision making.<\/p>\n\n\n\n Innovation therefore requires more than capital expenditure.<\/p>\n\n\n\n It requires organisational conversion<\/strong>: transforming investment into new capabilities and those capabilities into commercially useful outcomes.<\/p>\n\n\n\n The phrase \u201cinnovation paradox\u201d makes a useful headline, but a more precise description may be an innovation conversion gap<\/strong>.<\/p>\n\n\n\n The gap can be represented simply:<\/p>\n\n\n\n Technology Access \u2192 Adoption \u2192 Integration \u2192 Organisational Change \u2192 Innovation \u2192 Commercial Value<\/strong><\/p>\n\n\n\n Many organisations are moving rapidly through the first two stages.<\/p>\n\n\n\n Far fewer have reached the final stages.<\/p>\n\n\n\n The difference explains why statistics showing rapid AI adoption can coexist with more modest indicators of innovation and productivity.<\/p>\n\n\n\n Buying an AI subscription takes minutes.<\/p>\n\n\n\n Integrating AI across a company may take years.<\/p>\n\n\n\n The same was true of earlier general-purpose technologies.<\/p>\n\n\n\n Computers did not produce their maximum productivity impact the moment organisations purchased PCs. Companies had to redesign work around digital information.<\/p>\n\n\n\n The internet did not transform retail simply because companies created websites. Businesses had to rebuild logistics, payments, marketing, customer service and fulfilment.<\/p>\n\n\n\n AI is likely to require comparable organisational adaptation.<\/p>\n\n\n\n For managers, several differences are increasingly important.<\/p>\n\n\n\n The distinction is not technological.<\/p>\n\n\n\n It is managerial.<\/p>\n\n\n\n The first lesson is to stop treating technology adoption as a performance indicator by itself.<\/p>\n\n\n\n Executives should ask what changed after adoption.<\/p>\n\n\n\n Did customer acquisition become cheaper?<\/p>\n\n\n\n Did product-development time fall?<\/p>\n\n\n\n Did employees become more productive?<\/p>\n\n\n\n Did error rates decline?<\/p>\n\n\n\n Did the company launch something that was previously impossible?<\/p>\n\n\n\n Did the technology create a new revenue stream?<\/p>\n\n\n\n If the answer to these questions is unclear, the organisation may have adopted technology without innovating.<\/p>\n\n\n\n Second, firms should treat complementary capabilities as part of the technology budget.<\/p>\n\n\n\n An AI investment may require spending on data infrastructure, employee education, cybersecurity, workflow redesign and change management.<\/p>\n\n\n\n Third, businesses should begin with specific problems rather than fashionable tools.<\/p>\n\n\n\n The question is not:<\/p>\n\n\n\n \u201cHow can we introduce more AI?\u201d<\/strong><\/p>\n\n\n\n It is:<\/p>\n\n\n\n \u201cWhich constraint on our growth, cost, customer experience or innovation can technology help us remove?\u201d<\/strong><\/p>\n\n\n\n That apparently small difference changes technology from an objective into an instrument.<\/p>\n\n\n\n The issue goes beyond individual firms.<\/p>\n\n\n\n Britain’s ability to compete in an increasingly AI-intensive global economy will depend not simply on how many companies adopt artificial intelligence.<\/p>\n\n\n\n It will depend on whether those companies use technology to create more productive processes, internationally competitive products, new services and scalable business models.<\/p>\n\n\n\n The regional dimensions are also significant.<\/p>\n\n\n\n The UK Innovation Survey found that 39% of businesses in the East of England and 38% in the South East were innovation-active in 2022\u20132024, compared with 30% in the North West. At more detailed geographical levels, the variation was greater still.<\/p>\n\n\n\n Digital technology is often presented as geographically neutral.<\/p>\n\n\n\n Innovation capacity clearly is not.<\/p>\n\n\n\n Skills, universities, finance, management capability, business networks and sectoral clusters remain important.<\/p>\n\n\n\n A laptop running the latest AI model may be available almost anywhere.<\/p>\n\n\n\n The ecosystem required to turn that model into a rapidly growing company is distributed much less evenly.<\/p>\n\n\n\n For researchers, this creates a substantial agenda.<\/p>\n\n\n\n One obvious question is whether businesses that integrate AI more deeply produce greater innovation outcomes than firms using it in isolated functions.<\/p>\n\n\n\n Researchers can also examine the role of managerial capability. Do better management practices strengthen the relationship between technology adoption and innovation performance?<\/p>\n\n\n\n The SME gap requires further investigation. Which combinations of finance, training, consultancy, digital infrastructure and management support allow smaller firms to extract value from advanced technology?<\/p>\n\n\n\n Regional studies could examine why innovation remains substantially stronger in some parts of the UK than others despite increasingly widespread access to digital technology.<\/p>\n\n\n\n Longitudinal research will be particularly valuable.<\/p>\n\n\n\n The most important impacts of the current AI adoption wave may not yet appear in innovation statistics because organisational transformation takes time.<\/p>\n\n\n\n Studies comparing the United Kingdom with the United States, European Union, Canada, Australia, Asia and emerging markets could also reveal how different skills systems, capital markets, regulations and management cultures affect firms’ ability to convert technology into innovation.<\/p>\n\n\n\n Britain does not appear to have a technology-access problem.<\/p>\n\n\n\n It has a more complicated challenge.<\/p>\n\n\n\n AI use is expanding rapidly. Business investment has strengthened. Advanced digital tools are becoming cheaper and more accessible.<\/p>\n\n\n\n Yet innovation remains uneven, shallow integration is common and smaller firms continue to lag large businesses.<\/p>\n\n\n\n That should change the way managers think about digital transformation.<\/p>\n\n\n\n The competitive advantage of the next decade may not belong to organisations with the most AI tools.<\/p>\n\n\n\n It may belong to organisations that are best at connecting technology, people, processes, management and strategy<\/strong>.<\/p>\n\n\n\n Technology can make innovation possible.<\/p>\n\n\n\n It cannot make an organisation innovative by itself.<\/p>\n\n\n\n The real question for British business is therefore no longer simply:<\/p>\n\n\n\n \u201cAre we adopting the latest technology?\u201d<\/strong><\/p>\n\n\n\n It is:<\/p>\n\n\n\n \u201cAre we converting technology into something meaningfully better than what we did before?\u201d<\/strong><\/p>\n\n\n\n That is the difference between digital adoption and genuine innovation.<\/p>\n\n\n\n Department for Business and Trade. (2026). UK innovation survey 2025: Report<\/em>. The survey found that 34% of UK businesses were innovation-active during 2022\u20132024, compared with 36% during 2020\u20132022.<\/p>\n\n\n\n Office for National Statistics. (2026). Artificial intelligence in UK businesses: 2023 to 2026<\/em>. AI use among businesses with at least 10 employees increased from approximately 12% in late 2023 to approximately 35% by June 2026.<\/p>\n\n\n\n Office for National Statistics. (2025). Management practices and the adoption of technology and artificial intelligence in UK firms: 2023<\/em>. The analysis examines the relationship between management quality, technology adoption and barriers to AI adoption.<\/p>\n\n\n\n Bonney, K., Breaux, C., Dinlersoz, E., Foster, L., Haltiwanger, J., & Pande, A. (2026). The microstructure of AI diffusion: Evidence from firms, business functions, and worker tasks<\/em>. U.S. Census Bureau Center for Economic Studies.<\/p>\n\n\n\n European Investment Bank. (2026). AI adoption, productivity and employment: Evidence from European firms<\/em>. The study finds productivity benefits from AI adoption, particularly when accompanied by complementary investments in data, software and training.<\/p>\n\n\n\n Bianchini, M., & Lasheras Sancho, M. (2025). SME digitalisation for competitiveness: The 2025 OECD D4SME Survey<\/em>. OECD SME and Entrepreneurship Papers.<\/p>\n\n\n\n Office for National Statistics. (2026). Business investment in the UK: April to June 2026 revised results<\/em>.<\/p>\n\n\n\n Does technology adoption actually make organisations more innovative?<\/strong><\/p>\n\n\n\n The Journal of Business Management & Innovation (JBMIJ)<\/em> welcomes original empirical research, comparative studies, systematic reviews, conceptual papers and organisational case studies examining artificial intelligence, digital transformation, innovation capability, SME digitalisation, strategic management, technology adoption and organisational performance.<\/p>\n\n\n\n Particular interest is encouraged in research investigating why some firms convert new technologies into productivity, innovation and competitive advantage while others struggle to move beyond experimentation<\/strong>.<\/p>\n\n\n\n Comparative research involving the United Kingdom, United States, Europe, Canada, Australia, Asia, Africa and other international contexts<\/strong> can make an important contribution to understanding how institutions, skills, management practices and business environments shape innovation outcomes.<\/p>\n\n\n\n Researchers may submit manuscripts through the JBMIJ online submission system.<\/p>\n","protected":false},"excerpt":{"rendered":" British businesses have access to more powerful technology than at almost any point in modern commercial history. Cloud computing is widespread. Generative artificial intelligence can draft documents, analyse information, write…<\/p>\n","protected":false},"author":1,"featured_media":110,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[12,13,9,8,14,11],"class_list":["post-109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business","tag-ai-adoption-uk","tag-business-innovation-uk","tag-future-of-work","tag-human-resource-management","tag-sme-innovation","tag-uk-innovation-and-technology-adoption"],"_links":{"self":[{"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/posts\/109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/comments?post=109"}],"version-history":[{"count":1,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/posts\/109\/revisions"}],"predecessor-version":[{"id":111,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/posts\/109\/revisions\/111"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/media\/110"}],"wp:attachment":[{"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/media?parent=109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/categories?post=109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jbmipublisher.org\/blog\/wp-json\/wp\/v2\/tags?post=109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Technology Adoption Is Not the Same as Innovation<\/h2>\n\n\n\n
AI Adoption Is Growing Faster Than AI Integration<\/h2>\n\n\n\n
The United States Is Seeing the Same Pattern<\/h2>\n\n\n\n
Management Capability May Be the Missing Variable<\/h2>\n\n\n\n
Many Businesses Still Do Not Know What Problem AI Should Solve<\/h2>\n\n\n\n
The Skills Gap Is Also an Innovation Gap<\/h2>\n\n\n\n
Large Firms Have an Advantage\u2014and That Should Concern SMEs<\/h2>\n\n\n\n
Europe Offers Another Important Lesson<\/h2>\n\n\n\n
Britain Is Investing\u2014But Investment Alone Is Not Transformation<\/h2>\n\n\n\n
The Real Problem May Be an \u201cInnovation Conversion Gap\u201d<\/h2>\n\n\n\n
What Separates Technology Users From Technology Innovators?<\/h2>\n\n\n\n
Technology User<\/th> Technology Innovator<\/th><\/tr> Buys tools because competitors use them<\/td> Starts with a business problem<\/td><\/tr> Runs isolated pilots<\/td> Connects technology to strategy<\/td><\/tr> Measures adoption<\/td> Measures business outcomes<\/td><\/tr> Leaves existing processes unchanged<\/td> Redesigns workflows<\/td><\/tr> Gives employees access<\/td> Develops employee capability<\/td><\/tr> Treats AI as an IT project<\/td> Treats AI as organisational change<\/td><\/tr> Accumulates software<\/td> Builds complementary capabilities<\/td><\/tr> Focuses on efficiency alone<\/td> Explores new products and business models<\/td><\/tr> Experiments without ownership<\/td> Assigns clear managerial responsibility<\/td><\/tr> Asks \u201cDo we have AI?\u201d<\/td> Asks \u201cWhat value has AI created?\u201d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n What Should Business Leaders Do Differently?<\/h2>\n\n\n\n
What This Means for UK Competitiveness<\/h2>\n\n\n\n
A Research Agenda for Business and Innovation Scholars<\/h2>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
Sources and Further Reading<\/h2>\n\n\n\n
Call for Research<\/h2>\n\n\n\n