Innovation

From Wikipedia, the free encyclopedia

 

Innovation can be simply defined as a "new idea, creative thoughts, new imaginations in form of device or method". However, innovation is often also viewed as the application of better solutions that meet new requirements, unarticulated needs, or existing market needs. Such innovation takes place through the provision of more-effective products, processes, services, technologies, or business models that are made available to markets, governments and society. The term "innovation" can be defined as something original and more effective and, as a consequence, new, that "breaks into" the market or society. Innovation is related to, but not the same as, invention, as innovation is more apt to involve the practical implementation of an invention (i.e. new/improved ability) to make a meaningful impact in the market or society, and not all innovations require an invention. Innovation often manifests itself via the engineering process, when the problem being solved is of a technical or scientific nature. The opposite of innovation is exnovation.

 

While a novel device is often described as an innovation, in economics, management science, and other fields of practice and analysis, innovation is generally considered to be the result of a process that brings together various novel ideas in such a way that they affect society. In industrial economics, innovations are created and found empirically from services to meet growing consumer demand.

Definition

A 2014 survey of literature on innovation found over 40 definitions. In an industrial survey of how the software industry defined innovation, the following definition given by Crossan and Apaydin was considered to be the most complete, which builds on the Organization for Economic Co-operation and Development (OECD) manual's definition:

Innovation is production or adoption, assimilation, and exploitation of a value-added novelty in economic and social spheres; renewal and enlargement of products, services, and markets; development of new methods of production; and the establishment of new management systems. It is both a process and an outcome.

According to Kanter innovation includes original invention and creative use and defines innovation as a generation, admission and realization of new ideas, products, services and processes.

Two main dimensions of innovation were degree of novelty (patent) (i.e. whether an innovation is new to the firm, new to the market, new to the industry, or new to the world) and kind of innovation (i.e. whether it is processor product-service system innovation). In recent organizational scholarship, researchers of workplaces have also distinguished innovation to be separate from creativity, by providing an updated definition of these two related but distinct constructs:

Workplace creativity concerns the cognitive and behavioral processes applied when attempting to generate novel ideas. Workplace innovation concerns the processes applied when attempting to implement new ideas. Specifically, innovation involves some combination of problem/opportunity identification, the introduction, adoption or modification of new ideas germane to organizational needs, the promotion of these ideas, and the practical implementation of these ideas.

Inter-disciplinary views

Business and economics

In business and in economics, innovation can become a catalyst for growth. With rapid advancements in transportation and communications over the past few decades, the old-world concepts of factor endowments and comparative advantage which focused on an area's unique inputs are outmoded for today's global economy. Economist Joseph Schumpeter (1883–1950), who contributed greatly to the study of innovation economics, argued that industries must incessantly revolutionize the economic structure from within, that is innovate with better or more effective processes and products, as well as market distribution, such as the connection from the craft shop to factory. He famously asserted that "creative destruction is the essential fact about capitalism". Entrepreneurs continuously look for better ways to satisfy their consumer base with improved quality, durability, service and price which come to fruition in innovation with advanced technologies and organizational strategies.

A prime example of innovation involved the explosive boom of Silicon Valley startups out of the Stanford Industrial Park. In 1957, dissatisfied employees of Shockley Semiconductor, the company of Nobel laureate and co-inventor of the transistor William Shockley, left to form an independent firm, Fairchild Semiconductor. After several years, Fairchild developed into a formidable presence in the sector. Eventually, these founders left to start their own companies based on their own, unique, latest ideas, and then leading employees started their own firms. Over the next 20 years, this snowball process launched the momentous startup-company explosion of information-technology firms. Essentially, Silicon Valley began as 65 new enterprises born out of Shockley's eight former employees. Since then, hubs of innovation have sprung up globally with similar metonyms, including Silicon Alley encompassing New York City. 

Another example involves business incubators – a phenomenon nurtured by governments around the world, close to knowledge clusters (mostly research-based) like universities or other Government Excellence Centers – which aim primarily to channel generated knowledge to applied innovation outcomes in order to stimulate regional or national economic growth.

Organizations

In the organizational context, innovation may be linked to positive changes in efficiency, productivity, quality, competitiveness, and market share. However, recent research findings highlight the complementary role of organizational culture in enabling organizations to translate innovative activity into tangible performance improvements. Organizations can also improve profits and performance by providing work groups opportunities and resources to innovate, in addition to employee's core job tasks. Peter Drucker wrote:

Innovation is the specific function of entrepreneurship, whether in an existing business, a public service institution, or a new venture started by a lone individual in the family kitchen. It is the means by which the entrepreneur either creates new wealth-producing resources or endows existing resources with enhanced potential for creating wealth. –Drucker

According to Clayton Christensen, disruptive innovation is the key to future success in business. The organisation requires a proper structure in order to retain competitive advantage. It is necessary to create and nurture an environment of innovation. Executives and managers need to break away from traditional ways of thinking and use change to their advantage. It is a time of risk but even greater opportunity. The world of work is changing with the increase in the use of technology and both companies and businesses are becoming increasingly competitive. Companies will have to downsize and re-engineer their operations to remain competitive. This will affect employment as businesses will be forced to reduce the number of people employed while accomplishing the same amount of work if not more.

While disruptive innovation will typically "attack a traditional business model with a lower-cost solution and overtake incumbent firms quickly," foundational innovation is slower, and typically has the potential to create new foundations for global technology systems over the longer term. Foundational innovation tends to transform business operating models as entirely new business models emerge over many years, with gradual and steady adoption of the innovation leading to waves of technological and institutional change that gain momentum more slowly. The advent of the packet-switched communication protocol TCP/IP—originally introduced in 1972 to support a single use case for United States Department of Defense electronic communication (email), and which gained widespread adoption only in the mid-1990s with the advent of the World Wide Web—is a foundational technology.

All organizations can innovate, including for example hospitals, universities, and local governments. For instance, former Mayor Martin O’Malley pushed the City of Baltimore to use CitiStat, a performance-measurement data and management system that allows city officials to maintain statistics on several areas from crime trends to the conditions of potholes. This system aids in better evaluation of policies and procedures with accountability and efficiency in terms of time and money. In its first year, CitiStat saved the city $13.2 million. Even mass transit systems have innovated with hybrid bus fleets to real-time tracking at bus stands. In addition, the growing use of mobile data terminals in vehicles, that serve as communication hubs between vehicles and a control center, automatically send data on location, passenger counts, engine performance, mileage and other information. This tool helps to deliver and manage transportation systems.

Still other innovative strategies include hospitals digitizing medical information in electronic medical records. For example, the U.S. Department of Housing and Urban Development's HOPE VI initiatives turned severely distressed public housing in urban areas into revitalized, mixed-income environments; the Harlem Children’s Zone used a community-based approach to educate local area children; and the Environmental Protection Agency's brownfield grants facilitates turning over brownfields for environmental protection, green spaces, community and commercial development.

Sources

There are several sources of innovation. It can occur as a result of a focus effort by a range of different agents, by chance, or as a result of a major system failure. 

According to Peter F. Drucker, the general sources of innovations are different changes in industry structure, in market structure, in local and global demographics, in human perception, mood and meaning, in the amount of already available scientific knowledge, etc.

Original model of three phases of the process of Technological Change

 

In the simplest linear model of innovation the traditionally recognized source is manufacturer innovation. This is where an agent (person or business) innovates in order to sell the innovation. Specifically, R&D measurement is the commonly used input for innovation, in particular in the business sector, named Business Expenditure on R&D (BERD) that grew over the years on the expenses of the declining R&D invested by the public sector.

Another source of innovation, only now becoming widely recognized, is end-user innovation. This is where an agent (person or company) develops an innovation for their own (personal or in-house) use because existing products do not meet their needs. MIT economist Eric von Hippel has identified end-user innovation as, by far, the most important and critical in his classic book on the subject, The Sources of Innovation.

The robotics engineer Joseph F. Engelberger asserts that innovations require only three things:

1. A recognized need,
2. Competent people with relevant technology, and
3. Financial support.

However, innovation processes usually involve: identifying customer needs, macro and meso trends, developing competences, and finding financial support.

The Kline chain-linked model of innovation places emphasis on potential market needs as drivers of the innovation process, and describes the complex and often iterative feedback loops between marketing, design, manufacturing, and R&D.

Innovation by businesses is achieved in many ways, with much attention now given to formal research and development (R&D) for "breakthrough innovations". R&D help spur on patents and other scientific innovations that leads to productive growth in such areas as industry, medicine, engineering, and government. Yet, innovations can be developed by less formal on-the-job modifications of practice, through exchange and combination of professional experience and by many other routes. Investigation of relationship between the concepts of innovation and technology transfer revealed overlap. The more radical and revolutionary innovations tend to emerge from R&D, while more incremental innovations may emerge from practice – but there are many exceptions to each of these trends.

Information technology and changing business processes and management style can produce a work climate favorable to innovation. For example, the software tool company Atlassian conducts quarterly "ShipIt Days" in which employees may work on anything related to the company's products. Google employees work on self-directed projects for 20% of their time. Both companies cite these bottom-up processes as major sources for new products and features.

An important innovation factor includes customers buying products or using services. As a result, firms may incorporate users in focus groups (user centred approach), work closely with so called lead users (lead user approach) or users might adapt their products themselves. The lead user method focuses on idea generation based on leading users to develop breakthrough innovations. U-STIR, a project to innovate Europe’s surface transportation system, employs such workshops. Regarding this user innovation, a great deal of innovation is done by those actually implementing and using technologies and products as part of their normal activities. Sometimes user-innovators may become entrepreneurs, selling their product, they may choose to trade their innovation in exchange for other innovations, or they may be adopted by their suppliers. Nowadays, they may also choose to freely reveal their innovations, using methods like open source. In such networks of innovation the users or communities of users can further develop technologies and reinvent their social meaning.

One technique for innovating a solution to an identified problem is to actually attempt an experiment with many possible solutions. This technique was famously used by Thomas Edison's laboratory to find a version of the incandescent light bulb economically viable for home use, which involved searching through thousands of possible filament designs before settling on carbonized bamboo.

This technique is sometimes used in pharmaceutical drug discovery. Thousands of chemical compounds are subjected to high-throughput screening to see if they have any activity against a target molecule which has been identified as biologically significant to a disease. Promising compounds can then be studied; modified to improve efficacy, reduce side effects, and reduce cost of manufacture; and if successful turned into treatments.

The related technique of A/B testing is often used to help optimize the design of web sites and mobile apps. This is used by major sites such as amazon.com, Facebook, Google, and Netflix. Procter & Gamble uses computer-simulated products and onlinen user panels to conduct larger numbers of experiments to guide the design, packaging, and shelf placement of consumer products. Capital One uses this technique to drive credit card marketing offers.

Goals and failures

Programs of organizational innovation are typically tightly linked to organizational goals and objectives, to the business plan, and to market competitive positioning. One driver for innovation programs in corporations is to achieve growth objectives. As Davila et al. (2006) notes, "Companies cannot grow through cost reduction and re-engineering alone... Innovation is the key element in providing aggressive top-line growth, and for increasing bottom-line results".

One survey across a large number of manufacturing and services organizations found, ranked in decreasing order of popularity, that systematic programs of organizational innovation are most frequently driven by: improved quality, creation of new markets, extension of the product range, reduced labor costs, improved production processes, reduced materials, reduced environmental damage, replacement of products/services, reduced energy consumption, conformance to regulations.

These goals vary between improvements to products, processes and services and dispel a popular myth that innovation deals mainly with new product development. Most of the goals could apply to any organization be it a manufacturing facility, marketing firm, hospital or local government. Whether innovation goals are successfully achieved or otherwise depends greatly on the environment prevailing in the firm.

Conversely, failure can develop in programs of innovations. The causes of failure have been widely researched and can vary considerably. Some causes will be external to the organization and outside its influence of control. Others will be internal and ultimately within the control of the organization. Internal causes of failure can be divided into causes associated with the cultural infrastructure and causes associated with the innovation process itself. Common causes of failure within the innovation process in most organizations can be distilled into five types: poor goal definition, poor alignment of actions to goals, poor participation in teams, poor monitoring of results, poor communication and access to information.

Diffusion

Diffusion of innovation research was first started in 1903 by seminal researcher Gabriel Tarde, who first plotted the S-shaped diffusion curve. Tarde defined the innovation-decision process as a series of steps that includes:

1. First knowledge
2. Forming an attitude
3. A decision to adopt or reject
4. Implementation and use
5. Confirmation of the decision

Once innovation occurs, innovations may be spread from the innovator to other individuals and groups. This process has been proposed that the life cycle of innovations can be described using the 's-curve' or diffusion curve. The s-curve maps growth of revenue or productivity against time. In the early stage of a particular innovation, growth is relatively slow as the new product establishes itself. At some point, customers begin to demand and the product growth increases more rapidly. New incremental innovations or changes to the product allow growth to continue. Towards the end of its lifecycle, growth slows and may even begin to decline. In the later stages, no amount of new investment in that product will yield a normal rate of return.

The s-curve derives from an assumption that new products are likely to have "product life" – i.e., a start-up phase, a rapid increase in revenue and eventual decline. In fact, the great majority of innovations never get off the bottom of the curve, and never produce normal returns. 

Innovative companies will typically be working on new innovations that will eventually replace older ones. Successive s-curves will come along to replace older ones and continue to drive growth upwards. In the figure above the first curve shows a current technology. The second shows an emerging technology that currently yields lower growth but will eventually overtake current technology and lead to even greater levels of growth. The length of life will depend on many factors.

Measures

Measuring innovation is inherently difficult as it implies commensurability so that comparisons can be made in quantitative terms. Innovation, however, is by definition novelty. Comparisons are thus often meaningless across products or service. Nevertheless, Edison et al. in their review of literature on innovation management found 232 innovation metrics. They categorized these measures along five dimensions i.e. inputs to the innovation process, output from the innovation process, effect of the innovation output, measures to access the activities in an innovation process and availability of factors that facilitate such a process.

There are two different types of measures for innovation: the organizational level and the political level.

Organizational level

The measure of innovation at the organizational level relates to individuals, team-level assessments, and private companies from the smallest to the largest company. Measure of innovation for organizations can be conducted by surveys, workshops, consultants, or internal bench marking. There is today no established general way to measure organizational innovation. Corporate measurements are generally structured around balanced scorecards which cover several aspects of innovation such as business measures related to finances, innovation process efficiency, employees' contribution and motivation, as well benefits for customers. Measured values will vary widely between businesses, covering for example new product revenue, spending in R&D, time to market, customer and employee perception & satisfaction, number of patents, additional sales resulting from past innovations.

Political level

For the political level, measures of innovation are more focused on a country or region competitive advantage through innovation. In this context, organizational capabilities can be evaluated through various evaluation frameworks, such as those of the European Foundation for Quality Management. The OECD Oslo Manual (1995) suggests standard guidelines on measuring technological product and process innovation. Some people consider the Oslo Manual complementary to the Frascati Manual from 1963. The new Oslo manual from 2005 takes a wider perspective to innovation, and includes marketing and organizational innovation. These standards are used for example in the European Community Innovation Surveys.

Other ways of measuring innovation have traditionally been expenditure, for example, investment in R&D (Research and Development) as percentage of GNP (Gross National Product). Whether this is a good measurement of innovation has been widely discussed and the Oslo Manual has incorporated some of the critique against earlier methods of measuring. The traditional methods of measuring still inform many policy decisions. The EU Lisbon Strategy has set as a goal that their average expenditure on R&D should be 3% of GDP.

Level Down of Innovation in Silicon Valley

Innovation is starting to decline due to the newly introduced immigration policies. The new immigration policies have effects on the economy because 25% of the companies were created by foreign entrepreneurs. 

Indicators

Many scholars claim that there is a great bias towards the "science and technology mode" (S&T-mode or STI-mode), while the "learning by doing, using and interacting mode" (DUI-mode) is ignored and measurements and research about it rarely done. For example, an institution may be high tech with the latest equipment, but lacks crucial doing, using and interacting tasks important for innovation.

A common industry view (unsupported by empirical evidence) is that comparative cost-effectiveness research is a form of price control which reduces returns to industry, and thus limits R&D expenditure, stifles future innovation and compromises new products access to markets. Some academics claim cost-effectiveness research is a valuable value-based measure of innovation which accords "truly significant" therapeutic advances (i.e. providing "health gain") higher prices than free market mechanisms. Such value-based pricing has been viewed as a means of indicating to industry the type of innovation that should be rewarded from the public purse.

An Australian academic developed the case that national comparative cost-effectiveness analysis systems should be viewed as measuring "health innovation" as an evidence-based policy concept for valuing innovation distinct from valuing through competitive markets, a method which requires strong anti-trust laws to be effective, on the basis that both methods of assessing pharmaceutical innovations are mentioned in annex 2C.1 of the Australia-United States Free Trade Agreement.

Indices

Several indices attempt to measure innovation and rank entities based on these measures, such as:

• The Bloomberg Innovation Index
• The "Bogota Manual" similar to the Oslo Manual, is focused on Latin America and the Caribbean countries.
• The "Creative Class" developed by Richard Florida
• The EIU Innovation Ranking
• The Global Competitiveness Report
• The Global Innovation Index (GII), by INSEAD
• The Information Technology and Innovation Foundation (ITIF) Index
• Innovation 360 – From the World Bank. Aggregates innovation indicators (and more) from a number of different public sources
• The Innovation Capacity Index (ICI) published by a large number of international professors working in a collaborative fashion. The top scorers of ICI 2009–2010 were: 1. Sweden 82.2; 2. Finland 77.8; and 3. United States 77.5.
• The Innovation Index, developed by the Indiana Business Research Center, to measure innovation capacity at the county or regional level in the United States.
• The Innovation Union Scoreboard
• The innovationsindikator for Germany, developed by the Federation of German Industries (Bundesverband der Deutschen Industrie) in 2005
• The INSEAD Innovation Efficacy Index
• The International Innovation Index, produced jointly by The Boston Consulting Group, the National Association of Manufacturers and its nonpartisan research affiliate The Manufacturing Institute, is a worldwide index measuring the level of innovation in a country. NAM describes it as the "largest and most comprehensive global index of its kind".
• The Management Innovation Index – Model for Managing Intangibility of Organizational Creativity: Management Innovation Index
• The NYCEDC Innovation Index, by the New York City Economic Development Corporation, tracks New York City's "transformation into a center for high-tech innovation. It measures innovation in the City's growing science and technology industries and is designed to capture the effect of innovation on the City's economy."
• The Oslo Manual is focused on North America, Europe, and other rich economies.
• The State Technology and Science Index, developed by the Milken Institute, is a U.S.-wide benchmark to measure the science and technology capabilities that furnish high paying jobs based around key components.
• The World Competitiveness Scoreboard

Rankings

Many research studies try to rank countries based on measures of innovation. Common areas of focus include: high-tech companies, manufacturing, patents, post secondary education, research and development, and research personnel. The left ranking of the top 10 countries below is based on the 2016 Bloomberg Innovation Index. However, studies may vary widely; for example the Global Innovation Index 2016 ranks Switzerland as number one wherein countries like South Korea and Japan do not even make the top ten.

Bloomberg Innovation Index 2016 Rank Country/Territory Index 1  South Korea 91.31 2  Germany 85.54 3  Sweden 85.21 4  Japan 85.07 5   Switzerland 84.96 6  Singapore 84.54 7  Finland 83.80 8  United States 82.84 9  Denmark 81.40 10  France 80.39

Global Innovation Index 2016 Rank Country/Territory Index 1   Switzerland 66.3 2  Sweden 63.6 3  United Kingdom 61.9 4  United States 61.4 5  Finland 59.9 6  Singapore 59.2 7  Ireland 59.0 8  Denmark 58.5 9  Netherlands 58.3 10  Germany 57.9

Future

In 2005 Jonathan Huebner, a physicist working at the Pentagon's Naval Air Warfare Center, argued on the basis of both U.S. patents and world technological breakthroughs, per capita, that the rate of human technological innovation peaked in 1873 and has been slowing ever since. In his article, he asked "Will the level of technology reach a maximum and then decline as in the Dark Ages?" In later comments to New Scientist magazine, Huebner clarified that while he believed that we will reach a rate of innovation in 2024 equivalent to that of the Dark Ages, he was not predicting the recurrence of the Dark Ages themselves.

John Smart criticized the claim and asserted that technological singularity researcher Ray Kurzweil and others showed a "clear trend of acceleration, not deceleration" when it came to innovations. The foundation replied to Huebner the journal his article was published in, citing Second Life and eHarmony as proof of accelerating innovation; to which Huebner replied. However, Huebner's findings were confirmed in 2010 with U.S. Patent Office data. and in a 2012 paper.

Innovation and development

The theme of innovation as a tool to disrupting patterns of poverty has gained momentum since the mid-2000s among major international development actors such as DFID, Gates Foundation's use of the Grand Challenge funding model, and USAID's Global Development Lab. Networks have been established to support innovation in development, such as D-Lab at MIT. Investment funds have been established to identify and catalyze innovations in developing countries, such as DFID's Global Innovation Fund, Human Development Innovation Fund, and (in partnership with USAID) the Global Development Innovation Ventures.

Government policies

Given the noticeable effects on efficiency, quality of life, and productive growth, innovation is a key factor in society and economy. Consequently, policymakers have long worked to develop environments that will foster innovation and its resulting positive benefits, from funding Research and Development to supporting regulatory change, funding the development of innovation clusters, and using public purchasing and standardization to 'pull' innovation through.

For instance, experts are advocating that the U.S. federal government launch a National Infrastructure Foundation, a nimble, collaborative strategic intervention organization that will house innovations programs from fragmented silos under one entity, inform federal officials on innovation performance metrics, strengthen industry-university partnerships, and support innovation economic development initiatives, especially to strengthen regional clusters. Because clusters are the geographic incubators of innovative products and processes, a cluster development grant program would also be targeted for implementation. By focusing on innovating in such areas as precision manufacturing, information technology, and clean energy, other areas of national concern would be tackled including government debt, carbon footprint, and oil dependence. The U.S. Economic Development Administration understand this reality in their continued Regional Innovation Clusters initiative. In addition, federal grants in R&D, a crucial driver of innovation and productive growth, should be expanded to levels similar to Japan, Finland, South Korea, and Switzerland in order to stay globally competitive. Also, such grants should be better procured to metropolitan areas, the essential engines of the American economy.

Many countries recognize the importance of research and development as well as innovation including Japan's Ministry of Education, Culture, Sports, Science and Technology (MEXT); Germany's Federal Ministry of Education and Research; and the Ministry of Science and Technology in the People's Republic of China. Furthermore, Russia's innovation program is the Medvedev modernisation programme which aims at creating a diversified economy based on high technology and innovation. Also, the Government of Western Australia has established a number of innovation incentives for government departments. Landgate was the first Western Australian government agency to establish its Innovation Program.

Regions have taken a more proactive role in supporting innovation. Many regional governments are setting up regional innovation agency to strengthen regional innovation capabilities. In Medellin, Colombia, the municipality of Medellin created in 2009 Ruta N to transform the city into a knowledge city.

Open innovation

From Wikipedia, the free encyclopedia

Open innovation is a term used to promote an information age mindset toward innovation that runs counter to the secrecy and silo mentality of traditional corporate research labs. The benefits and driving forces behind increased openness have been noted and discussed as far back as the 1960s, especially as it pertains to inter-firm cooperation in R&D. Use of the term 'open innovation' in reference to the increasing embrace of external cooperation in a complex world has been promoted in particular by Henry Chesbrough, adjunct professor and faculty director of the Center for Open Innovation of the Haas School of Business at the University of California, who articulated a modern perspective in his book Open Innovation: The new imperative for creating and profiting from technology (2003).

The term was originally referred to as "a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology". More recently, it is defined as "a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-pecuniary mechanisms in line with the organization's business model". This more recent definition acknowledges that open innovation is not solely firm-centric: it also includes creative consumers and communities of user innovators. The boundaries between a firm and its environment have become more permeable; innovations can easily transfer inward and outward between firms and other firms and between firms and creative consumers, resulting in impacts at the level of the consumer, the firm, an industry, and society.

Because innovations tend to be produced by outsiders and founders in startups, rather than existing organizations, the central idea behind open innovation is that, in a world of widely distributed knowledge, companies cannot afford to rely entirely on their own research, but should instead buy or license processes or inventions (i.e. patents) from other companies. In addition, internal inventions not being used in a firm's business should be taken outside the company (e.g. through licensing, joint ventures or spin-offs).

The open innovation paradigm can be interpreted to go beyond just using external sources of innovation such as customers, rival companies, and academic institutions, and can be as much a change in the use, management, and employment of intellectual property as it is in the technical and research driven generation of intellectual property. In this sense, it is understood as the systematic encouragement and exploration of a wide range of internal and external sources for innovative opportunities, the integration of this exploration with firm capabilities and resources, and the exploitation of these opportunities through multiple channels.

Advantages

Open innovation offers several benefits to companies operating on a program of global collaboration:

• Reduced cost of conducting research and development;
• Potential for improvement in development productivity;
• Incorporation of customers early in the development process;
• Increase in accuracy for market research and customer targeting;
• Potential for synergism between internal and external innovations;
• Potential for viral marketing.

Disadvantages

Implementing a model of open innovation is naturally associated with a number of risks and challenges, including:

• Possibility of revealing information not intended for sharing;
• Potential for the hosting organization to lose their competitive advantage as a consequence of revealing intellectual property;
• Increased complexity of controlling innovation and regulating how contributors affect a project;
• Devising a means to properly identify and incorporate external innovation;
• Realigning innovation strategies to extend beyond the firm in order to maximize the return from external innovation.

Models

Government driven

In the UK the Knowledge Transfer Partnerships (KTP) is a funding mechanism encouraging the partnership between a firm and a knowledge-based partner. A KTP is a collaboration program between a knowledge-based partner (i.e. a research institution), a company partner and one or more associates (i.e. recently qualified persons such as graduates). KTP initiatives aim to deliver significant improvement in business partners’ profitability as a direct result of the partnership through enhanced quality and operations, increased sales and access to new markets. At the end of their KTP project, the three actors involved have to prepare a final report that describes KTP initiative supported the achievement of the project’s innovation goals.

Product platforming

This approach involves developing and introducing a partially completed product, for the purpose of providing a framework or tool-kit for contributors to access, customize, and exploit. The goal is for the contributors to extend the platform product's functionality while increasing the overall value of the product for everyone involved. 

Readily available software frameworks such as a software development kit (SDK), or an application programming interface (API) are common examples of product platforms. This approach is common in markets with strong network effects where demand for the product implementing the framework (such as a mobile phone, or an online application) increases with the number of developers that are attracted to use the platform tool-kit. The high scalability of platforming often results in an increased complexity of administration and quality assurance.

Idea competitions

This model entails implementing a system that encourages competitiveness among contributors by rewarding successful submissions. Developer competitions such as hackathon events fall under this category of open innovation. This method provides organizations with inexpensive access to a large quantity of innovative ideas, while also providing a deeper insight into the needs of their customers and contributors.

Customer immersion

While mostly oriented toward the end of the product development cycle, this technique involves extensive customer interaction through employees of the host organization. Companies are thus able to accurately incorporate customer input, while also allowing them to be more closely involved in the design process and product management cycle.

Collaborative product design and development

Similarly to product platforming, an organization incorporates their contributors into the development of the product. This differs from platforming in the sense that, in addition to the provision of the framework on which contributors develop, the hosting organization still controls and maintains the eventual products developed in collaboration with their contributors. This method gives organizations more control by ensuring that the correct product is developed as fast as possible, while reducing the overall cost of development. Dr. Henry Chesbrough recently supported this model for open innovation in the optics and photonics industry.

Innovation networks

Similarly to idea competitions, an organization leverages a network of contributors in the design process by offering a reward in the form of an incentive. The difference relates to the fact that the network of contributors are used to develop solutions to identified problems within the development process, as opposed to new products. Emphasis needs to be placed on assessing organizational capabilities to ensure value creation in open innovation.

In science

In Austria the Ludwig Boltzmann Gesellschaft started a project named "Tell us!" about mental health issues and used the concept of open innovation to crowdsource research questions. The institute also launched the first "Lab for Open Innovation in Science" to teach 20 selected scientists the concept of open innovation over the course of one year. On Facebook the Ludwig Boltzmann Gesellschaft informs about the lab, the participants and teachers and on news on open innovation in science.

In engineering

A European startup has proved that engineering crowdsourcing delivers good results for open innovation in technology. This startup, ennomotive, organizes competitions to solve real-life engineering challenges coming from companies. Its global community of engineers submits solutions through an online platform and, after a multi-round filtering process, the company selects and awards the best solutions. This way, complex issues like asphalting in the rain or monitoring wildfires in the forest through IoT have been solved.

Versus closed innovation

The paradigm of closed innovation holds that successful innovation requires control. Particularly, a company should control the generation of their own ideas, as well as production, marketing, distribution, servicing, financing, and supporting. What drove this idea is that, in the early twentieth century, academic and government institutions were not involved in the commercial application of science. As a result, it was left up to other corporations to take the new product development cycle into their own hands. There just was not the time to wait for the scientific community to become more involved in the practical application of science. There also was not enough time to wait for other companies to start producing some of the components that were required in their final product. These companies became relatively self-sufficient, with little communication directed outwards to other companies or universities. 

Throughout the years several factors emerged that paved the way for open innovation paradigms:

• The increasing availability and mobility of skilled workers;
• The growth of the venture capital market;
• External options for ideas sitting on the shelf;
• The increasing capability of external suppliers.

These four factors have resulted in a new market of knowledge. Knowledge is not anymore proprietary to the company. It resides in employees, suppliers, customers, competitors and universities. If companies do not use the knowledge they have inside, someone else will. Innovation can be generated either by means of closed innovation or by open innovation paradigms. There is an ongoing debate on which paradigm will dominate in the future.

Terminology

Modern research of open innovation is divided into two groups, which have several names, but are similar in their essence (discovery and exploitation; outside-in and inside-out; inbound and outbound). The common factor for different names is the direction of innovation, whether from outside the company in, or from inside the company out:

Revealing (non-pecuniary outbound innovation)

This type of open innovation is when a company freely shares its resources with other partners, without an instant financial reward. The source of profit has an indirect nature and is manifested as a new type of business model.

Selling (pecuniary outbound innovation)

In this type of open innovation a company commercializes its inventions and technology through selling or licensing technology to a third party.

Sourcing (non-pecuniary inbound innovation)

This type of open innovation is when companies use freely available external knowledge, as a source of internal innovation. Before starting any internal R&D project a company should monitor the external environment in search for existing solutions, thus, in this case, internal R&D become tools to absorb external ideas for internal needs.

Acquiring (pecuniary inbound innovation)

In this type of open innovation a company is buying innovation from its partners through licensing, or other procedures, involving monetary reward for external knowledge

Versus open source

Open source and open innovation might conflict on patent issues. This conflict is particularly apparent when considering technologies that may save lives, or other open-source-appropriate technologies that may assist in poverty reduction or sustainable development. However, open source and open innovation are not mutually exclusive, because participating companies can donate their patents to an independent organization, put them in a common pool, or grant unlimited license use to anybody. Hence some open-source initiatives can merge these two concepts: this is the case for instance for IBM with its Eclipse platform, which the company presents as a case of open innovation, where competing companies are invited to cooperate inside an open-innovation network.

In 1997, Eric Raymond, writing about the open-source software movement, coined the term the cathedral and the bazaar. The cathedral represented the conventional method of employing a group of experts to design and develop software (though it could apply to any large-scale creative or innovative work). The bazaar represented the open-source approach. This idea has been amplified by a lot of people, notably Don Tapscott and Anthony D. Williams in their book Wikinomics. Eric Raymond himself is also quoted as saying that 'one cannot code from the ground up in bazaar style. One can test, debug, and improve in bazaar style, but it would be very hard to originate a project in bazaar mode'. In the same vein, Raymond is also quoted as saying 'The individual wizard is where successful bazaar projects generally start'.

The next level

In 2014, Chesbrough and Bogers describe open innovation as a distributed innovation process that is based on purposefully managed knowledge flows across enterprise boundaries. Open innovation is hardly aligned with the ecosystem theory and not a linear process. Fasnacht's adoption for the financial services uses open innovation as basis and includes alternative forms of mass collaboration, hence, this makes it complex, iterative, non-linear, and barely controllable. The increasing interactions between business partners, competitors, suppliers, customers, and communities create a constant growth of data and cognitive tools. Open innovation ecosystems bring together the symbiotic forces of all supportive firms from various sectors and businesses that collectively seek to create differentiated offerings. Accordingly, the value captured from a network of multiple actors and the linear value chain of individual firms combined, creates the new delivery model that Fasnacht declares "value constellation".

Open innovation ecosystem

The term Open Innovation Ecosystem consists of three parts that describe the foundations of the approach of open innovation, innovation systems and business ecosystems.

While James F. Moore researched business ecosystems in manufacturing around a specific business or branch, the open model of innovation with the ecosystem theory was recently studied in various industries. Traitler et all. researched it 2010 and used it for R&D, stating that global innovation needs alliances based on compatible differences. Innovation partnerships based on sharing knowledge represents a paradigm shift toward accelerating co‐development of sustainable innovation. West researched open innovation ecosystems in the software industry, following studies in the food industry that show how a small firm thrived and became a business success based on building an ecosystem that shares knowledge, encourages individuals' growth, and embeds trust among participants such as suppliers, alumni chef and staff, and food writers. Other adoptions include the telecom industry or smart cities.

Ecosystems foster collaboration and accelerate the dissemination of knowledge through the network effect, in fact, value creation increases with each actor in the ecosystem, which in turn nurtures the ecosystem as such.

A digital platform is essential to make the innovation ecosystem work as it aligns various actors to achieve a mutually beneficial purpose. Parker explained that with platform revolution and described how networked Markets are transforming the economy.

Business ecosystems are increasingly used and drive digital growth, and pioneering firms in China use their technological capabilities and link client data to historical transactions and social behavior to offer tailored financial services among luxury goods or health services. Such open collaborative environment changes the client experience and adds value to consumers. The drawback is that it is also threatening incumbent banks from the U.S. and Europe due to its legacies and lack of agility and flexibility.

Digital native

From Wikipedia, the free encyclopedia

A child using a tablet

 

The term digital native describes a person that grows up in the digital age, rather than acquiring familiarity with digital systems as an adult, as a digital immigrant. Both terms were used as early as 1996 as part of the Declaration of the Independence of Cyberspace. They were popularized by education consultant Marc Prensky in his 2001 article entitled Digital Natives, Digital Immigrants, in which he relates the contemporary decline in American education to educators' failure to understand the needs of modern students. His article posited that "the arrival and rapid dissemination of digital technology in the last decade of the 20th century" had changed the way students think and process information, making it difficult for them to excel academically using the outdated teaching methods of the day. In other words, children raised in a digital, media-saturated world, require a media-rich learning environment to hold their attention, and Prensky dubbed these children "digital natives". 

Globally, 30 percent of the population born between 1988 and 1998 had used the Internet for over five years as of 2013.

Origins

Digital Natives, Digital Immigrants Marc Prensky defines the term "digital native" and applies it to a new group of students enrolling in educational establishments referring to the young generation as "native speakers" of the digital language of computers, videos, video games, social media and other sites on the internet. Contextually, his ideas were introduced after a decade of worry over increased diagnosis of children with ADD and ADHD, which itself turned out to be largely overblown. Prensky did not strictly define the digital native in his 2001 article, but it was later, arbitrarily, applied to children born after 1980, because computer bulletin board systems and Usenet were already in use at the time.

The idea became popular among educators and parents, whose children fell within Prensky's definition of a digital native, and has since been embraced as an effective marketing tool. It is important to note that Prensky's original paper was not a scientific one, and that no empirical data exists to support his claims. However, the concept has been widely addressed in the academic literature since, mainly in education research, but also in health research.

Prensky has since abandoned his digital native metaphor in favor "digital wisdom". More recently, the Digital Visitor and Resident idea has been proposed as an alternative to understanding the various ways individuals engage with digital technology. 

People who were "born digital", first appeared in a series of presentations by Josh Spear beginning in May 2007. A Digital Native research project is being run jointly by the Berkman Center for Internet & Society at Harvard Law School and the Research Center for Information Law at the University of St. Gallen in Switzerland. A collaborative research project is being run by Hivos, Netherlands and the Bangalore-based Centre for Internet and Society. The Net Generation Encountering e-learning at university project funded by the UK research councils was completed in March 2010. More recently the Museum of Social Media, launched in 2012, has included an exhibition about "Digital Natives & Friends."

Conflicts between generations

Due to the obvious divide set between digital natives and digital immigrants, sometimes both generations are forced to meet which commonly results in conflicting ideologies of digital technology.^{[citation needed]} The everyday regimen of work-life is becoming more technologically advanced with improved computers in offices, more complicated machinery in industry etc. With technology moving so fast, it is hard for digital immigrants to keep up. This creates conflicts among older supervisors and managers with the increasingly younger workforce. Similarly, parents clash with their children at home over gaming, texting, YouTube, Facebook and other Internet technology issues. Much of the world's Millennials and Generation Z members are digital natives. According to law professor and educator John Palfrey, there may be substantial differences between digital natives and non digital natives, in terms of how people see relationships and institutions and how they access information. In spite of this, the timetable for training young and old on new technology is about the same.

Prensky states that education is the single largest problem facing the digital world as our digital immigrant instructors, who speak an outdated language (that of the pre-digital age), are struggling to teach a population that speaks an entirely new language. Digital natives have had an increased exposure to technology, which has changed the way they interact and respond to digital devices. In order to meet the unique learning needs of digital natives, teachers need to move away from traditional teaching methods that are disconnected with the way students learn today. For the last 20 years, technology preparation for teachers has been at the forefront of policy. However, Immigrants suffer complications in teaching natives how to understand an environment which is "native" to them and foreign to Immigrants. Teachers not only struggle with proficiency levels and their abilities to integrate technology into the classroom, but also, display resistance towards the integration of digital tools. Since technology can be frustrating and complicated at times, some teachers worry about maintaining their level or professionalism within the classroom. Teachers worry about appearing "unprofessional" in front of their students. Although technology presents challenges in the classroom, it is still very important for teachers to understand how natural and useful these digital tools are for students.

To meet the unique learning needs of digital natives, Forzani and Leu suggest that digital tools are able to respond immediately to the natural, exploratory, and interactive learning style of students today. Learning how to use these digital tools not only provides unique learning opportunities for digital natives, but they also provide necessary skills that will define their future success in the digital age. One preference to this problem is to invent computer games to teach digital natives the lessons they need to learn, no matter how serious. This ideology has already been introduced to a number of serious practicalities. For example, piloting an unmanned aerial vehicle (UAV) in the army consists of someone sitting in front of a computer screen issuing commands to the UAV via a hand-held controller which resembles, in detail, the model of controllers that are used to play games on an Xbox 360 game console. (Jodie C Spreadbury, Army Recruiting and Training Division).

Gamification as a teaching tool has sparked interest in education, and Gee suggests this is because games have special properties that books cannot offer for digital natives. For instance, gamification provides an interactive environment for students to engage and practice 21st century skills such as collaboration, critical thinking, problem solving, and digital literacy. Gee presents four reasons why gamification provides a distinct way of learning to promote 21st century skills. First, games are based on problem solving and not on ones ability to memorize content knowledge. Second, gamification promotes creativity in digital natives where they are encouraged to think like a designer or modify to redesign games. Third, digital natives are beginning to co-author their games through the choices they make to solve problems and face challenges. Therefore, students' thinking is stimulated to promote meta-cognition since they have to think about their choices and how they will alter the course and outcome of the game. Lastly, through online gaming, digital natives are able to collaborate and learn in a more social environment. Based on the literature, one can see the potential and unique benefits digital tools have. For example, online games help digital natives meet their unique learning needs. Furthermore, online gaming seems to provide an interactive and engaging environment that promotes the necessary skills digital natives will need to be successful in their future.

Discourse

Different approaches to educate the digital native

 

Not everyone agrees with the language and underlying connotations of the digital native. The term, by definition, suggests a familiarity with technology that not all children and young adults who would be considered digital natives have; some instead have an awkwardness with technology that not all digital immigrants have. For instance, those on the disadvantaged side of the digital divide lack access to technology. In its application, the concept of the digital native preferences those who grow up with technology as having a special status, ignoring the significant difference between familiarity and creative application.

The classification of people into digital natives and digital immigrants is controversial. Some digital immigrants surpass digital natives in tech savvy, but there is a belief that early exposure to technology fundamentally changes the way people learn. The term "digital immigrant" overlooks the fact that many people born before the digital age were the inventors, designers, developers and first users of digital technology and in this sense could be regarded as the original "natives". To confuse the prolific (and arguably superficial) use of digital technology by current adolescents as deep knowledge and understanding is potentially misleading and unhelpful to the discourse. The term also discounts the broader and more holistic knowledge, experience and understandings that older generations may have about digital technologies and their potential place in society. Digital immigrants are believed to be less quick to pick up new technologies than digital natives. This results in the equivalent of a speaking accent when it comes to the way in which they learn and adopt technology. A commonly used example is that a digital immigrant may prefer to print out a document to edit it by hand rather than doing onscreen editing. 

The actual classification of people into immigrants and natives is tricky as the adoption of digital technology hasn't been a unified phenomenon worldwide. For North America, most people born prior to 1980 are considered digital immigrants. Those closer to the cutoff are sometimes called digital intermediates, which means they started using digital technology in their early teens and thus are closer to digital natives in terms of their understanding and abilities. 

The term "digital native" is synonymous with the term "digital inclusion". Being digitally included means that you are innately able in using a smartphone or computer tablets: modern technology has enabled the non-speaking to speak, the non-hearing to hear and the non-seeing to see. Crucially, there is debate over whether there is any adequate evidence for claims made about digital natives and their implications for education. Bennett, Maton & Kervin (2008), for example, critically review the research evidence and describe some accounts of digital natives as having an academic form of a moral panic. concluded that generation does not explain differences in how learners use technology and that there is no empirical research to support claims made by Prensky and other proponents of the idea of the digital native. Using such a terminology is rather a sign of unfamiliarity and exoticism in relation to digital culture. Of course, nobody is "born digital"; as with any cultural technology, such as reading and writing, it is matter of access to education and experience.

It considers that all youths are digital natives in the modern age. However, this is not the case. It is primarily based on cultural differences and not by age. According to Henry Jenkins (2007), "Part of the challenge of this research is to understand the dynamics of who exactly is, and who is not, a digital native and what that means." There are underlying conflicts on the definition of the term "digital natives" and it is wrong to say that all modern age youths are placed in that particular category or that all older adults can be described as digital immigrants. Some adults are more tech savvy than a lot of children, depending on socio-economic standings, personal interests, etc., but as teachers we must include the world outside with which the children are familiar and use it inside the classroom.

The formulation of digital native is also challenged by researchers looking at emerging technology landscapes. The current discourse concentrates largely on developed technology and has a particular bias towards white, middle-class youth who have the privilege of access to technology. Nishant Shah (2009) says, "It is necessary to promote research that grasps that not all Digital Natives are equal. Each context will have certain norms by which digital nativity is understood and experienced. Dismantling the universal Digital Native and considering contextualized Digital Native identities might also help us move away from speaking of the Digital Native as a necessarily elite power-user of technology and understand the identity as a point of departure from earlier technology-mediated identities within those contexts." He also suggests that one way of understanding "digital natives" is to look at how they use digital technologies to engage with their immediate environments and initiate processes of social and personal change.

It is possible to argue that digitality is not a birth-right but instead a product of cultural capital. According to its originator, Pierre Bordieu, cultural capital is defined as "the possession of certain cultural competencies, bodies of cultural knowledge, that provide for distinguished modes of cultural consumption". Familiarity with technology and ease of use is a form of social capital that allows those who possess it to advance in society. In fact, scholars have commented on the variability of technological literacy in different social groups. In "Communities, Cultural Capital and the Digital Divide", Viviana Rojas calls this phenomenon a person's "techno-disposition". This familiarity with technology is one of many privileges granted by cultural capital. She defines techno-disposition more explicitly as "practices, perceptions and attitudes, technical education, awareness of technology, desires for information, job requirements, social relations with community members and community organizations, and geographical location". One's techno-disposition, not simply one's access to technology, she argues, is at the root of any digital divide.

As we move into the second decade of the 21st century, others are calling into question Prensky's Digital/Immigrant dichotomy on different grounds. Jones & Shao (2011) recently conducted a literature review for the UK Higher Education Academy which found that there was no empirical evidence of a single new generation of young students. They argued that complex changes were taking place but there was no evidence of a generation gap. The nature of the metaphor itself is challenged, with White and Le Cornu (2011) drawing attention to the difficulties that a language-based analogy introduces, especially when then linked to age and place. They also highlight the rapid technological advances that have been made in the last ten years, most notably in the advent of social networking platforms. White and Le Cornu therefore propose an alternative metaphor of Visitors and Residents which they suggest more accurately represents the ways in which learners engage with technology in a social networking age. 

Ignoring debate on definitions, "Digital Natives mastering our world", published in 2017, assumes that we are already living in a digital world, and that it will be more intense in the future. Hence, educating all children to become citizens of this world is a necessary goal. The book is indeed a proposal for teaching the 4th R, rendering the remote, including the creation of learning materials, digital, visual, and narrative, each employing the principles of the others. To establish an alphabet of creative need, experiments in visual thinking and pattern recognition must guide the process. For author Elihu Blotnick, native means natural, and digital means coded. Coding then is but a translatable language, best understood through immersion. Nature-centered learning, even at a two-room school in a redwood grove, suggests a reconsidered lesson plan, digitally oriented, to demonstrate the promise and the possibilities ahead, where the digital is seen not just as a tool but as the subject itself, essential to all other subjects and the basis for MeTech, a new curriculum. From the oral culture of pre-school we can then advance to the written library that shapes abstract understanding. When technology shadows art, art also creates technology, and learning will be naturally received.