It should be noted that this advice is aimed mostly at management science students doing social science dissertations.

The first thing to do is to read a book like Business Research by Collis and Hussey to get a good grounding in the undertaking of research in the social science arena. This book goes through the theory of research paradigms and methodologies but also includes advice on the more practical side like organizing surveys, conducting interviews, what software to get, etc.

I found it useful to access more learning materials pertaining to my chosen methodology. For example, because I was conducting exploratory research from interviews, I watched several lectures from the University of Huddersfield's Graham Gibbs to gain a better understanding of Grounded Theory. I also read various texts on Grounded Theory and this helped me structure in my mind how the research was going to be conducted.

Once you start to make head or tail of how you might go about conducting the research, you can start reading the relevant literature on your topic. I found it very useful to create a spreadsheet listing the name of the paper, authors, rating out of 10 (how useful/relevant/interesting it was) and key takeaways. This is going to come in very handy later on when you've totally lost track of what concepts you've read where, and you have to trace certain ideas back to their source papers in order to substantiate your own observations in your dissertation writing. I used a Google Spreadsheet.

I also found it useful to use a software tool called Dropbox. Dropbox makes a folder on your computer sync with any other computers/mobiles/tablets on which you've also installed Dropbox. This is handy for searching for research papers and saving copies when on a computer, but leaving the reading of those papers for somewhere more comfortable like an iPad. Dropbox also stores copies of all your files in the cloud, so nothing is ever lost.

If you are doing qualitative research, you will probably find NVIVO to be your main tool of choice. This application lets you input a number of data sources (for example, interview transcripts) and lets you code them into categories and themes. Be sure to watch some of the numerous YouTube videos covering coding in NVIVO (and general NVIVO usage for that matter) if you intend to include this activity in your research. The latest version is NVIVO 9 and a student license is around £80, although you should have it available through the university as well. I ended up recording interviews using my iPhone, playing them back at halfspeed using Windows Media Player and transcribing-on-the-go straight into NVIVO.

When actually writing the dissertation, I found Google Documents more than sufficed. Again, stored in the cloud so it's accessible anywhere and never lost, even if your computer dies (avoiding that nightmare dissertation situation!). I know people use more sophisticated tools like LaTeX and MS Word, but I don't understand why more people don't use Google Docs - it's simple and reliable, and all you need to use it is a browser.

Here is an academic essay I did for university recently. It explores how the social elements of the web, via Github and others, have contributed to Open Innovation and specifically open source software. Enjoy.

Open Innovation meets Social Media:

The Rise of Social Coding

by Mark Gibaud

Introduction

This essay explores the new phenomenon of ‘social coding’ within the context of open innovation. Social coding is a radical evolution in open source software development caused largely by the convergence of traditional open source software development and the new social networking paradigm. In our analysis we begin by exploring the transition from closed innovation to open innovation that has taken place largely over the last decade. We then look the open source software movement, and specifically review the literature explaining the motivations of software developers for contributing to open source projects. We briefly review the rise of social networking on the internet before examining the current leading ‘social coding’ website, Github.com, and analyse how this website allows software developers to experience traditional OSS contribution motivations more acutely than before and thus sustain and even increase their open innovation activity. Finally, we consider the opportunities for non high-tech sectors and discuss the challenges that might be faced in transplanting this specific flavour of open innovation embodied by social coding into arenas outside of software engineering.

From Closed to Open

For much of the 20th century there was only one way to innovate. Firms would invest as many resources as they could afford into their own private R&D labs while simultaneously seeking to fill those labs with the best and brightest engineers and scientists in an effort to invent, develop and commercialize innovations that would yield profit and growth (Chesbrough 2003). They also aggressively managed intellectual property related to these products which would protect their revenues and in turn allow them to further invest in R&D and thus fund the next wave products, reinforcing a cycle of “closed innovation” (Chesbrough 2003). The prevailing school of thought was to have “innovation through total control” (Chesbrough 2003).

Figure 1: Closed Innovation (Chesborough 2003)

But several changes in the economic landscape of the late 20th and early 21st centuries have induced an eroding effect of this model of innovation. Firstly, the steady and persistent emergence of the knowledge-powered economy (Drucker 1992) has dramatically reduced the chance of any single company owning even a majority of a particular industry’s leading thinkers, much less all. Secondly, Chesbrough (2006) cites the increase in quantity and employment mobility of knowledge workers as contributory to the eroding of closed innovation, as firms naturally find it more difficult to own their employees’ ideas when they no longer work for the same company for decades at a time, as was prevalent in the 20th century. Along with this, the rise of globalisation has meant that even within a specific technology or science, “knowledge is distributed” (Chesbrough 2003) and experts in a particular field may often find themselves on opposite sides of the planet employed by different companies or even in different sectors. Another major catalyst for open innovation was the growth of the venture capital market, which better enabled innovation spillovers from both corporate research labs and universities to become commodotized within startups and therefore serve the marketplace, whereby previously, an innovation that was not considered related to the core offering of a firm would’ve died out. For these reasons, it is increasingly recognized (Chesbrough 2003, von Hippel 2001) that supplementing internal R&D with knowledge from outside the boundary of the firm can give rise to commercialization prospects previously unavailable.

By the same token, organisations are more vigorously exploring efforts for internal innovations that may not relate intimately to their core offerings to follow novel paths to market and realise benefits other than profit for these products and services. For example, a firm could license out intellectual property (IP) for financial gain, contibute to a patent pool to access new technologies to use internally, or making technology freely available so as to encourage the market adoption of a standard (von Krogh and von Hippel, 2006). Organisations are finding out that the usual idea-commoditization route is only one way to capitalize from internal R&D, and that through exploring other ways to the market an organisation can benefit in novel ways.

*

Figure 2: Open Innovation (Chesborough 2006)

Chesbrough’s defined open innovation in his 2003 book “Open Innovation: The new imperative for creating and profiting from technology”:

“Open innovation is 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.”

- Henry Chesbrough (2006)

A definition that extends Chesbrough's original by usefully including the notions of the learning organisation, knowledge management and organisational boundaries is provided by Ulrich Lichtenthaler:

"Open innovation is defined as systematically performing knowledge exploration, retention, and exploitation inside and outside an organization’s boundaries throughout the innovation process.”

-  Ulrich Lichtenthaler (2010)

*  Figure 3: A typical Github developer’s workflow

The team behind the design of Github have taken a number of decisions to greatly foster collaboration. First and foremost, the site charges users for wanting to host their source code privately, ie. in a private repository where only certain users may access the code. This in turn means that the vast majority of projects hosted on Github are open source by default, as users get “public” code hosting at no cost. Therefore, any user can fork these projects and begin contributing.

Github also redefined the concept of forking an open source project. Most research on open source software has concurred that forking a project is a harmful last-resort (Kogut and Metiu 2001), but Github has turned this phenomenon on it’s head by greatly encouraging developers to fork projects, innovate on the product and then issue pull requests.

But most critically, Github has a mature and developed social layer that is designed to make key OSS operations easier, quicker and more satisfying to do than ever before, and leverages key motivations of open source programmers as outlined earlier. Users have profiles with job descriptions and pictures, can “watch” projects they are interested in and “follow” other programmers activity. They can also mention usernames in discussions (for eg. @markgibaud) immediately notifying those users. Mechanisms like these greatly encourage community collaboration and increase participation behaviours, foster belonging, and enable the pursuing of reputational gains among developers. Users can easily and clearly engage in pull request discussions, providing and receiving feedback on code changes and thereby providing all participants with greater and more satisfying learning opportunities (Raymond 2001).

Github also continues to add new functionality that caters to the most common motivations of OSS developers. For example, it recently made Github user data available to other websites. Stack Overflow Careers is a website where thousands of programmers have created online curriculum vitaes and users can now incorporate a view of their Github activity (see Addendum E) directly to their online CV. This is a direct facilitation of the motivation of OSS developers to increase their marketability.

Github has been very successful in creating an active, motivated community of open source developers to collaborate and communicate through a social web application to be able to produce superior quality innovations in the high tech and software industries. It remains the goal of other industries to find ways to replicate this successful open innovation in their own sectors.

Challenges

When referencing the innovative practices that open source software displays, it has been propounded that “There is no reason to believe that those practices cannot spread to other areas of economic and social activity.” (Von Krogh and von Hippe,l 2006). This author disagrees. It will take very careful and considerate analysis of sectors that do not share a few key traits with OSS to allow them to enjoy open innovation as successfully as OSS.

OSS is a natural leader in open innovation because of a few factors. Firstly, the actual output of innovative activity, computer code, is highly distributable at negligible cost. Along with this, the components of contributions in open source software (textual differentials) are sufficiently defined and discrete so as to easily allow focused discussion on each individually, before that contribution is accepted into the product (or rejected). The fact that incremental innovations can be so readily appraised, discussed and accepted using a low-friction process through an easy-to-use social website is a major factor enabling high levels of open innovation in software development. It is not easy to intuit how more sophisticated forms of data representation, for example architectural blueprints or 3D models, can take on forms that allow the same kind of distributed online collaboration that OSS enjoys.

Software application development itself is also at an advantage when exploiting open innovation because of it’s inherently component-based nature. Solutions to software “problems” like database access, integration with Twitter.com and authentication mechanisms are discrete enough that they can be implemented without impacting other modules, thus making decisions regarding one problem independent from another. What this translates into is massively increased cross-pollination and collaboration, rather than developer communities forming ‘silos’ around particular technologies and this in turn facilitates innovation across the sector.

Lastly, one critical distinction between software development and most other sectors is that open innovation in software has traditionally occurred in non-competitive areas. For example, for the most part, banks don’t compete with other banks when it comes to database access, and airlines don’t compete with other airlines on their website login systems. This enables developers across organisations to freely help each other with solving these non-competitive problems, such that they can then spend more time on improving their respective organisations’ core offerings. It remains a challenge for organisations in other industries to more clearly separate their core and differentiated offerings from problems encountered by all players in a particular market. This author argues that the lessons learnt from open innovation literature could, for example, advise the airline industry to form a coalition to create a global flight-searching platform that all players would benefit from. Individual airlines should then focus on increasing the value of their differentiated offerings such as customer service, in-flight comfort and entertainment, etc.

Conclusion

In this essay we have reviewed how organisations are increasingly undergoing a change in their R&D regime from a closed model to a open model of innovation. At the forefront of this movement are the participants of the open source software industry who have been innovating across firm boundaries for decades. We also reviewed specific motivations as to why this behaviour is sustained. We discussed how, in parallel to the increase of open innovation, social media has enabled people from all over the world to communicate and collaborate in unprecedented ways and at unprecedented scale. We then looked at how the most popular source code hosting website leverages many popular social media mechanisms in order to facilitate OSS developers experiencing their motivations for contribution more acutely than ever before. Finally, we elucidated the challenges that other sectors face if they wish to replicate the open innovation success enjoyed by the OSS fraternity, including desegregating their core offerings from their non-competitive pursuits so as to better enable openly innovating on the latter.

Today's organisations have to take into account a number of different aspects when attempting to deepen their capacity for, and evolve capabilities facilitating, innovation. Distilling the most relevant academic literature results in several organisational capabilities that must underpin several distinct phases of innovation if the firm is to achieve sustainable economic rents and ongoing growth from it's efforts in this direction. These capabilities are separated broadly into those supporting the identification of potentially lucrative innovations and those that support the exploitation of those innovations along lines that maximize their opportunity for profit. This essay will attempt to enumerate the most important of these capabilities together with said phases and summarise the operational details of each giving examples where possible.

Mastering innovation requires mature capabilities in several phases identified by Professor Joe Tidd (2005). The first of these are concerned with searching or scanning the environment for "signals" (Tidd 2005) that indicate an opportunity for innovation. These could form new technological advances in the market place; for example, the success of the iPhone presents many brands with a more compelling opportunity than ever before to represent themselves on a mobile platform. Signals could also be changes in legislation or changing behaviour in a competitor (who perhaps themselves are reacting to signals). Signals can also appear simply "in the wild", an example being on the Facebook page of Contiki Holidays (http://www.facebook.com/contiki), where users are indicating a strong desire to make contact with other travellers sharing their specific tour departure. Successfully innovating organisations have developed sophisticated methods for continuously capturing signals from a broad range of sources.

While signals might originate from outside the firm, the opportunities for innovation that they represent might not be immediately obvious. The right information in the right place at the right time can spark a profitable innovation that would otherwise delay coming to market if at all. Organisations naturally amplify knowledge through the efforts of their workers collaborating on projects (Nonaka & Takeuchi 1995), and firms should put resources and practices in place that support capturing and codifying what would otherwise remain less accessible but valuable tacit knowledge (Polanyi 1966). Referencing Knowledge Management best practices, creating a knowledge repository with an emphasis on collaboration, such as an enterprise wiki solution or groupware, broadens the reach of the sum of organisational knowledge and can quickly become a breeding ground of innovative ideas, evolving from signals captured from outside the firm. The paper and packaging group Mondi have a web-based system named the "Innovation Zone" through which employees can share and improve ideas (Koudal 2005). A dedicated team evaluates especially innovative ideas and creates plans to develop them further. In this way Mondi managers can enable the entire company to contribute to it's innovation efforts, with a team possessing the required authority and resources to facilitate the commercialization of ideas into new products or processes.

If an organisation is successful in it's search for and processing of innovation signals, it may well identify more opportunities than it can realistically afford to commit to. Thus, selection of the correct opportunities is critical to maximising return on investment in innovation efforts. It quickly becomes apparent that successful innovation is an interactive process between what the organisation can supply as per it's technological competences (technology push) and the demand it may identify from signals in the market (customer pull) (Tidd et al 2001). The organisation should select opportunities that are likely good answers to one or more marketplace questions, achievable in terms of it's current capabilities (though a little stretching of current capabilities is desirable, as this presents the opportunity for learning), and finally, well aligned with the broader strategy of the business. A mark of a successfully innovating firm is a mature mechanism for the "coupling and matching process" (Tidd 2005) of innovation supply and demand. A secondary point here is the implication that an organisation should be constantly deepening it's technological capabilities as this will result in the potential for diversification and thus more opportunities to map supply to demand regarding innovation efforts.

Following from actually preparing the market (this could be advertising for a new product or evangelising for a new process), successfully launching an innovation should be accompanied by mechanisms by which feedback can be both gained and acted upon rapidly. Customer feedback on products and services, especially those characterised by higher novelty and frequent change such as fashion and high technology, has become an especially valuable source of 'sustaining' innovation (ie. incremental improvements) (Christensen 1997). Efforts in this area range from the more loosely connected, such as the ever popular anonymous feedback forms, to a much more tightly integrated collaboration with "lead users" (Von Hippel 1986) that steer ongoing incremental improvements to existing products or services as they typically experience needs before more mainstream users. To pick an example from midway along this continuum, Zara store staff are equipped with PDA's and are able to gather information from browsing customers about what more they would like to see in store, like existing designs in different formats or colours. Once the store doors close, information is also gathered from the shelves that are more depleted than others and the stock of clothes that customers tried on but didn't buy. All this data is linked up to the point of sale system which allows branches to issue re-buy orders based on hard data instead of hunches and guesswork. The qualitative data gained by the PDA users makes its way back to Inditex  (Zara's parent company) headquarters in La Coruña, Spain, where the hundreds of designers employed there are tasked to respond to the data in designing their future products. Innovations like these have spurred Inditex to grow from $2.43 billion in 2001 to $13.6 billion in 2007 (Gallaugher 2010) and fueled massive global expansion, opening hundreds of new stores around the world. Inditex's ability to continuously launch new products and then refine them rapidly using customer feedback has paid dividends.

Finally, it is recommended that after an innovation is successfully brought to it's target, the firm retrospects on the entire development process and draws lessons that may be used in future innovation efforts (Tidd 2005). Furthermore, management is compelled to create new processes and routines around lessons learnt such that the organisation can form new behaviours that eventually make innovation-inducing practices the new norm. But what gives firms the ability to carry out the Tidd's phases of innovation successfully?

Successfully innovating companies put full time resources behind innovation endeavors (Koudal 2005). Whether individual employees are tasked with a few weeks of dedicated research and experimentation, new middle management teams are created with innovation-centric remits, or perhaps entire departments spun off as new innovation-chasing entities, organisations are learning that they can no longer concern themselves with sustaining the status quo and flirting with the odd idea on the side. Following the examples of 3M and Google, more and more companies are formally dedicating a percentage of their employees time to innovation to better incentivise ideation. This practice leverages the growing agreement that "front-line staff" are in the best position to generate ideas (Poppendieck & Poppendieck 2006, Mesaglio 2010) since usually they are regularly exposed to the customer and have most of the information available with which they might think of a better or entirely novel way to satisfy customers' needs. Van de Ven (1999) suggests that employees be given sufficient time to experiment with new ways of doing the work, while Tidd (2005) recommends that employees have the opportunity to work cross-functionally. A compelling method for gaining new organisational resources for innovation is put forward by Geoffrey Moore in his book Dealing with Darwin: that of "extracting resources from context to repurpose for core" (Moore 2006). Moore defines context as the efforts of the organisation concerned with continuing to provide existent products and services to customers, whereas core is the effort behind new products and services which seek to further differentiate the organisation. Moore investigates how the networking infrastructure products company Cisco goes about extracting resources from existing operations to apply to new ventures, enabling them to innovate on their own products but also quickly regain parity with any products from competitors who attempt to disrupt Cisco's market. Moore suggests that firms perform a "context/core analysis" followed by a resource allocation strategy that complements this analysis with the goal of setting a path to a more innovative future. It is clear that successfully innovating companies both allocate sufficient resources to innovation and also make it easier to do so by innovatively driving down the resource cost of day-to-day overhead.

Clayton Christensen articulates another innovation management question to be answered: The Innovator's Dilemma. In his 1997 book of the same name, Christensen discusses the question facing every innovating organisation: What is the most beneficial distribution of resources between sustaining or incremental innovation efforts and efforts behind disruptive or radical innovation? Essentially, organisations need to develop an "ambidextrous capability" (Tidd 2005) that allows them to effectively pursue both courses of innovation, with considerable allowance made for their different risk/reward profiles, the different paths they follow and the different "innovation architectures" (Tidd et al 2001) they require. While it's likely that sustaining innovations can be facilitated by relatively established routines, disruptive innovations will require innovation management that is highly flexible, tolerant of ambiguity and uncertainty, and seeks to hedge risk by limiting commitment (Tidd 2005).

In a discussion of how enterprises become successful innovators it is also prudent to discuss how enterprises might stay successful innovators. Mastering the complexity in their value-chain and innovation capabilties has gained successfully innovating firms the ability to sustain their competitive advantage as their operations and infrastructure is characterised by several attributes evident in the resource-based view of the firm (Wernerfelt 1984, Penrose & Pitelis 2009). Firm resources can include highly-skilled personnel, valuable branding, intellectual property rights, an advanced ICT infrastructure, and even customer loyalty (Wernerfelt 1984).  Possibly the most compelling attribute is that their operational configuration and infrastructure is difficult to imitate. Referring back to the example of Inditex, other fashion organisations may attempt to copy their infrastructure wholesale but this is a high risk strategy since Inditex regards people (specifically, the store managers and designers) as key resources. These employees are empowered to make decisions that drive Zara's rapid response to customer trends in fashion. Also present in successfully innovating firms is a managerial capability required to achieve rewarding innovations in the first place, and these managers are supported by a corporate culture that allows them to do their best work. Perhaps the best example is that of Jonothan Ive, who joined Apple in 1992, became VP of Industrial Design in 1998 and Senior VP of Design in 2005, and has been the main proponent in the design (and thus success) of the PowerBook G4, the Macbook, the unibody Macbook Pro, the iPod, iPhone and now the iPad. Apple doing their best to retain Jonothan and his team is a major contributor to Apple's ongoing successful innovation. Finally, it is worth remembering that no firm is an open book, and there are likely many more innovative processes and practices at work inside Inditex and Apple that have not found their way to the public domain but are central to sustaining each their competitive edge. This is the idea of 'causal ambiguity' (King 2007) and it represents a major problem for imitators in that they cannot exactly articulate how Inditex, Apple, etc remain successful innovators. Indeed, Tidd asserts (2005) that any successful "innovation management routines" are difficult to copy simply because they represent the accumulation of a long line of lessons learnt along the way. This essentially means that firms should seek to forge their own learning path to successful innovation and avoid the temptation to copy "fashions" if they are to sustain their competitive edge through successful innovation management.

There is no sure way to guarantee value-creating, growth-inducing innovation. Ultimately, to 'manage innovation' is to establish the resources and develop the capabilities to merely increase the chances, radically or less so according to the skill of management, that successful innovation will flow from the organisation. Organisations must evolve capabilities to scan the marketplace for signals, select high-potential ideas, resource and manage their execution effectively, quickly gain and react to customer feedback, and in the final stages, retrospect on the initiative and learn as much as possible regardless of success or failure. In parallel, firms must constantly broaden their resources for knowledge management, cross-functional communication and collaboration of employees, effective collaboration with customers, suppliers and partners, and leverage advanced ICT infrastructures to support all of the above. This represents a major challenge for organisations, but one that cannot be ignored, because to ignore the challenge of innovating successfully is to doom the organisation to disruption by another and eventual extinction.

References:

Christensen, C.M., 1997. The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail illustrated edition., Harvard Business School Press.

Gallaugher, John., Information Systems: A Manager's Guide To Harnessing Technology. Retrieved Mar 19, 2010 from http://www.flatworldknowledge.com/node/41126 .

King, A., 2007. Disentangling Inter-and Intra-Firm Causal Ambiguity: A Conceptual Model of Causal Ambiguity and Sustainable Competitive Advantage. Academy of Management Review, 32(1), 156–178.

Koudal, P., 2005. Mastering Innovation–Exploiting Ideas for Profitable Growth. Deloitte research.

Mesaglio, Mary., IT-Enabled Innovation, lecture given at Travel Corporation luncheon, Rubens Hotel, London. 24th March 2010.

Moore, G., 2006. Dealing with Darwin: How Great Companies Innovate at Every Phase of Their Evolution, Capstone.

Nonaka, I.A. & Takeuchi, H.A., 1995. The knowledge-creating company: How Japanese companies create the dynamics of innovation, Oxford university press.

Penrose, E. & Pitelis, C., 2009. The Theory of the Growth of the Firm 4th ed., Oxford University Press, USA.

Polanyi, M., 1966. The tacit dimension. New York.

Poppendieck, M. & Poppendieck, T., 2006. Implementing Lean Software Development: From Concept to Cash (The Addison-Wesley Signature Series), Addison-Wesley Professional.

Tidd, J., Bessant, J. & Pavitt, K., 2001. Managing Innovation: Integrating Technological, Market, and Organizational Change, 2nd Edition 2nd ed., John Wiley & Sons.

Van de Ven, A.H. et al., 1999. The innovation journey, Oxford University Press New York.

Von Hippel, E., 1986. Lead users: A source of novel product concepts. Management science, 32(7), 791–805.

Wernerfelt, B., 1984. A Resource-Based View of the Firm. Strategic Management Journal, 5(2), 171-180.

[Update: This essay received 70%]

[Updated 15 Dec with final version]

Organisational Knowledge as One of the Core Assets of our Time:

Implications for Management

by Mark Gibaud

Knowledge is one of the key assets of the intellectually-powered "new" economy. Organisations can achieve critical competitive differentiation if they learn to leverage the collective and cumulative knowledge of their employees. Starting earnestly in the mid 90's, Knowledge Management (KM) has emerged as the key discipline to support this endeavour, but many organisations have not yet fully recognised the benefits of managing their knowledge due to a poor understanding of the nature of workplace knowledge itself and how to effectively manage its capture, storage and distribution within the organisation. This essay aims to explore the implications for management by illuminating the nature of knowledge in the workplace, how it is generated and how to further amplify its generation such that an organisation can be in a better position to capture and diffuse it. Also highlighted are the compelling reasons as to why knowledge should be managed. Finally, one leading strategy, of moving from "channels" to "platforms", is more fully explored through the analysis of a new type of "wiki" software.

Knowledge is defined by the Oxford English Dictionary as "expertise and skills acquired by a person through experience or education". One might think that simply by extending this definition to include the situational context of the workplace, one can then arrive at an accurate assessment of organisational knowledge. However, this is only a part of the story. Organisations amplify knowledge in a number of ways from the cumulative efforts of its staff (Nonaka 1994), potentially creating a knowledge repository contributed to and in turn leveraged by all employees. Michael Polyani popularised the nature of tacit or implicit knowledge which is embodied in an individual and not easily codified or transferred (Polanyi 1966). Later, Ikujiro Nonaka focused on the distinction between tacit knowledge and explicit knowledge, which can be written down and transferred (Ikujiro & Takeuchi 1991). Polyani also argued (1966:55) that all knowledge is either tacit or rooted in tacit knowledge.  Pairing this observation with the fact that the world is full of visible codified knowledge, it follows that tacit knowledge could be further broken down into that which is codifiable, albeit with more intensive effort and management, and that which remains uncodifiable, such as intuition gained from experience, vis-à-vis Polanyi's "We can know more than we can tell". Organisations have historically exhibited a high level of explicit knowledge codification such as operational manuals, process documentation and annual financial reports. However, it is the generation, codification and dissemination of knowledge further along the knowledge continuum (Leonard & Sensiper 1999) (that is, increasingly tacit knowledge) that represents the major challenge for contemporary organisations and thus becomes the primary opportunity for knowledge management solutions (Twentyman 2006) (see diagram 1). In turn, the term "Knowledge Management" has many definitions. The common elements of the most-cited definitions centre around defining a strategy for creating, capturing, storing, accessing, and distributing knowledge across individuals, teams and the entire organisation (and sometimes even outside it) in an effort to generate value (increased skill in employees, more innovative products or services, etc) from an organisation's knowledge-based assets (Stankosky 2005, Davenport et al. 2008, O'dell & Grayson 1998, Rumizen 2001, Brooking 1998).

Nonaka, through the development of his SECI model (Nonaka 1994), argued that organisational knowledge can take on a self-generative nature when knowledge is transferred between tacit and tacit, tacit and explicit, and explicit and explicit states. He argued, after closely studying Japanese firms, that organisational knowledge takes on a "spiral" nature when transferring between these states, inferring that this "knowledge conversion" process serves to continually increase the organisation's total stock of knowledge as learning becomes embedded in the organisation's culture. A deeper analysis of Nonaka's SECI model is beyond the scope of this essay, but the key implication for management is that in order to create a useful knowledge base from which employees may leverage value, an organisation must first take the necessary steps to begin to support and facilitate the generation of that knowledge.

Another useful lens through which to define knowledge is with reference to the Knowledge Hierarchy (Zeleny & von Hayek 1980) or DIKW (Data to Information to Knowledge to Wisdom) taxonomy, if only to assert that while an organisation might record data and make sense of that data turning it to information, gaining knowledge from that information is indeed the next level entirely. Consider when Google asked users in a survey whether they would prefer more search results per page (Millis 2006). Users responded that "more is better", so Google lengthened the results from 10 to 30 for some users. However, it turned out that the extra half-second that the page took to load drastically decreased user satisfaction such that users conducted fewer searches. Google took the data from the initial survey, turned it to information (a recommendation to provide more search results per page and thereby increase user satisfaction) but then, only after going forward with an experiment, did Google gain the knowledge that users actually far preferred faster load times than more results per page. The lesson here is that the management of knowledge, is very different to the management of mere data and information.

Much reference is also made to the dynamic nature of organisational knowledge (McInerney 2002), in that it is constantly being updated, upgraded and refreshed, and should be regarded and managed as a living entity rather than a static artifact.  Davenport & Prusak argue that knowledge is not only bound to "documents and repositories" but also "routines, processes, practices and norms" (Davenport & Prusak 2000). Knowledge is produced, captured and transferred in many ways, from formal mentoring arrangements, apprenticeships and training programs to informal workplace discussions. Since these workplace practices occur on an ongoing basis, it is imperative that any KM strategy and/or solution needs to facilitate the consistent and continual upgrading and improving of the organisation's knowledge.

There is broad and growing consensus that the strategic management of knowledge assets contributes directly to an organisation's competitive advantage (Teece 2000) and is key to providing new products and services (Kessels 2001). On the other hand, neglecting this need leads to organisations suffering redundancy in effort and repeating past mistakes (Garfield 2007). International Data Corporation, a technology research group, reported that a poor capability to manage knowledge cost the Fortune 500 $12 billion per year (Stewart 2001, cited by McInerney 2002). But knowledge cannot be bought in and of itself; rather, it has to be cultivated in-house (Teece 2000) before any opportunity to embed it into new products or services can be fully exploited. The more salient knowledge an organisation has, and the more accessible it is, the more opportunities employees will find to leverage that knowledge in the innovation of new products and services. This will not only  decrease time-to-market and increase quality, but also expand the potential for future improvements to said products and services. It would appear that executives can no longer afford to lack a strategy for managing the collective of their employees' knowledge.

There are many opportunities to both capture and diffuse knowledge already present in most organisations. Any design, development or engineering of a product is fundamentally a knowledge creation process, and the lessons of bringing a product to market are invariably valuable to future efforts (M. Poppendieck & T. Poppendieck 2006). Davenport and Prusak, in their book "Working Knowledge: How Organisations Manage What They Know", highlight the value of interpersonal communication when facilitating knowledge transfer (Davenport & Prusak 2000). Time should be allotted, they argue, to allow staff members to engage in spontaneous conversations, organised group KT (knowledge transfer) sessions, demonstrations, presentations and experience reports, and the idea of "productivity" should be broadened to include these activities. Swap et al. suggest (2001) that storytelling and mentoring are also powerful ways to diffuse knowledge if well managed, and highly recommend the practice of joint-problem solving involving a senior and junior practitioner (Leonard & Swap 2004). A good example of this is in the software development industry where two programmers engage in "pair programming" (Williams & Kessler 2002). Two programmers sit at one computer, taking turns to command the keyboard and mouse while the other reviews the code as it is written. The result is a higher quality piece of work, and a good deal of knowledge transfer between the programmers involved. Peter Senge also advocates creating learning situations (Senge 1993). In his book "The Fifth Discipline", he champions the creation of a "learning organisation" through five key disciplines including "team learning" through dialogue and group discussion. It is clear that there is already a wealth of key opportunities for the capturing of knowledge in the workplace, but the key is to institutionalise the culture of knowledge capture and sharing. Workers should not feel harried or rushed; rather, they must have the opportunity to constantly reflect and analyse how practices and processes might be improved (Garvin 2003). Projects should end with (or be punctuated by) "retrospectives" or "post-mortems" where questions such as "What did we do right?", "What did we do wrong?" and "How can we improve?" are asked and provoke discussion in their answers (Derby et al. 2006). Another key aspect to evolving towards a culture that facilitates KM is the breaking down of boundaries and isolated teams and individuals. Along with the aforementioned communities of practice, working groups provide an opportunity for individuals to share and collaborate with employees outside of their day-to-day teams. Management teams that cultivate this kind of culture will, in addition to fostering innovation, prime themselves for return on investment when engaging in KM practices.

But how exactly do we use these opportunities to capture a deeper set of knowledge? Andrew McAfee recommends that organisations move away from communication "channels" where knowledge is one-to-one, formulated haphazardly and expended quickly, to communication "platforms" where knowledge has a bigger audience (and thus often displays more care in its creation) and can be kept relevant, salient and accessible (McAfee 2006). An instruction sent in an email to a new employee detailing the tools, practices and processes of the organisation is redundant effort, likely to be incomplete, and loses accessibility and visibility rapidly after its immediate use has expired. But a web page on the corporate intranet titled "Things you need to know to start" that is both easy to find, and easy to update, is instantly accessible to anyone, likely to be kept relevant and updated, and also likely to be a product of a group of people rather than one individual, greatly increasing the chance that it is of a higher quality (Mader 2007). A "wiki" is a website that allows users to create and edit HTML pages with rich content for immediate consumption by potentially everyone with a web browser. Wikipedia has built its success and huge popularity on this model. Contemporary wiki products include features like configurable access rights, a WYSIWYG (what-you-see-is-what-you-get) text editor, document revision history and several more features that make it an extremely compelling KM tool for the enterprise. A number of organisations have identified wikis as an excellent tool to facilitate communication and collaboration (Grace 2009) and one of the easiest ways to move from the "channel" of communication (often email) to a "platform" of wiki documents, photo galleries and blog entries. Management teams should look keenly to this and other "Web 2.0" technologies that contribute in bringing people together, as Nonaka's spiral can be greatly infused when large groups of employees, who are otherwise distributed across the organisation, are brought together in the same virtual space.

However, it is important to note that the role of technology in Knowledge Management has historically been misdiagnosed and overstated. Consequently a number of organisations have missed the mark by focusing too much on the technology (Davenport et al. 2008). According to Teech (2000), IT solutions are only an enabler, and they should be implemented not to enable the storage and retrieval of knowledge, but the connection of people. This should be self-evident in today's Facebook dominated world, but as Joel Spolsky says, to promote the successful community-driven collaboration tool he helped create, "A successful Exchange [an installation of the product] depends on whether you [the product buyer] can supply the community", implying that even the best software solution will not create a useful knowledge base without the engagement of a community.

Microsoft chairman Bill Gates framed the challenge precisely in an interview for a Newsweek magazine article in 2006: “While information wants to be free, knowledge is much ‘stickier’ – harder to communicate, more subjective, less easy to define.” He continued that an employee's value to their firm is wrapped up in their tacit knowledge. “Your ability to combine it with the knowledge of co-workers, partners and customers can make the difference between success and failure – for you and your employer.” (Twentyman 2006) The message to management is clear: the cultivation and generation of strategic knowledge, followed by the effective capture, diffusion and consequent amplification will define competitiveness in tomorrow’s businesses. Organisations are highly advised to adopt strategies for the effective management of their employees' collective knowledge, as it is critical to survival in a knowledge-driven economy of the 21st century.

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