Knowledge management and information technology - Ingenta Connect

Knowledge management and information technology: can they work in perfect harmony? Mirghani Mohamed, Michael Stankosky and Arthur Murray

Abstract Purpose – Aims to impart new insights into the role of information technology (IT) in knowledge extraction, capture, distribution and personalization. The paper seeks to pin-point the strengths and weaknesses of IT in the domain of knowledge management (KM) and to explain why the technology promise remains unfulfilled, as seen by many KM practitioners. Design/methodology/approach – The discussion in this paper is fundamentally based on Stankosky’s four KM pillars conceptual framework. Within this framework the authors attempted to shed some light on the IT role and the hidden reasons that make knowledge prominently unreachable via IT. Findings – IT assimilation and representation of knowledge intangibility, dynamism, experience and other humanistic cognitive dimensions remain debatable. The current technology is immature to resolve such problems. For IT to be effective for KM it must shred its bivalent logic and instead learn to operate within an authentic continuum.

Mirghani Mohamed is an Assistant Director based at the Information Systems and Services, The George Washington University, Washington, DC, USA. Michael Stankosky is a Lead Professor based at the Institute for Knowledge and Innovation, The George Washington University, Washington, DC, USA. Arthur Murray is a CEO based at Telart Inc., Boyce, VA, USA.

Originality/value – Knowledge managers need to understand that a KM initiative that considers IT as a Utopian panacea will fail. Equally, the KM initiative that undervalues IT will follow suit. Owing to IT immaturity in the area of cognitive behavior, the situation is still perplexing. This elusiveness imposes some obstacles to sufficiently represent the context of tacit knowledge. Hence, codifying knowledge with the poser of the existing IT and without the support from socio-cultural inputs, will result in de-contextualization, i.e. ‘‘knowledge dilution.’’ Hence, special considerations should be given to applications that offer some behavioral context and human cognitive dimensions. Keywords Knowledge management, Binary logic, Tacit knowledge, Content management Paper type Literature review

Introduction The advent of communications networks and internet access brought greater speed and agility, knowledge sharing, collaboration, lower costs and greater satisfaction through customer and supplier integration and self-services. In its natural progression technology moves from supporting functional systems to process oriented systems. This helped to lead a technology-enabled revolution dominated by the perceived efficiencies of process reengineering. The role of information technology (IT) in sharing knowledge has been a center of debate. Many investigators insisted that knowledge management (KM) initiatives could be successful without using IT tools (McDermott and O’Dell, 2001; Hibbard and Carillo, 1998), and IT should be adopted only when it is necessary. Others argued that IT is strategically essential for global reach when organizations are geographically distributed (Duffy, 2000; Lang, 2001). The KM conceptual framework developed by Stankosky and Baldanza (2000) has considered technology equally important as any of the other three pillars of KM (Figure 1), i.e. organization, learning and leadership. These four pillars form the ‘‘foundation’’ of any KM system. Without all of them in some kind of harmony, a knowledge management system (KMS) does not exist.

DOI 10.1108/13673270610670885

VOL. 10 NO. 3 2006, pp. 103-116, Q Emerald Group Publishing Limited, ISSN 1367-3270

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Figure 1 Knowledge management four pillars

Borghoff and Pareschi (1998) reported that the Nonaka and Takeuchi model tackles issues directly related to IT infrastructure. Knowledge itself is an integral part of technology definition as explained by Pe´rez-Bustamante (1999) who defines technology itself as any applied knowledge that fulfils market expectations or market needs. Linguistically technology refers to the combination of technical expertise (technos) and knowledge bases (logos). In its development life cycle, a majority of investigators considered knowledge itself as a superior phase that is proceeded by data and information. However, historically there has been no commonsense argument sited in the literature concerning the trivial role of IT in either of these two forms. Although there are differences on how knowledge and information or data are obtained, interpreted and managed, these differences do not offer a coherent rationale for alienating IT’s role in KM. Recently, the vividness and immediacy of the role of collaborative computing and virtual spaces role in sharing knowledge and shaping the decision have become even more compelling. Well-managed knowledge technologies can provide an enterprise with an invaluable asset: true and lasting alignment. However, Reneker and Buntzen (2000) argued that many discussions of knowledge management seem to view humans primarily as knowledge managers, rather than to conceptualize knowledge management as an approach or set of processes and practices to improve human seeking and utilizing knowledge. Keeping this caution in mind, practitioners need to look carefully at KM tools, to see exactly what benefits (and costs) can be anticipated in implementing KM and networking projects; how the benefits and costs will be measured; how organizational performance can be improved; and how the knowledge gained will be incorporated in the design of knowledge portals of the future. If properly used IT can accelerate knowledge-sharing capabilities in both time and space dimensions. Locality, timing, and relevancy factors determine the expediency and the strength of IT’s role in KM initiatives. On the other hand, due to the difficulty of incorporating

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most of human behavior aspects in technology, IT cannot fully put into operation many of KM’s humanistic features. Therefore, IT cannot be considered the magic bullet that makes a KM initiative a complete success. Hence, IT has to be part of a balanced and integrated set of components. The intermingling relationship and the balance between the four KM pillars is complex and unique to each environment. Too much emphasis on technology without incorporating the other critical elements could easily result in a failed system. Conversely, an organization can also place too much emphasis on strategy and organization and not necessarily capitalize on technology to implement that strategy or to provide continuous learning environment.

Binary logic versus learning loops It is accurate to say that current technology does not and may not offer the absolute cognitive dimension, which is exercised by the human brain. This is because the cognitive process involves socio-cultural perspectives built and sustained by social units, such as organizations, in a harmonized social network maintained by human beings. McDermott (1999) reports that leveraging knowledge involves a unique combination of human and information systems. Haldin-Herrgard (2000) state that a great deal can be done through modern IT to diffuse explicit knowledge, but tacitness is hard to diffuse technologically. Perhaps today and in the future high technology will facilitate this diffusion in an artificial face-to-face interaction, through different forms of meetings in real-time and with images and different forms of simulations. The knowing and learning machines are still a dream of fuzzy logic, chaos theory and artificial intelligence to transfer non-contextual data into pragmatic solutions by understanding the patterns. As has been described by many investigators Senge (1990), Argyris (1990), Murdoch (1995), and Henderson (1997), there are two types of learning, namely, single-loop and double-loop learning. Hosley et al. (1994) differentiated between the two loops in an interesting way. Single-loop learning is where individuals respond to changes in their internal or external environment by detecting and correcting errors so as to maintain the central features of the organizational norms. In contrast, double-loop learning is where the current organizational norms and assumptions are questioned to establish a new set of norms. Nevertheless, Henderson (1997) advised writers to be much more appreciative of the importance of single-loop learning. Far from being a short-run expedient, it is, perhaps, an entirely sensible way of dealing with problems. Provided that organizational structures and systems generally are not too rigid, individuals and groups might grow toward solutions in a context-specific fashion. Other investigators lean towards double-loop learning because it is generative more than just corrective measures. In addition, double-loop learning comes through a social process of sharing knowledge and interests. Romme and Witteloostuijn (1999) report that double-loop learning involves reframing, that is, learning to see things in totally new ways. But, Snell and Man-Kuen Chak (1998) observed that double-loop learning manifests itself as a transformation process, that is creating changes in the organization’s knowledge and competency base by collectively reframing problems and developing new policies, objectives and mental maps. Technology plays a major role in facilitating single-loop learning, but falls short in achieving the intricacy required of double-loop learning. Although there have been some attempts to address this deficiency, this standpoint has never adequately addresses those factors that keep technology imprisoned to its binary logic. This binary logic forms a repellent effect that is largely reflected in the role of IT in KM initiatives. The binary logic only accepts extremes of black and white, while the double-loop adds the reality of grayness by asking questions about the binary vision (deuteronomy learning). For instance the light switch could be on (light) or off (dark) there is no intermediate continuum. However, the question arises: how to control the amount of light? Or, how to adjust the color of light or the type of wire that creates the light according to its resistance. In this double-loop learning the feedback from the learning process itself is essential. To resolve this problem, IT attempts to introduce similar

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‘‘ Well-managed knowledge technologies can provide an enterprise with an invaluable asset: true and lasting alignment. ’’

concepts in databases such as ‘‘unstructured query language’’ (UQL) and ‘‘reflective query,’’ i.e. self-sustaining feedback. Technology does not have the capability to react quickly and effectively to outside influences. This is because the change depends on a deep understanding of external environmental parameters and the free flow and exchange of contextual information to ensure that expertise is available where and when it is required. The bivalent logic of the digital computer described by Kosko (1993) as a high-speed binary strings of ones and zeros, stand as the emblem of the black and white and its triumph over the scientific mind. Following Kosko’s line of reasoning, this bivalent logic hampers the freedom and dynamism of knowledge, which is too complex to be effectively governed by rigid binary rules. One of the acceptable rationalizations as to why many KM practitioners do not accept the close relationship between KM and IT is that IT is founded on the binary strings of ones and zeros. In other words, technical rationality is built on the discrete thinking of binary logic in which the whole world is expressed with 0 and/or 1. In fact this is the main IT limitation when it deals with themes of continuums and social disciplines like KM. The usage of binary logic implies that the value is true or false. This is based on the assumption that knowledge either exists or does not exist, there is no intermediate position. However, the lack of proper knowledge does exist within the knowledge continuum. This simply implies the philosophy of existence or the non-existence, i.e. ontology, may lead to a very controversial question: can knowledge be created or it can only be discovered? KM philosophy deals with knowledge as people-embodied activity and the complexity of culture. Hence, binary rule undoubtedly does not apply with the precision to the gray area in between. For instance to answer the following question is not a clear-cut ‘‘yes’’, or ‘‘no’’: can tacit knowledge be captured? The answer is a matter of degree. Most people believe that some tacit knowledge can be codified, but most of it cannot be articulated and cannot be captured at all through technology or documentation. From this example it is obvious that it is difficult, if not impossible to apply binary strings to socially constructed concepts. Tenkasi and Boland (1996) state that information technologies are increasingly playing an integrative role in knowledge-intensive firms as a way of achieving mutual learning. And, the information systems field has predominantly been driven by the notion of integration as a rational design process and an end state to be achieved through a static incorporation of knowledge domains. It has failed to consider the interpretive dynamics associated with the integration of differentiated knowledge and expertise. Recent collaborative software in the market improved the effectiveness and the efficiency of collaborative-commerce (c-commerce). But, it is a long way before technology that completely fulfills the totality of KM requirements is reached.

Tacit knowledge and software humanization The role of IT in capturing tacit knowledge is as blurred as the definition of tacit knowledge itself. Many investigators identified different types of knowledge (Polanyi, 1958; Nonaka and Takeuchi, 1995). Choo (2000) classified knowledge into three categories of organizational knowledge: tacit knowledge, explicit knowledge, and cultural knowledge. Tacit knowledge is the personal knowledge used by individuals to perform their work and to make sense of their world. Earlier, Polanyi (1958) coined the term ‘‘tacit knowledge’’ and distinguished between objective and tacit knowledge, with tacit knowledge as being highly individual, and achievable only through personal experience, but cannot be articulated. This

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idea is shared by Haldin-Herrgard (2000) who concluded that internal individual processes like experience and talent obtain tacit knowledge that is difficult to code. Therefore it cannot be managed and shared as explicit knowledge. But, he agrees that conversion of tacit knowledge to explicit or at least ability to share it offers greater value to an organization. The exact point of tacit knowledge transmission is defined by Hannabuss (2000) who stated that when unconscious tacit knowledge becomes conscious tacit knowledge is the point at which awareness hits us. That is the tacit meta-knowledge point. For individual thinkers, this is a critical moment of awareness. The partial role of IT in the transformation of tacit to explicit knowledge is described by McDermott (1999) who reported that while the knowledge revolution is inspired by new information systems, it takes human systems to realize it. This is not because people are reluctant to use IT. It is because knowledge involves thinking with information. If all what is accomplished is increase the circulation of information, then only one of the components of knowledge is addressed. But, Nonaka and Takeuchi (1995) did not put any limit to such transformation process and suggested that tacit knowledge becomes explicit through the process of externalization, i.e. by sharing metaphors and analogies during social interaction. Once knowledge becomes explicit it can be stored in databases and manuals. More specifically regarding IT’s role, Baker et al. (1997) concludes that technology is the obvious solution to assist communication. To be effective, communication needs to be based around a structured framework, so that people can put forward ideas and encourage response in a way that all team members are included in the communication. Nevertheless, technology alone is not a solution as stated by Terrett (1998) who found that intranet projects are seen as a useful new way of addressing the issue of know-how. However, once again, this initiative is often based on an incorrect assumption. The mere existence of a particular type of technology does not turn a knowledge-hoarding organization into a knowledge-sharing one. Technology and cultural change must go hand-in-hand. Technology can be used as an opportunity to change behavior, but it has to be introduced carefully, cautiously and in a structured manner. Although the difference between information and knowledge is often blurred, the software industry dealt with structured data using statistical analysis systems and dealt with information using databases and its various applications. But both approaches have fallen short in abundantly addressing the KM question. The dynamism of technology and in particular the rapid transformation in software development methodologies and life cycles have led to an enormous gap between those within the IT community and the specialized end-users such as KM practitioners. This may be considered, as one of the reasons that the experienced practitioners are not fully aware of what is available to them and/or what is potentially feasible to be offered in the future. We can safely say that knowledge is human-driven and depends heavily on human relationships and community communication and interaction. The mental model of IT professionals and especially those who sit for long hours interacting with their computers has been greatly influenced by the binary or Boolean logic of computers, i.e. (if . . . then . . .). This takes them away from appreciating the grayness of reality, which in fact, sits somewhere between the two poles of their logic, and its closeness to any of the poles is a matter of degree. Kaye and Little (1996) advise that to gain the potential benefits of emerging technologies, the conflict between the needs of developers and users must be dealt with. In addition, the technical focus of information technology must be broadened to encompass cultural concerns at both organizational and social levels. Hence, there are many efforts that encourage the implementation of techniques and languages that narrow the gap between IT and KM. Bair (1998) advises that developers should consider artificial intelligence (AI) as a source of technology enhancement for knowledge management. One of the most other promising knowledge representation approaches is case-based reasoning (CBR). Pfeffer and Sutton (2000) observed that a focus on technology and transfer of codified information, limited possibility to the transfer of tacit knowledge using formal

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systems and lack of understanding of the actual work among knowledge managers (staff department, etc.) are among the most common practices driving a wedge between knowing and doing. Stenmark (2000) argues that agent-based retrieval system technology could act as a facilitator in the KM process of capturing tacit knowledge on an intra-organizational web and making it tangible. Wiig (1999) predicted that future KM practices and methods would be systematic, explicit, and relatively dependent upon advanced technology in several areas. Overall, authors expect KM to become more people-centric as the recognition spreads that it is the networking of competent and collaborating people that forms the basis for the behavior and success of any organization. The real challenge that faces IT evangelists and their role in KM is to revolutionize the strategic objectives to select, develop and employ the appropriate technology that better serves KM. This can only be achieved by a better understanding of what KM means and how IT can be aligned with the business process to support problem-solving or help in decision-making processes.

Boundaryless organizations and virtual domains Most of the knowledge managed via communities of practice or communities of interest is socially based. Nevertheless, not all social relationships are good for knowledge artifacts discovery and not all knowledge is socially constructed. The proportionality of each to the total pool of knowledge depends on the contribution of the individual alone or the individual in relation to the community. Most knowledge is discovered on foundations of community interaction, but very minor breakthroughs exist from individuals such as inventions that result from an abrupt paradigm shift or mental model metanoia. However, the driving force for such breakthroughs comes from the need of the community for that invention. It is known that flat and less structured organizations encourage intradepartmental and interdepartmental communique´s that directly correlate with the organization’s ability to renovate and innovate. IT has significantly contributed to this communication as stated by Grieves (2000) that organizations continue to change to suit the needs of a society dominated by immediacy, speed, instant solutions and just-in-time approaches. The explosion in information in the twentieth century, created by the revolution in micro technology, presented the possibility of new organizational forms. These have been referred to as knowledge-centered, knowledge-intensive and virtual organizations. In business, this is particularly critical since the leverage of exotic knowledge becomes central in shaping the sharable and reusable interdisciplinary decisions. In more recent business models, there is a great tendency for infusion of KM concepts in the business process relating to marketing and sales. These resulted in the consolidation of back-office systems such as enterprise resource planning (ERP) systems and front-office systems such as customer relationship management (CRM). This collaborative link of the entire value network results in transparency and decreased response times. It also promotes knowledge accessibility for vigorous cooperative dialogue, which in turns, resulted in the production of innovative ideas and sharing of optimal solutions for complex problems. This led companies not only to integrate internal resources, but also to reach for the external ones for performance improvement. For instance, Cisco portal system collaboratively connected the suppliers, customers and the employees in such a way that the resultant knowledge was consistently used to improve future performance.

‘‘ If properly used IT can accelerate knowledge sharing capabilities in both time and space dimensions. ’’

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Virtual organizations are where the technology and organization pillars meet to create a high degree of co-operation over time, space and organizational structures. As the classical organizational boundaries vanished the only limits for collaboration remaining were the network bandwidth. Mohanty and Deshmukh (1999) argued that borderless global economies create an international competitive environment in which the commercial success of individual firms is strongly conditioned by their belonging to a network of collectively interacting firms. As such, the knowledge-building requirement is not confined to the organization itself, but transcends to the network of organizations referred to as ‘‘network capabilities.’’ Cleveland (1990) described a world increasingly ‘‘people-driven’’ as ‘‘knowledge’’ becomes accessible globally. This transition is directly facilitated by IT, which also assists in overcoming the cultural and linguistic barriers. However, the authors see that the degree of cultural acceptance to these technological advances varies from one organization to another. When technology intensive cultures, use technology as an effective tool for its operations, there is often no choice for a KM initiative but to use technology in that particular environment. Likewise, it should be understood that procedures need to be adapted to the culture to make best use of it and not to adopt ‘‘exotic’’ procedures in an attempt to change an existing culture. This challenging philosophy has been pursued by Stankosky and Baldanza (2000) who reported that the most cited major barriers to KM success is ‘‘culture’’. The best approach is to work with the existing culture through various strategies. For instance, Green and Ruhleder (1995) point out that the coupling of technology, consumerism, and social welfare is tight and unchallenged. Globalization is often cast as an imperative driven by technological forces, consumers and ‘‘the market’’. The forces, which will lead to the establishment of the global, borderless society that cannot, and should not, be restrained. It is also obvious that the effectiveness of a KM initiative that lacks IT implementation will always be limited to an internal and centralized focus versus the, distributed transfer of knowledge when the proper technology is employed. IT pulls together requisite individuals and groups such as virtual teams, virtual communities, electronic commerce and collaborative commerce. These groups can work from any location at anytime. On the other hand, these groupings form structures of virtual workplaces such as virtual banks, virtual corporations, virtual departments, virtual offices, virtual university, virtual departments and virtual or networked organizations. Kim and Lee (1996) observed that a wide, easy access to corporate data makes virtual organizations feasible because physical locations of employees can become irrelevant. Information exchange, easy access to data, and telecommunications enable the employees of an organization to establish a work unit dynamically over different time zones and geographical locations. Thus, an organization can have a better chance at becoming world-class by being flexible, fluid and virtual.

Content management Content management (CM) is one of the few fields within the KM discipline that has not been criticized for not delivering actionable information through technology. Gilbert et al. (2000) explain that CM is an ambiguous phrase with meanings that vary depending on what a user may need or a vendor may offer. IT offers an easy and versatile conduit for communication that helps in content filtering and pushing the relevant information to the desktop via a predefined user profile. Although document management is an important part of CM and can be achieved without technology, the knowledge dynamic in an organization enforces the continuous maintenance, improvement and distribution of critical-mission content, which is in some cases, cannot be achieved without technology. An end-to-end CM capability allows the user to deal with the back-end content repositories through a ubiquitous interface, such as a web browser. This may hide the user from direct contact that may result in revealing his/her ignorance and to some extent flatten the workforce learning curve. Technology plays a major role in shared content such as audiovisuals, maps, text, data, and images. Many different techniques can be applied to

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‘‘ Technology plays a major role in facilitating single-loop learning, but falls short in achieving the intricacy required of double-loop learning. ’’

deploy required information such as complex search, collaborative authoring, video streaming, interactive media and other smart technologies. Conversational content that has been proposed by Nishida (2002) provides the user with a means for interacting with the content in a conversational fashion, and presents a traveling conversation model in which the community in which knowledge process can be shared. The importance of the digital intellectual content has been stressed by Paskin (1999) who states that the digital technology community takes as its starting point all digital mechanisms, and views intellectual content mechanisms as a sub-set. In contrast, the intellectual content community takes as its starting point all creative works, and views digital mechanisms as a sub-set. From the standpoint of creation or dissemination of intellectual content, ‘‘digital’’ is one of many possible carrier mechanisms, but an increasingly important one. The increasingly broad spectrum of new technologies such as portals, natural language, knowledge query language (KQL), hierarchical distributed dynamic indexing (HDDI), media asset management (MAM), reflective query (RQ) and unstructured query language (UQL) have significantly contributed to the execution of complex queries. These technologies and others will become imbedded in many devices and networks that facilitate instantaneous knowledge sharing and dynamic content delivery. Caldwell et al. (2002) predicted a proliferation of ‘‘smart’’ technologies for mobile and wireless to include virtual team collaboration, media CM, voice portals, geospatial information management and personal KM through a wide-range of smart mobile devices. One important branch of the CM is the de-briefing using video clips that represent the most important knowledge objects. However, this technology will be overwhelming if there are no searching mechanisms such as the retrieval of audio-visual documents from their repository. This also would be most useful if integrated with the text authoring. For example an employee who writes a report would be required to navigate ‘‘speed-bumps’’ of short audio-visual and text from albums and other collected works that are dynamically created and distributed across a network. For instance, the user would be prompted to refer to company statistics, policies, strategies or vision when mentioning the relevant information in the text body of the research. Streaming technology is an approach to managing the ever-growing complexity of imaging. It has been described by Claxton (2001) as a shift in audiovisual content delivery, which pushes content to the ‘‘edge’’ of the internet, thereby reducing bandwidth loads and improving the user experience. It transmits multimedia files so that playback occurs upon arrival of the first data packets. Thus, streaming provides instant gratification for users, makes efficient use of network resources, and offers a compelling alternative to downloading. It might be argued that effects information-overload effects still exist in CM, but simple solutions might help. Adams (2001) argues that the precision and efficiency of information access improves when digital content is organized into tables within a relational database. The two main methods of information extraction technology – natural language processing and wrapper induction – offer a number of important benefits. Theses include helping the user to cope with the overwhelming amount of digital information, generating web pages directly from the databases as a result of a query and breaking up the web into small, more manageable pieces.

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IT and KM metrics Measurement is the nuclei of the performance process. In many organizations KM is considered a strategically critical for decision-making that directly impact the organizational effectiveness and competitiveness. Hence, its measurement is a key for productivity, effectiveness, efficiency and innovation of the organization. However, due to the intangibility of its effects, measurement is always a significant challenge to both researchers and practitioners. The problem of most performance measurements in KM initiatives is that they are always prone to measure or incorporate the confounded effects. The KM initiative performance should be defined before the commencement of KM activities, when a clear plan on how to eliminate the confounding effects can be addressed. McCampbell et al. (1999) conclude that performance measurement will be a key issue in KM initiatives since there is little precedent upon which to establish returns on investment (ROI). As an emerging and dynamic discipline, the creation of a standard measurement of KM reflected on the balance sheets is still in the formation stage. Once achieved, the result will be a rapid response from global business leaders to implement KM ‘‘best practices’’ to remain competitive. However, Carneiro (2000) reports that performance measurement is key to the learning process of employees and organizations in resource-competitive environments, but it needs to be well structured, communicated, and continually developed in order to remain effective. Selen (2000) suggests a roadmap for building a learning organization in a resource-competitive environment. This occurs by identifying a number of learning issues emerged relating to the dynamic nature of performance measurement, knowledge sharing, knowledge repositories, complex performance systems, and feedback mechanisms for both current and future decision-making. However, most performance improvement programs require changes in processes, structures and systems, as well as in technology and human resources. Regarding technology, Stankosky and Baldanza (2000) find that technology must support the business strategy, add value, and be measured. Levett and Guenov (2000) propose eight metrics for KM analysis motivation, knowledge capture, stored knowledge, personal training, knowledge transfer, creative thinking, knowledge identification, and knowledge access. It is obvious that IT can significantly contribute to all of these metrics. In terms of sophistication, ease of use and cost, IT continues to improve its position as a source of help to management evaluation processes. Nevertheless, Austin (1998) reports that some have argued that comprehensive measurement – which precludes dysfunction by measuring all dimensions of performance that are critical to value creation – is generally feasible. But that realization depends on the effective design and implementation of measurement technology. Others maintain that comprehensive measurement is not generally feasible, and that infeasibility can be inherent in organizational situations in ways that cannot be overcome by measurement technology. Out of the four KM pillars, the effects of technology are the closest to be measured. IT enables the manifestation of tangible results and the value-added benefits, compared to the primary anecdotal measures of KM initiatives without or with minimal technology contribution. KM metrics such as the increased market share, ROI, quality improvement and organizational behavioral effects may be confounded with the effects of other factors. These factors may be claimed by more powerful departments within the same organization that might actually marginalize the payback of KM. Thus the usage of knowledge content by employees or customers can be estimated by the number of database hits, time spent per visit, or frequency of visits. In addition, it can go further to identify the customers through the internet protocol (IP) addresses of their machines or through information obtained from web cookies. By using technology it is much easier to see the pattern of database access or the number of hit queries for a certain knowledge object. Although the insights gained by using these nuggets may not be quantified, but the associated innovation can be traced and accounted for. Hence, managers deal with all individual measures as parameters within a multivariate

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domain. On the contrary, the lack of technology in a KM initiative makes it difficult to measure activities when the KM initiative is faced with the question about its ROI. The level of success when non-technology techniques are used is indeterminate due to the lack of pragmatic quantifiable parameters. Nevertheless, IT measures should be used with care, and where possible, taking into account the most unambiguous complementary effects of other non-quantifiable measures.

In summary: a simple metaphor The key to achieving harmony between KM and IT is to understand the very basic principles: there are things that computer and technology do well, and there are things that humans do well. Many of the failures of IT and KM, and much of the tension between the two, are the result of repeated attempts to force one paradigm to operate within the realm of the other. Both ‘‘islands’’ speak different languages, and use different currencies (measures). Instead of forcing a ‘‘merger’’ or a ‘‘hostile takeover’’, KM managers and practitioners should look for ways to build ‘‘bridges’’ between these two islands. Just as bridges can connect, but not necessarily unite, two distinctly economies, KM and IT can be similarly inter-connected. Some key ‘‘arteries’’ that have been discussed include: collapsing the time factor through ‘‘fast lanes’’ (algorithmic optimization), or ‘‘high occupancy vehicles, HOV lanes’’ (communities); knowing when to apply binary logic vs continuums; knowing when to switch ‘‘transportation modes’’ from tacit to explicit, knowing when to remove ‘‘trade barriers’’ understanding what ‘‘cargo’’ is being stored and shipped (i.e. content); and what to measure (qualitative vs quantitative). By focusing efforts on bridging these disparate yet complementary concepts, much of the tension and frustration can be removed within the KM and IT communities, and indeed begin to establish greater harmony.

The bottom line With the ongoing expansion of KM into enterprises’ body, practitioners from different backgrounds have attempted to better understand the question of the exact role of IT in contributing to the success of KM initiatives. There is no clear-cut answer to the question; however, knowledge managers need to understand that the KM initiative that considers IT as a utopian panacea will fail. Equally, the KM initiative that undervalues IT will follow suit. In general, due to IT immaturity in the area of cognitive behavior, the situation is still perplexing. This elusiveness imposes some obstacles to sufficiently represent the context of tacit knowledge. Hence, codifying knowledge with the power of the existing IT and without the support from socio-cultural inputs, will result in de-contextualization, i.e. ‘‘knowledge dilution’’. IT must be accompanied by social networks such as communities of practice and other human interventions to create the requisite synergetic effects. IT success in KM initiative is unique to the enterprise’s ecosystem; one size fits all approach does not work here. The choice of any KM tool must follow the typical technology selection principles, but at the end it must assimilate the enterprise socio-cultural environment. Software, hardware and other IT infrastructure must be seriously evaluated and matched with the environment requirements. Special considerations should be given to applications that offer some behavioral context and human cognitive dimensions. To avoid sub-optimization, changes in enterprise culture and structure as well as changes in IT architecture may be inevitable. This in turn may require an amendment on the enterprise’s

‘‘ We can safely say that knowledge is human-driven and depends heavily on human relationships and community communication and interaction. ’’

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technology strategic plan to align with these changes. Understanding exactly what IT provides and what the enterprise needs will facilitate not only funding for the continuation and the expansion of the program, but also help in creating a value in a shorter time. This value, however, must be quantified to reflect the real ROI of the program. Although, the measurement of KM ROI is intricate process and remain confounded and ostensibly debatable, indicators from IT repositories can be used to illustrate part of this benefit. In conclusion, IT is ‘‘systemic/organic’’ to the enterprise, and must be designed, implemented, and measured within the context of the entire enterprise. The traditional role of the CIO has to be expanded into the strategic and operational areas, as well as the cultural and human resources. Organization leaders are witnessing the beginnings of a new way of looking at and evaluating IT. All the pillars of leadership/management, organization, learning, and technology must be in harmony, and interact smartly together to produce improvements in efficiency, effectiveness, and innovation.

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About the authors Mirghani Mohamed is the Assistant Director for the Data Center at The George Washington University, DC, he also serves as the Director of the Knowledge Management (KM) Technology Center, KM Institute at the same university. He holds MSc and PhD in Agronomy/Statistics, MSc in Computer Science and DSc in Systems Engineering and Engineering Management with emphasis on knowledge management. In addition, he is an Oracle Certified Professional. Dr Mohamed has wealth of operational experience in ICT roadmapping, technology operations, change management, content management, and technology strategic and capacity planning. He supervised many community building, communication and collaboration technologies in the areas of technology operations, financial systems and KM. He worked as a technical lead for deployments of complex ERP and many other enterprise-wide systems. He has worked in many socio-technical projects with the intention to narrow the digital divide and to deliver and share the knowledge with the poor. During his work with INSTORMIL CRSP Dr Mohamed was involved in various regional and national sustainable development and humanitarian efforts in Sudan. Mirghani Mohamed is the corresponding author and can be contacted at: [email protected] Michael Stankosky is an Associate Professor of Engineering Management and Systems Engineering and Co-founder/Co-director of the Institute for Knowledge Management at George Washington University. He is the Lead Professor for Knowledge Management, Marketing of Technology and Technology Issues Analysis. He has established the first US Masters, Graduate Certificate, and Doctoral programs in Knowledge Management (KM); and is directing extensive KM research (over 50 researchers), with the objective of creating an academic discipline and a theory of knowledge management. He has written several seminal articles, addressed numerous conferences, and is a contributing editor to KMWorld on this topic. He consults to several corporations, not-for-profit organizations, and government agencies on KM. He is participating in a government-sponsored workshop to establish a KM framework for governmental KM training and certification. Dr Stankosky joined George Washington University in 1998. Prior to that, he was a Senior Vice President for Business Development at QuesTech, Inc., a Systems Engineering and Integration Corporation. He spent five years at Science Applications International Corporation as Vice President for Commercial and International Business Development, specializing in information technology solutions. He has 26 years experience in government, encompassing many areas of research, development, and acquisition. His expertise ranges from command, control, communications, computers, modeling and simulation to systems and software engineering, to program management and direction activities, to policy formulation and operational leadership positions. He also served as a diplomat at the American Embassy, Paris, brokering relationships among US and French governments and various enterprises. Dr Stankosky has been active in developing information technology (IT) architectures that cut across many domains. He pioneered the widely implemented Department of Defense Information Infrastructure Common Operating Environment, a technical architecture now mandated for all IT systems throughout the Department of Defense. He recently developed an enterprise information architecture for use in a global environment. Dr Stankosky has been active for 25 years with numerous universities as an adjunct faculty member, delivering courses at the graduate-level that span over 30 different courses in leadership/management, systems engineering, program management, information systems/technology, international transactions, economics, finance, marketing of technology, management of research, development and acquisition, information systems management, software management, information assurance/security management, organizational effectiveness and knowledge management. He has experience in both curriculum and faculty development, and created a series of international transaction courses for the University of Denver’s Systems Management program. He authored the theoretical construct for KM, upon which is based the current KM curriculum and research map. He is directing the teaching responsibilities for ten part-time faculty, and is the adviser for over 120 students in the various KM graduate programs. Dr Stankosky served the government on a variety of panels, to include: the National Academy of Sciences, the Armed

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Forces Communications and Electronics Association Panel on Evolutionary Acquisition, and the National Correlation Working Group, recommending IT architectures for several government agencies. He was a member of the International Business Advisory Council at the Center for International Business Education and Research, University of Maryland; a member of the Board of Governors at the Marine Corps Association; participated in the High Technology Council of Maryland; and was Director of the Cyberspace Policy Institute and a member of the Advisory Panel at the Continuing Engineering Education program at the George Washington University. He is active with the National Task Force on Intellectual Property and Knowledge Management. He is a member of the American Society of Engineering Management, and an advisory member of the World Wide Web Consortium. Dr Stankosky holds a Doctor of Science in Engineering Management from George Washington University, majoring in Information and Process Engineering; two Master of Science degrees in Systems Management and Education from the University of Southern California; a Master of Arts in International Relations from Salve Regina University; and an LL.B. from Blackstone School of Law. He attended the National War College, the Naval War College, the Foreign Services Institute, the Defense Systems Management College, and the Senior Executive program at the Kennedy School of Government, Harvard University. He is a recipient of the French National Order of Merit, the US Legion of Merit, the Defense Superior Service Medal, and many other government honors and awards. Arthur Murray is CEO of Telart Technologies, Inc., a company he founded in 1993 to help organizations boost the performance of their knowledge workforce. He has led the development and deployment of advanced information and knowledge systems for over 25 years. Through many graduate courses, seminars and workshops in North America and Europe, Dr Murray has helped hundreds in the government and private sector find and re-deploy their hidden knowledge assets. He is the co-founder of the Behavioral and Computational Neuropsychology (BCN) Group, Inc., an international organization of knowledge scientists. He is managing director of the George Washington University Institute for Knowledge Management, and sits on the advisory boards of international organizations in the fields of science, medicine and organizational learning. He is a keynote speaker, an editorial board member and reviewer for several scientific journals. He has been featured and interviewed in numerous trade publications and radio programs. He holds the DSc and MEA degrees in Engineering Administration from The George Washington University, and the BSEE from Lehigh University.

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