Distributed Knowledge

Current innovations in information and communication technologies (ICT) provide new opportunities for engaging in geographically distributed work (Hinds & Mortensen, 2005). A workforce is distributed if: 1) knowledge workers operate in different physical locations, 2) team members communicate asynchronously for most normal interchanges even with collocated colleagues, or 3) team members work with different firms or within different entities of the same parent organization (Ware & Grantham, 2003). The term distributed teams refers to teams that rarely use face-to-face communications because they are geographically dispersed and linked by ICT (Townsend et al., 1998). In this socio-technical perspective (Hazy, 2006), distributed teams are synonymous to virtual teams.

According to Curseu, Schalk, and Wessel (2007), the concept of distributed teams can be defined through three dimensions: 1) the degree and type of interdependence among teams, 2) the nature of teams (temporary or permanent), and 3) the extent to which teams rely computer-mediated communication systems. These dimensions and the characteristics of the various types of distributed networks affect information processing, information flows and knowledge transfer (Curseu et al., 2007; Arling & Subramani, 2006).  The purpose of this essay is twofold: 1) identify five criteria that should be considered when choosing tools to connect distributed teams, and 2) describe how the knowledge created by these teams should be captured and used.

Connecting Distributed Teams: Models and Tools

As mentioned earlier, the construct of distributed teams refers to teams that rarely use face-to-face communications because they are geographically dispersed and linked by ICT. From the three dimensions of distributed teams identified by Curseu and his colleagues, two main configurations of distributed teams exist: 1) collocated teams with few remote team members and 2) multiple geographically distributed subgroups. Members of distributed teams experience a mix of communication modes such as face-to-face interaction and electronic communication (Arling & Subramani, 2006).

Distributed computing environments link teams and resources dispersed across networks. Distributed systems consist of several interoperable multi-platform processing components including operating systems and hardware architectures (Gunwani, 1999). These differences are masked by middleware technologies that provide additional services to solve some issues inherent to programming in a distributed environment such as transactions, naming, security, and reliability (Gorappa, 2007). The foundation for distributed computing systems is N-tier client-server systems (Hoganson & Guimaraes, 2003). In fact, distributed database systems are client-server systems that provide concurrent access to clients to data stored in various servers through a distributed database and a distributed database management systems (Cavazos & Jarquin, 2004). Some characteristics of distributed systems are openness, resource sharing, scalability, concurrency, transparency, and fault tolerance.

In recent years, the demand for Internet-based distributed systems and applications has expanded rapidly. These technologies and applications are cluster computing, grid computing, web services, mobile systems programming, distributed algorithms, sensor networks, DCE, CORBA, J2EE and .NET industry standards. The expectation is that they enable the creation of new types of enterprises and services by virtualizing resources that are geographically distributed (Buyya & Ramamohanarao, 2007). Kurdi, Li, and Al-Raweshidy (2008) used six criteria to categorize distributed systems: size, solution, interactivity, accessibility, manageability, and user-centricity. Depending of the types of services delivered, these systems might be computational, global, interactive, mobile, voluntary, personalized, and automatic, whereas another might be data oriented, project based, for batch processing, restricted, centralized, and nonpersonalized.

Knowledge-Based View of the Firm and Knowledge Management Systems

The creation of a global society with possibilities of knowledge sharing is among the contributions of the IT revolution and globalization. In the knowledge society, the value-creating strategies and long-term viability of a firm depend on sustaining its competitive advantage. The knowledge-based view of the firm draws upon the resourced-based view (Levitas & Ndofor, 2006; Williamson, 1957; Chandler 1962; Stigler, 1961) and considers knowledge as a distinctively unique resource that should be managed. Organizational knowledge can be characterized as explicit and tacit (Regan & O’Connor, 2002), and embedded (Bourdeau & Couillard, 1999). Knowledge management (KM) refers to the ability to create and manage a culture that encourages and facilitates the creation, appropriate use, and sharing of knowledge to improve organizational performance and effectiveness (Walczak, 2005).

Organizational KM includes the identification, acquisition, storing, and dissemination of tacit, explicit, and embedded knowledge. Conceptualizations of knowledge management (KM) as well as of intellectual and human capital in organizational design are usually guided by various perspectives such as information-processing theory (Tushman & Nader, 1978; Galbraith, 1973), organizational learning theory (Senge, 1990), knowledge creation (Kearns & Sabherwal, 2007), dynamic capabilities (Collis, 1991), and resource-based theory of the firm (Rugman & Verbeke, 2002; Wernerfelt, 1984; Penrose, 1959).

Good KM, as Charles (2005) noted, involves three elements: people, processes and technology. Organizational technologies that support KM initiatives and KWs are called knowledge management systems (KMS). KMS are IT-based tools developed to support corporate processes of knowledge management (Feng, Chen, & Liou, 2005). KMS are classified in terms of knowledge dimensions (tacit and explicit) and the extent of codifiability required (Becerra-Fernandez, 2000), codification versus personalization strategy (Hansen et al., 1999), KM processes that are supported (Alavi and Leidner, 2001; Tiwana and Ramesh, 2000). Benbya and Belbaly (2005) have provided a classification of KMS based on the tacit and explicit dimensions. Examples of such applications are knowledge bases, business intelligence services, corporate information portals, and customer relationship management services. Five indicators are used to measure their success (Benbya & Belbaly, 2005): 1) system quality, 2) knowledge quality, 3) use and user satisfaction, 4) perceived benefits, and 5) net impact.

Conclusion

Continued globalization, coupled with the technology revolution, has changed the way many corporations operate. Organizational design and change are not easy in the increased global competitive pressures combined with the increasing use of advanced IT. The future of work and the business success depend directly on an organization’s ability to redefine its business strategies, workplace, workforce, and technology. Geographically distributed teams are increasingly the new workforce and workplace strategies that give firms the required agility and flexibility to meet dynamically changing needs in the volatile contemporary business environment. This essay discussed criteria that should be considered when choosing technology systems to connect distributed teams. Based on the fact that knowledge is considered the main source of competitive advantage in today organizations, this essay also described IT-based technologies that are available to support corporate processes of knowledge management.

The achievement of corporate agility and flexibility goals require a deep rethinking of the missing element among the four identified above, that is, business strategies. To improve corporate internal features, appropriate management and leadership approaches should be implemented. On the other hand, geographically distributed teams generate new types of issues. These global issues could be handled by using the contingency theory that emphasizes that design decisions depend on environmental conditions and are guided by the general orienting hypothesis that organizations whose internal features are aligned with the demands of their environments will increase organizational performance and minimize uncertainty.