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.

Two Types of Innovative Leadership: Application to Knowledge Management Initiatives

Amar (1998) observed that

organizations whose success depends on innovation require a

leadership style totally different from the one typically used by

most leaders. Whereas leaders of traditional organizations succeed on

their ability to artfully manipulate their environment, innovation

leadership emanates from manager’s creative initiatives, intellectual

preeminence, and technical or unique expertise that is of value to

each individual in the group and which translates to direct benefit

for all (Amar, 1998).

The literature distinguishes two types of innovation

leadership: the transformational-transactional leadership model in

the organizational behavior literature, and the leadership role model in the

innovation management literature (Bossink, 2004). In the

organizational behavior literature, leadership relates to: the

personal traits of the leader such as intelligence, values and

physical appearance; the leader’s behavior such as the use of power,

the control of rewards and the delegation of authority; and the

organizational situation the leader is in such as the structure, age

and environment.

The

innovation management literature presents leadership as a role to be

performed by managers but also by employees. These roles are:

inventor: the leader promotes the technological know-how that is

translated into innovative products and services; champion: the

leader promotes organizational adoption of innovations; entrepreneur:

the leader initiates, drives and controls the innovation strategies

and processes in the organization; gatekeeper: the leader gathers and

processes information about changes in the organization and its

environment; and sponsor.

Bossink (2004) identified four leadership styles in the

innovation leadership roles: 1) charismatic: the leader communicates an

innovation vision, energizes others to innovate, and accelerates

innovation processes; 2) instrumental: the leader structures and

controls innovation processes; 3) strategic: the leader uses hierarchical

power in favor of organizational innovation; 4) interactive: the leader empowers other to

innovate, cooperates with them to innovate and shows them how to

become innovation leaders in the organization themselves.

My question is:

How could we apply these two types of

innovative leadership to a knowledge management initiative?

Thanks for sharing your

thoughts.