The Relationship Between Social Interaction And
Knowledge Management System Success
Anthony J. Delmonte, Kennedy Space Center, Jay E. Aronson, The University of Georgia
ABSTRACT:
While recent developments in information technology have provided the tools that enable companies to explore knowledge management solutions, it has become well recognized that information technology does not in itself create knowledge or guarantee knowledge creation. Additionally, most companies are finding that leveraging knowledge is extremely difficult, and dependent more on building a culture based on effective communicating teams, and interdepartmental trust, than on information technology.
The purpose of this study was to expand the base of knowledge in that area, and empirically test the relationship between social interaction within an organization and knowledge management system success. Two aspects of social interaction were measured: interdepartmental connectedness, and interdepartmental conflict. The results of the study indicate that there is a significant relationship between both factors and knowledge management system success.
1. Introduction
Early literature on knowledge management posited that organizational capability is the outcome of knowledge integration, and the extent to which this capability is distinctive and contributing to competitive advantage depends on how well the company accesses and integrates the knowledge of its employees (Grant, 1996). Increasingly, management and organization literature claims that organizational knowledge is a valuable strategic asset (Zack, 1999).
While recent developments in information technology have provided tools that enable companies to explore knowledge management solutions, it has become well recognized that information technology does not in itself create knowledge or guarantee knowledge creation. Technology is not a substitute for knowledge, but rather a pipeline and a vehicle for delivering data and information (Spiegler, 2000). Most companies embarking on knowledge management system implementations are finding that leveraging knowledge is extremely difficult, and dependent more on building a culture based on social interaction, effective communicating teams, and interdepartmental trust, than on information technology (McDermott, 1999). Consistently, literature on knowledge management contends that the sharing of knowledge requires collaboration across organizations (McDermott, 1999). Empirical determinants of the effectiveness of knowledge management systems, however, are relatively sparse, and there is little field data available upon which to base expectations for their success. Even the definition of the term “knowledge” has met with debate in the literature, so it is not difficult to understand that the effective management of knowledge is elusive to most managers (Herbert, 2000; Spiegler, 2000).
We discuss an evaluation of the relationship between two aspects of social interaction within organizations, interdepartmental connectedness and interdepartmental conflict, and the success of knowledge management systems implementations. Specifically, we investigate two hypotheses. The first states that higher levels of interdepartmental connectedness result in higher levels of knowledge management system success. The second states that higher levels of interdepartmental conflict result in lower levels of knowledge management system success. We begin with a review of some of the pertinent literature on knowledge management and organizational culture. The study and findings are discussed. We conclude with implications for future researchers as well as practitioners.
2. Review
Of The Literature
2.1. Knowledge Management
Knowledge management is a strategic and systematic approach to capitalize on what an organization knows (Knapp, 1998). One view of knowledge management defines it as the concept under which information is turned into actionable knowledge and made available in a usable form to the individuals who need it and can apply it to solving problems (Angus, Patel, and Harty, 1998). The basic functions for knowledge management systems involve the processes of data capture, storage, classification, and retrieval. Because these are typical functions inherent in any information system, the focus of most knowledge management systems initiatives tends toward the technology.
The uniqueness of knowledge management systems, stems from the necessity to draw from the firm's intellectual capital to form the basis for their value (Smith, 1998). Companies that consider themselves successful in knowledge management systems implementation appear to agree that the biggest challenge is to properly address the cultural-change issues associated with the creation of effective communicating teams and where the sharing of knowledge is viewed as a benefit rather than a risk (Davenport, 2000).
A consortium benchmarking study on knowledge sharing by the American Productivity and Quality Center (APQC) concluded that, even in companies considered to be best practice organizations in the area of knowledge management, greater management focus is required in order to improve the sharing of knowledge. The study found that while level of management involvement is closely tied to the scale, scope, and focus of knowledge-sharing activities, most organizations reported that less than 25% of their leadership was active as knowledge-sharing champions and participants. When sharing knowledge is tied to local, grassroots problem solving, senior managers failed to act as advocates or role models. It is only when sharing knowledge is explicitly a part of a corporate strategy, that senior leaders become actively involved as champions (Carlin and Womack, 1999).
Mueller and Dyerson (1999) looked at the changing role of management, with a specific focus on the accumulation of knowledge in organizations undergoing technological change. Based on longitudinal research in three organizations over a four-year period, they observed that in addition to the acquisition of a new technology and the training of employees to use it, appropriate mechanisms are required to retain key staff as well as to integrate the newly acquired knowledge and skills into existing routines. This leads to both internal “appropriability” (p. 225) problems, which arise from the organization’s inability to fully appropriate all of the knowledge, skills and abilities that it stores, as well as external appropriability problems due to the organization’s inability to keep people who truly want to leave the organization. To effectively appropriate organizational knowledge internally without losing the ability to draw in new knowledge from external sources, both problems must be considered simultaneously. This increased organizational complexity requires that greater management focus be placed on the lateral flow of knowledge in order to address the informational interdependence between specialties, functions and projects.
Eriksson, et al. (2000) conducted an experiential study aimed at testing the premise that in order to use knowledge to improve business processes, it is necessary to understand how sharing and knowledge creation takes place, in general as well as in team and organizational sessions. Using the new product development process within a host organization, they focused on the areas of team dynamics, learning strategies, knowledge sharing, and the requisite aspects of the business process. While the result of the study was a set of “lessons learned,” which can prove useful to future researchers, several recommendations were made with respect to knowledge-sharing strategies. The first was that attempts should be made to compensate for the differing communications style of team members, in that some tend to spend the majority of their time working alone, while others work in groups. Recognizing these differences, and taking some action to bring them together, could enhance the probability of knowledge being shared.
Gray and Chan (2000) looked at the integration of knowledge management practices through a problem-solving framework. They posited that a primary goal of knowledge management is to ensure that newly generated knowledge in one phase of problem solving becomes an input to the next phase. The Gray and Chan (2000) framework, which was developed from empirical literature, is made up of two axes and four cells (Figure 1). The vertical axis, “Process Supported,” distinguishes between the recognition of the existence of a problem and taking actions to solve it. The horizontal axis, “Class of Problem,” distinguishes between knowledge management practices that help people to identify and resolve new or unique problems, or those that have been experienced before.
Figure 1 – The Knowledge Management Framework – Gray
and Chan (2000)
Cell 1 represents practices that encourage people to expose themselves to new information and ideas. From a management standpoint this requires the creation of a setting that allows that to take place (Davenport and Prusak, 1998). Cell 2 involves the active creation of knowledge once a problem is recognized. Mechanisms for accomplishing this include the use of cross-functional teams, customer- or product-focused work groups, or the most recent organizational form that has grown out of the knowledge management literature, “communities of practice” (Wenger and Snyder, 2000, p. 139).
Cell 3 of the framework represents the capture, retention, and dissemination of knowledge. This could include the creation of repositories of knowledge, databases of lessons learned, knowledge maps, or merely directories of individuals and their associated knowledge (Skyrme, 1999). Finally, Cell 4 represents the activities undertaken by organizations to raise the awareness of individuals that are facing a problem that has already been encountered and addressed. These activities include mentoring programs, peer reviews, and training programs (Allee, 1997).
The conclusion drawn by Gray and Chan (2000) is that organizations need to help individuals solve problems by developing practices that improve the likelihood of finding a solution. The use of “dysfunctional processes” such as bypassing the organizational knowledge base and “reinventing the wheel,” are an inefficient use of problem-solving resources and organizations should promote a learning culture, which avoids their use. This is consistent with the conclusions drawn by Lipshitz (2000), which state that if organizations are to benefit consistently from learning, they must create the conditions that facilitate people’s ability to detect and correct problems. This is also borne out in organizations deemed to be best practice organizations, where knowledge sharing is integrated with people’s work, through the use of visible knowledge-sharing events and also embedding knowledge sharing into routine work processes (Carlin and Womack, 1999).
2.2. Knowledge
Management And Collaboration
One of the newest organizational forms beginning to emerge, specifically focused on learning and the sharing of knowledge, is communities of practice. Wenger and Snyder (2000) define communities of practice as “groups of people informally bound together by shared expertise and passion for a joint enterprise” (p. 139). Unlike formal work groups or project teams, the members themselves form communities of practice, for the sole purpose of developing the capabilities of the members and to build and exchange knowledge. Due to the informal nature of communities of practice, there is a managerial paradox associated with their use. Because the members essentially select themselves based on common interests and desires, management cannot mandate communities of practice. Some management may even spurn the idea. A small number of companies who have found ways to seed and nurture communities of practice, however, are finding that they can result in significant value to the company. Best practice organizations in the APQC benchmarking study were found to enable and embrace informal human networks, without attempting to formalize them. A key to the networks in those organizations whether the network was formal or informal, was that a single facilitator “owned” (p. 70) the network and ensured that people actively participated (Carlin and Womack, 1999).
Lesser and Storck (2001) found that the social capital resident in communities of practice leads to behavioral changes, which in turn positively influence business performance. In a study of seven companies, they identified four areas of performance that were positively impacted in the organizations studied. The first area was in decreasing the learning curve of new employees. Guiding new employees toward appropriate communities of practice shortened the time needed to learn both the technical and cultural aspects of their jobs. The second area was in responding more rapidly to customer needs and inquiries. It was shown that communities of practice were extremely helpful in the identification of individuals with the subject matter expertise needed to solve a particular client problem. This was especially true in organizations where the expertise needed was separated by geographic or organizational boundaries.
The third area identified by Lesser and Storck (2001) was in reducing rework and preventing “reinvention of the wheel.” This was identified as the most valuable contribution made by communities of practice, as it allowed for the sponsor organization to more easily reuse existing knowledge assets. All of the communities studied cited the ability to locate, access, and apply existing intellectual capital to new situations as one of the most important benefits. The final area identified was spawning new ideas for products and services. It was determined that communities of practice served as breeding grounds for innovation. One of the reasons identified for this was that the communities of practice provided an environment of trust, where people felt comfortable sharing challenges, ideas, and knowledge.
In a discussion on knowledge creation, Nonaka (1994) described two types of knowledge: “explicit” knowledge and “tacit” knowledge. Explicit knowledge is codified knowledge that has been captured in records of the past, and is transmittable in formal, systematic language. Tacit knowledge has both cognitive and technical elements and contains a personal quality that is rooted in action and involvement. Tacit knowledge is very often difficult to formalize and communicate, as it is very closely related to the concept of “mental models” as described by Senge (1990). As the economy shifts more and more to knowledge work, collaboration at the interorganizational level is necessary to turn tacit knowledge into explicit knowledge (Scott 2000).
This premise is consistent with research conducted by the Delphi Consulting Group Inc. in Boston, which identified cultural issues as the largest obstacle to implementing knowledge management systems. Getting people to break the culture of hoarding knowledge requires new business processes combined with a new form of trust between employers and employees (Hibbard and Carrillo, 1998). This is also consistent with the findings of Mueller and Dyerson (1999), who pointed out that even though experts within an organization may be encouraged to engage in free sharing of information with colleagues, many question whether the use of their knowledge in that way will result in negative consequences with respect to their own careers. Buckman Laboratories, a $300 million chemical company, provides an interesting illustration.
In 1989, Robert Buckman, founder of Buckman Laboratories, made a pledge that knowledge would become the foundation of his company’s competitive advantage. This transformation to a knowledge-based company was started in 1992 with an objective that all customer knowledge, competitive intelligence, process knowledge, and product knowledge would be captured, stored, and shared across the company. A Knowledge Management Transfer Department was even set up to monitor and support the sharing of both explicit and tacit knowledge within the organization. By the end of 1992, Buckman had invested over $8 million to lay the groundwork and implement the technical infrastructure for the system. It wasn’t until 1998, however, that Buckman was satisfied that the necessary culture change had taken place to stimulate the climate of continuity and trust required for such a system to begin to reap benefits (Pan and Scarbrough, 1999). At that time, CEO Bob Buckman was quoted as saying, “What’s happened here is 90 percent culture change. You need to change the way you relate to one another. If you can’t do that you won’t succeed” (De Long and Fahey, 2000).
2.3. Knowledge
Management And Conflict
Like Buckman, other companies which consider themselves successful in knowledge management systems implementation appear to agree that the biggest challenge is properly addressing the cultural change issues throughout the organization, associated with the creation of effective communicating teams and where the sharing of knowledge is viewed as a benefit rather than a risk (Davenport, 2000). In a case study conducted in a pharmaceutical company, Brown and Woodland (1999) found that unless negative attitudes among coworkers can be stifled, both knowledge and learning are stifled. Accomplishing a state of open sharing of knowledge involves the questioning of previously accepted assumptions, a reflection on existing mental models, and positive dialogue based on groups of people viewing each other as colleagues in a mutual quest for clarity and insight. This type of reflection, inquiry, and dialogue is indicative of an organization focused on learning and its absence results in conflict (Senge, 1990).
Using observations from interviews over a period of years, De Long and Seeman (2000) attempted to show how knowledge management initiatives are hindered by conceptual confusion resulting in conflict. This arises primarily due to the differing perspectives on knowledge management that must be integrated in order to implement a long-term strategy. They identified four sources of conflict that are likely to threaten the credibility of knowledge management initiatives. The first, and most common, is the struggle for control over specific knowledge resources. Finance people, for example, may have strong objections to requests by engineering people for cost information. The second source of conflict is associated with the varying subcultures or ideologies that coexist within an organization. Sharing knowledge across functional or geographic boundaries can result in a perceived infringement on other’s belief systems and norms. The third conflict source results from the need to integrate highly autonomous departments, functions, or business units. Knowledge management initiatives require that very different perspectives on “valid” knowledge, such as marketing and finance, be integrated, potentially resulting in a lack of understanding and defensiveness. The fourth source of conflict stems from the fact that, if successful, knowledge management becomes a source of power. Infighting over who controls that power diverts attention from the true strategic value of the initiative.
Nonaka (1994) described the process of knowledge creation at the organizational level as “creative chaos.” This is generated naturally when a crisis arises. The crisis may be real, such as those generated by market or technology pressures, or contrived by the organization’s leaders to heighten awareness. In either case the result is increased tension in the organization, which focuses attention on the forming and solving of new problems. Drawing from Schon (1983) he cautions that this process only takes place when the leaders and members of the organization openly reflect on their own actions. Without this reflection, the result is “destructive” rather than “creative” chaos, and a source of conflict within the organization.
When attempts at knowledge creation become the foundation of an organization’s knowledge management systems, this destructive chaos can have a debilitating effect on the outcome of the system. Consider the responses of 109 participants in an executive development program conducted at Northeast University in 1997. When asked about their key concerns about knowledge management, they indicated that the primary concern was with regard to the difficulties in convincing people and business units to volunteer their knowledge, particularly when each business unit was responsible for generating a profit (Alavi and Leidner, 1999). Employees of best practice companies in the knowledge management area also report that strong bonds and genuine friendships result from the human networks within the company, whether they are formal or informal. They share knowledge not because they are forced or provided incentives to do it, but rather because they feel they owe it to their colleagues in the network (Carlin and Womack, 1999).
The discussions in the literature cited on social interaction and knowledge sharing leads to the belief that higher levels of social interaction combined with lower levels of conflict can enhance the probability for success in knowledge management initiatives. While there is a great deal of direct and indirect speculation in the literature leading to this belief, it does not appear to ever have been directly correlated or empirically tested. This concept of culture and conflict in the midst of knowledge management systems implementation led to the motivation for this study.
3. The Subject Study
There were three variables associated with the subject study: interdepartmental connectedness, interdepartmental conflict and knowledge management systems success. The constructs for interdepartmental connectedness and interdepartmental conflict were taken directly from existing literature, while the construct for knowledge management systems success was developed specifically for this study. The following sections briefly discuss the development of these variables.
3.1. Interdepartmental
Connectedness
Interdepartmental connectedness is defined as the degree of formal and informal direct contact among employees across departments (Kohli and Jaworski, 1990; Tjosvold, 1990). Related literature (Deshpande and Zaltman, 1982) suggests that connectedness facilitates the exchange and use of information across organizational boundaries. There are several examples of recent studies which indicate that informal networks rather than formal organization structures are increasingly affecting organizational activities and outcomes (Menon, et al., 1997). This is related to knowledge management projects in the discussions on communities of practice (Carlin and Womack, 1999; Wenger and Snyder, 2000). Lesser and Storck (2001) showed how the social capital resident in communities of practice leads to behavioral changes, which in turn positively influence business performance. Adams and Freeman (2000) also concluded that successful knowledge management implementation requires that the human side of the equation be looked as well as the data side, and that implementation can only be done successfully if a community of practice is in place.
3.2. Interdepartmental
Conflict
Interdepartmental conflict is the tension among departments arising from the incompatibility of actual or desired responses and goals (Raven and Kruglanski, 1970). Recent studies have shown the relationship between conflict and product quality (Menon et.al., 1997) as well as the effect on the perception of commitment in dyadic relationships (Anderson and Weitz, 1992). In their studies on market orientation, Jaworski and Kohli (1993) showed that interdepartmental conflict inhibits intelligence dissemination. This was specifically related to knowledge management initiatives by De Long and Seeman (2000), in their identification of four sources of conflict that are likely to threaten credibility during the course of the initiative.
3.3. Knowledge
Management Systems Success
The knowledge management systems success variable was developed from a number of studies on the success of various types of information systems. Over the past 10 years, determinants of the success of information systems implementations have been the subject of numerous studies. DeLone and McLean (1992) undertook an extensive study to provide an integrated view of the concept of information systems success. In that study a comprehensive taxonomy was introduced which provided six major categories of information systems success. Using that taxonomy, 180 empirical and conceptual studies were reviewed and drawn together to provide a descriptive model for information systems research.
Seddon, et al. (1999) built on the DeLone and McLean (1992) model, as well as other research, to develop an alternative framework, which classifies information systems effectiveness based on two dimensions: Type of Stakeholders and their Primary Focus; and Type of System Being Evaluated. Using a review of empirical measures in 186 studies on information systems effectiveness, the frequency of occurrence for each stakeholder/system type combination was tallied. The results are shown in Table 1.
|
SystemType |
An Aspect of IT Design |
A Single IT Application |
A Type of IT or IT Application |
All IT Applications in an Organization |
An Aspect of a System Development Methodology |
An IT Function |
|
Stakeholder |
||||||
|
Independent Observer |
21 |
5 |
12 |
1 |
8 |
1 |
|
Individual |
10 |
11 |
25 |
3 |
11 |
10 |
|
Group |
1 |
|
26 |
|
1 |
|
|
Management |
1 |
6 |
15 |
9 |
6 |
13 |
|
Country |
|
|
2 |
2 |
|
|
Table 1
– Framework for information systems effectiveness (Seddon, et al., 1999)
Davenport, et al. (1998) de-emphasized the information systems aspect and focused specifically on the success of knowledge management projects. They conducted an evaluation of thirty-one knowledge management projects (with varying types and degrees of IT usage) in twenty-four companies. The success of the projects was evaluated at a single point in time, using the following set of success factors:
3.4. Growth
In The Resources Attached To The Project
Growth in the volume of knowledge content and usage (i.e. the number of documents or accesses for repositories or participants)
The likelihood that the project would survive without the support of a particular individual or two (ie, an organizational effort rather than an individual project)
3.5. Some Evidence Of Financial Return Either For The Project Or The Organization.
Wixom and Watson (2001) conducted an empirical investigation of the factors affecting the success of data warehousing projects. They drew on the work of Seddon (1997) in their selection of the dimensions of data quality, system quality, and perceived net benefits to measure implementation success. Within these dimensions, two surveys were developed to explore the factors deemed to be representative of success. Questions related to management support, user participation, and the availability of a champion were asked. The conclusions of the study were consistent with Davenport, et al. (1998) with respect to participation and support, but did not show the availability of a champion as a major factor in system success.
For purposes of the subject study, an aggregate measure was developed combining the knowledge management specific components of the Davenport, et al. (1998) study with the information systems aspects of the Seddon, et al. (1999) and DeLone and McLean (1993) models.
3.6. Research
Methodology
The purpose of the subject study was to provide empirical evidence for the relationship between interdepartmental connectedness and interdepartmental conflict and knowledge management success. Specifically, two research questions were examined:
§ Is there a relationship between interdepartmental connectedness and interdepartmental conflict, and the success of knowledge management systems implementations?
§ Does the degree of interdepartmental connectedness or interdepartmental conflict affect the success of knowledge management systems implementation?
From these research questions, two hypotheses were derived:
§ H1A–Higher levels of interdepartmental connectedness result in higher levels of knowledge management systems success.
§ H10–Higher levels of interdepartmental connectedness result in lower or unchanged levels of knowledge management systems success.
§ H2A–Higher levels of interdepartmental conflict result in lower levels of knowledge management systems success.
§ H20–Higher levels of interdepartmental conflict result in higher or unchanged levels of knowledge management systems success.
The research was conducted using a combination of mailed and Web-based surveys. Three mailed surveys were conducted over a three month period, and the Web-based survey was available on line for three months. The exact same questions were asked in all surveys regardless of form. Complete confidentiality was guaranteed and provisions were made for anonymity in all cases.
Participation in the Web-based survey was offered through postings placed on the Web sites of knowledge management interest groups, supplemented by emails sent to a variety of contacts in companies known to have knowledge management initiatives. As an inducement, the survey included a place for participants to enter their name, company name, and email address if they were interested in receiving a copy of the research results. This information was optional, to allow for surveys to be submitted anonymously.
3.7. Design Of The
Survey Instrument
A 34-question survey was designed to collect the data for the study. The questions were divided into three sections. The first section contained 12 questions related to the organization’s knowledge management system. The first five questions were designed to determine the organization type and size, current state of the knowledge management system, and the role of the respondent in the implementation process. These questions were not scored, but simply used to classify and review descriptive analysis of the data.
The next seven questions in the first section were intended to measure the success of the knowledge management system within the respondent’s business unit. This scale was formulated from the success indicators used by Davenport, et al. (1998) in their study of knowledge management projects. The wording of the questions was formulated to provide focus on the system type and stakeholder dimensions of the Seddon, et al. (1999) framework describe above. A five point Likert scale, ranging from “strongly disagree” to “strongly agree,” was used to measure all scales.
The second section contained seven questions for each of the interdepartmental conflict and interdepartmental connectedness scales. Both scales were used verbatim from the Jaworski and Kohli (1993) study, except for the deletion of one reference to the marketing and manufacturing departments. This change was made because of the general nature of the subject study as opposed to the specific focus on market orientation in the Jaworski and Kohli study.
The third section contained eight questions, which were demographic in nature, usedto gather information about the respondent’s position with in the company, number of years of experience, and level of experience with knowledge management systems.
3.8. Reliability and Validity
The reliability of the measures of interdepartmental conflict and interdepartmental connectedness were methodically tested and refined by Jaworski and Kohli (1993) in previous studies. Reliability coefficients were recalculated in the subject study using a sample of the survey responses. Cronbach alpha scores of .80 and .86 were calculated for the conflict and connectedness scales respectively, which were consistent with the Jaworski and Kohli calculations. The validity of the interdepartmental conflict and interdepartmental connectedness scales, was also tested by Jaworski and Kohli (1993), but this was not revalidated for the current study.
Since the items for the knowledge management system success scale were formulated specifically for this study, there was no published reliability or validity data available. Using the same sample of responses indicated above, the Cronbach alpha score for the success scale was calculated as .85. Content validity was established prior to administration of the survey by obtaining the feedback of 12 content area experts. They were asked to review the scale and answer specific questions regarding any ambiguities, or the failure to accurately measure the intended characteristic. Only minor wording changes were suggested, which were incorporated into the survey questions. This revised scale was then presented to three academic experts, who were also asked to provide feedback. No additional changes were incorporated into the scale.
Subsequent to the collection of data further validity testing was performed using a portion of the survey responses. Concurrent validity was assessed by correlating the response to a question asking the respondents provide their opinion of the success of the system, with questions related to the factors deemed to be indicative of system success in the Seddon, et al. (1999) and Wixom and Watson (2001) studies. The Pearson correlation coefficient was determined to be .76 for this analysis.
A total of 80 responses were received from the three mailed surveys. In the first mailing, 245 surveys were mailed with a response rate of 24.6 percent. A second mailing was sent to 100 senior executives in 31 companies with a response rate of only two percent. A third mailing was made to 600 professionals in Fortune 100 companies with a response rate of five percent.
Calculation of the response rate for the Web-based survey is a little less precise. Postings were placed on four interest group Web sites, and approximately 100 emails were sent to contacts obtained from a variety of sources. While there is no way to tell how many individuals viewed the postings on the interest group Web sites, 461 individuals followed the link from the interest group site, or from the email received, to the survey Web site and viewed the introduction. Of those individuals 257 viewed the survey itself, with 26 actually submitting a completed survey. The response rate therefore from individuals viewing the introduction was 5.6 percent, and 10.1 percent from those viewing the actual survey. A total of 104 valid, useable surveys were received from the mailed and Web-based surveys.
Survey Question Number Survey Question Component 1 .746 14 Tensions run
high when departments get together .776 15 People dislike
interacting with other departments .694 16 Employees feel
goals are in harmony with those of others .790 17 Protecting turf
is a way of life .792 18 Objectives are
incompatible across departments .579 19 There is little
or no interdepartmental conflict .784
Factor analysis was used to empirically assess the
dimensionality of the scales to be used in the testing of the hypotheses. This
factor analysis was done using principal components analysis with no rotation.
Factors were selected using Eigenvalues greater than 1 and factor loadings
exceeding ± .55. Table 2, Table 3 and
Table 4 show the results of this analysis.
13
Departments get along well with each other
Table 2 – Principal
Components Analysis for Conflict Scale
|
Survey Question Number |
Survey Question |
Component 1 |
Component 2 |
|
20 |
It’s easy to talk to anyone regardless of position |
.139 |
|
|
21 |
There is ample opportunity for hall talk |
.461 |
.680 |
|
22 |
Employees are comfortable calling others |
.778 |
.171 |
|
23 |
Managers discourage discussion of work related matters |
.754 |
-.346 |
|
24 |
People are accessible to those in other departments |
.808 |
5.711E-02 |
|
25 |
Communications are expected to use proper channels |
.503 |
-.651 |
|
26 |
Junior managers can easily schedule meetings with those in other departments |
.691 |
1.597E-03 |
Table 3 – Principal
Components Analysis for Connectedness Scale
|
Survey Question Number |
Survey Question |
Component 1 |
Component 2 |
|
6 |
The KMS can generally be considered a success |
.870 |
-.213 |
|
7 |
The KMS has received sufficient resources |
.708 |
3.980E-02 |
|
8 |
Usage of the KMS has increased since inception |
.818 |
4.456E-02 |
|
9 |
The volume of knowledge in the KMS has increased since inception |
.751 |
.246 |
|
10 |
The KMS would not survive without support of key individuals |
.204 |
.946 |
|
11 |
Business function has derived benefits from the KMS |
.844 |
-.125 |
|
12 |
Business unit has derived benefits from the KMS |
.868 |
-.175 |
Table 4 – Principal
Components Analysis for Success Scale
Unidimensionality was identified in the conflict scale (see Table 2). Hence a summated scale was created using the mean of all seven variables on the conflict scale.
On the connectedness scale, Table 3 shows two dimensions identified with “Ample opportunity for hall talk” and “Communications expected to use proper channels” occurring on one dimension and the remaining five variables on the other. In analyzing the content of the survey questions falling into each dimension, the first grouping appears to relate to ease of access to other individuals in the organization, while the second relates to the availability of informal communications mechanisms. Hence, two summated scales were created for hypothesis testing using the mean of each grouping of variables. One dimension was called the “Access” dimension and the other the “Formality” dimension.
On the success scale (see Table 4), unidimensionality was determined in six of the seven variables, with only “KMS would not survive without key individuals”, falling out. Based on this result, that specific variable was dropped from the summated scale used for the hypothesis testing.
Hypothesis testing was performed using One-Way ANOVA to determine significance, and correlation analysis was used to determine directionality. The analysis is described in the following sections.
Hypothesis 1
The first hypothesis stated that higher levels of interdepartmental connectedness result in higher levels of knowledge management systems success. Table 5 shows the One-Way ANOVA results for the access dimension of connectedness. Table 6 shows the One-Way ANOVA results for the formality dimension of connectedness. Success was used as the dependent variable in both cases.
Table 5 – ANOVA Results
for Connectedness Access Dimension
Table 6 –
ANOVA Results for Connectedness Formality Dimension
An analysis of Table 5 shows an F statistic of 2.05 with 16 and 85 degrees of freedom, which is well within the reject region using an interpolated critical value of 1.78 at the .95 percentile. Likewise, Table 6 shows an F statistic of 2.17 with 7 and 94 degrees of freedom, which also falls in the reject region using an interpolated critical value of 2.03 at the .95 percentile. Based on these data, and the confirmation of directionality shown in Table 7, sufficient evidence exists to reject the null hypothesis that higher levels of interdepartmental connectedness result in lower or unchanged levels of knowledge management systems success at a confidence level of 95 percent.
Table 7 – Correlation
Analysis – Connectedness to Success
Table 7 also shows that with a correlation of .37, the access dimension is more closely correlated to success than is the formality dimension with a coefficient of .29. Both are significant at the .01 level.
Hypothesis 2
The second hypothesis stated that higher levels of interdepartmental conflict result in lower levels of knowledge management system success. Table 8 shows the results of the One-Way ANOVA for conflict using success as the dependent variable.
Table 8 – ANOVA Results
for Conflict
An analysis of Table 8 shows an F statistic of 2.43 with 21 and 80 degrees of freedom. Using an interpolated critical value of 1.79 at the .95 percentile this falls in the reject region. Based on these data and the directionality indicated by the correlation analysis in Table 9, sufficient evidence exists to reject the null hypothesis that higher levels of interdepartmental conflict result in higher or unchanged levels of knowledge management systems success.
Table 9 – Correlation
Analysis – Conflict to Success
To further test the relationship between the two dimensions of connectedness and conflict to success, linear regression was conducted using the forward stepwise method, which examined the variables at each step for entry or removal. The analysis indicated a significant relationship at the .05 level with only the conflict variable entered, but did not indicate a significant relationship with all three variables entered. To confirm the analysis, the regression was rerun using the “Enter” method, which results in all three variables being entered in a single step. The analysis using the Enter method indicated that with all three variables entered into the model a significant relationship at .05 does exist. The Coefficient of Determination (R2) reflected that 23.1 percent of the variation in success is explained by the combination of the three variables. The standard error of the estimate was .72, indicating that the three variables represent a reasonably strong predictor of Success, and a Durbin-Watson of 1.87 indicated a low autocorrelation among the variables.
4. Limitations
Of The study
This study was exploratory in nature, and subject to a number of
limitations. As mentioned earlier, while the response rate from the first
mailing was good (24.6 percent), the response rates from the second and third
mailings were low, resulting in a relatively small overall sample size.
Additionally, no testing was performed to evaluate whether differences existed
between responses from the mailed surveys versus the Web-based surveys.
With respect to the survey instrument, no specific validation was performed
on the change, referenced earlier, which was made to the Jaworski and Kohli
(1993) instrument, deleting the reference to the marketing and manufacturing
departments. Additionally, the
success data were based purely on the opinion of the respondent, rather than
using a quantitative measure of the success of the knowledge management
initiative. Likewise, the study did not attempt to isolate specific conditions
that may tend to moderate the results within specific organizations.
5. Conclusions
And Implications
The focus of the subject study was on the relationship between the social interactions in an organization and the success of knowledge management system initiatives. Two aspects of social interaction were studied; one assumed to have positive affects, interdepartmental connectedness; and the other assumed negative, interdepartmental conflict. Throughout the literature, there is speculation that organizational culture and various forms of social interaction, both negative and positive, impact the success and effectiveness of knowledge management system implementations. The conclusions drawn from the study provide strong empirical evidence to back up that speculation, and the data indicate that from the perspective of both connectedness and conflict, a significant relationship exists between social interaction and knowledge management system success.
The data related to positive interaction, interdepartmental connectedness, show that there are two different dimensions of social interaction that impact success. Both dimensions showed a moderate positive and significant correlation to knowledge management system success. The first dimension is that of access to other individuals and sources of information within the organization. We refer to this as the access dimension. In some respects, this aspect is at the very core of knowledge sharing. Davenport and Prusak (1998) state that the most effective way for an organization to transfer knowledge is to hire smart people and let them talk to one another. They point out, however, that most organizations hire bright people and then either isolate them or overburden them with tasks that limit their availability to others in the organization.
In his discussion of the organizational school of knowledge management, Earl (2001) points out the importance of “knowledge communities” which brings together people with common interests, problems or experiences. In analyzing several examples of organizations with effective knowledge communities, Earl (2001) concludes that they are more likely to work in organizations where there is a tradition of sociability and networking. Burn and Ash (2000) also point out that “knowledge does not come from processes or activities; it comes from people and communities of people” (p. 20). They go on to say that before developing information and communications technology solutions for knowledge management, companies need to understand what knowledge they have, what knowledge they need, and who knows about what, and then apply the technology appropriately. Meso and Smith (2000) agree that the core of organizational knowledge management systems is with people and the way that they interact. They point out that the teams formed by employees, and the synergies resulting from those teams, result in organizational learning. Since organizational learning is viewed as a strategic asset, when people work together or collaborate, they also constitute a strategic asset of the organization.
The second dimension of interdepartmental connectedness refers to the formality of the communications channels within the organization. This is referred to in the study as the formality dimension, and relates to the availability of informal communications mechanisms within the organization. While not as strongly correlated to knowledge management system success as the access dimension, there is a moderate positive correlation. Swap, et al. (2001) discuss at length the importance of informal communications mechanisms in the learning and knowledge sharing process in organizations. They point out numerous examples of how mentoring is used to convey knowledge about organizational routines and informal managerial systems. Rather than formal teaching mechanisms, this mentoring role, in many cases relies on the use of storytelling and observation of a mentor’s behavior. Many companies are also finding that water cooler types of conversation are key mechanisms for the transfer of knowledge related to effective problem-solving (Anthes, 2000).
Earl (2001) also discussed the importance of informal communications in his description of the “spatial school” of knowledge management. Alternatively called the social school, the key focus is on encouraging socialization as a means of knowledge exchange. Examples of mechanisms employed by organizations that are proponents of the spatial school are meeting places called “water coolers” or “knowledge cafes.” The essential element of the spatial school is one of “contactivity”, rather than focusing on electronic communications. Hinds and Aronson (2002) also point out that while many organizations have come to rely on electronic communications as a major form of internal communication, this is not necessarily the optimal mechanism for sharing knowledge. While electronic communications may tend to improve the efficiency of communications, the knowledge transfer required for knowledge management success requires both efficient and effective communications. This is consistent with the findings of Roberts (2000), which indicate that the trust and mutual understanding required for the transfer of tacit knowledge, while possible through electronic communications, is much more effective through face-to-face contact.
The negative aspect of social interaction measured in this study was interdepartmental conflict. The data show a moderately strong negative correlation between interdepartmental conflict and knowledge management success. This would indicate that higher levels of interdepartmental conflict within an organization are likely to result in lower levels of knowledge management system success. Conflict takes on many forms within organizations. On occasion, conflict in organizations results in dramatic confrontations such as strikes, walkouts, and firings, however more often than not it is embedded in routine interactions between individuals as they go about their daily activities (Kolb and Putnam, 1992). The elements measured in this study related specifically to those types of routine interactions such as interpersonal communications across department, compatibility of goals and objectives, and protecting of one’s turf.
The literature continues to indicate that the effective sharing of knowledge requires interorganizational collaboration and a focus on organizational learning (McDermott, 1999; Scott, 2000). Organizational learning, as we have seen, will not take place without an environment of trust and respect (DeLong and Fahey, 2000; Hibbard and Carrillo, 1998). Lack of trust is cited as the number one cultural barrier, or “friction,” to knowledge management success by Davenport and Prusak (1998). DeLong and Fahey (2000) also stated that lack of trust relates directly to the organizational norms associated with sharing information. They point out that if employees believe sharing what they know has the potential for them to incur personal risk, or reduce their perceived power, then “the social norms governing how individuals should interact will not support the behaviors needed to create and sustain the exchange of knowledge” (p 4). In their research in more than 50 companies pursuing knowledge management projects, they found that a low-trust culture within an organization greatly decreases the amount of knowledge that flows from individual to individual as well as into the firm’s knowledge base.
The freedom to question and provide constructive criticism is also an important factor in the sharing of knowledge. Open criticism of ideas is an essential component of organizational learning, and hence knowledge management, but when the environment fails to recognize such criticism as healthy, conflict results and questioning is seen as merely sticking one’s neck out for no apparent value (Coates, 2001). This is exacerbated by the reality that the transfer of tacit knowledge within an organization often brings together individuals and groups that would not otherwise be in regular contact, and are more likely accustomed to being in competition, rather than in collaboration, with one another (Hinds and Aronson, 2002).
6. Implications
for Practitioners
There are a number of implications of this study, for organizations involved in, or embarking on knowledge management initiatives. First and foremost, as was pointed out early in this study, and is increasingly noted in the knowledge management literature, successful knowledge management is not a technology issue. The results of this analysis provide additional evidence that organizations must first look to the culture inherent in the organization, and the state of the social interactions among its members before embarking upon a quest to capture and share knowledge. This culture must begin with the CEO and become a critical part of the mission and values of the organization.
The specific findings of this study point to several areas of focus. The data regarding the formality dimension of connectedness imply that organizations which take overt steps to facilitate the bringing together of individuals with common interests, improve their likelihood of success in knowledge sharing. Going as far as setting up specific areas such as the “knowledge café” referred to earlier may be more than an organization is prepared to do, but allowing individuals the time to interact without the insinuation of wasting time would be a step in the right direction. Encouragement and facilitation of cross-organizational communities of practice is a positive step toward bringing down the smokestacks that are a death knell to effective knowledge management.
The data regarding the access dimension of connectedness also reinforce the need for the facilitation of face-to-face communication. The literature is replete with strategies for team building and team development. While some appear to be more effective than others, the effective use of teams and teamwork provide an excellent mechanism for nurturing the open communications structure required for effective knowledge sharing. Once again, the encouragement and facilitation of communities of practice that span the entire organization can be an important component of an overall knowledge management strategy.
The implications of the conflict data also point out a number of areas for focus. Convincing people to share their knowledge requires a combination of new processes, as well as a level of trust among employees as well as between employees and employer. People must feel comfortable that they will not lose their value to the company after sharing their knowledge with others and committing it to electronic form. This requires a clear statement of values set out from the top management levels of the organization. If an organization is truly committed to the seeking out and sharing of the tacit and explicit knowledge within the organization, then the company mission and value statements must clearly represent that commitment. Secondly, it must be effectively communicated down through the entire organization. Most importantly, as Argyris and Schon (1978) have so adequately described, management must follow through and ensure that their theories in use are consistent with their theories espoused.
Several possibilities for future research emerge from the results of the current study. First, the current study was exploratory in nature with a relatively few number of respondents from a each responding organization. It did not attempt in any way to isolate specific conditions that may tend to moderate the results within a specific organization. Likewise, the success data were based purely on the opinion of the respondent. A focused study within several organizations, using a cross-section of individuals, combined with an objective evaluation of the success of the knowledge management initiative, would provide useful follow-on research.
Additionally, due to the nature of this study, there was no attempt to classify results based on type or size of the organizations. Opportunities for similar research would appear to exist in this area, to determine if the study factors differ based on organization type, makeup, or organizational structure.
While this study was focused on the effect of social interaction on knowledge management initiatives, there is evidence in the literature that an effective knowledge management strategy may itself tend to enhance social interaction. In that regard, it would appear that a longer-term study examining the changes in social interaction before and after knowledge management system implementation would yield useful and interesting results.
Adams, E. C. and Freeman, C. “Communities of Practice: Bridging Technology and Knowledge Assessment,” Journal of Knowledge Management (4:1), 2000, pp. 38-42.
Alavi, M. and Leidner, D. E. “Knowledge Management Systems: Issues, Challenges, and Benefits,” Communications of the Association for Information Systems (1:1), February 1999, Article 7.
Allee, V. The Knowledge Evolution: Expanding Organizational Intelligence, Boston: Butterworth-Heinemann, 1997.
Anderson, E. and Weitz, B. “The Use of Pledges to Build and Sustain Commitment in Distribution Channels,” Journal of Marketing Research (29:1), February 1992, pp. 18-34.
Angus, J. Patel, J., and Harty, J. “Knowledge Management: Great Concept…But What Is It?” InformationWeek, (1:673), March 1998, pp 58-70.
Argyris, C. and Schon, D. Organizational Learning: A Theory of Action Perspective, Addison-Wesley, Reading, Ma, 1978.
Anthes, G. H. “Software Development Goes Global,” ComputerWorld (34:26), June 2000, pp. 94-95.
Brown, R. B. and Woodland, M. J. “Managing Knowledge Wisely: A Case Study in Organizational Behaviour,” Journal of Applied Management Studies (8:2), December 1999, pp. 175-198.
Burn, Janice M. and Ash, Colin “Knowledge Management Strategies for Virtual Organizations,” Information Resources Management Journal, (13:1), January-March 2000, pp. 15-23.
Carlin, S. and Womack, A. Creating a Knowledge Sharing Culture, American Productivity And Quality Center (APQC), Houston, TX, 1999.
Coates, J. F. “Knowledge Management Is a Person to Person Enterprise,” Research Technology Management (9:44), May-June 2001, pp. 3-13.
Davenport, T. H., Delong, D. W. and Beers, M. C. “Successful Knowledge Management Projects,” Sloan Management Review (39:2), Winter 1998, pp. 43-57.
Davenport, T. H. and Prusak, L. Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston, MA, 1998.
Davenport, T. “The Last Big Thing,” CIO (14:3), November 2000, pp. 53-58.
Delone, W. H. and McLean, E. R. “Information Systems Success: The Quest for the Dependent Variable,” Information Systems Research (3:1), March 1992, pp. 60-94.
De Long, D. W. and Fahey, L. “Diagnosing Cultural Barriers to Knowledge Management,” The Academy of Management Executive (14:4), November 2000, pp. 113-127.
De Long, D. and Seeman, P. “Confronting Conceptual Confusion and Conflict in Knowledge Management,” Organizational Dynamics (29:1), Summer 2000, pp. 33-44.
Deshpande, R. and Zaltman, G. “Factors Affecting the Use of Market Research Information: A Path Analysis,” Journal of Marketing Research (19:1), February 1982, pp. 14-31.
Earl, M. “Knowledge Management Strategies: Toward a Taxonomy,” Journal of Management Information Systems (18:1), Summer 2001, pp. 215-233.
Eriksson, I. V., Dickson, G. W. and El Sawy, O. A. “Reflections on Designing Field Research for Emerging IS Topics: The Case of Knowledge Management,” Communications of the Association for Information Systems, (3:1), March 2000, Article 6.
Grant, R. M. “Toward a Knowledge Based Theory of the Firm,” Strategic Management Journal (17:1), Winter 1996, pp. 109-122.
Gray, Peter H. and Chan, Y. E. “Integrating Knowledge Management Practices through A Problem-Solving Framework,” Communications of the Association for Information Systems (4:1), November 2000, Article 12.
Herbert, I. “Knowledge is a Noun Learning is a Verb,” Financial Management (78:2), February 2000, pp. 68-69.
Hibbard, J. and Carillo, K. M. “Knowledge Revolution,” InformationWeek (1:698), January 1998, pp. 49-54.
Hinds, R. S. and Aronson, J. E. “Developing the Requisite Organizational, Attitudinal, and Behavioral Conditions For Effective Knowledge Management: A Review of Current Research,” Proceedings of the Eighth Americas Conference on Information Systems, August 8-10 2002, Dallas, TX.
Jaworski, Bernard J. and Kohli, Ajay K. “Market Orientation: Antecedents and Consequences,” Journal of Marketing (57:3), July 1993, pp. 53-71.
Knapp, Ellen M. “Knowledge Management,” Business and Economic Review, (44:4), July-September 1998, pp. 3-6.
Kohli, A. K. and Jaworski, B. “Market Orientation: The Construct, Research Propositions, and Managerial Implications,” Journal of Marketing (54:1), April 1990, pp. 1-18.
Kolb, D. M. and Putnam, L. L. “The Dialectics of Disputing,” in D. M. Kolb and J. M. Bartunek, (Eds.) Hidden Conflict in Organizations. Sage Publications, Newbury Park, CA, 1992.
Lesser, E. L. and Storck, J. “Communities of Practice and Organizational Performance,” IBM Systems Journal (40:4), 2001, pp. 831-841.
Lipshitz, R. “Chic, Mystique, and Misconception: Argyris and Schon and the Rhetoric of Organizational Learning,” The Journal of Applied Behavioral Science (36:4), December 2000, pp. 456-473.
McDermott, R. “Why Information Technology Inspired but Cannot Deliver Knowledge Management,” California Management Review (41:4), Summer 1999, pp. 103-117.
Menon, A., Jaworski, B. J. and Kohli, A. K. “Product Quality: Impact of Interdepartmental Interactions,” Academy of Marketing Science (25:3), Summer 1997, pp. 187-200.
Meso, Peter and Smith, Robert “A Resource-Based View of Organizational Knowledge Management Systems,” Journal of Knowledge Management (4:3), 2000, pp. 224-231.
Mueller, F. and Dyerson, R. “Expert Humans or Expert Organizations?”Organization Studies, (20:2), pp. 225-256.
Nonaka, I. “A Dynamic Theory of Organizational Knowledge Creation” Organization Science (5:1), February 1994, pp. 14-35.
Pan, S. L. and Scarbrough, H. “Knowledge Management in Practice: An Exploratory Case Study,” Technology Analysis and Strategic Management (11:3), September 1999, pp. 359-374.
Raven, B. H. and Kruglanski, A. W. “Conflict and Power,” In Paul Swingle, (Ed.) The Structure of Conflict, pp. 69-109, Academic Press, New York NY, 1970.
Roberts, J. “From Know-How to Show-How? Questioning the Role of Information and Communication Technologies in Knowledge Transfer,” Technology Analysis and Strategic Management (12:4), February 2000, pp. 429-443.
Schon, D. “Some of What a Planner Knows: A Case Study of Knowing-in-Practice,” Journal of the American Planning Association (48:3), Summer 1983, pp. 351-365.
Scott, J. E. “Facilitating Organizational Learning with Information Technology,” Journal of Management Information Systems (17:2), Fall 2000, pp. 81-113.
Seddon, P. B., Staples, S., Patnayakuni, R., and Bowtell, M. “Dimensions of Information Systems Success,” Communications of the Association for Information Systems (2:1), November 1999, Article 20.
Senge, P. M. The Fifth Discipline: The Art and Practice of the Learning Organization, Currency Doubleday, New York, NY, 1990.
Spiegler, I. “Knowledge Management: A New Idea or a Recycled Concept?” Communications of the Association for Information Systems (3:1), June 2000, Article 14.
Skyrme, D. Knowledge Networking: Creating the Collaborative Enterprise, Butterworth-Heinemann, Oxford, UK 1999.
Smith, R. A. “Knowledge: Researching the Power Base of the Organization,” Information Outlook, (2:6), June 1998, pp. 12-14.
Swap, W., Leonard, D., Shields, M. and Abrams, L. “Using Mentoring and Storytelling to Transfer Knowledge in the Workplace,” Journal of Management Information Systems (18:1), Summer 2001, pp. 95-114.
Tjosvold, Dean “Power in Cooperative and Competitive Organizational Contexts,” Journal of Social Psychology (130:2), April 1990, pp. 249-258.
Wenger, E. C., and Snyder, W. M. “Communities of Practice: The Organizational Frontier,” Harvard Business Review, January-February 2000, pp. 78-85.
Wixom, B. H., And Watson, H. J. “An Empirical Investigation of the Factors Affecting Data Warehouse Success,” MIS Quarterly (25:1), March 2001, pp. 17-41.
Zack, Michael H. “Managing Codified Knowledge,” Sloan Management Review (40:4), Summer 1999, pp. 45-58.
9. Appendix
– Survey Questions (Demographic questions have been deleted)
Strongly Neither strongly
Disagree Disagree Agree
nor Agree
Agree
Disagree
1 2 3 4 5
The Knowledge Management System implementation in this business unit
could generally be considered a success
The Knowledge Management System initiative has received sufficient
resources (people, money, etc.) to facilitate its success
Since its inception, the number of participants using the Knowledge
Management System has consistently increased
Since its inception, the volume of knowledge content within the Knowledge
Management System has consistently increased
Without the support of one or two key individuals, the Knowledge
Management System would not be likely to survive
The process or function that I am most closely associated with has
enjoyed benefits, in terms of efficiencies or financial returns, from the use
of the Knowledge Management System
This business unit as a whole has enjoyed benefits, in terms of
efficiencies or financial returns, from the use of the Knowledge Management
System
Most departments in this business unit get along well with each other
When members of several departments get together, tensions frequently run
high
People in one department generally dislike interacting with those from
other departments
Employees from different departments feel that the goals of their
respective departments are in harmony with each other
Protecting one’s departmental turf is considered to be a way of life in
this business unit
The objectives pursued by some departments are incompatible with those of
other departments
There is little or no interdepartmental conflict in this business unit
In this business unit it is easy to talk with virtually anyone you need
to, regardless of rank or position
There is ample opportunity for informal “hall talk” among individuals
from different departments in this business unit
In this business unit, employees from different departments feel
comfortable calling each other when the need arises
Managers here discourage employees from discussing work related matters
with those who are not their immediate superiors or subordinates
People around here are quite accessible to those in other departments
Communications from one department to another are expected to be routed
through “proper channels”
Junior managers in my department can easily schedule meetings with junior
managers in other departments
Meet The Authiors:
Anthony J. Delmonte, 2967 S. Atlantic Ave., Daytona Beach Shores, FL 32118; Tel: 386-760-1719: Email: proftony@bellsouth.net
Jay E. Aronson, Department of Management Information Systems, Terry College of Business, The University of Georgia, Brooks Hall, Athens, GA 30602-6273; Tel: 706-542-0991; Email: jaronson@uga.edu
Anthony J. Delmonte (B.S., M.S., D.B.A.) is Director, Launch Operations for United Space Alliance at Kennedy Space Center, and an Adjunct Associate Professor at Embry-Riddle Aeronautical University. He holds a BS in Management from Indiana University, an MS in Aeronautical Science from Embry-Riddle Aeronautical University, and a Doctorate in Business Administration from Nova Southeastern University.
Jay E. Aronson B.S.E.E, M.S.E.E., M.S.O.R., Ph.D. I.A., Carnegie Mellon University, is a professor of Management Information Systems in the Terry College of Business at The University of Georgia.