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Accelerating Knowledge Adoption: A Perspective of Social Network Analysis

Hung-Chun Huang1, Les Davy2, Hsin-Yu Shih1 1Department of International Business Studies, National Chi Nan University, Puli, Taiwan

2Department of Computer Science and Information Engineering, National Chi Nan University, Puli, Taiwan

Abstract--An individual who has the ability to focus and learn quickly is at a distinct competitive advantage over those who do not. As difficult as it may be to accelerate an individual’s learning rate, it is even more complicated to accelerate the learning rate of a group. Knowledge management has devoted a great amount of study and research into learning efficiency. In theory, managing knowledge behaviors greatly affects knowledge management. However in practice, knowledge is difficult to manage directly. The structure of a working team represents a miniature social system as well as an internal collaboration network. Differing teamwork structures conduct different knowledge behaviors. Social influence theories suggest that different social proximities evoke distinguishing contagion effects. This study applies a social network perspective to explore the knowledge behaviors of computer software developers. Our findings show that controlling network redundancies can effectively enhance knowledge diffusion efficiency. Furthermore, if a team fails to manage knowledge diffusion, it will potentially offset any competitive advantages that might be gained via upgrading technology. Based on our findings, this study suggests a new approach for implementing knowledge management and R&D strategic planning.

I. INTRODUCTION

How to make a team exploit and utilize new technology faster is an important issue for knowledge management. Learning through teamwork does not merely concern knowledge acquisition, but also absorption capacity. Speaking in terms of absorption, team membership structure affects the performance of team activity. In general, new technology is introduced to an individual or a few members of a team. These early adopters gain initial knowledge, and then translate this tacit knowledge into explicit documents or other forms of diffusion to other members of the group. Early technology adopters are the first to apply a knowledge spiral to a new technology. Although the new technology may be public knowledge, the initial process of team learning transforms this awareness of a new technology into team-specific knowledge. Nonaka & Takeuchi [1] studied Japanese companies and technology development; they found that successful teams master the dynamics of innovative progress to seize opportunities. Facing technological change, they nurture wellsprings of knowledge to sustain sources of learning.

Knowledge as a strategic resource represents the capability to create and utilize knowledge, thus allowing a firm to develop a sustainable, specific, competitive advantage. This resource-based view of strategic resources holds that knowledge behaviors are causally ambiguous, unique, and imperfectly imitable. Although previous studies have suggested this critical point of knowledge creation in

successful organizations [3], specific discussion regarding how team structure influences knowledge diffusion remains vague. Our study approaches the latter topic by studying a team of computer software developers to examine the influence of team structure in learning new technology.

As global competition forces rapid changes technology, an efficient knowledge absorptive structure should be considered a strategic resource. This paper will refer to social network analysis to explore knowledge diffusion within differential teamwork structures. Additionally, a reinterpretation of Nonaka & Takeuchi’s redundancy structure in knowledge creation is proposed.

II. RESEARCH BACKGROUND

A. Knowledge Creating and diffusion Nonaka & Takeuchi[1] proposed a model of the

knowledge creation process in order to understand the dynamic nature of knowledge creation and diffusion. The knowledge creating process consists of three elements: a knowledge spiral, social context “Ba”, and knowledge assets. When these elements interact with each other organically and dynamically, the knowledge assets of an organization are mobilized and shared in a social context. In their model, the tacit knowledge held by individuals is converted and amplified through a spiral of knowledge using socialization, externalization, combination and internalization [2]. Under a clear leadership structure, the three elements integrate so that the organization can create knowledge continuously and dynamically, creating a disciplined approach for organization members. These aspects come together into what is referred to as “the process model of creating organization knowledge”. Additionally, Nonaka[1] proposed critical enabling conditions in terms of environment or social context, as well as a five-phase model of sharing tacit knowledge, creating concepts, justifying concepts, building an archetype, and cross-leveling knowledge for a good environment to support individual and organizational knowledge creation.

Using this model, an environment with redundant communication is important for both the knowledge diffusion and convergent knowledge necessary to organize knowledge activities. It not only retains the advantages of organizational knowledge but also enhances the speed of knowledge creation. The model stresses three aspects of redundancy in knowledge creation and diffusion. First, information redundancy[3]: information redundancy among team members is an advantage under conditions of unexpected environmental fluctuations. This provides additional working information and allows members to focus on an overall goal. This is achieved through consistent informational meetings,

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such as regular and irregular conferences, formal and informal communication management, and off-duty sessions. Moreover, team members can readily access information, and share the benefits of clear information. Secondly, through internal competition [1]: A developed production panel consists of several competitive sections, developing independent solutions to the same cases. Additionally, these groups explain and defend their work to each other. Using this method, a group panel can examine many different possible solutions and methodologies, and forge a common consensus. Thirdly, strategic technology diffusion[2]: employees can tactically understand their firms’ affairs from several different points, in terms of task assignment or job rotation. This method provides and additional benefit of diffusing a common language of technique and approach, creating “common knowledge” in an organization.

However, information redundancy, internal competition and strategic technology diffusion concern team structure more in terms of theory, leaving discussions of actual practice and implementation as vague at best and non-existent at worst. Further study in actual practice is necessary to prove the validity of these theories.

B. Social context and the influence of Social Structure

Lundvall [4] indicates that cooperation or display of a singular form in a organization is conducive to the development and diffusion of new technology in an organization. Accordingly, by studying the influence on the performance of technology transformation, our study can recommend an appropriate mode of social interaction among team members. Social influence occurs when actors’ behavior, attitudes, or beliefs involuntarily follow others’ in a social system. This social influence processes is often referred to as contagion. Within the field of social influence theory, there are two mechanisms, communication and comparison, used to diffuse knowledge among people[5].

Team membership is a social network activity. Two nodes in a term contact for some propose, and as this linkage provides an increasingly valuable resource, their relationship becomes more and more interdependent. Meanwhile, other nodes are deriving benefits indirectly. These groups depend upon this; a unique bridge node will turn into many bridge nodes, diffusing benefits. It is important to note that network redundancy aggregates transmission obstruction until unhindered. Watts[6] indicates that redundant linkage causes by network traffic crowding. On the other hand, redundant network linkage can be interpreted as a consequence of resource interdependence or value contagion.

In contrast, Granovetter [7] proposed the strength of weak ties for acquiring information benefits. Since strong ties contain redundant information, weak ties provide information that is much more useful. The more open a network structure is, the more weak ties exist. Open social contexts more easily introduce new ideas or assess job opportunities to their members than redundant tie-laden closed networks. In other words, social contexts with overly redundant ties only assess

information comprised of the same knowledge and opportunities. That is not to say that hard ties are not effective; Krackhardt [8] noted the strength of strong ties. Strong ties may be beneficial by providing a strong social environment and mutual support for network players. Network redundancy not only provides transmission benefits but also produces network robustness. Burt [9] pointed out the spread of new ideas and a practice often argued to be contingent on the way in which social structure brings people together. Adopting an innovation entails a risk, an uncertain balance of costs and benefits, and people manage that uncertainty by drawing on others to define a socially acceptable interpretation of the risk. Social contagion arises when people in a social structure use one another to manage the uncertainty of innovation. This network closure gives rise to a form of interpersonal trust. Social closure can reduce the risks posed by cheating. The more closed the network, the more misbehavior will be detected. Network actors who don’t wish to lose a reputation built up over the course of a long-term relationship with a group of colleagues will cooperate with other people in the network. There is clear evidence of trust being more likely in a strong tie than in a weak tie relationship[10]. This feedback loop of longer-term relationships both increases and depends on network redundancy in their teamwork structures.

C. Knowledge creation and redundant structures

Nonaka and Burt provide a meso-view to an organizational activity and knowledge creation. Nonaka provides a redundant framework for organizational knowledge generation through the means of information redundancy to organize personal knowledge, and invoke the knowledge spiral process between explicit, tacit knowledge and individual, interpersonal knowledge. Additionally, Burt proposed social network perspective for new knowledge diffusion in network redundancy and provided a structural interpretation to manage information benefits.

However, these previous studies fail to distinguish between structural redundancy and personal redundancy in knowledge management. Teamwork is a micro-social structure, which requires internal trust to achieve some purpose [11].

III. METHODOLOGY AND DATA COLLATION

This research applies Nonaka’s proposed knowledge

creation and redundancy structure for knowledge behaviors. However, knowledge behaviors are often causally ambiguous. Thus, this study combines quantitative and qualitative methodology as necessary for data collection.

This study examines case studies using two principles: First, and industry characteristic; the chosen teams are both knowledge creators and knowledge consumers. Explicit knowledge is their product and tacit knowledge is their foundation. As a result, the value creation process and knowledge flow can be clearly observed. Secondly, the team background: the targeted cases concern the same event and

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share a common foundation, allowing for a comparison of organizational performance.

The team activity of computer software development is a form of knowledge creation; investigating team activity allows for direct observation of knowledge behaviors. A major organizational event, in this case platform transformation, causes the workers to discard a familiar platform and shift to a new technology. This provides a clear study of organizational knowledge management discrepancies between groups involved in the transformation process.

A. Data Collection and Analysis

For qualitative purposes, this study applies Nonaka’s field study redundancy pattern. This provides a basis from which to construct a case data for pattern matching. After internal-case pattern matching, our study compares the activities of two teams for cross-case analysis. In the cross-case analysis, redundant patterns that distinguish differences between the two groups are identified as the quantitative investigation is concluded.

In terms of quantitative investigation, this study uses Burt’s [12] social capital survey questionnaire. In the study of personal networks, the name generator has become the standard method to enumerate networks and delineate network characteristics and structure. Team members are given one or a series of questions that elicit a list of network alters, such as those people with whom they discuss important matters, or the people with whom they chat or visit. Once a list of names has been produced, participants are presented with a series of name interpreters: follow up questions that gather information on the demographic characteristics of each alter, the relationship between ego and alter, and the relationships between alters. The data collected through name generators and interpreters provide individual profiles of respondents’ personal network members that can be aggregated into measures of network composition. According to the results of social network analysis, two higher redundancy degree members are taken for a second stage of investigation.

B. Network redundant measurement

Burt [12] suggests the network size minus the redundant size is the effective network size. The redundant size can calculate the network size and deduce the effective network size. Below are the analysis formulations: Network Size: The number of contacts in a network is the

network size. Effective size: Burt [13] develops a set of measures based on

ego networks. It defines the effective size of a person's ego network as

ji,q,m p-1 j

jqq

iq ≠⎥⎦

⎤⎢⎣

⎡∑ ∑ (1)

where j indexes all of the people that ego i has contact with, and q is every third person other than i or j. The quantity (piqmjq) inside the brackets is the level of redundancy between

ego and a particular alter, j. The piq is the proportion of actor i’s relations that are spent with q. The mjq is the marginal strength of contact j’s relation with contact q. this is j’s interaction with q divided by j’s strongest interaction with anyone. For a binary network, the strongest link is always 1 and thus mjq reduces to 0 or 1 (whether j is connected to q or not - that is, the adjacency matrix). The sum of the product piqmjq measures the portion of i’s relation with j that is redundant to i’s relation with other primary contacts. Redundant size: The network size N minus effective

network size is the redundant size, R.

ji,q, m p-1-NRj

jqq

iq ≠⎥⎥⎦

⎤

⎢⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡= ∑ ∑

(2)

IV. CASE ANALYSIS

Our study selected two teams with a similar background

in technology transformation, whose different team structures influence their overall goal achievement.

A. The profile of case team and diffusion process of new technology

Team A is a computer software development department founded in 1994, whose main business centers on the mapping design of information systems. It maintains a lead in integrating technology, even though it has experienced various technological transitions, including changes in client/server hardware, distributed databases, and data warehousing. Team A is primarily a project-based collaboration; after a given project terminates, the working group is dismissed and incorporated into other projects.

Team B is an information management department formed 1990, whose major domain focuses on development, management and maintenance of information systems for the organization. Due to hardware technology and application software renovations, their information systems developed from large-sized to small-sized hosts.

An essential expertise of software engineers is being conversant in the technology of platform development. However, gaining engineering expertise requires long-term accumulation; thus, an engineer continually faces career challenges as technology shifts. Meanwhile, technology shifting impacts an organization in terms of market competition. An information system is not a constantly operating structure, especially in terms of decision-making, it requires coordination with the overall commercial environment. A system development platform will not merely influence developmental efficiency, it will also determine information system performance. The speed of acquiring leading technology greatly affects an organization’s competitive advantage.

On Team A, seven members studied a new platform. These members represent those directly concerned with management and information resources. The average time of membership in the group is 5.8 years. The administration hierarchy consists of one executive manager, one project

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leader, three senior engineers, and two junior engineers. Team B also involves seven members. The average

membership time of each participant is 10 years. The team members include one department manager, two senior managers, and four engineers (See Appendix A. team member table).

Each team adopted different technology diffusion processes. In Figure 1, Team A selects member A2 as a new technology explorer, with A1 and A3 forming a panel studying the practical benefits of the new technology from a competitive advantage standpoint, and then begin preparing

formal technology documents. After obtaining preliminary technology results, they pass their experimental findings on to the other members of the team.

Team B has three technology explorers; the department manager, senior manager and one engineer. After an initial new technology evaluation, the department manager assigns engineer B5 as early-phase researcher. Then, after acquiring preliminary technology, B5 acts as a diffusion source to other members of the team. Team B expands its technology diffusion from a single point, and builds common technology documents via group discussion.

Fig. 1 the team structure of new technology diffusion. The time required to accomplish technology shifting is

significantly different between the two teams. Team A spends four months to complete the shift and implement projects using the new platform technology. However, B team spends ten months finishing technology shifting.

B. Network analysis of team member

In Team A, the degree of personal redundancy of members A3 and A5 were higher than the other team

members. Member A5 was a software system engineer. As a senior engineer, member A3 also played a major role in diffusing knowledge of the new technology.

In Team B, the personal redundancy degree of members B5 and B2 was higher than the others. Furthermore, all of them were system engineers. B5 was the only member acting as a diffusion point for the new technology. B2 was the only engineer responsible for maintaining hardware and software systems.

TABLET 1 NETWORK ANALYSIS OF TEAM MEMBER

Team Team A Team B Member A1 A2 A3 A4 A5 A6 A7 B1 B2 B3 B4 B5 B6 B7

Network Size 8 13 8 7 7 9 4 9 7 7 9 7 10 8 Effective Size 5.50 10.93 5.05 4.17 3.85 7.00 1.80 6.23 3.22 4.10 5.93 3.58 7.18 4.69

Personal Redundancy 2.50 2.070 2.95 2.84 3.15 2.00 2.20 2.78 3.79 2.90 3.07 3.42 2.82 3.31

Team Redundancy 2.53 3.15

FIG. 2 Network graph

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V. RESEARCH FINDINGS AND DISCUSSION

A. On organizational structure and knowledge diffusion The team redundancy of Team A is 2.53, significantly less

than Team B. Furthermore, Team A spent only four months to shift technology, compared to Team B’s ten months. The process of knowledge diffusion occurs when elements of one culture spread to another without wholesale dislocation or migration[14]. The knowledge creator used certain channels to communicate among the members of a social system[15]. Team A’s success shows that properly arranging the teamwork structure can greatly influence knowledge diffusion.

On the other hand, the information benefits of a network define who can identify these opportunities, when they find them, and who gets to participate in them. Players with a network optimally structured to provide these benefits enjoy higher rates of return to their investments, because such players know about, and have a hand in, more rewarding opportunities[13]. So the information benefit is naturally affected by information sharing.

However, this finding does not conflict with the debate of weak ties versus strong ties, as the information property differs. Strong ties between business units facilitate the transfer of complex knowledge that in itself can contribute to an improved technological performance of the organization[16]. Restated, weak ties are benefits for codifying and explicating knowledge diffusion, strong ties are benefits for ambiguous and tacit knowledge transfer. Information redundancy can potentially nurture group think. While information redundancy can translate into flexible and rapid innovation generation, it can also give rise to compromise in the pursuit of innovation to its ultimate, potential limits. Even within the most heterogeneous of groups, information redundancy implies that there are no

eccentric ideas and even that, from time to time, there is some hesitation in the submission of creative ideas [3]. However, Nonaka did not discuss the information redundant to knowledge transfer and rarely discussed internal competition mechanisms as enhancing knowledge creation.

However, if a given network is overly redundant, it will experience difficulty in transmitting knowledge. Organizational viscosity [17] illustrates an organizational structure for a new technology unhindered by diffusion. Once structural redundancy is effectively managed, it not only provides a competitive cooperation situation, but also releases personal redundancy to the group. Structural redundancy can prevent personal over redundancy from reducing efficacy.

In terms of structural redundancy, this study concludes with the finding that structural redundancy can reduce personal redundancy and efficiently increase knowledge diffusion.

Despite Nonaka’s [3] arguments that information redundancy in the innovation generation process is of an unusually high cost, there exists a tremendous reward for teams to implement new technology. Therefore, this study infers that properly placed team manager can control structural redundancy, allowing the team to exploit and utilize new technology faster. B. On Personal Redundancy and Member Specific Property

Our case analysis shows that members with a higher degree of redundancy member can be regarded as those who have wide network range with others. However, based on the interview questions regarding for their job duties, this study finds the more redundant member plays a major role in team activity. This appears to be in agreement with Nonaka’s [18]assertion that redundancy of information helps team members share their tacit knowledge.

B1

B3B4 B2

B5

B6

B7B5

B1 B7

A1 A3

A4

A2

A5 A6 A7

Exploring Phase

Early adopt phase

Late adoptphase

Team A Team B

FIG. 3 The higher personal redundancy member in team structure

In this study, A3, A5, B5, and B2 members with higher redundancy act as technological developer or chief system maintainer (see Figure. 3). For instance, member B2 was both an MIS maintainer and software engineer. Members A3 and B5 member were early adopters for the new technology. Those members were required to internalize extensive external knowledge and turn it into personal tacit knowledge.

Then the member acting as a knowledge supplier, had to transfer their tacit knowledge into explicit knowledge, externalizing the technical specifications, teaching materials and working manuals through common technique language. The new technology was disseminated out to larger group through meetings and discussions with the members to combine workable methods into a general technology.

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Additionally, examinations of redundant linkages in the initial network stages proved unusually illuminating. In regular network linkage, strong ties and redundant linkage existed[13]. Therefore, redundant linkage gave the appearance of the larger state of communication within a network. In personal knowledge acquisition, redundant linkages ensured someone would maintain plentiful information access, with strong ties appearing to reinforce this structure of frequent communication. This networking phenomenon can be represented as knowledge diffusion. The network linkage demonstrates preferential attachment[19]; which means that the more connected a node is, the more likely it is to receive new links. Nodes with a higher degree have a stronger ability to grab links added to the network. Intuitively, this preferential attachment can interpreted as people in social networks connecting.

A node with heavily redundant linkage represents a node with lots of resources. Therefore, the early technology adopters A1 & A3 should be more highly redundant members of the team. However, this study did not find such a result in Team A. member A5 was the highest redundant member, acting as new developing project leader after Team A had shifted technology. This result infers that new technology was diffused to A5 successfully, and A5 acted as a new source to provide working knowledge. In contrast, the highest redundant member in Team B was not the early adopter B5. Member B2 was heavily redundant, but B2 acted as both programmer and hardware engineer. Member B2 was another new technology provider in the new system implementation.

In terms of the personal redundancy, this study finds that a heavily redundant person can provide more specialized technology or knowledge.

VI. CONCLUSION

In theory, Nonaka argues that information redundancy and

internal competition affects team members to create working knowledge. However, what mechanism accelerates this knowledge creation is not clearly expressed. This study provides a network structural perspective to explore the knowledge behaviors of teamwork, and finds that the structural redundancy of a team affects knowledge diffusion. In terms of theoretical implementation, information redundancy and internal competition can speed up technology upgrading and knowledge diffusion. However, failing to manage teamwork structure in terms of lead-in and early adoption of new technology will offset the benefits to be gained. More specifically, a structural team lead member performing with internal competition is critical in transforming new technological knowledge into team-specific knowledge or competence. Moreover, an internal competition structure eases team redundancy for tacit knowledge diffusion. Restated, controlling the structural redundancy can

accelerate knowledge diffusion. A person with the more redundant networks is the one provides more technical knowledge. On the result, we can not only identify the major technology provider, but also can rearrange team structure for further technology diffusion. Accelerating team adoption of new technology enhances a company’s overall competitive advantage. This finding suggests a new implementation for strategic planning and managing R&D activity.

REFERENCES

[1] I. Nonaka, “A Dynamic Theory of Organizational Knowledge

Creation,” Organization Science, vol. 5, no. 1, pp. 14-37, Feb., 1994, 1994.

[2] I. Nonaka, and H. Takeuchi, Hitotsubashi on Knowledge Management: John Wiley & Sons, USA, 2004.

[3] I. Nonaka, “Redundant, overlapping organization : A Japanese approach to managing the innovation process.,” California Management Review, vol. 32, no. 3, pp. 27-38, 1990.

[4] B.-A. Lundvall, National Systems of Innovation: Towards a Theorem of Innovation and Interactive Learning, 1992, Ed. ed., London: Pinter Publications, 1992.

[5] R. T. h. A. J. Leenders, “Modeling social influence through network autocorrelation: constructing the weight matrix,” Social Networks, vol. 24, pp. 21-47, 2002.

[6] D. J. Watts, Six Degrees: The Science of a Connected Age: W. W. Norton & Company., 2004.

[7] M. Granovetter, "The strength of weak ties: A network theory revisited.," Social structure and network analysis, P. V.Marsden and N. Lin, eds., pp. 105–130, Beverly Hills, CA: Sage, 1982.

[8] D. Krackhardt, "The strength of strong ties: The importance of philos in organizations.," Networks and organizations: Structure form and action, N. Nohria and R. Eccles, eds., pp. 216-239, Cambridge, MA: Harvard University Press, 1992.

[9] R. S. Burt, “Social contagion and innovation, cohesion versus structural equivalence.,” American Journal of Sociology, vol. 92, pp. 1287-1335, 1987.

[10] R. S. Burt, Brokerage and Closure: An Introduction to Social Capital Oxford University Press, 2005.

[11] M. Granovetter, “Economic Action and Social Structure: The Problem of Embeddedness,” American Journal of Sociology, vol. 91, no. 3, pp. 481-510, 1985.

[12] R. S. Burt, The network structure of social capital., New York, NY,: JAI Press, 2000.

[13] R. S. Burt, Structural holes: The structure of competition., Cambridge, MA: Harvard University Press, 1992.

[14] J. Srinivasan, R. Narasimha, and S. K. Biswas, The dynamics of technology : creation and diffusion of skills and knowledge / edited by Roddam Narasimha, J. Srinivasan, S.K. Biswas, New Delhi ; Thousand Oaks, CA :: Sage Publications, 2003.

[15] E. M. Rogers, Diffusion of Innovations, New York: Free Press, 1985. [16] M. T. Hansen, “Knowledge Networks: Explaining Effective

Knowledge Sharing in Multiunit Companies,” Organization Science, vol. 13, no. 3, pp. 232-248, 2002.

[17] D. Krackhardt, “Organizational viscosity and the diffusion of controversial innovations. ,” Journal of Mathematical Sociology, vol. 22, no. 2, pp. 177-199, 1997.

[18] I. Nonaka, and N. Konno, “The Concept of Ba : Building a Foundation for Knowledge Creation.,” California Management Review, vol. 40, no. 3, pp. 40-54, 1998.

[19] A.-L. Barabási, and R. Albert, “Emergence of Scaling in Random Networks ” Science vol. 286, no. 5439, pp. 509-512, 1999.

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APPENDIX A

TABLE OF TEAM MEMBER DESCRIPTIONS

Member Job position Working Field Years on teamwork

A1 senior engineer programmer 4 A2 executive manager System Planning 10 A3 senior engineer programmer 5 A4 project leader System Planning 8 A5 junior engineer programmer 4 A6 senior engineer programmer 8 A7 junior engineer System pilot 2 B1 Senior manager System Planning 8 B2 Junior engineer programmer/ hardware Maintain 5 B3 Senior engineer programmer 15 B4 Senior manager programmer 10 B5 Senior engineer programmer 15 B6 Junior engineer programmer 2 B7 Department manager System Planning 15

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