Systems and complexity

A classification framework of knowledge transfer issues across value networks

Creating integrated solutions with the customers of a business requires the collaboration of different partners in a value network. A value network consists of nodes (people or roles) that are connected by interactions representing tangible and intangible deliverables.

Knowledge is considered a foundation for value co-creation, so the authors of a recent paper1 contend that identifying knowledge transfer issues can significantly assist knowledge exchange initiatives in value networks.

The authors carried out a systematic literature review to identify the issues, and then used a structured classification approach to classify the issues into six main categories and 29 subcategories. From 6720 initial sources, 54 papers were selected for full review and data extraction. At the end of the classification process, 29 categories emerged. Six main categories also emerged, representing higher order concepts and groupings of commonality across the categories. Both tacit and explicit knowledge were considered.

The six main categories and 29 subcategories are as follows:

Category: Network structure issues

Subcategories:

  1. Transactive memory issues: Refers to the set of knowledge possessed by group members coupled with an awareness of who knows what.
  2. Complex network issues: Extreme complexity in terms of relationships, communications, and use of knowledge.
  3. Relationship issues: Collaborations between actors are hindered because of personal relationships. One firm feels superior over the other.
  4. General distance issues: Physical or time distance between actors creates difficulties in knowledge sharing.
  5. Cultural distance issues: All actors must know each other’s respective cultural backgrounds. Views and ideas can be negatively influenced by not knowing languages people speak, their habits, and what is acceptable and what is not.
  6. Lack of communication facilities: Lack of opportunities for communication and lack of formal/informal mechanism, making it difficult to transfer knowledge across a network.

Category: Generic issues

Subcategories:

  1. Difficulty in expressing tacit knowledge: People are unable to externalize/codify their tacit knowledge.

Category: Social issues

Subcategories:

  1. Knowledge source reliability issues: Knowledge is not perceived as true because its source is unreliable.
  2. Fear of losing knowledge: Since knowledge is a source of competitive advantage, there is fear that when it is shared, it is shared with partners that could be competitors.
  3. Lack of willingness: People don’t want or are unmotivated to engage in knowledge sharing for reasons including knowledge as a power syndrome, lack of trust in people, resistance to change, or fear of exploitation.
  4. Lack of trust: A belief that the other party might act opportunistically or in an unfavourable way hinders knowledge sharing across a network.

Category: Language / understanding issues

Subcategories:

  1. Insufficient mutual understanding: Unable to make good use of the others’ knowledge due to a lack of common ground, casual ambiguity, difference in perception, or lack of knowledge of exactly how the knowledge is supposed to be used.
  2. Contextualization issues: Context can be defined as information about the situation, intentions, and feelings about an issue or action. Losing the context of knowledge can be an issue, especially for tacit knowledge.
  3. Semantic issues: Use of different terminology or different meanings of words can cause misunderstanding.

Category: Organisational aspect issues

Subcategories:

  1. Organizational issues: The organization does not have sufficient formal planning, guidelines or regulations for knowledge sharing. This makes it unclear who is responsible, and what and how data should be shared.
  2. Lack of top management commitment: Due to lack of top management commitment and involvement, knowledge sharing initiatives lack a mandate, causing them to fail.
  3. Network level objective and benefit issues: Given power asymmetry and goal problems at the network level, actors do not equally benefit from knowledge sharing.
  4. Insufficient resource: Lack of resources such as expertise, training, time, funds, and network structure cause difficulties for knowledge sharing.
  5. Organization structural issues: Inflexibility results from excessive hierarchy and centralization, or too many guidelines and regulations. People may be willing to share, but lack the authorization.
  6. Lack of incentive: People are not motivated to share their knowledge due to a lack of incentives in the form of accolades or rewards.

Category: Technical issues

Subcategories:

  1. Authorization / data flow: Data exists but is not mobile. People cannot access it and therefore they cannot derive value out of it.
  2. Performance measurement issue: With no monitoring control or evaluation procedure, it is impossible to tell how the KM system is performing.
  3. Legal issues: Laws and regulations may put constrains on inter-organizational knowledge sharing.
  4. Failure to meet technological demand: Technology in place is inadequate (e.g. lack of functionality, architectural issues, system security) to support a network’s actual knowledge transfer process.
  5. Lack of user-friendly IS: The system is not adequately user friendly.
  6. Data quality issues: Refers to availability, privacy, accessibility, accuracy, and completeness of shared data.
  7. Data overload issues: There is more data available than that there is processing capacity available.
  8. Data security issues: Technological issues generate reliability and security concerns in knowledge transfer.
  9. Data integration issue: Different information systems are not capable of exchanging data.

Reference:

  1. Bagheri, S., Kusters, R. J., Trienekens, J. J., & van der Zandt, H. V. (2016). Classification Framework of Knowledge Transfer Issues Across Value Networks. Procedia CIRP, 47, 382-387.

Also published on Medium.

Bruce Boyes

Bruce Boyes (www.bruceboyes.info) is editor, lead writer, and a director of the award-winning RealKM Magazine (www.realkm.com) and currently also teaches in the University of NSW (UNSW) Foundation Studies program in China. He has expertise and experience in a wide range of areas including knowledge management (KM), environmental management, program and project management, writing and editing, stakeholder engagement, communications, and research. Bruce holds a Master of Environmental Management with Distinction and a Certificate of Technology (Electronics). With a demonstrated ability to identify and implement innovative solutions to social and ecological complexity, Bruce's many career highlights include establishing RealKM Magazine as an award-winning resource for knowledge managers, using agile and knowledge management approaches to oversee the implementation of an award-winning $77.4 million river recovery program in western Sydney on time and under budget, leading a knowledge strategy process for Australia's 56 natural resource management (NRM) regional organisations, pioneering collaborative learning and governance approaches to support communities to sustainably manage landscapes and catchments, and initiating and teaching two new knowledge management subjects at Shanxi University in China.

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