Creating Value in Networks: A Value Network Mapping Method for Assessing the Current and Potential Value Networks in Cross-Sector Collaboration
Bibliographic record
Abstract
ABSTRACTHow to manage value creation and how to maximize the potential of collaborations are challenging issues in the management of cross-sector collaborations. One strategic tool for addressing these areas of concern is value network mapping; this serves as the starting point for value network analysis, which is used to maximize innovations by forging stronger value creation links with strategic partners. The objective of this study was to design and implement a method for value network mapping that would increase our understanding of how new value can be created in cross-sector collaborations. The proposed mapping method also permits the assessment of current and potential value networks; e.g., what value is exchanged in the current collaborative state and what new value can be created through the proper management of a value network? Using the case study of a collaboration among the public, private, and non-profit sectors for elderly care, this paper describes, step by step, how the proposed value network mapping method can be applied in practice to help managers and experts understand how to develop complex value network maps, how new values can be created, and how to maximize the potential of a collaboration.Key Words: Value network, value network mapping, value creation, cross-sector collaboration, conceptual framework, elderly careIntroductionHow to manage value creation and how to maximize the potential of collaborations are challenging issues in the management of cross-sector collaborations. In today's dynamic economic environment, which is facing complex socioeconomic problems, cross-sector collaborations have continuously increased (Handy, 1996; Lipman-Blumen, 1996; Cleveland, 2002; Le Ber and Branzei, 2010; Oystein et al., 2012). However, despite sustained efforts to find or develop innovative and powerful methods for effective management and value creation within the context of cross-sector collaborations, this remains a challenge.Collaborations and partnerships enable people and organizations to support each other by combining and leveraging their complementary strengths and capabilities (Dyer and Singh, 1998; Adegbesan and Higgins, 2011). The power of a partnership is determined by the value network that results from the collaboration, and managing value creation in a knowledge economy requires a strong appreciation of the intangible aspects of a business model and an understanding of network dynamics (Allee, 2002).Traditionally, value creation has been seen as a linear process (i.e., value is created through a value chain) (Vargoa, Magliob ,and Akakaa, 2008). In a knowledge economy, however, the situation is much more complex. Organizations themselves are highly complex systems consisting of many variables that cannot easily be controlled, which complicates the management of value creation (Chatain and Zemsky, 2011). Managers will often use visual tools like strategic maps to more easily piece together a huge amount of information (de Benedetto and Klemes, 2009). Value network maps (Allee, 2011) are one such visual tool used to manage value creation. Value network mapping is the starting point in value network analysis, which is used to maximize innovations by forging stronger value creation links between strategic partners (Allee and Taug, 2006).Although the literature on value networks and value network analysis has reached an increasingly interest (Allee, 2002, 2006, 2008; Lock Lee, 2007; Meggitt and Allee, 2011; Optimice Pty. Ltd., 2008; Anger, 2008; Plambeck and Denend, 2008), there is not a specific method for describing in detail how to map a value network. Allee (2011) has described the basics of value network mapping but provides no details about how to identify the added value that every partner brings to a network or how to identify the participants' assets (e.g., the value flows within the network). Therefore, more research is needed in this area.The objective and benefits of the studyThe main objective of this study was to design a method for value network mapping that provides increased understanding of the value creation process. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".