Creating Value in Networks: A Value Network Mapping Method for Assessing the Current and Potential Value Networks in Cross-Sector Collaboration
Notice bibliographique
Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».