Project description:
Regional knowledge in terms of specialist skills and knowledge gained through experience and culture is used to implement innovations. This is essential to remain competitive and economically resilient in a globalised business environment. Knowledge exchange between regional authorities is becoming increasingly important, especially given the devolution of power to local authorities across Europe and the need to increase the importance of the learning process. Regions are
The study explores how tourism destinations can innovate by looking beyond their own borders and industries.
The Big Idea: Usually, people look at tourism innovation only within one specific town or city. This paper argues that real innovation happens when different sectors (like tech, transport, and food) and different regions share knowledge through “super-networks.”
The Problem: Many tourism networks are currently a bit of a mess—they are “unmanaged,” meaning people join randomly, there’s no clear goal, and it’s hard to tell who is actually contributing.
The “System” Checklist: For a network to actually work well, the authors say it needs three things:
Unified Function: Everyone needs to be working toward the same goal.
Coherence: The different parts (like hotels and tech startups) need to actually fit together and make sense.
Boundedness: There should be clear boundaries about what the network does and who is involved so it doesn’t get too bloated.
Thinking Outside the Box: The authors suggest that destinations should stop being “insular” and start looking for “extra-regional” knowledge. Basically, a beach destination might learn more about sustainability from a mountain region or a tech hub than from another similar beach.
The “Network-System” Scale: The paper places different types of networks on a scale. On one end, you have loose groups where people just chat; on the other, you have highly organized “systems” where knowledge flows efficiently and everyone is in sync.
Why This Matters: For tourism to survive big shocks (like pandemics or climate change), it needs to be part of these bigger, cross-sectoral networks to get fresh ideas and better technology.
Methodology This is a “conceptual paper,” which means the authors didn’t go out and do new interviews or surveys this time. Instead, they used a “neo-Schumpeterian” lens—a fancy way of saying they looked at how economies evolve and innovate over time. They re-analyzed existing theories and data about tourism and regional development to build a new framework that researchers can use in the future to study how destination networks actually behave.
Real-world exemplars, are as follows:
- Neighboring Destinations
These networks consist of proximate regions that benefit from frequent face-to-face interactions, leading to joint innovation and creativity.
Type 1: Cross-border (Transnational)
Description: Neighbouring destination regions from different countries, typically in border regions.
Exemplar: AlpNet, a network of neighboring Alpine regions.
Details: Members share similar tourism resources, markets, and challenges, focusing knowledge exchange on sustainability and global challenges.
Type 2: Neighboring destination regions (National)
Description: Proximate destination regions within the same national boundaries.
Exemplar: ‘Beskidzka 5’ in Poland.
Details: Comprises five municipal authorities that previously competed but developed an innovative “coopetition” approach to sell a combined tourism product.
- Distant Destinations
These networks involve destinations that are not geographically close but share thematic priorities or specialized innovation needs.
Type 3: Transnational (Distant)
Description: Distant or non-contiguous destinations from different countries.
Exemplar: Network of European Regions for Sustainable and Competitive Tourism (NECSTouR).
Details: Identified as the most “systemic” exemplar, it facilitates shared knowledge on sustainable development through project participation across a heterogeneous membership base.
Type 4: Distant destination regions within national boundaries (National)
Description: Destination regions from across the same country.
Exemplar: American Destinations Council (part of the US Travel Association).
Details: Functions primarily as a marketing knowledge dissemination platform for about 400 U.S. destination marketing organizations, focusing on peer learning and annual training events.
Summary of Systemic Qualities
The table and subsequent analysis use these types to evaluate systemic qualities such as coherence, unified function, and boundedness. For instance, NECSTouR (Type 3) is positioned closest to the “system” end of the continuum due to its high member heterogeneity and clear mission, while the Destinations Council (Type 4) is positioned closest to the “no system” edge because it focuses on general marketing rather than specific innovative priorities.
Weidenfeld, A., Hall, C. M., & Baggio, R. (2025). Knowledge Networks of Destination Regions: A Systemic Cross-Sectoral and Regional Perspective. Journal of Travel Research, 1–19. https://doi.org/10.1177/00472875251388338
aware of the need to cooperate with other regions in learning to achieve development goals.
Previous research has focused on bilateral links between organizations from different regions rather than on learning within regional groups or associations. This is surprising, especially when we consider that these networks, in addition to collecting membership fees, often receive public financial support to boost regional economies and represent the interests of the regions and their inhabitants. Examining the learning and knowledge exchange processes between these networks in comparison with the regions in each network will help us understand their role in generating new knowledge and using it for innovation. It will also lead to recommendations for improving these processes and the functioning of institutions. More specifically, the structures, organization, and activities of such networks will be examined.
Through this comparison, different types of knowledge networks will be identified, their organisation, structure and how they function in terms of sharing knowledge exchange. The project will also investigate to what extent these networks behave as systems. When networks behave as systems, they are characterised by identifiable components, feedback, common development goals and clear geographical boundaries, which are important for managing and facilitating knowledge exchange and designing new policies to improve knowledge exchange. In order to increase the relevance of the study to many regions facing the same challenges around the world, networks of regions in two countries were selected: Poland and the UK. This will allow for the study of regions in different geopolitical contexts; the networks in the UK will represent networks with regions where the transfer of power from central to regional governments is at an advanced stage and the importance of regions in cooperation with other actors is likely to increase even more after Brexit.
In contrast, the Polish networks represent regions in an EU country where decentralization is still in its early stages. Finally, the study will benefit end users and policy makers by suggesting some policy implications for the management and functioning of networks and knowledge systems in general, as well as improving knowledge exchange and innovation in regions in particular.